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   <news:title>パノラミック深度マップによる4D人体対応関係の学習（4D Human Body Correspondences from Panoramic Depth Maps）</news:title>
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   <news:title>深部準位の同定に第一原理計算を適用する手法（Defect identification based on first-principles calculations for deep level transient spectroscopy）</news:title>
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   <news:title>部分帯域ベースの時系列ニューラル音声合成モデル（A Fully Time-domain Neural Model for Subband-based Speech Synthesizer）</news:title>
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   <news:title>ブロック安定性によるMAP推定の解析（Block Stability for MAP Inference）</news:title>
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    <news:language>ja</news:language>
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   <news:title>学習による推論の自動化（Learning to Reason: Theorem proving at first order via reinforcement learning）</news:title>
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    <news:language>ja</news:language>
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   <news:title>変更毎のテストを予測で絞る仕組み（Predictive Test Selection）</news:title>
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    <news:language>ja</news:language>
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   <news:title>オンライン多クラスブースティングとバンディットフィードバック（Online Multiclass Boosting with Bandit Feedback）</news:title>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>SIGNSGDと多数決による頑健で通信効率の良い分散学習（SIGNSGD WITH MAJORITY VOTE IS COMMUNICATION EFFICIENT AND FAULT TOLERANT）</news:title>
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    <news:language>ja</news:language>
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   <news:title>ネットワーク剪定の再考（RETHINKING THE VALUE OF NETWORK PRUNING）</news:title>
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   <news:title>ドローン撮影による大規模車両軌跡データセットの構築と意義（The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems）</news:title>
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   <news:title>強化学習を使わない敵対的テキスト生成（Adversarial Text Generation Without Reinforcement Learning）</news:title>
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   <news:genres>Blog</news:genres>
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    <news:language>ja</news:language>
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   <news:title>任意の線形方程式に収束する二重確率的ガウス・ザイデル法（A Linearly Convergent Doubly Stochastic Gauss-Seidel Algorithm）</news:title>
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   <news:genres>Blog</news:genres>
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    <news:language>ja</news:language>
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   <news:title>誰でも即興ピアノ演奏を可能にする「Piano Genie」の仕組み（Piano Genie）</news:title>
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   <news:genres>Blog</news:genres>
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    <news:language>ja</news:language>
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   <news:title>送電線故障のリアルタイム局所化とPMU設置最適化（Real-time Faulted Line Localization and PMU Placement in Power Systems through Convolutional Neural Networks）</news:title>
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   <news:genres>Blog</news:genres>
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    <news:language>ja</news:language>
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   <news:title>文書ごとに最適なキーフレーズ数を生成・評価する方法（One Size Does Not Fit All: Generating and Evaluating Variable Number of Keyphrases）</news:title>
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   <news:genres>Blog</news:genres>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>実用的な設計空間探索の手法（Practical Design Space Exploration）</news:title>
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   <news:genres>Blog</news:genres>
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  <lastmod>2026-06-22T20:38:46Z</lastmod>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>在庫配分とオンライン学習の統合による収益最適化（Inventory Balancing with Online Learning）</news:title>
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   <news:genres>Blog</news:genres>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>データのサブサンプリングによる効率的なデータ拡張（EFFICIENT AUGMENTATION VIA DATA SUBSAMPLING）</news:title>
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   <news:genres>Blog</news:genres>
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   <news:title>MDGANによる異常検知の強化（MDGAN: Boosting Anomaly Detection Using Multi-Discriminator Generative Adversarial Networks）</news:title>
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   <news:genres>Blog</news:genres>
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   <news:title>分布ロバストな送電網拡張計画（Distributionally Robust Transmission Expansion Planning）</news:title>
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   <news:genres>Blog</news:genres>
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   <news:title>ランダムフーリエ特徴を用いたカーネル導関数近似（On Kernel Derivative Approximation with Random Fourier Features）</news:title>
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   <news:genres>Blog</news:genres>
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   <news:title>視覚認識のための敵対的メッシュ（MeshAdv: Adversarial Meshes for Visual Recognition）</news:title>
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   <news:genres>Blog</news:genres>
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    <news:language>ja</news:language>
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   <news:title>ユニットレベルで見るベイズニューラルネットワークの事前分布の理解（Understanding Priors in Bayesian Neural Networks at the Unit Level）</news:title>
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   <news:genres>Blog</news:genres>
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    <news:language>ja</news:language>
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   <news:title>モンテカルロ法のための量子システムと分数確率過程への応用 (Quantum Systems for Monte Carlo Methods and Applications to Fractional Stochastic Processes)</news:title>
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   <news:genres>Blog</news:genres>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>Riemannの未発表ノートが示した解析的整数論の道筋（On Riemann’s Nachlass for Analytic Number Theory）</news:title>
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   <news:genres>Blog</news:genres>
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  <news:news>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>インフレーションを情報ボトルネックとして捉える戦略（Inflation as an Information Bottleneck: A strategy for identifying universality classes and making robust predictions）</news:title>
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   <news:genres>Blog</news:genres>
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  <lastmod>2026-06-22T18:53:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>若い星J1407の環を持つ伴星の軌道周期の制約（Constraining the period of the ringed secondary companion to the young star J1407）</news:title>
   <news:publication_date>2026-06-22T18:53:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-06-22T18:45:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>ヒト細胞アトラス（The Human Cell Atlas）</news:title>
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   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/703683</loc>
  <lastmod>2026-06-22T18:45:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>赤方偏移空間マップからのベイズ的宇宙密度復元（Bayesian cosmography from redshift space maps）</news:title>
   <news:publication_date>2026-06-22T18:45:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-06-22T18:43:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>二乗でも1/2乗でもない低ランク復元の実用性を高める手法（Bilinear Factor Matrix Norm Minimization for Robust PCA）</news:title>
   <news:publication_date>2026-06-22T18:43:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>アプリレビューからの機能抽出における注釈ガイドラインとデータの影響（The Impact of Annotation Guidelines and Annotated Data on Extracting App Features from App Reviews）</news:title>
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   <news:genres>Blog</news:genres>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>インフレーションを学ぶ（Learning to Inflate: A Gradient Ascent Approach to Random Inflation）</news:title>
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   <news:genres>Blog</news:genres>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>エネルギーフローネットワーク：粒子ジェットのためのDeep Sets（Energy Flow Networks: Deep Sets for Particle Jets）</news:title>
   <news:publication_date>2026-06-22T18:42:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-06-22T17:51:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>ミススペシファイドな目的空間下での学習（Learning under Misspecified Objective Spaces）</news:title>
   <news:publication_date>2026-06-22T17:51:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703671</loc>
  <lastmod>2026-06-22T17:49:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ボトムアップ注意（Bottom-up Attention, Models of）</news:title>
   <news:publication_date>2026-06-22T17:49:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703669</loc>
  <lastmod>2026-06-22T17:48:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>チャネル数が多いベイズ畳み込みニューラルネットワークはガウス過程である（Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes）</news:title>
   <news:publication_date>2026-06-22T17:48:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703667</loc>
  <lastmod>2026-06-22T17:48:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>空間的一貫性情報に基づく敵対的事例の特徴付け（Characterizing Adversarial Examples Based on Spatial Consistency Information for Semantic Segmentation）</news:title>
   <news:publication_date>2026-06-22T17:48:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703665</loc>
  <lastmod>2026-06-22T17:48:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>物理法則に基づく深層ニューラルネットの正則化（Physics-Driven Regularization of Deep Neural Networks for Enhanced Engineering Design and Analysis）</news:title>
   <news:publication_date>2026-06-22T17:48:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703663</loc>
  <lastmod>2026-06-22T17:47:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>連続ランダムエネルギーモデルにおけるアルゴリズム的困難度の閾値（The algorithmic hardness threshold for continuous random energy models）</news:title>
   <news:publication_date>2026-06-22T17:47:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703661</loc>
  <lastmod>2026-06-22T17:47:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>解釈可能な機械による複数の秩序パラメータ学習（Learning multiple order parameters with interpretable machines）</news:title>
   <news:publication_date>2026-06-22T17:47:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703659</loc>
  <lastmod>2026-06-22T16:56:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文間をまたぐ関係抽出のニューラル手法（Neural Relation Extraction Within and Across Sentence Boundaries）</news:title>
   <news:publication_date>2026-06-22T16:56:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703657</loc>
  <lastmod>2026-06-22T16:55:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>現実世界のネットワークの統計物理学（The Statistical Physics of Real-World Networks）</news:title>
   <news:publication_date>2026-06-22T16:55:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703655</loc>
  <lastmod>2026-06-22T16:55:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>乳児脳MRIのボクセル単位セグメンテーションを効率化するInfiNet（INFINET: FULLY CONVOLUTIONAL NETWORKS FOR INFANT BRAIN MRI SEGMENTATION）</news:title>
   <news:publication_date>2026-06-22T16:55:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703653</loc>
  <lastmod>2026-06-22T16:55:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>18F-FDG PETイメージングの最適な深層射影学習によるパーキンソン症候群の早期鑑別診断（Learning Optimal Deep Projection of 18F-FDG PET Imaging for Early Differential Diagnosis of Parkinsonian Syndromes）</news:title>
   <news:publication_date>2026-06-22T16:55:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703651</loc>
  <lastmod>2026-06-22T16:54:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>線形パラメトリック偏微分方程式のバイアス排除型ディープソルバー（Unbiased Deep Solvers for Linear Parametric PDEs）</news:title>
   <news:publication_date>2026-06-22T16:54:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703649</loc>
  <lastmod>2026-06-22T16:54:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>交差エントロピーの抑制版が示す頑健性の道筋（Taming the Cross Entropy Loss）</news:title>
   <news:publication_date>2026-06-22T16:54:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703647</loc>
  <lastmod>2026-06-22T16:54:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>正則化付き文脈バンディット（Regularized Contextual Bandits）</news:title>
   <news:publication_date>2026-06-22T16:54:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703645</loc>
  <lastmod>2026-06-22T16:02:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>近傍探索モデルの理論に基づく実践評価（A Theory-Based Evaluation of Nearest Neighbor Models Put Into Practice）</news:title>
   <news:publication_date>2026-06-22T16:02:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703643</loc>
  <lastmod>2026-06-22T16:02:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層学習を用いた画像デノイジングの概観（Deep Learning for Image Denoising: A Survey）</news:title>
   <news:publication_date>2026-06-22T16:02:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703641</loc>
  <lastmod>2026-06-22T16:01:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>希薄ハドロンにおけるグルーオン密度の揺らぎ（Gluon density fluctuations in dilute hadrons）</news:title>
   <news:publication_date>2026-06-22T16:01:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703639</loc>
  <lastmod>2026-06-22T16:00:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ワンショット高忠実度模倣学習（ONE-SHOT HIGH-FIDELITY IMITATION: TRAINING LARGE-SCALE DEEP NETS WITH RL）</news:title>
   <news:publication_date>2026-06-22T16:00:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703637</loc>
  <lastmod>2026-06-22T16:00:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>視覚特徴を用いた階層注意による食品推薦（Hierarchical Attention Network for Visually-aware Food Recommendation）</news:title>
   <news:publication_date>2026-06-22T16:00:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703635</loc>
  <lastmod>2026-06-22T16:00:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>見た目から判断する適正速度予測の新領域（ISA2: Intelligent Speed Adaptation from Appearance）</news:title>
   <news:publication_date>2026-06-22T16:00:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703633</loc>
  <lastmod>2026-06-22T16:00:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>曲率が導く終末加速と永遠回帰の宇宙モデル（Curvature Late-Time Acceleration in an Eternal Universe）</news:title>
   <news:publication_date>2026-06-22T16:00:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703631</loc>
  <lastmod>2026-06-22T15:08:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>チェリーピッキングへの簡単な対処法（A Simple Way to Deal with Cherry-picking）</news:title>
   <news:publication_date>2026-06-22T15:08:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703629</loc>
  <lastmod>2026-06-22T15:08:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SingleGANによる単一ジェネレータでのマルチドメイン画像変換（SingleGAN: Image-to-Image Translation by a Single-Generator Network using Multiple Generative Adversarial Learning）</news:title>
   <news:publication_date>2026-06-22T15:08:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703627</loc>
  <lastmod>2026-06-22T15:08:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分数階微分方程式を解くための人工ニューラルネットワーク手法（Artificial Neural Network Approach for Solving Fractional order initial value problems）</news:title>
   <news:publication_date>2026-06-22T15:08:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703625</loc>
  <lastmod>2026-06-22T15:07:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>パーシステンス・ランドスケープとその性質（The persistence landscape and some of its properties）</news:title>
   <news:publication_date>2026-06-22T15:07:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703623</loc>
  <lastmod>2026-06-22T15:07:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>サイレンを聴く：都市シーンにおける音響アラームの検出と位置特定（Listening for Sirens: Locating and Classifying Acoustic Alarms in City Scenes）</news:title>
   <news:publication_date>2026-06-22T15:07:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703621</loc>
  <lastmod>2026-06-22T15:07:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>どの世代が最も慎重にデータを共有するか（Which Generation Shows the Most Prudent Data Sharing Behaviour?）</news:title>
   <news:publication_date>2026-06-22T15:07:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703619</loc>
  <lastmod>2026-06-22T15:06:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グローバルに連続した非マルコフ群衆活動解析（Globally Continuous and Non-Markovian Crowd Activity Analysis from Videos）</news:title>
   <news:publication_date>2026-06-22T15:06:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703617</loc>
  <lastmod>2026-06-22T14:15:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>インタラクティブ認知評価ツール：認知症臨床評価のためのデジタルペンのケーススタディ（Interactive Cognitive Assessment Tools: A Case Study on Digital Pens for the Clinical Assessment of Dementia）</news:title>
   <news:publication_date>2026-06-22T14:15:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703615</loc>
  <lastmod>2026-06-22T14:14:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>NGC1052-DF2における原子水素の欠乏（A dearth of atomic hydrogen in NGC1052-DF2）</news:title>
   <news:publication_date>2026-06-22T14:14:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703613</loc>
  <lastmod>2026-06-22T14:14:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>同一外観ロボットのオンライン視覚追跡と識別（Online Visual Robot Tracking and Identification using Deep LSTM Networks）</news:title>
   <news:publication_date>2026-06-22T14:14:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703611</loc>
  <lastmod>2026-06-22T14:13:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ペアワイズ拡張GANによる敵対的再構成損失（PAIRWISE AUGMENTED GANS WITH ADVERSARIAL RECONSTRUCTION LOSS）</news:title>
   <news:publication_date>2026-06-22T14:13:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703609</loc>
  <lastmod>2026-06-22T14:13:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ビデオ予測における位置依存性（Location Dependency in Video Prediction）</news:title>
   <news:publication_date>2026-06-22T14:13:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703607</loc>
  <lastmod>2026-06-22T14:13:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>VIPL-HR: 動画から非接触で心拍を推定する大規模マルチモーダルデータベース（VIPL-HR: A Multi-modal Database for Pulse Estimation from Less-constrained Face Video）</news:title>
   <news:publication_date>2026-06-22T14:13:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703605</loc>
  <lastmod>2026-06-22T14:13:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>リンク予測における体系的バイアス（Systematic Biases in Link Prediction: comparing heuristic and graph embedding based methods）</news:title>
   <news:publication_date>2026-06-22T14:13:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703603</loc>
  <lastmod>2026-06-22T13:21:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層双密結合ネットワークによる画像超解像（Deep Bi-Dense Networks for Image Super-Resolution）</news:title>
   <news:publication_date>2026-06-22T13:21:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703601</loc>
  <lastmod>2026-06-22T13:13:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>感情を含む会話環境での話者識別を高精度化するGMM–DNNカスケード分類器（Novel Cascaded Gaussian Mixture Model–Deep Neural Network Classifier for Speaker Identification in Emotional Talking Environments）</news:title>
   <news:publication_date>2026-06-22T13:13:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703599</loc>
  <lastmod>2026-06-22T13:13:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多目的自動交渉に基づくオンライン特徴選択システム（MOANOFS: Multi-Objective Automated Negotiation based Online Feature Selection System for Big Data Classification）</news:title>
   <news:publication_date>2026-06-22T13:13:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703597</loc>
  <lastmod>2026-06-22T13:12:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>埋め込み型深層形状表現を用いたRGBD画像からの高密度物体再構築（Dense Object Reconstruction from RGBD Images with Embedded Deep Shape Representations）</news:title>
   <news:publication_date>2026-06-22T13:12:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703595</loc>
  <lastmod>2026-06-22T13:11:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>衛星・航空画像による太陽光パネル検出の自動化（Automated Detection of Solar Panels from Satellite and Aerial Imagery）</news:title>
   <news:publication_date>2026-06-22T13:11:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703593</loc>
  <lastmod>2026-06-22T13:11:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ロボットが人間の動作を模倣し身体的制約を理解するための共有潜在変数生成（Generating Shared Latent Variables for Robots to Imitate Human Movements and Understand their Physical Limitations）</news:title>
   <news:publication_date>2026-06-22T13:11:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703591</loc>
  <lastmod>2026-06-22T13:11:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>相互に関連する複数タスクを段階的に学ぶ仕組み（Learning a set of interrelated tasks by using sequences of motor policies for a strategic intrinsically motivated learner）</news:title>
   <news:publication_date>2026-06-22T13:11:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703589</loc>
  <lastmod>2026-06-22T12:19:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>共振型パワーコンバータのための深層学習ベースのモデル予測制御（Deep Learning-based Model Predictive Control for Resonant Power Converters）</news:title>
   <news:publication_date>2026-06-22T12:19:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703587</loc>
  <lastmod>2026-06-22T12:19:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>データ効率の高いナビゲーション方策の学習とシムツーリアル転移の枠組み（A Data-Efﬁcient Framework for Training and Sim-to-Real Transfer of Navigation Policies）</news:title>
   <news:publication_date>2026-06-22T12:19:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703585</loc>
  <lastmod>2026-06-22T12:18:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>データから自然文を作るSeq2Seqの比較：単語処理と文字処理の違い（Sequence-to-Sequence Models for Data-to-Text Natural Language Generation: Word- vs. Character-based Processing and Output Diversity）</news:title>
   <news:publication_date>2026-06-22T12:18:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703583</loc>
  <lastmod>2026-06-22T12:17:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>能動的逐次仮説検定のための方策設計と深層学習（Policy Design for Active Sequential Hypothesis Testing using Deep Learning）</news:title>
   <news:publication_date>2026-06-22T12:17:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703581</loc>
  <lastmod>2026-06-22T12:17:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>エンパワーメントに基づく探索と相互情報推定（Empowerment-driven Exploration using Mutual Information Estimation）</news:title>
   <news:publication_date>2026-06-22T12:17:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703579</loc>
  <lastmod>2026-06-22T12:17:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>適応的ノイズ増強による無向グラフィカルモデルの正則化（AdaPtive Noisy Data Augmentation for Regularized Estimation of Undirected Graphical Models）</news:title>
   <news:publication_date>2026-06-22T12:17:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703577</loc>
  <lastmod>2026-06-22T12:17:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マージン追求による分類（Classification using margin pursuit）</news:title>
   <news:publication_date>2026-06-22T12:17:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703575</loc>
  <lastmod>2026-06-22T11:25:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ターゲット音声抽出のためのVoiceFilter（VoiceFilter: Targeted Voice Separation by Speaker-Conditioned Spectrogram Masking）</news:title>
   <news:publication_date>2026-06-22T11:25:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703573</loc>
  <lastmod>2026-06-22T11:25:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>医用画像分割のための新しいドメイン適応フレームワーク（A Novel Domain Adaptation Framework for Medical Image Segmentation）</news:title>
   <news:publication_date>2026-06-22T11:25:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703571</loc>
  <lastmod>2026-06-22T11:25:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>都市部におけるLiDAR単独のNDT–basedグラフSLAM性能解析（Performance Analysis of NDT-based Graph SLAM）</news:title>
   <news:publication_date>2026-06-22T11:25:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703569</loc>
  <lastmod>2026-06-22T11:25:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>双方向Transformerによる言語表現の事前学習（BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding）</news:title>
   <news:publication_date>2026-06-22T11:25:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703567</loc>
  <lastmod>2026-06-22T11:24:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的ブロッキングとシリア紛争データへの応用（Probabilistic Blocking with An Application to the Syrian Conflict）</news:title>
   <news:publication_date>2026-06-22T11:24:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703565</loc>
  <lastmod>2026-06-22T11:24:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ユーザーの意図的行動予測のための混合深層学習アプローチ（A Blended Deep Learning Approach for Predicting User Intended Actions）</news:title>
   <news:publication_date>2026-06-22T11:24:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703563</loc>
  <lastmod>2026-06-22T11:24:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オフライン多選択ポリシー学習の最適化と一般化（Offline Multi-Action Policy Learning: Generalization and Optimization）</news:title>
   <news:publication_date>2026-06-22T11:24:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703561</loc>
  <lastmod>2026-06-22T10:33:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>離散分布に対するラオ＝ブラックウェル化確率勾配法（Rao-Blackwellized Stochastic Gradients for Discrete Distributions）</news:title>
   <news:publication_date>2026-06-22T10:33:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703559</loc>
  <lastmod>2026-06-22T10:33:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Boolean因子を持つテンソル分解の実用化とBMPアルゴリズム（Efficient Tensor Decomposition with Boolean Factors）</news:title>
   <news:publication_date>2026-06-22T10:33:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703557</loc>
  <lastmod>2026-06-22T10:32:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>プロトコル文書を使った文法ベースのファジング（Leveraging Textual Specifications for Grammar-based Fuzzing of Network Protocols）</news:title>
   <news:publication_date>2026-06-22T10:32:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703555</loc>
  <lastmod>2026-06-22T10:32:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>最大行列ノルム結合による確率的クラスタリング（Probabilistic Clustering using Maximal Matrix Norm Couplings）</news:title>
   <news:publication_date>2026-06-22T10:32:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703553</loc>
  <lastmod>2026-06-22T10:31:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Deep Recurrent Level Setを用いた脳腫瘍セグメンテーション（Deep Recurrent Level Set for Segmenting Brain Tumors）</news:title>
   <news:publication_date>2026-06-22T10:31:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703551</loc>
  <lastmod>2026-06-22T10:31:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>アンドロメダの向こうに見つかった小さな銀河の発見が示すこと（Mirach’s Goblin: Discovery of a dwarf spheroidal galaxy behind the Andromeda galaxy）</news:title>
   <news:publication_date>2026-06-22T10:31:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703549</loc>
  <lastmod>2026-06-22T10:31:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>音楽の感情を読み解くマルチモーダル手法（A MULTIMODAL APPROACH TOWARDS EMOTION RECOGNITION OF MUSIC USING AUDIO AND LYRICAL CONTENT）</news:title>
   <news:publication_date>2026-06-22T10:31:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703547</loc>
  <lastmod>2026-06-22T09:39:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>漸減ステップサイズのSGDに対する次元非依存の厳密下限（Tight Dimension Independent Lower Bound on the Expected Convergence Rate for Diminishing Step Sizes in SGD）</news:title>
   <news:publication_date>2026-06-22T09:39:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703545</loc>
  <lastmod>2026-06-22T09:39:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>完全教師付き話者ダイアライゼーションの実現（Fully Supervised Speaker Diarization）</news:title>
   <news:publication_date>2026-06-22T09:39:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703543</loc>
  <lastmod>2026-06-22T09:39:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習コミュニティを活用した学業成績予測（Leveraging local network communities to predict academic performance）</news:title>
   <news:publication_date>2026-06-22T09:39:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703541</loc>
  <lastmod>2026-06-22T09:38:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>フォーサイトを持ったクラウドタスクスケジューリング（A Stochastic Approximation Approach for Foresighted Task Scheduling in Cloud Computing）</news:title>
   <news:publication_date>2026-06-22T09:38:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703539</loc>
  <lastmod>2026-06-22T09:38:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>VDSR-ResNeXt と SRCGAN による単一画像超解像（Image Super-Resolution Using VDSR-ResNeXt and SRCGAN）</news:title>
   <news:publication_date>2026-06-22T09:38:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703537</loc>
  <lastmod>2026-06-22T09:38:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DEAとデータマイニングを統合した支店効率評価手法（Introducing a hybrid model of DEA and data mining in evaluating efficiency. Case study: Bank Branches）</news:title>
   <news:publication_date>2026-06-22T09:38:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703535</loc>
  <lastmod>2026-06-22T09:38:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>バイナリニューロンで学ぶGANの訓練（Training Generative Adversarial Networks with Binary Neurons by End-to-end Backpropagation）</news:title>
   <news:publication_date>2026-06-22T09:38:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703533</loc>
  <lastmod>2026-06-22T08:46:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>少数IMUでリアルタイムに人の姿勢を復元する技術（Deep Inertial Poser: Learning to Reconstruct Human Pose from Sparse Inertial Measurements in Real Time）</news:title>
   <news:publication_date>2026-06-22T08:46:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703531</loc>
  <lastmod>2026-06-22T08:46:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>データから文章を作る最前線──End-to-End Content and Plan Selection for Data-to-Text Generation（End-to-End Content and Plan Selection for Data-to-Text Generation）</news:title>
   <news:publication_date>2026-06-22T08:46:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703529</loc>
  <lastmod>2026-06-22T08:46:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マウス動作による侵入検知の実証研究（Intrusion Detection Using Mouse Dynamics）</news:title>
   <news:publication_date>2026-06-22T08:46:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703527</loc>
  <lastmod>2026-06-22T08:45:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ギャップのある非閉じ込め多段階ダイナミクス理論の質量スペクトル（Mass spectrum of gapped, non-confining theories with multi-scale dynamics）</news:title>
   <news:publication_date>2026-06-22T08:45:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703525</loc>
  <lastmod>2026-06-22T08:45:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>銀河のX線とAGN発生率の関係（X-rays across the galaxy population - III. The incidence of AGN as a function of star formation rate）</news:title>
   <news:publication_date>2026-06-22T08:45:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703523</loc>
  <lastmod>2026-06-22T08:44:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動的リスク特徴を用いた適応型不正検知システム（Adaptive Fraud Detection System Using Dynamic Risk Features）</news:title>
   <news:publication_date>2026-06-22T08:44:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703521</loc>
  <lastmod>2026-06-22T08:44:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>密度汎関数理論計算の高速化に向けた機械学習アプローチ（A Machine Learning Approach for Increased Throughput of Density Functional Theory Substitutional Alloy Studies）</news:title>
   <news:publication_date>2026-06-22T08:44:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703519</loc>
  <lastmod>2026-06-22T07:53:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>主要成分に導かれたスパース回帰（Principal component-guided sparse regression）</news:title>
   <news:publication_date>2026-06-22T07:53:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703517</loc>
  <lastmod>2026-06-22T07:53:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチタスク学習を多目的最適化として捉える（Multi-Task Learning as Multi-Objective Optimization）</news:title>
   <news:publication_date>2026-06-22T07:53:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703515</loc>
  <lastmod>2026-06-22T07:52:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>商品ビジュアル検索のための埋め込み学習とオンラインサンプリング（Learning Embeddings for Product Visual Search with Triplet Loss and Online Sampling）</news:title>
   <news:publication_date>2026-06-22T07:52:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703513</loc>
  <lastmod>2026-06-22T07:52:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Patient2Vecによる個別化可能なEHR深層表現（Patient2Vec: A Personalized Interpretable Deep Representation of the Longitudinal Electronic Health Record）</news:title>
   <news:publication_date>2026-06-22T07:52:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703511</loc>
  <lastmod>2026-06-22T07:51:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>仮想バッテリーのパラメータ同定と転移学習によるスタック型オートエンコーダ（Virtual Battery Parameter Identification using Transfer Learning based Stacked Autoencoder）</news:title>
   <news:publication_date>2026-06-22T07:51:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703509</loc>
  <lastmod>2026-06-22T07:51:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非線形プロセス畳み込みによるマルチ出力ガウス過程（Non-linear process convolutions for multi-output Gaussian processes）</news:title>
   <news:publication_date>2026-06-22T07:51:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703507</loc>
  <lastmod>2026-06-22T07:51:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>音声とテキストを用いたマルチモーダル音声感情認識（MULTIMODAL SPEECH EMOTION RECOGNITION USING AUDIO AND TEXT）</news:title>
   <news:publication_date>2026-06-22T07:51:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703505</loc>
  <lastmod>2026-06-22T07:00:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>リザバー・コンピューティングの進化的側面（Evolutionary aspects of Reservoir Computing）</news:title>
   <news:publication_date>2026-06-22T07:00:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703503</loc>
  <lastmod>2026-06-22T06:59:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>構造化プルーニングの実態と実務的示唆（A Closer Look at Structured Pruning for Neural Network Compression）</news:title>
   <news:publication_date>2026-06-22T06:59:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703501</loc>
  <lastmod>2026-06-22T06:59:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>公平性を組み込む回帰の総合枠組み（A General Framework for Fair Regression）</news:title>
   <news:publication_date>2026-06-22T06:59:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703499</loc>
  <lastmod>2026-06-22T06:58:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>乱流、重力、そしてマルチメッセンジャーの星震学（Turbulence, Gravity, and Multimessenger Asteroseismology）</news:title>
   <news:publication_date>2026-06-22T06:58:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703497</loc>
  <lastmod>2026-06-22T06:58:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時間を意識したクロスメディア検索とソフトスムージング（Temporal Cross-Media Retrieval with Soft-Smoothing）</news:title>
   <news:publication_date>2026-06-22T06:58:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703495</loc>
  <lastmod>2026-06-22T06:58:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>市販脳波計による感情認識の実用性検証（Consumer Grade Brain Sensing for Emotion Recognition）</news:title>
   <news:publication_date>2026-06-22T06:58:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703493</loc>
  <lastmod>2026-06-22T06:57:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ラプラシアンに基づく強化学習の表現学習（The Laplacian in RL: Learning Representations with Efficient Approximations）</news:title>
   <news:publication_date>2026-06-22T06:57:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703491</loc>
  <lastmod>2026-06-22T06:06:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ランダム行列理論に基づく共分散行列距離の改良推定（Random Matrix-Improved Estimation of Covariance Matrix Distances）</news:title>
   <news:publication_date>2026-06-22T06:06:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703489</loc>
  <lastmod>2026-06-22T06:06:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>連続ランダムハッシュによるストリーミング符号化のシンプルなベンチマーク（CRH: A Simple Benchmark Approach to Continuous Hashing）</news:title>
   <news:publication_date>2026-06-22T06:06:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703487</loc>
  <lastmod>2026-06-22T06:06:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Secure Deep Learning Engineeringの品質保証視点からの意義（Secure Deep Learning Engineering: A Software Quality Assurance Perspective）</news:title>
   <news:publication_date>2026-06-22T06:06:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703485</loc>
  <lastmod>2026-06-22T06:04:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ET-Lassoによる効率的なLassoチューニング（ET-LASSO: A NEW EFFICIENT TUNING OF LASSO-TYPE REGULARIZATION FOR HIGH-DIMENSIONAL DATA）</news:title>
   <news:publication_date>2026-06-22T06:04:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703483</loc>
  <lastmod>2026-06-22T06:04:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>プラズマ表面インターフェースの機械学習モデル（Machine learning plasma-surface interface for coupling sputtering and gas-phase transport simulations）</news:title>
   <news:publication_date>2026-06-22T06:04:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703481</loc>
  <lastmod>2026-06-22T06:04:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ユーザー生成画像からの性別推定（Inferring User Gender from User Generated Visual Content on a Deep Semantic Space）</news:title>
   <news:publication_date>2026-06-22T06:04:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703479</loc>
  <lastmod>2026-06-22T06:04:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>小学校教師が最も重視する生徒特性（THE MOST CONSIDERED TYPE OF STUDENT CHARACTERISTICS BY PRIMARY SCHOOL TEACHERS）</news:title>
   <news:publication_date>2026-06-22T06:04:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703477</loc>
  <lastmod>2026-06-22T05:12:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>低深度アンサッツによるVQE出力の一般化（Generalization of the output of variational quantum eigensolver by parameter interpolation with low-depth ansatz）</news:title>
   <news:publication_date>2026-06-22T05:12:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703475</loc>
  <lastmod>2026-06-22T05:12:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>NVM時代の学習データシャッフル最適化（LIRS: Enabling efficient machine learning on NVM-based storage via a lightweight implementation of random shuffling）</news:title>
   <news:publication_date>2026-06-22T05:12:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703473</loc>
  <lastmod>2026-06-22T05:11:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>量子検出理論に触発された多クラス分類モデル（Multi-class Classification Model Inspired by Quantum Detection Theory）</news:title>
   <news:publication_date>2026-06-22T05:11:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703471</loc>
  <lastmod>2026-06-22T05:10:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Leapfrogの刻みを学習して高速化するHMC（Faster Hamiltonian Monte Carlo by Learning Leapfrog Scale）</news:title>
   <news:publication_date>2026-06-22T05:10:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703469</loc>
  <lastmod>2026-06-22T05:10:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SECaps: 逐次情報を取り込むカプセルネットワークによる刑事事案の判決支援（SECaps: A Sequence Enhanced Capsule Model for Charge Prediction）</news:title>
   <news:publication_date>2026-06-22T05:10:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703467</loc>
  <lastmod>2026-06-22T05:10:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散協調確率的マルチアームバンディット（Decentralized Cooperative Stochastic Bandits）</news:title>
   <news:publication_date>2026-06-22T05:10:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703465</loc>
  <lastmod>2026-06-22T05:10:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>視覚的注目（サリエンシー）モデルと人間の視線の不変性解析（Invariance Analysis of Saliency Models versus Human Gaze During Scene Free Viewing）</news:title>
   <news:publication_date>2026-06-22T05:10:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703463</loc>
  <lastmod>2026-06-22T04:18:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>デコーダ内部のアテンションを用いたイディオム翻訳の探究（Exploring the Use of Attention within an Neural Machine Translation Decoder States to Translate Idioms）</news:title>
   <news:publication_date>2026-06-22T04:18:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703461</loc>
  <lastmod>2026-06-22T04:18:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>行動で暗黙に伝える学習（Learning to Communicate Implicitly by Actions）</news:title>
   <news:publication_date>2026-06-22T04:18:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703459</loc>
  <lastmod>2026-06-22T04:17:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>シミュレーション知見を現場で活かす全探索手法（Global Search with Bernoulli Alternation Kernel for Task-oriented Grasping Informed by Simulation）</news:title>
   <news:publication_date>2026-06-22T04:17:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703457</loc>
  <lastmod>2026-06-22T04:17:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>LSTMの持続性に着目した記憶参照法（Persistence pays off: Paying Attention to What the LSTM Gating Mechanism Persists）</news:title>
   <news:publication_date>2026-06-22T04:17:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
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 <url>
  <loc>https://aibr.jp/archives/703455</loc>
  <lastmod>2026-06-22T04:16:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Lazy-CFR: 広大な不完全情報ゲームを速く解く（Lazy-CFR: fast and near-optimal regret minimization for extensive games with imperfect information）</news:title>
   <news:publication_date>2026-06-22T04:16:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703453</loc>
  <lastmod>2026-06-22T04:16:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>簡約化コーパスを用いたニューラル文章簡易化モデルの改善（Improving Neural Text Simplification Model with Simplified Corpora）</news:title>
   <news:publication_date>2026-06-22T04:16:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703451</loc>
  <lastmod>2026-06-22T04:16:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層ニューラルネットワークの自動構成と並列EGO最適化（Automatic Configuration of Deep Neural Networks with Parallel Efficient Global Optimization）</news:title>
   <news:publication_date>2026-06-22T04:16:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703449</loc>
  <lastmod>2026-06-22T03:25:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Harmonizable mixture kernels と変分フーリエ特徴量による非定常性の捉え方（Harmonizable mixture kernels with variational Fourier features）</news:title>
   <news:publication_date>2026-06-22T03:25:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703447</loc>
  <lastmod>2026-06-22T03:25:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非対応・高解像度でスケーラブルなスタイル変換（Unpaired High-Resolution and Scalable Style Transfer Using Generative Adversarial Networks）</news:title>
   <news:publication_date>2026-06-22T03:25:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703445</loc>
  <lastmod>2026-06-22T03:24:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>パラメータ化深層Qネットワーク学習（Parametrized Deep Q-Networks Learning）</news:title>
   <news:publication_date>2026-06-22T03:24:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703443</loc>
  <lastmod>2026-06-22T03:23:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ソフトウェア定義無線で測るハードウェア劣化（Measuring hardware impairments with software-defined radios）</news:title>
   <news:publication_date>2026-06-22T03:23:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703441</loc>
  <lastmod>2026-06-22T03:23:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多資産マーケットメイキングにおける閉形式近似（Closed-form approximations in multi-asset market making）</news:title>
   <news:publication_date>2026-06-22T03:23:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703439</loc>
  <lastmod>2026-06-22T03:23:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>回折型ディープニューラルネットワークに対する反論への応答（Response to Comment on “All-optical machine learning using diffractive deep neural networks”）</news:title>
   <news:publication_date>2026-06-22T03:23:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703437</loc>
  <lastmod>2026-06-22T03:23:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>意味的画像解析の深層表現学習（Learning Deep Representations for Semantic Image Parsing: a Comprehensive Overview）</news:title>
   <news:publication_date>2026-06-22T03:23:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703435</loc>
  <lastmod>2026-06-22T02:31:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ランダムReLU特徴とReLUネットワークの近似能力（On the Approximation Capabilities of ReLU Neural Networks and Random ReLU Features）</news:title>
   <news:publication_date>2026-06-22T02:31:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703433</loc>
  <lastmod>2026-06-22T02:31:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ソフトウェア無線チャレンジが教育とイノベーションを加速する（A Software Radio Challenge Accelerating Education and Innovation in Wireless Communications）</news:title>
   <news:publication_date>2026-06-22T02:31:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703431</loc>
  <lastmod>2026-06-22T02:31:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>セミ監督クラスタリングによる重複排除の実務的意義（Semi-supervised clustering for de-duplication）</news:title>
   <news:publication_date>2026-06-22T02:31:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703429</loc>
  <lastmod>2026-06-22T02:30:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>タスク実行時間予測のためのオンライン逐次学習法（Task Runtime Prediction in Scientific Workflows Using an Online Incremental Learning Approach）</news:title>
   <news:publication_date>2026-06-22T02:30:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703427</loc>
  <lastmod>2026-06-22T02:30:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CuをドープしたZnSにおける欠陥準位と持続発光の起源（Defect energy levels and persistent luminescence in Cu-doped ZnS）</news:title>
   <news:publication_date>2026-06-22T02:30:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703425</loc>
  <lastmod>2026-06-22T02:30:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>適切に重み付けされたグラフ・ラプラシアンによる半教師あり学習（PROPERLY-WEIGHTED GRAPH LAPLACIAN FOR SEMI-SUPERVISED LEARNING）</news:title>
   <news:publication_date>2026-06-22T02:30:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703423</loc>
  <lastmod>2026-06-22T02:29:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Bayesian最適化とLipschitz最適化を組み合わせる手法の要点（Combining Bayesian Optimization and Lipschitz Optimization）</news:title>
   <news:publication_date>2026-06-22T02:29:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703421</loc>
  <lastmod>2026-06-22T01:39:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>相補ラベル学習：任意の損失関数とモデルに対する枠組み（Complementary-Label Learning for Arbitrary Losses and Models）</news:title>
   <news:publication_date>2026-06-22T01:39:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703419</loc>
  <lastmod>2026-06-22T01:39:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ACL2のJavaコード生成と深い埋め込みによる実務的利点（A Simple Java Code Generator for ACL2 Based on a Deep Embedding of ACL2 in Java）</news:title>
   <news:publication_date>2026-06-22T01:39:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703417</loc>
  <lastmod>2026-06-22T01:38:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>患者データを共有しない医療画像の分散学習が可能に（Multi-Institutional Deep Learning Modeling Without Sharing Patient Data）</news:title>
   <news:publication_date>2026-06-22T01:38:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703415</loc>
  <lastmod>2026-06-22T01:38:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>3つの確率モデルの物語（A Tale of Three Probabilistic Families: Discriminative, Descriptive and Generative Models）</news:title>
   <news:publication_date>2026-06-22T01:38:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703413</loc>
  <lastmod>2026-06-22T01:38:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>極端分類を対数メモリで回す発想（Extreme Classification in Log Memory）</news:title>
   <news:publication_date>2026-06-22T01:38:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703411</loc>
  <lastmod>2026-06-22T01:37:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>報酬関数のバッチ能動的選好学習（Batch Active Preference-Based Learning of Reward Functions）</news:title>
   <news:publication_date>2026-06-22T01:37:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703409</loc>
  <lastmod>2026-06-22T01:37:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スキャナ間のHARDIデータを揃える新手法：Null Space Deep Networkによる調和化（Inter-Scanner Harmonization of High Angular Resolution DW-MRI using Null Space Deep Learning）</news:title>
   <news:publication_date>2026-06-22T01:37:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703407</loc>
  <lastmod>2026-06-22T00:45:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多段階トレーニングを用いた転移学習による鳥種分類（Bird Species Classification using Transfer Learning with Multistage Training）</news:title>
   <news:publication_date>2026-06-22T00:45:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703405</loc>
  <lastmod>2026-06-22T00:45:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>データに依存したランダム特徴の圧縮による大規模カーネル近似（Data-dependent compression of random features for large-scale kernel approximation）</news:title>
   <news:publication_date>2026-06-22T00:45:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703403</loc>
  <lastmod>2026-06-22T00:45:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的ゲートによる特徴選択の実用性と要点（Feature selection using Stochastic Gates）</news:title>
   <news:publication_date>2026-06-22T00:45:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703401</loc>
  <lastmod>2026-06-22T00:44:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自律機体による分散森林火災監視（Distributed Wildfire Surveillance with Autonomous Aircraft using Deep Reinforcement Learning）</news:title>
   <news:publication_date>2026-06-22T00:44:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703399</loc>
  <lastmod>2026-06-22T00:44:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>衝撃を受けたHMXの中間スケールにおけるエネルギー局在のモデリング（Modeling meso-scale energy localization in shocked HMX, Part I: machine-learned surrogate model for effect of loading and void size）</news:title>
   <news:publication_date>2026-06-22T00:44:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703397</loc>
  <lastmod>2026-06-22T00:43:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習と予測モデリングによる心臓電気生理の再考（Rethinking multiscale cardiac electrophysiology with machine learning and predictive modelling）</news:title>
   <news:publication_date>2026-06-22T00:43:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703395</loc>
  <lastmod>2026-06-22T00:43:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>航空機衝突回避システムのための深層ニューラルネットワーク圧縮（Deep Neural Network Compression for Aircraft Collision Avoidance Systems）</news:title>
   <news:publication_date>2026-06-22T00:43:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703393</loc>
  <lastmod>2026-06-21T23:52:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的シミュレーションから異種反応速度を学ぶ（Learning heterogenous reaction rates from stochastic simulations）</news:title>
   <news:publication_date>2026-06-21T23:52:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703391</loc>
  <lastmod>2026-06-21T23:51:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ReLU学習の計算複雑性と実務的含意（The Computational Complexity of Training ReLU(s))</news:title>
   <news:publication_date>2026-06-21T23:51:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703389</loc>
  <lastmod>2026-06-21T23:51:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>モデル圧縮の理論と実践をつなぐレート・ディストーションの視点（Rate Distortion For Model Compression: From Theory To Practice）</news:title>
   <news:publication_date>2026-06-21T23:51:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703387</loc>
  <lastmod>2026-06-21T23:51:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>人工アバターで人の運動協調を学ばせる方法（Using learning to control artificial avatars in human motor coordination tasks）</news:title>
   <news:publication_date>2026-06-21T23:51:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703385</loc>
  <lastmod>2026-06-21T23:50:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>二重再パラメータ化勾配推定器（Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives）</news:title>
   <news:publication_date>2026-06-21T23:50:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703383</loc>
  <lastmod>2026-06-21T23:50:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>銀河進化から見たAGNの成長シナリオ（AGN Evolution from Galaxy Evolution Viewpoint - II）</news:title>
   <news:publication_date>2026-06-21T23:50:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703381</loc>
  <lastmod>2026-06-21T23:50:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>実画像を幾何学領域へ写像する新手法（Seeing Beyond Appearance – Mapping Real Images into Geometrical Domains for Unsupervised CAD-based Recognition）</news:title>
   <news:publication_date>2026-06-21T23:50:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703379</loc>
  <lastmod>2026-06-21T22:58:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>再現可能な機械学習パイプラインの構築（Building a Reproducible Machine Learning Pipeline）</news:title>
   <news:publication_date>2026-06-21T22:58:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703377</loc>
  <lastmod>2026-06-21T22:58:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>矮小銀河スケールのシミュレーションが示す宇宙の時間旅行（Simulations at the Dwarf Scale: From Violent Dwarfs at Cosmic Dawn and Cosmic Noon to Quiet Discs today）</news:title>
   <news:publication_date>2026-06-21T22:58:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703375</loc>
  <lastmod>2026-06-21T22:58:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>エントロピックGANとVAEの接点（Entropic GANs meet VAEs: A Statistical Approach to Compute Sample Likelihoods in GANs）</news:title>
   <news:publication_date>2026-06-21T22:58:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703373</loc>
  <lastmod>2026-06-21T22:57:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>適応学習のための最大マージン基準の系列（A Family of Maximum Margin Criterion for Adaptive Learning）</news:title>
   <news:publication_date>2026-06-21T22:57:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703371</loc>
  <lastmod>2026-06-21T22:57:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>半教師あり深層強化学習によるIoT・スマートシティ支援（Semi-supervised Deep Reinforcement Learning in Support of IoT and Smart City Services）</news:title>
   <news:publication_date>2026-06-21T22:57:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703369</loc>
  <lastmod>2026-06-21T22:56:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>一般入力分布下での一層隠れ層ニューラルネットワークの学習（Learning One-hidden-layer Neural Networks under General Input Distributions）</news:title>
   <news:publication_date>2026-06-21T22:56:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703367</loc>
  <lastmod>2026-06-21T22:56:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>汎用的能動学習戦略の発見（Discovering General-Purpose Active Learning Strategies）</news:title>
   <news:publication_date>2026-06-21T22:56:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703365</loc>
  <lastmod>2026-06-21T22:05:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>養殖における曝気機の異常検出のためのリアルタイム専門家システム（Real time expert system for anomaly detection of aerators based on computer vision technology and existing surveillance cameras）</news:title>
   <news:publication_date>2026-06-21T22:05:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703363</loc>
  <lastmod>2026-06-21T22:04:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニュース品質のマルチメディアのランキング（Ranking News-Quality Multimedia）</news:title>
   <news:publication_date>2026-06-21T22:04:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703361</loc>
  <lastmod>2026-06-21T22:04:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ビッグデータと機械学習で実現する認知的スマートシティ（Enabling Cognitive Smart Cities Using Big Data and Machine Learning: Approaches and Challenges）</news:title>
   <news:publication_date>2026-06-21T22:04:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703359</loc>
  <lastmod>2026-06-21T22:04:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Geometry meets semantics for semi-supervised monocular depth estimation（Geometry meets semantics for semi-supervised monocular depth estimation）</news:title>
   <news:publication_date>2026-06-21T22:04:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703357</loc>
  <lastmod>2026-06-21T22:04:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>後悔最小化と最良選択同定の溝を埋める（Bridging the gap between regret minimization and best arm identification, with application to A/B tests）</news:title>
   <news:publication_date>2026-06-21T22:04:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703355</loc>
  <lastmod>2026-06-21T22:03:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>画像の説明生成を機械翻訳として扱う試み（Image Captioning as Neural Machine Translation Task in SOCKEYE）</news:title>
   <news:publication_date>2026-06-21T22:03:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703353</loc>
  <lastmod>2026-06-21T22:03:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>凸目的関数の特徴づけとSGDの最適期待収束率（Characterization of Convex Objective Functions and Optimal Expected Convergence Rates for SGD）</news:title>
   <news:publication_date>2026-06-21T22:03:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703351</loc>
  <lastmod>2026-06-21T21:12:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>たわむ物体操作を学ぶ：接線空間ポイントセットレジストレーション（Learn the Manipulation of Deformable Objects Using Tangent Space Point Set Registration）</news:title>
   <news:publication_date>2026-06-21T21:12:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703349</loc>
  <lastmod>2026-06-21T21:12:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自転車再配分問題に対する知識転送機能を備えた分散強化学習ソリューション (A Distributed Reinforcement Learning Solution With Knowledge Transfer Capability for A Bike Rebalancing Problem)</news:title>
   <news:publication_date>2026-06-21T21:12:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703347</loc>
  <lastmod>2026-06-21T21:12:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>微分ガウス過程フローによる深層学習（Deep learning with differential Gaussian process flows）</news:title>
   <news:publication_date>2026-06-21T21:12:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703345</loc>
  <lastmod>2026-06-21T21:10:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多核子移動反応のランジュバン型解析（Analysis of multinucleon transfer reactions involving spherical and statically deformed nuclei using a Langevin-type approach）</news:title>
   <news:publication_date>2026-06-21T21:10:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703343</loc>
  <lastmod>2026-06-21T21:10:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>弱ラベルGTDの選択的蒸留によるスラブ識別（Selective Distillation of Weakly Annotated GTD for Vision-based Slab Identification System）</news:title>
   <news:publication_date>2026-06-21T21:10:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703341</loc>
  <lastmod>2026-06-21T21:10:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Dropoutを構造化された収縮事前分布として（Dropout as a Structured Shrinkage Prior）</news:title>
   <news:publication_date>2026-06-21T21:10:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703339</loc>
  <lastmod>2026-06-21T21:10:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層事前アンサンブルによる収束伝播で実現する画像強調（Learning Converged Propagations with Deep Prior Ensemble for Image Enhancement）</news:title>
   <news:publication_date>2026-06-21T21:10:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703337</loc>
  <lastmod>2026-06-21T20:18:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>カスケードUNetによるグリオーマ（膠芽腫）セグメンテーション（Glioma Segmentation with Cascaded Unet）</news:title>
   <news:publication_date>2026-06-21T20:18:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703335</loc>
  <lastmod>2026-06-21T20:18:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>名詞格の学習と系列ニューラルネットワーク（Learning Noun Cases Using Sequential Neural Networks）</news:title>
   <news:publication_date>2026-06-21T20:18:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703333</loc>
  <lastmod>2026-06-21T20:18:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>UOLOによる医用画像の同時計測と検出（UOLO - automatic object detection and segmentation in biomedical images）</news:title>
   <news:publication_date>2026-06-21T20:18:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703331</loc>
  <lastmod>2026-06-21T20:17:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分数拡散写像（Fractional Diffusion Maps）</news:title>
   <news:publication_date>2026-06-21T20:17:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703329</loc>
  <lastmod>2026-06-21T20:17:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>畳み込み内の畳み込み（Convolutional Neural Networks In Convolution）</news:title>
   <news:publication_date>2026-06-21T20:17:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703327</loc>
  <lastmod>2026-06-21T20:17:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文脈を取り込むニューラル自己回帰トピックモデル（textTOvec: DEEP CONTEXTUALIZED NEURAL AUTOREGRESSIVE TOPIC MODELS OF LANGUAGE WITH DISTRIBUTED COMPOSITIONAL PRIOR）</news:title>
   <news:publication_date>2026-06-21T20:17:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703325</loc>
  <lastmod>2026-06-21T20:16:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>決定論的変分推論による頑健なベイズニューラルネットワーク（DETERMINISTIC VARIATIONAL INFERENCE FOR ROBUST BAYESIAN NEURAL NETWORKS）</news:title>
   <news:publication_date>2026-06-21T20:16:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703323</loc>
  <lastmod>2026-06-21T19:25:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>転移距離計量学の体系化（Transfer Metric Learning）</news:title>
   <news:publication_date>2026-06-21T19:25:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703321</loc>
  <lastmod>2026-06-21T19:25:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>キャッシュ非依存なタスクベース双曲型PDEソルバーのメモリとエネルギー挙動（Studies on the energy and deep memory behaviour of a cache-oblivious, task-based hyperbolic PDE solver）</news:title>
   <news:publication_date>2026-06-21T19:25:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703319</loc>
  <lastmod>2026-06-21T19:25:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ns3-gym: OpenAI Gymをネットワーク研究に拡張する試み（ns3-gym: Extending OpenAI Gym for Networking）</news:title>
   <news:publication_date>2026-06-21T19:25:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703317</loc>
  <lastmod>2026-06-21T19:24:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文脈ベースの増分検索メカニズムの形式的特徴と実証分析（Characterization Formal and Empirical Analysis of Incremental Context-Based Search Mechanisms）</news:title>
   <news:publication_date>2026-06-21T19:24:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703315</loc>
  <lastmod>2026-06-21T19:24:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層ニューラルネットワークのノイズ耐性の解析（Analyzing the Noise Robustness of Deep Neural Networks）</news:title>
   <news:publication_date>2026-06-21T19:24:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703313</loc>
  <lastmod>2026-06-21T19:24:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>決定論的スリム化による空間点過程の制御（Determinantal thinning of point processes with network learning applications）</news:title>
   <news:publication_date>2026-06-21T19:24:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703311</loc>
  <lastmod>2026-06-21T19:23:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>密度に基づくK平均クラスタリングの改良（Improvement of K Mean Clustering Algorithm Based on Density）</news:title>
   <news:publication_date>2026-06-21T19:23:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703309</loc>
  <lastmod>2026-06-21T18:32:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>トランスポート層プロトコルの最近の進展（A Survey on Recent Advances in Transport Layer Protocols）</news:title>
   <news:publication_date>2026-06-21T18:32:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703307</loc>
  <lastmod>2026-06-21T18:31:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>埋め込み型ウェイクワード検出器に対する連合学習の実践（Federated Learning for Keyword Spotting）</news:title>
   <news:publication_date>2026-06-21T18:31:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703305</loc>
  <lastmod>2026-06-21T18:31:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>継続的な状態表現学習と生成的リプレイ（Continual State Representation Learning for Reinforcement Learning using Generative Replay）</news:title>
   <news:publication_date>2026-06-21T18:31:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703303</loc>
  <lastmod>2026-06-21T18:31:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>脳活動から顔画像を再構成する新手法の衝撃（Face reconstruction from fMRI using VAE-GAN）</news:title>
   <news:publication_date>2026-06-21T18:31:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703301</loc>
  <lastmod>2026-06-21T18:31:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>屋外自然風景の記憶されやすさの理解と予測（Understanding and Predicting the Memorability of Outdoor Natural Scenes）</news:title>
   <news:publication_date>2026-06-21T18:31:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703299</loc>
  <lastmod>2026-06-21T18:31:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機能的にモジュール化され解釈可能な時系列フィルタ（Functionally Modular and Interpretable Temporal Filtering for Robust Segmentation）</news:title>
   <news:publication_date>2026-06-21T18:31:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703297</loc>
  <lastmod>2026-06-21T18:30:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>不均衡な医療画像セマンティックセグメンテーションのための条件付き生成改良対抗ネットワーク（Conditional Generative Refinement Adversarial Networks for Unbalanced Medical Image Semantic Segmentation）</news:title>
   <news:publication_date>2026-06-21T18:30:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703295</loc>
  <lastmod>2026-06-21T17:39:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習された四足歩行行動を運動原始で実現する（Realizing Learned Quadruped Locomotion Behaviors through Kinematic Motion Primitives）</news:title>
   <news:publication_date>2026-06-21T17:39:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703293</loc>
  <lastmod>2026-06-21T17:39:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スパイクトレイン分類のための公開ベンチマークの提案（Spikebench: an open benchmark for spike train time-series classification）</news:title>
   <news:publication_date>2026-06-21T17:39:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703291</loc>
  <lastmod>2026-06-21T17:38:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>相互学習による深層注意追跡（Deep Attentive Tracking via Reciprocative Learning）</news:title>
   <news:publication_date>2026-06-21T17:38:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703289</loc>
  <lastmod>2026-06-21T17:38:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次元ℓ1ボール上の滑らかな対数損失に対する適応的ミニマックス後悔（Adaptive Minimax Regret against Smooth Logarithmic Losses over High-Dimensional ℓ1-Balls via Envelope Complexity）</news:title>
   <news:publication_date>2026-06-21T17:38:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703287</loc>
  <lastmod>2026-06-21T17:38:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>感情表現学習を用いた性別分類の強化（Using Sentiment Representation Learning to Enhance Gender Classification for User Profiling）</news:title>
   <news:publication_date>2026-06-21T17:38:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703285</loc>
  <lastmod>2026-06-21T17:38:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>類似領域からのインスタンス転移によるドメイン固有固有表現認識の改善（An Instance Transfer based Approach Using Enhanced Recurrent Neural Network for Domain Named Entity Recognition）</news:title>
   <news:publication_date>2026-06-21T17:38:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703283</loc>
  <lastmod>2026-06-21T17:37:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>小惑星採掘の技術経済分析（A Techno-Economic Analysis of Asteroid Mining）</news:title>
   <news:publication_date>2026-06-21T17:37:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703281</loc>
  <lastmod>2026-06-21T16:46:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数カーネルからの明示的特徴写像を用いた貪欲近似の学習境界（Learning Bounds for Greedy Approximation with Explicit Feature Maps from Multiple Kernels）</news:title>
   <news:publication_date>2026-06-21T16:46:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703279</loc>
  <lastmod>2026-06-21T16:45:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>視覚質問応答の注意機構はどこを見ているか（Knowing Where to Look? Analysis on Attention of Visual Question Answering System）</news:title>
   <news:publication_date>2026-06-21T16:45:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703277</loc>
  <lastmod>2026-06-21T16:45:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DeepWeedsによるラングランド雑草の画像データセット（DeepWeeds: A Multiclass Weed Species Image Dataset for Deep Learning）</news:title>
   <news:publication_date>2026-06-21T16:45:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703275</loc>
  <lastmod>2026-06-21T16:44:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SNAPによる準滑らかニュートン法で経路最適化を速める（SNAP: A semismooth Newton algorithm for pathwise optimization with optimal local convergence rate and oracle properties）</news:title>
   <news:publication_date>2026-06-21T16:44:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703273</loc>
  <lastmod>2026-06-21T16:44:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>フィッシャー情報計量に基づく敵対的攻撃と検出（The Adversarial Attack and Detection under the Fisher Information Metric）</news:title>
   <news:publication_date>2026-06-21T16:44:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703271</loc>
  <lastmod>2026-06-21T16:44:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>何がその判断を生んだのか：Sufficient Input Subsetsによるブラックボックス説明（What made you do this? Understanding black-box decisions with sufficient input subsets）</news:title>
   <news:publication_date>2026-06-21T16:44:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703269</loc>
  <lastmod>2026-06-21T16:43:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>志望理由書は合否を語るか？志望理由書に基づくコンピュータサイエンス大学院合格予測（Is your Statement Purposeless? Predicting Computer Science Graduation Admission Acceptance based on Statement Of Purpose）</news:title>
   <news:publication_date>2026-06-21T16:43:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703267</loc>
  <lastmod>2026-06-21T15:51:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>エナクティブ学習による自律知能エージェントの研究（Investigating Enactive Learning for Autonomous Intelligent Agents）</news:title>
   <news:publication_date>2026-06-21T15:51:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703265</loc>
  <lastmod>2026-06-21T15:51:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューラルネットワークの導関数に潜む外積構造（The Outer Product Structure of Neural Network Derivatives）</news:title>
   <news:publication_date>2026-06-21T15:51:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703263</loc>
  <lastmod>2026-06-21T15:51:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>εエリダニ系の深掘り：ケック望遠鏡Vortexコロナグラフィと視線速度で読み解く巨大外惑星の質量と軌道（DEEP EXPLORATION OF ε ERIDANI WITH KECK MS-BAND VORTEX CORONAGRAPHY AND RADIAL VELOCITIES: MASS AND ORBITAL PARAMETERS OF THE GIANT EXOPLANET）</news:title>
   <news:publication_date>2026-06-21T15:51:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703261</loc>
  <lastmod>2026-06-21T15:50:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動きの性質に基づく教師なしオンライン動画物体分割（Unsupervised Online Video Object Segmentation with Motion Property Understanding）</news:title>
   <news:publication_date>2026-06-21T15:50:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703259</loc>
  <lastmod>2026-06-21T15:50:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>直交初期化と訓練の情報幾何学（Information Geometry of Orthogonal Initializations and Training）</news:title>
   <news:publication_date>2026-06-21T15:50:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703257</loc>
  <lastmod>2026-06-21T15:50:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>量子畳み込みニューラルネットワーク（Quantum Convolutional Neural Networks）</news:title>
   <news:publication_date>2026-06-21T15:50:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703255</loc>
  <lastmod>2026-06-21T15:49:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>エージェント設計を強化学習で最適化する手法（Reinforcement Learning for Improving Agent Design）</news:title>
   <news:publication_date>2026-06-21T15:49:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703253</loc>
  <lastmod>2026-06-21T14:58:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>関係データにおける教師なしオブジェクト照合（Unsupervised Object Matching for Relational Data）</news:title>
   <news:publication_date>2026-06-21T14:58:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703251</loc>
  <lastmod>2026-06-21T14:58:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>平均マージン正則化が変える分類器設計（Average Margin Regularization for Classifiers）</news:title>
   <news:publication_date>2026-06-21T14:58:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703249</loc>
  <lastmod>2026-06-21T14:58:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GANの潜在変数復元の一般化手法（Generalized Latent Variable Recovery for Generative Adversarial Networks）</news:title>
   <news:publication_date>2026-06-21T14:58:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703247</loc>
  <lastmod>2026-06-21T14:56:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>生ポリソムノグラム波形に対する深層残差ネットワークによる自動睡眠段階分類（Deep residual networks for automatic sleep stage classification of raw polysomnographic waveforms）</news:title>
   <news:publication_date>2026-06-21T14:56:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703245</loc>
  <lastmod>2026-06-21T14:56:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マジック：ザ・ギャザリングのカード解析のためのニューラルネットワークモデル（Neural Networks Models for Analyzing Magic: the Gathering Cards）</news:title>
   <news:publication_date>2026-06-21T14:56:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703243</loc>
  <lastmod>2026-06-21T14:56:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>シミュレータの内部情報を使うドメイン適応手法（SPIGAN: PRIVILEGED ADVERSARIAL LEARNING FROM SIMULATION）</news:title>
   <news:publication_date>2026-06-21T14:56:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703241</loc>
  <lastmod>2026-06-21T14:56:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>キュービック正則化にモメンタムを組み合わせた非凸最適化の加速（Cubic Regularization with Momentum for Nonconvex Optimization）</news:title>
   <news:publication_date>2026-06-21T14:56:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703239</loc>
  <lastmod>2026-06-21T14:04:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ロックマンホール領域における1.4 GHzワイドエリアモザイクによる微弱電波源数の新制約（The Lockman Hole Project: New constraints on the sub-mJy source counts from a wide-area 1.4 GHz mosaic）</news:title>
   <news:publication_date>2026-06-21T14:04:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703237</loc>
  <lastmod>2026-06-21T13:57:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ブートストラップで頑健化するスパース復元手法の提案（JOBS: Joint-Sparse Optimization from Bootstrap Samples）</news:title>
   <news:publication_date>2026-06-21T13:57:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703235</loc>
  <lastmod>2026-06-21T13:56:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>効率的な二段階敵対的防御（Efficient Two-Step Adversarial Defense for Deep Neural Networks）</news:title>
   <news:publication_date>2026-06-21T13:56:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703233</loc>
  <lastmod>2026-06-21T13:55:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>効率的な非パラメトリック・ベイジアンによるホークス過程推定（Efficient Non-parametric Bayesian Hawkes Processes）</news:title>
   <news:publication_date>2026-06-21T13:55:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703231</loc>
  <lastmod>2026-06-21T13:55:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>重要な次元の見つけ方（Find the dimension that counts: Fast dimension estimation and Krylov PCA）</news:title>
   <news:publication_date>2026-06-21T13:55:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703229</loc>
  <lastmod>2026-06-21T13:55:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的セマンティック補完とPixel Constrained CNN（Probabilistic Semantic Inpainting with Pixel Constrained CNNs）</news:title>
   <news:publication_date>2026-06-21T13:55:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703227</loc>
  <lastmod>2026-06-21T13:54:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>道徳的責任と非難を扱う可解確率モデルの学習（Learning Tractable Probabilistic Models for Moral Responsibility and Blame）</news:title>
   <news:publication_date>2026-06-21T13:54:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703225</loc>
  <lastmod>2026-06-21T13:03:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>住宅用機器のデータ駆動負荷モデルと短期予測（Data-Driven Load Modeling and Forecasting of Residential Appliances）</news:title>
   <news:publication_date>2026-06-21T13:03:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703223</loc>
  <lastmod>2026-06-21T13:03:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>連想意味のモデル比較：単純な言語ゲームにおける参照の実証的検討（Comparing Models of Associative Meaning: An Empirical Investigation of Reference in Simple Language Games）</news:title>
   <news:publication_date>2026-06-21T13:03:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703221</loc>
  <lastmod>2026-06-21T13:02:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層学習時代の視覚的注目予測の到達点と課題（Saliency Prediction in the Deep Learning Era: Successes, Limitations, and Future Challenges）</news:title>
   <news:publication_date>2026-06-21T13:02:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703219</loc>
  <lastmod>2026-06-21T13:02:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動的マルチチャネル選択のためのアクター・クリティック深層強化学習（Actor-Critic Deep Reinforcement Learning for Dynamic Multichannel Access）</news:title>
   <news:publication_date>2026-06-21T13:02:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703217</loc>
  <lastmod>2026-06-21T13:02:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>軌道追従制御におけるハイブリッド設計（A Hybrid Approach for Trajectory Control Design）</news:title>
   <news:publication_date>2026-06-21T13:02:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703215</loc>
  <lastmod>2026-06-21T13:01:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>色画像からの3D姿勢推定におけるドメイン転移（Domain Transfer for 3D Pose Estimation from Color Images without Manual Annotations）</news:title>
   <news:publication_date>2026-06-21T13:01:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703213</loc>
  <lastmod>2026-06-21T13:01:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>適応サンプリングによる設計（Design by Adaptive Sampling）</news:title>
   <news:publication_date>2026-06-21T13:01:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703211</loc>
  <lastmod>2026-06-21T12:09:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>乱流サブグリッド反応率を推定する畳み込みニューラルネットワークの訓練（Training convolutional neural networks to estimate turbulent sub-grid scale reaction rates）</news:title>
   <news:publication_date>2026-06-21T12:09:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703209</loc>
  <lastmod>2026-06-21T12:09:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>交通シミュレータに対する実践的ベイズ最適化（Practical Bayesian Optimization for Transportation Simulators）</news:title>
   <news:publication_date>2026-06-21T12:09:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703207</loc>
  <lastmod>2026-06-21T12:09:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ホルムアルデヒド・ディープフィールド（A Formaldehyde Deep Field）</news:title>
   <news:publication_date>2026-06-21T12:09:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703205</loc>
  <lastmod>2026-06-21T12:08:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DepecheMood++：バイリンガル感情語彙の構築（DepecheMood++: a Bilingual Emotion Lexicon Built Through Simple Yet Powerful Techniques）</news:title>
   <news:publication_date>2026-06-21T12:08:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703203</loc>
  <lastmod>2026-06-21T12:08:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ワイヤレス制御プレーンの概観と今後の研究方向（The Wireless Control Plane: An Overview and Directions for Future Research）</news:title>
   <news:publication_date>2026-06-21T12:08:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703201</loc>
  <lastmod>2026-06-21T12:08:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ゼロエネルギーコミュニティのためのマルチエージェント深層強化学習（Multi-agent Deep Reinforcement Learning for Zero Energy Communities）</news:title>
   <news:publication_date>2026-06-21T12:08:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703199</loc>
  <lastmod>2026-06-21T12:08:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ステレオ動画から同時に学ぶ深度と光学フロー（Joint Unsupervised Learning of Optical Flow and Depth by Watching Stereo Videos）</news:title>
   <news:publication_date>2026-06-21T12:08:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703197</loc>
  <lastmod>2026-06-21T11:17:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多目的進化スペクトル法とアンサンブルによるグラフクラスタリングの提案（An ensemble based on a bi-objective evolutionary spectral algorithm for graph clustering）</news:title>
   <news:publication_date>2026-06-21T11:17:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703195</loc>
  <lastmod>2026-06-21T11:16:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>視覚質問応答における言語バイアス克服のための敵対的正則化（Overcoming Language Priors in Visual Question Answering with Adversarial Regularization）</news:title>
   <news:publication_date>2026-06-21T11:16:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703193</loc>
  <lastmod>2026-06-21T11:16:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>センサー融合のための最適化ゲート付き深層学習アーキテクチャ（OPTIMIZED GATED DEEP LEARNING ARCHITECTURES FOR SENSOR FUSION）</news:title>
   <news:publication_date>2026-06-21T11:16:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703191</loc>
  <lastmod>2026-06-21T11:15:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単語埋め込みにおけるバイアスの起源の解明（Understanding the Origins of Bias in Word Embeddings）</news:title>
   <news:publication_date>2026-06-21T11:15:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703189</loc>
  <lastmod>2026-06-21T11:15:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高速な文脈適応を実現するメタラーニング手法の実践的解説（CAVIA: Fast Context Adaptation via Meta-Learning）</news:title>
   <news:publication_date>2026-06-21T11:15:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703187</loc>
  <lastmod>2026-06-21T11:15:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>量子情報ボトルネック関数の凸性と操作的解釈（Convexity and Operational Interpretation of the Quantum Information Bottleneck Function）</news:title>
   <news:publication_date>2026-06-21T11:15:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703185</loc>
  <lastmod>2026-06-21T11:14:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>モアレ絶縁体を三次元対称性保護トポロジカル相の表面として見る（Moiré Insulators viewed as the Surface of three dimensional Symmetry Protected Topological Phases）</news:title>
   <news:publication_date>2026-06-21T11:14:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703183</loc>
  <lastmod>2026-06-21T10:23:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>映像から学ぶ物理スキル——SFV: Reinforcement Learning of Physical Skills from Videos（SFV: Reinforcement Learning of Physical Skills from Videos）</news:title>
   <news:publication_date>2026-06-21T10:23:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703181</loc>
  <lastmod>2026-06-21T10:23:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文字画像を用いたエンドツーエンドのテキスト分類（End-to-End Text Classification via Image-based Embedding using Character-level Networks）</news:title>
   <news:publication_date>2026-06-21T10:23:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703179</loc>
  <lastmod>2026-06-21T10:23:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動的レグレットに対する近接オンライン勾配法の最適性（Proximal Online Gradient is Optimum for Dynamic Regret: A General Lower Bound）</news:title>
   <news:publication_date>2026-06-21T10:23:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703177</loc>
  <lastmod>2026-06-21T10:22:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>強相互作用二次元電子系のスピン感受性の探査（Probing spin susceptibility of a correlated two-dimensional electron system by transport and magnetization measurements）</news:title>
   <news:publication_date>2026-06-21T10:22:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703175</loc>
  <lastmod>2026-06-21T10:22:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ロボット中心の概念知識獲得の提案（Towards Robot-Centric Conceptual Knowledge Acquisition）</news:title>
   <news:publication_date>2026-06-21T10:22:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703173</loc>
  <lastmod>2026-06-21T10:21:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>生成モデルを用いる逆問題のアルゴリズム的側面 (Algorithmic Aspects of Inverse Problems Using Generative Models)</news:title>
   <news:publication_date>2026-06-21T10:21:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703171</loc>
  <lastmod>2026-06-21T10:21:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>One-Node ReLUニューラルネットワークの学習に関する近似アルゴリズム（An Approximation Algorithm for training One-Node ReLU Neural Network）</news:title>
   <news:publication_date>2026-06-21T10:21:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703169</loc>
  <lastmod>2026-06-21T09:30:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Kerrブラックホールのモデリング：基底学習、準正準振動数、球面—扁球面混成係数（On modeling for Kerr black holes: Basis learning, QNM frequencies, and spherical-spheroidal mixing coefficients）</news:title>
   <news:publication_date>2026-06-21T09:30:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703167</loc>
  <lastmod>2026-06-21T09:30:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多言語ソース間モデル転移：何を共有するかを学ぶ（Multi-Source Cross-Lingual Model Transfer: Learning What to Share）</news:title>
   <news:publication_date>2026-06-21T09:30:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703165</loc>
  <lastmod>2026-06-21T09:29:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>無限Viterbiアライメントと減衰凸性（The infinite Viterbi alignment and decay-convexity）</news:title>
   <news:publication_date>2026-06-21T09:29:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703163</loc>
  <lastmod>2026-06-21T09:29:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Steinに基づくニューラルサンプラー（STEIN NEURAL SAMPLER）</news:title>
   <news:publication_date>2026-06-21T09:29:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703161</loc>
  <lastmod>2026-06-21T09:29:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>二値化ニューラルネットワークに対する組合せ攻撃（COMBINATORIAL ATTACKS ON BINARIZED NEURAL NETWORKS）</news:title>
   <news:publication_date>2026-06-21T09:29:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703159</loc>
  <lastmod>2026-06-21T09:28:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>メタラーニングの全体像と経営への示唆（Meta-Learning: A Survey）</news:title>
   <news:publication_date>2026-06-21T09:28:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703157</loc>
  <lastmod>2026-06-21T09:28:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習のための効果的並列化（Effective Parallelisation for Machine Learning）</news:title>
   <news:publication_date>2026-06-21T09:28:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703155</loc>
  <lastmod>2026-06-21T08:37:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CHOPT：クラウド上での自動ハイパーパラメータ最適化フレームワーク（CHOPT : Automated Hyperparameter Optimization Framework for Cloud-Based Machine Learning Platforms）</news:title>
   <news:publication_date>2026-06-21T08:37:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703153</loc>
  <lastmod>2026-06-21T08:36:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多目的最適化で設計するニューラルネットワーク（NSGA-Net: Neural Architecture Search using Multi-Objective Genetic Algorithm）</news:title>
   <news:publication_date>2026-06-21T08:36:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703151</loc>
  <lastmod>2026-06-21T08:36:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スパース性先行知識を用いたトレース商による低次元画像表現学習（Trace Quotient with Sparsity Priors for Learning Low Dimensional Image Representations）</news:title>
   <news:publication_date>2026-06-21T08:36:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703149</loc>
  <lastmod>2026-06-21T08:35:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SO/PHIのオンボード自律データ処理と較正ソフトウェア（Autonomous on-board data processing and instrument calibration software for the SO/PHI）</news:title>
   <news:publication_date>2026-06-21T08:35:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703147</loc>
  <lastmod>2026-06-21T08:35:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>RANSモデルにおける波吸収の拡張手法（Enhancing active wave absorption in RANS models）</news:title>
   <news:publication_date>2026-06-21T08:35:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703145</loc>
  <lastmod>2026-06-21T08:35:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ランダムニューラルネットワークを用いた深層学習と応用（Deep Learning with the Random Neural Network and its Applications）</news:title>
   <news:publication_date>2026-06-21T08:35:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703143</loc>
  <lastmod>2026-06-21T08:34:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>安全に探索できる空間の設計（Safe–To–Explore State Spaces: Ensuring Safe Exploration in Policy Search with Hierarchical Task Optimization）</news:title>
   <news:publication_date>2026-06-21T08:34:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703141</loc>
  <lastmod>2026-06-21T07:43:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ドロップレット方式によるラプタ符号を用いた分散計算とレイテンシ低減（A Droplet Approach Based on Raptor Codes for Distributed Computing With Straggling Servers）</news:title>
   <news:publication_date>2026-06-21T07:43:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703139</loc>
  <lastmod>2026-06-21T07:43:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>キャッシュ副チャネルが明かすニューラルネットワークの設計情報（Security Analysis of Deep Neural Networks Operating in the Presence of Cache Side-Channel Attacks）</news:title>
   <news:publication_date>2026-06-21T07:43:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703137</loc>
  <lastmod>2026-06-21T07:42:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>潜在トランスクリプトームへの道（Towards the Latent Transcriptome）</news:title>
   <news:publication_date>2026-06-21T07:42:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703135</loc>
  <lastmod>2026-06-21T07:41:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>クラスタリングの健康指標と手法横断比較（Unique Metric for Health Analysis with Optimization of Clustering Activity and Cross Comparison of Results from Different Approach）</news:title>
   <news:publication_date>2026-06-21T07:41:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703133</loc>
  <lastmod>2026-06-21T07:41:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>統一的動的アプローチによるスパースモデル選択（A Unified Dynamic Approach to Sparse Model Selection）</news:title>
   <news:publication_date>2026-06-21T07:41:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703131</loc>
  <lastmod>2026-06-21T07:40:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>常微分方程式の確率的解法を非線形ベイズフィルタとして捉える新視点（Probabilistic Solutions To Ordinary Differential Equations As Non-Linear Bayesian Filtering: A New Perspective）</news:title>
   <news:publication_date>2026-06-21T07:40:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703129</loc>
  <lastmod>2026-06-21T07:40:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Wikipediaを用いたヒンディー語─英語クロススクリプト固有表現コーパスの自動構築（Cross Script Hindi English NER Corpus from Wikipedia）</news:title>
   <news:publication_date>2026-06-21T07:40:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703127</loc>
  <lastmod>2026-06-21T06:49:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>POLO: ポリシーベース設計による最適化ライブラリの実用性（POLO: a POLicy-based Optimization library）</news:title>
   <news:publication_date>2026-06-21T06:49:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703125</loc>
  <lastmod>2026-06-21T06:38:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層的に結合された表現を実現する密なマルチモーダル融合（Dense Multimodal Fusion for Hierarchically Joint Representation）</news:title>
   <news:publication_date>2026-06-21T06:38:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703123</loc>
  <lastmod>2026-06-21T06:38:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Deep LDA Hashingの深層化によるハッシュ学習（Deep LDA Hashing）</news:title>
   <news:publication_date>2026-06-21T06:38:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703121</loc>
  <lastmod>2026-06-21T06:37:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>心臓動作の深層学習による生存予測（Deep learning cardiac motion analysis for human survival prediction）</news:title>
   <news:publication_date>2026-06-21T06:37:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703119</loc>
  <lastmod>2026-06-21T06:37:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>密度ピークと密度連結を活かす階層的クラスタリング（Hierarchical clustering that takes advantage of both density-peak and density-connectivity）</news:title>
   <news:publication_date>2026-06-21T06:37:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703117</loc>
  <lastmod>2026-06-21T06:36:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ラフ確率的ボラティリティモデルの深層キャリブレーション（Deep calibration of rough stochastic volatility models）</news:title>
   <news:publication_date>2026-06-21T06:36:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703115</loc>
  <lastmod>2026-06-21T06:36:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューラルネットワークにおけるBreimanのジレンマ再考（Rethinking Breiman’s Dilemma in Neural Networks: Phase Transitions of Margin Dynamics）</news:title>
   <news:publication_date>2026-06-21T06:36:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703113</loc>
  <lastmod>2026-06-21T05:45:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>受動光フォトニックリザーバの集積光学リードアウトの訓練（Training Passive Photonic Reservoirs with Integrated Optical Readout）</news:title>
   <news:publication_date>2026-06-21T05:45:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703111</loc>
  <lastmod>2026-06-21T05:44:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層レクチファイアネットワークの線形領域に関する経験的境界 (Empirical Bounds on Linear Regions of Deep Rectifier Networks)</news:title>
   <news:publication_date>2026-06-21T05:44:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703109</loc>
  <lastmod>2026-06-21T05:44:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ReLUネットワークにおける記憶化の検出（Detecting Memorization in ReLU Networks）</news:title>
   <news:publication_date>2026-06-21T05:44:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703107</loc>
  <lastmod>2026-06-21T05:43:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マゼラン雲における恒星集団の形態（Morphology of stellar populations in the Magellanic Clouds using the VMC survey）</news:title>
   <news:publication_date>2026-06-21T05:43:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703105</loc>
  <lastmod>2026-06-21T05:43:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>2D遷移金属ジカルコゲナイドにおける置換酸素欠陥の同定（Identifying substitutional oxygen as a prolific point defect in monolayer transition metal dichalcogenides with experiment and theory）</news:title>
   <news:publication_date>2026-06-21T05:43:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703103</loc>
  <lastmod>2026-06-21T05:42:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>敵対的頑健性の限界（Limitations of Adversarial Robustness: Strong No Free Lunch Theorem）</news:title>
   <news:publication_date>2026-06-21T05:42:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703101</loc>
  <lastmod>2026-06-21T05:42:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所原子環境を特徴づける電子状態に基づく記述子（Electronic structure based descriptor for characterizing local atomic environments）</news:title>
   <news:publication_date>2026-06-21T05:42:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703099</loc>
  <lastmod>2026-06-21T04:50:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>サポート局所化とフィッシャー計量によるオフグリッド疎正則化（Support Localization and the Fisher Metric for off-the-grid Sparse Regularization）</news:title>
   <news:publication_date>2026-06-21T04:50:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703097</loc>
  <lastmod>2026-06-21T04:50:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>対話システムにおける発話の「乱れ」を逐次検出する多仕事学習（Multi-Task Learning for Domain-General Spoken Disfluency Detection in Dialogue Systems）</news:title>
   <news:publication_date>2026-06-21T04:50:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703095</loc>
  <lastmod>2026-06-21T04:50:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習による合成で導く監視向け視線推定（Guiding Intelligent Surveillance System by learning-by-synthesis gaze estimation）</news:title>
   <news:publication_date>2026-06-21T04:50:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703093</loc>
  <lastmod>2026-06-21T04:50:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>状態機械における待ち時間閾値の最適化（Optimizing Waiting Thresholds Within A State Machine）</news:title>
   <news:publication_date>2026-06-21T04:50:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703091</loc>
  <lastmod>2026-06-21T04:49:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>サリェンシーマップの健全性チェック（Sanity Checks for Saliency Maps）</news:title>
   <news:publication_date>2026-06-21T04:49:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703089</loc>
  <lastmod>2026-06-21T04:49:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>浅いバンドと深いバンドが作る高温超伝導の道（Screening of pair fluctuations in superconductors with coupled shallow and deep bands）</news:title>
   <news:publication_date>2026-06-21T04:49:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703087</loc>
  <lastmod>2026-06-21T04:49:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所説明手法が学習済みパラメータに敏感でないという発見（LOCAL EXPLANATION METHODS FOR DEEP NEURAL NETWORKS LACK SENSITIVITY TO PARAMETER VALUES）</news:title>
   <news:publication_date>2026-06-21T04:49:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703085</loc>
  <lastmod>2026-06-21T03:58:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Novel Massive MIMO Channel Sounding Dataを用いた屋内位置推定の実務的示唆（Novel Massive MIMO Channel Sounding Data applied to Deep Learning-based Indoor Positioning）</news:title>
   <news:publication_date>2026-06-21T03:58:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703083</loc>
  <lastmod>2026-06-21T03:57:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>NSMLに関する実運用MLaaSの事例研究（NSML: Meet the MLaaS platform with a real-world case study）</news:title>
   <news:publication_date>2026-06-21T03:57:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703081</loc>
  <lastmod>2026-06-21T03:57:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>人材と職務の最適適合を測る――Joint Representation LearningによるPerson-Job Fitの実装（Person-Job Fit: Adapting the Right Talent for the Right Job with Joint Representation Learning）</news:title>
   <news:publication_date>2026-06-21T03:57:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703079</loc>
  <lastmod>2026-06-21T03:57:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散機械学習における「遅延（staleness）」の影響を解きほぐす（TOWARD UNDERSTANDING THE IMPACT OF STALENESS IN DISTRIBUTED MACHINE LEARNING）</news:title>
   <news:publication_date>2026-06-21T03:57:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703077</loc>
  <lastmod>2026-06-21T03:57:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Capsule Networkの学習と表現の特徴（On Learning and Learned Representation with Dynamic Routing in Capsule Networks）</news:title>
   <news:publication_date>2026-06-21T03:57:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703075</loc>
  <lastmod>2026-06-21T03:56:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>インターネット輻輳制御への深層強化学習の視点（A Deep Reinforcement Learning Perspective on Internet Congestion Control）</news:title>
   <news:publication_date>2026-06-21T03:56:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703073</loc>
  <lastmod>2026-06-21T03:56:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>インフルエンス関数の直感を可視化して教える方法（Visually Communicating and Teaching Intuition for Influence Functions）</news:title>
   <news:publication_date>2026-06-21T03:56:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703071</loc>
  <lastmod>2026-06-21T03:05:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単一画像超解像のための三重注意混合リンクネットワーク（Triple Attention Mixed Link Network for Single Image Super Resolution）</news:title>
   <news:publication_date>2026-06-21T03:05:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703069</loc>
  <lastmod>2026-06-21T03:05:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層可微同相正規化フロー（Deep Diffeomorphic Normalizing Flows）</news:title>
   <news:publication_date>2026-06-21T03:05:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703067</loc>
  <lastmod>2026-06-21T03:05:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CNNのスペクトル応答による診断（Diagnosing Convolutional Neural Networks using their Spectral Response）</news:title>
   <news:publication_date>2026-06-21T03:05:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703065</loc>
  <lastmod>2026-06-21T03:04:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>勾配不要・投影不要の確率的最適化の現実的意義（Towards Gradient Free and Projection Free Stochastic Optimization）</news:title>
   <news:publication_date>2026-06-21T03:04:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703063</loc>
  <lastmod>2026-06-21T03:04:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューラルネットワークの位相の解説と解釈 (Exposition and Interpretation of the Topology of Neural Networks)</news:title>
   <news:publication_date>2026-06-21T03:04:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703061</loc>
  <lastmod>2026-06-21T03:04:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>正則性条件下における非凸最適化の加速勾配法の解析的収束領域（Analytical Convergence Regions of Accelerated Gradient Descent in Nonconvex Optimization under Regularity Condition）</news:title>
   <news:publication_date>2026-06-21T03:04:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703059</loc>
  <lastmod>2026-06-21T03:04:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>タスク埋め込み制御ネットワークによる少数ショット模倣学習（Task-Embedded Control Networks for Few-Shot Imitation Learning）</news:title>
   <news:publication_date>2026-06-21T03:04:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703057</loc>
  <lastmod>2026-06-21T02:13:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>音楽生成における畳み込み変分再帰モデルの再考（Rethinking Recurrent Latent Variable Model for Music Composition）</news:title>
   <news:publication_date>2026-06-21T02:13:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703055</loc>
  <lastmod>2026-06-21T02:12:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>欠損情報を含む量子化データの復元（Recovering Quantized Data with Missing Information Using Bilinear Factorization and Augmented Lagrangian Method）</news:title>
   <news:publication_date>2026-06-21T02:12:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703053</loc>
  <lastmod>2026-06-21T02:12:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>線形計画による深層ニューラルネットワーク訓練の原則化（Principled Deep Neural Network Training Through Linear Programming）</news:title>
   <news:publication_date>2026-06-21T02:12:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703051</loc>
  <lastmod>2026-06-21T02:11:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>プレ・シナプティック・プール修正（Pre-Synaptic Pool Modification: PSPM）— 発火時系列を再現するための教師あり学習手法</news:title>
   <news:publication_date>2026-06-21T02:11:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703049</loc>
  <lastmod>2026-06-21T02:11:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>会話グループ検出のための深層畳み込みネットワーク（Conversational Group Detection With Deep Convolutional Networks）</news:title>
   <news:publication_date>2026-06-21T02:11:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703047</loc>
  <lastmod>2026-06-21T02:11:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>連続注意機構によるヒト行動認識の解明と改善（Understanding and Improving Recurrent Networks for Human Activity Recognition by Continuous Attention）</news:title>
   <news:publication_date>2026-06-21T02:11:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703045</loc>
  <lastmod>2026-06-21T02:11:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CIFAR-10の画像補完（Image Completion on CIFAR-10）</news:title>
   <news:publication_date>2026-06-21T02:11:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703043</loc>
  <lastmod>2026-06-21T01:19:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ペルシア語テキスト可読性評価の機械学習アプローチ (A Machine Learning Approach to Persian Text Readability Assessment Using a Crowdsourced Dataset)</news:title>
   <news:publication_date>2026-06-21T01:19:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703041</loc>
  <lastmod>2026-06-21T01:19:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自己学習のための強化進化学習手法（Reinforcement Evolutionary Learning Method for self-learning）</news:title>
   <news:publication_date>2026-06-21T01:19:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703039</loc>
  <lastmod>2026-06-21T01:19:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>再利用するADMM：分散アルゴリズムで計算量を減らしつつプライバシーと精度を向上させる手法（Recycled ADMM: Improve Privacy and Accuracy with Less Computation in Distributed Algorithms）</news:title>
   <news:publication_date>2026-06-21T01:19:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703037</loc>
  <lastmod>2026-06-21T01:19:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>計算機実験で明かす「相転移」の素過程（A Computational Study Explaining Processes Underlying Phase Transition）</news:title>
   <news:publication_date>2026-06-21T01:19:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703035</loc>
  <lastmod>2026-06-21T01:18:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>中国語の未監督語分割を神経的に解く──セグメンタル言語モデルの提案（Unsupervised Neural Word Segmentation for Chinese via Segmental Language Modeling）</news:title>
   <news:publication_date>2026-06-21T01:18:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703033</loc>
  <lastmod>2026-06-21T01:18:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習による原子核β崩壊半減期の予測とr過程への影響 (Predictions of nuclear β-decay half-lives with machine learning and their impacts on r process)</news:title>
   <news:publication_date>2026-06-21T01:18:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703031</loc>
  <lastmod>2026-06-21T01:18:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自動単発パルス検出と分類のスケーラブルな解法（Scalable Solutions for Automated Single Pulse Identification and Classification in Radio Astronomy）</news:title>
   <news:publication_date>2026-06-21T01:18:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703029</loc>
  <lastmod>2026-06-21T00:27:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>サブピクセル畳み込みを用いたエンコーダ−デコーダによる対応点検出（Finding Correspondences for Optical Flow and Disparity Estimations using a Sub-pixel Convolution-based Encoder-Decoder Network）</news:title>
   <news:publication_date>2026-06-21T00:27:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703027</loc>
  <lastmod>2026-06-21T00:26:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>運転中の認知負荷をリアルタイムで識別する手法（Real-Time Workload Classification during Driving using HyperNetworks）</news:title>
   <news:publication_date>2026-06-21T00:26:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703025</loc>
  <lastmod>2026-06-21T00:26:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多種フィーチャー正則化によるスパース回帰（Sparse regression with Multi-type Regularized Feature modeling）</news:title>
   <news:publication_date>2026-06-21T00:26:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703023</loc>
  <lastmod>2026-06-21T00:26:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>一旗（ユニフラジェラ）型ソフトロボットの低レイノルズ数での制御とバッキング不安定性の利用（Control of uniflagellar soft robots at low Reynolds number using buckling instability）</news:title>
   <news:publication_date>2026-06-21T00:26:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703021</loc>
  <lastmod>2026-06-21T00:25:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>欧州人権裁判所オープンデータ・プロジェクト（European Court of Human Rights Open Data project）</news:title>
   <news:publication_date>2026-06-21T00:25:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703019</loc>
  <lastmod>2026-06-21T00:25:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ASVRG: 単純化された加速確率的分散削減勾配法（ASVRG: Accelerated Proximal SVRG）</news:title>
   <news:publication_date>2026-06-21T00:25:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703017</loc>
  <lastmod>2026-06-21T00:25:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ミニバッチSGDの分散を減らす反対サンプリング（Accelerating Stochastic Gradient Descent Using Antithetic Sampling）</news:title>
   <news:publication_date>2026-06-21T00:25:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703015</loc>
  <lastmod>2026-06-20T23:34:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>銀河群におけるMONDの検証（MOND in galaxy groups）</news:title>
   <news:publication_date>2026-06-20T23:34:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703013</loc>
  <lastmod>2026-06-20T23:34:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ハートレー・スペクトルプーリングによる深層学習の効率化（Hartley Spectral Pooling for Deep Learning）</news:title>
   <news:publication_date>2026-06-20T23:34:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703011</loc>
  <lastmod>2026-06-20T23:34:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフレット数推定をCNNで高速化する手法（Graphlet Count Estimation via Convolutional Neural Networks）</news:title>
   <news:publication_date>2026-06-20T23:34:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703009</loc>
  <lastmod>2026-06-20T23:33:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>顕微鏡画像におけるCNNと圧縮センシングのEnd-to-End統合による細胞検出（Training Convolutional Neural Networks and Compressed Sensing End-to-End for Microscopy Cell Detection）</news:title>
   <news:publication_date>2026-06-20T23:33:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703007</loc>
  <lastmod>2026-06-20T23:32:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時空間的エッジサービス配置（Spatio-temporal Edge Service Placement: A Bandit Learning Approach）</news:title>
   <news:publication_date>2026-06-20T23:32:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703005</loc>
  <lastmod>2026-06-20T23:32:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DeepGeo: 画像から州を推定する技術（DeepGeo: Photo Localization with Deep Neural Network）</news:title>
   <news:publication_date>2026-06-20T23:32:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703003</loc>
  <lastmod>2026-06-20T23:32:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Humanoid WIPの重心推定をオンラインで改善する手法（Online Center of Mass Estimation for a Humanoid Wheeled Inverted Pendulum Robot）</news:title>
   <news:publication_date>2026-06-20T23:32:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/703001</loc>
  <lastmod>2026-06-20T22:41:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>RGBのみで6D姿勢を高精度に補正する手法の要点（Deep Model-Based 6D Pose Refinement in RGB）</news:title>
   <news:publication_date>2026-06-20T22:41:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702999</loc>
  <lastmod>2026-06-20T22:41:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフデータ解析のための幾何学的スキャッタリング（Geometric Scattering for Graph Data Analysis）</news:title>
   <news:publication_date>2026-06-20T22:41:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702997</loc>
  <lastmod>2026-06-20T22:41:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CSI-Net：WiFi信号で人体特徴と姿勢を一手に（CSI-Net: Uniﬁed Human Body Characterization and Pose Recognition）</news:title>
   <news:publication_date>2026-06-20T22:41:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702995</loc>
  <lastmod>2026-06-20T22:40:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>リトライによる堅牢性の実現：自己教師あり学習で作る閉ループロボット操作（Robustness via Retrying: Closed-Loop Robotic Manipulation with Self-Supervised Learning）</news:title>
   <news:publication_date>2026-06-20T22:40:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702993</loc>
  <lastmod>2026-06-20T22:40:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Bayes-CPACE：連続空間でのBayes適応型MDPに対するPAC最適探索（Bayes-CPACE: PAC Optimal Exploration in Continuous Space Bayes-Adaptive Markov Decision Processes）</news:title>
   <news:publication_date>2026-06-20T22:40:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702991</loc>
  <lastmod>2026-06-20T22:40:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>欠損データ下で証明可能なサブスペース追跡と行列補完（Provable Subspace Tracking from Missing Data and Matrix Completion）</news:title>
   <news:publication_date>2026-06-20T22:40:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702989</loc>
  <lastmod>2026-06-20T22:40:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層畳み込みガウス過程（Deep Convolutional Gaussian Processes）</news:title>
   <news:publication_date>2026-06-20T22:40:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702987</loc>
  <lastmod>2026-06-20T21:48:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>なぜ大きなモデルはより良く一般化するのか—XOR問題を通した理論的視点（Why do Larger Models Generalize Better? A Theoretical Perspective via the XOR Problem）</news:title>
   <news:publication_date>2026-06-20T21:48:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702985</loc>
  <lastmod>2026-06-20T21:39:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>合成ソーシャル接触ネットワークの複雑性と現実性の実証評価 (An Empirical Assessment of the Complexity and Realism of Synthetic Social Contact Networks)</news:title>
   <news:publication_date>2026-06-20T21:39:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702983</loc>
  <lastmod>2026-06-20T21:38:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>TensorFlowにおけるDeep Learningの入出力負荷の特徴付け（Characterizing Deep-Learning I/O Workloads in TensorFlow）</news:title>
   <news:publication_date>2026-06-20T21:38:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702981</loc>
  <lastmod>2026-06-20T21:37:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>楽曲の感情認識と歌詞感情分析（Sentiment Analysis of Songs）</news:title>
   <news:publication_date>2026-06-20T21:37:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702979</loc>
  <lastmod>2026-06-20T21:37:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層ジオデシック学習による顎骨のセグメンテーションと解剖学的ランドマーク認識（Deep Geodesic Learning for Segmentation and Anatomical Landmarking）</news:title>
   <news:publication_date>2026-06-20T21:37:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702977</loc>
  <lastmod>2026-06-20T21:37:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>類似度分布の識別によるグラフノード埋め込みの構築（Constructing Graph Node Embeddings via Discrimination of Similarity Distributions）</news:title>
   <news:publication_date>2026-06-20T21:37:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702975</loc>
  <lastmod>2026-06-20T21:36:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>因果効果推定における頑健な分散推定と検定（Robust Variance Estimation and Inference for Causal Effect Estimation）</news:title>
   <news:publication_date>2026-06-20T21:36:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702973</loc>
  <lastmod>2026-06-20T20:45:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非定常性下での最適化学習（Learning to Optimize under Non-Stationarity）</news:title>
   <news:publication_date>2026-06-20T20:45:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702971</loc>
  <lastmod>2026-06-20T20:45:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>LSTMの勾配を「整える」単純な工夫（h-DETACH: MODIFYING THE LSTM GRADIENT TOWARDS BETTER OPTIMIZATION）</news:title>
   <news:publication_date>2026-06-20T20:45:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702969</loc>
  <lastmod>2026-06-20T20:45:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>不規則時系列データの離散化が意思決定学習に与える偏り（Discretizing Logged Interaction Data Biases Learning for Decision-Making）</news:title>
   <news:publication_date>2026-06-20T20:45:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702967</loc>
  <lastmod>2026-06-20T20:44:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>MBZIRC 2017における自律工具操作ロボットの実装（Team NimbRo at MBZIRC 2017: Autonomous Valve Stem Turning using a Wrench）</news:title>
   <news:publication_date>2026-06-20T20:44:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702965</loc>
  <lastmod>2026-06-20T20:44:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>インフラ無しネットワークにおける協調スペクトラム学習（Distributed Learning Algorithms for Opportunistic Spectrum Access in Infrastructure-less Networks）</news:title>
   <news:publication_date>2026-06-20T20:44:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702963</loc>
  <lastmod>2026-06-20T20:44:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文脈を取り込む時空間手法による深層手指姿勢推定（Context-Aware Deep Spatio-Temporal Network for Hand Pose Estimation from Depth Images）</news:title>
   <news:publication_date>2026-06-20T20:44:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702961</loc>
  <lastmod>2026-06-20T20:43:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>画像キャプショニングに関する深層学習の包括的サーベイ（A Comprehensive Survey of Deep Learning for Image Captioning）</news:title>
   <news:publication_date>2026-06-20T20:43:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702959</loc>
  <lastmod>2026-06-20T19:52:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>カメラ機種判別を深層学習で高精度に行う手法（Camera Model Identification Using Convolutional Neural Networks）</news:title>
   <news:publication_date>2026-06-20T19:52:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702957</loc>
  <lastmod>2026-06-20T19:51:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>散乱した倉庫作業を高速化する物体学習と二腕協調（Fast Object Learning and Dual-arm Coordination for Cluttered Stowing, Picking, and Packing）</news:title>
   <news:publication_date>2026-06-20T19:51:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702955</loc>
  <lastmod>2026-06-20T19:51:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Anytime-Gradientsによる分散確率的勾配降下法の高速化（Anytime Stochastic Gradient Descent: A Time to Hear from all the Workers）</news:title>
   <news:publication_date>2026-06-20T19:51:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702953</loc>
  <lastmod>2026-06-20T19:50:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>異種ネットワークの高次スペクトルクラスタリング（Higher-order Spectral Clustering for Heterogeneous Graphs）</news:title>
   <news:publication_date>2026-06-20T19:50:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702951</loc>
  <lastmod>2026-06-20T19:50:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時系列データにおける新規多変量関係の発見（Mining Novel Multivariate Relationships in Time Series Data Using Correlation Networks）</news:title>
   <news:publication_date>2026-06-20T19:50:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702949</loc>
  <lastmod>2026-06-20T19:50:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>長距離依存を解析・合成して再帰型ニューラルネットの設計指針を導く（UNDERSTANDING RECURRENT NEURAL ARCHITECTURES BY ANALYZING AND SYNTHESIZING LONG DISTANCE DEPENDENCIES IN BENCHMARK SEQUENTIAL DATASETS）</news:title>
   <news:publication_date>2026-06-20T19:50:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702947</loc>
  <lastmod>2026-06-20T19:50:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>偏極ビョルケン和則の最適化的決定（Optimized determination of the polarized Bjorken sum rule in pQCD）</news:title>
   <news:publication_date>2026-06-20T19:50:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702945</loc>
  <lastmod>2026-06-20T18:59:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチレベル注意で深層テキスト照合を強化する手法（Co-Stack Residual Affinity Networks with Multi-level Attention Refinement for Matching Text Sequences）</news:title>
   <news:publication_date>2026-06-20T18:59:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702943</loc>
  <lastmod>2026-06-20T18:58:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自己チューニング型パラメータサーバの手法（Towards Self-Tuning Parameter Servers）</news:title>
   <news:publication_date>2026-06-20T18:58:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702941</loc>
  <lastmod>2026-06-20T18:58:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>FD-GAN: ポーズ指示付き特徴蒸留GANによる頑健な人物再識別（FD-GAN: Pose-guided Feature Distilling GAN for Robust Person Re-identification）</news:title>
   <news:publication_date>2026-06-20T18:58:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702939</loc>
  <lastmod>2026-06-20T18:57:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>全目標（All-Goals）一括更新を畳み込みネットワークで拡張する方法（Scaling All-Goals Updates in Reinforcement Learning Using Convolutional Neural Networks）</news:title>
   <news:publication_date>2026-06-20T18:57:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702937</loc>
  <lastmod>2026-06-20T18:57:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Deep learningを用いたab initio核理論の外挿手法（Deep learning: Extrapolation tool for ab initio nuclear theory）</news:title>
   <news:publication_date>2026-06-20T18:57:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702935</loc>
  <lastmod>2026-06-20T18:57:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>距離行列を変換して依存性を見抜く手法の提案（Adaptive Geo-Topological Independence Criterion）</news:title>
   <news:publication_date>2026-06-20T18:57:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702933</loc>
  <lastmod>2026-06-20T18:57:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Wi‑Fi信号を用いる操作不要の受動的個人識別（WiPIN: Operation-free Passive Person Identification Using Wi‑Fi Signals）</news:title>
   <news:publication_date>2026-06-20T18:57:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702931</loc>
  <lastmod>2026-06-20T18:05:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>説明可能な機械学習の技と科学（On the Art and Science of Explainable Machine Learning）</news:title>
   <news:publication_date>2026-06-20T18:05:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702929</loc>
  <lastmod>2026-06-20T18:05:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Actor-Attention-Criticによるマルチエージェント強化学習の要点（Actor-Attention-Critic for Multi-Agent Reinforcement Learning）</news:title>
   <news:publication_date>2026-06-20T18:05:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702927</loc>
  <lastmod>2026-06-20T18:04:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>検索API上のテキストマイニング実装を検証するSifaka（Sifaka: Text Mining Above a Search API）</news:title>
   <news:publication_date>2026-06-20T18:04:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702925</loc>
  <lastmod>2026-06-20T18:04:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単層WS2における硫黄欠陥の深いギャップ状態における大きなスピン軌道分裂（Large spin-orbit splitting of deep in-gap defect states of engineered sulfur vacancies in monolayer WS2）</news:title>
   <news:publication_date>2026-06-20T18:04:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702923</loc>
  <lastmod>2026-06-20T18:03:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>不均一なクラスタ密度に効くCDF変換とシフト（CDF Transform-and-Shift）</news:title>
   <news:publication_date>2026-06-20T18:03:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702921</loc>
  <lastmod>2026-06-20T18:03:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>個別因果効果の区間推定と不観測交絡への感度解析（Interval Estimation of Individual-Level Causal Effects Under Unobserved Confounding）</news:title>
   <news:publication_date>2026-06-20T18:03:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702919</loc>
  <lastmod>2026-06-20T18:03:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ラプラシアンフローに基づくネットワーク距離（NETWORK DISTANCE BASED ON LAPLACIAN FLOWS ON GRAPHS）</news:title>
   <news:publication_date>2026-06-20T18:03:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702917</loc>
  <lastmod>2026-06-20T17:11:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>構造化データからのスケーラブルな文生成（Scalable Micro-planned Generation of Discourse from Structured Data）</news:title>
   <news:publication_date>2026-06-20T17:11:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702915</loc>
  <lastmod>2026-06-20T17:11:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>HG-DAGGER: 人間専門家と協働する模倣学習の実践（HG-DAgger: Interactive Imitation Learning with Human Experts）</news:title>
   <news:publication_date>2026-06-20T17:11:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702913</loc>
  <lastmod>2026-06-20T17:11:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>モデル選択の呪い（The Model Selection Curse）</news:title>
   <news:publication_date>2026-06-20T17:11:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702911</loc>
  <lastmod>2026-06-20T17:10:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>位相整形レーザーパルスで制御するニューラルネットによるプラズモニックナノアンテナのホットスポット制御（Efficient Hotspot Switching in Plasmonic Nanoantennas using Phase-shaped Laser Pulses controlled by Neural Networks）</news:title>
   <news:publication_date>2026-06-20T17:10:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702909</loc>
  <lastmod>2026-06-20T17:10:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>順応的臨床試験：逐次的な患者募集と割り付けの最適化（Adaptive Clinical Trials: Exploiting Sequential Patient Recruitment and Allocation）</news:title>
   <news:publication_date>2026-06-20T17:10:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702907</loc>
  <lastmod>2026-06-20T17:10:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>物理知識で導く再帰型ニューラルネットワーク（Physics-Guided Recurrent Neural Networks for Modeling Dynamical Systems）</news:title>
   <news:publication_date>2026-06-20T17:10:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702905</loc>
  <lastmod>2026-06-20T17:09:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率モデルの可視化：集中的主成分分析（Visualizing probabilistic models: Intensive Principal Component Analysis）</news:title>
   <news:publication_date>2026-06-20T17:09:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702903</loc>
  <lastmod>2026-06-20T16:18:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>クラス分類を切り離して誤検出を抑える手法（Decoupled Classification Refinement: Hard False Positive Suppression for Object Detection）</news:title>
   <news:publication_date>2026-06-20T16:18:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702901</loc>
  <lastmod>2026-06-20T16:17:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>気象災害に対応するAI支援型送電網強靭化（Artificial Intelligence Assisted Power Grid Hardening in Response to Extreme Weather Events）</news:title>
   <news:publication_date>2026-06-20T16:17:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702899</loc>
  <lastmod>2026-06-20T16:17:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Zrの格子振動（フォノン）安定性を温度と機械学習で読み解く（Temperature Effect on Phonon Dispersion Stability of Zirconium by Machine Learning-driven Atomistic Simulations）</news:title>
   <news:publication_date>2026-06-20T16:17:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702897</loc>
  <lastmod>2026-06-20T16:16:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複雑モデルの学習におけるマルチタスク弱ラベルの統合（Training Complex Models with Multi-Task Weak Supervision）</news:title>
   <news:publication_date>2026-06-20T16:16:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702895</loc>
  <lastmod>2026-06-20T16:16:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>人が読める要約を学習するGAN（Learning to Encode Text as Human-Readable Summaries using Generative Adversarial Networks）</news:title>
   <news:publication_date>2026-06-20T16:16:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702893</loc>
  <lastmod>2026-06-20T16:16:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層生成による動画圧縮（Deep Generative Video Compression）</news:title>
   <news:publication_date>2026-06-20T16:16:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702891</loc>
  <lastmod>2026-06-20T16:15:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>クロス被験者転移学習が現実世界のBCI実装を現実的にする（Cross-Subject Transfer Learning Improves the Practicality of Real-World Applications of Brain-Computer Interfaces）</news:title>
   <news:publication_date>2026-06-20T16:15:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702889</loc>
  <lastmod>2026-06-20T15:23:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>計算効率の高い深層ニューラルネットワークによるCT再構成（Computationally Efficient Deep Neural Network for Computed Tomography Image Reconstruction）</news:title>
   <news:publication_date>2026-06-20T15:23:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702887</loc>
  <lastmod>2026-06-20T15:23:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ネットワーク制御におけるサブモジュラ最適化のスケーリング（Scaling Submodular Optimization Approaches for Control Applications in Networked Systems）</news:title>
   <news:publication_date>2026-06-20T15:23:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702885</loc>
  <lastmod>2026-06-20T15:23:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ResumeNetによる自動履歴書品質評価（ResumeNet: A Learning-based Framework for Automatic Resume Quality Assessment）</news:title>
   <news:publication_date>2026-06-20T15:23:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702883</loc>
  <lastmod>2026-06-20T15:22:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ローカル差分プライバシー下における線形クエリ推定（Linear Queries Estimation with Local Differential Privacy）</news:title>
   <news:publication_date>2026-06-20T15:22:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702881</loc>
  <lastmod>2026-06-20T15:22:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>銀河の年齢と金属量の解明（CLEAR I: Ages and Metallicities of Quiescent Galaxies at 1.0 &amp;lt; z &amp;lt; 1.8 Derived from Deep Hubble Space Telescope Grism Data）</news:title>
   <news:publication_date>2026-06-20T15:22:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702879</loc>
  <lastmod>2026-06-20T15:22:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DDO 68の化学組成と放射速度の解析（Chemical abundances and radial velocities in the extremely metal-poor galaxy DDO 68）</news:title>
   <news:publication_date>2026-06-20T15:22:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702877</loc>
  <lastmod>2026-06-20T15:21:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高赤方偏移における拡張型低イオン化エミッション領域の検出（Spatially resolved emission diagnostics for z~0.9 galaxies）</news:title>
   <news:publication_date>2026-06-20T15:21:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702875</loc>
  <lastmod>2026-06-20T14:31:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>行動データを予測し説明する構造化特徴空間分解（Predicting and Explaining Behavioral Data with Structured Feature Space Decomposition）</news:title>
   <news:publication_date>2026-06-20T14:31:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702873</loc>
  <lastmod>2026-06-20T14:30:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>二重半暗黙変分推論（Doubly Semi-Implicit Variational Inference）</news:title>
   <news:publication_date>2026-06-20T14:30:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702871</loc>
  <lastmod>2026-06-20T14:30:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単一ショットで高解像度を狙う照明設計と深層学習（Illumination Pattern Design with Deep Learning for Single-Shot Fourier Ptychographic Microscopy）</news:title>
   <news:publication_date>2026-06-20T14:30:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702869</loc>
  <lastmod>2026-06-20T14:29:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>予兆的加速地震活動のコーパスに対するテキスト分類（Text Classification of the Precursory Accelerating Seismicity Corpus: Inference on some Theoretical Trends in Earthquake Predictability Research from 1988 to 2018）</news:title>
   <news:publication_date>2026-06-20T14:29:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702867</loc>
  <lastmod>2026-06-20T14:29:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>M*プロキシによるスパース復元性能の評価（An M* Proxy for Sparse Recovery Performance）</news:title>
   <news:publication_date>2026-06-20T14:29:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702865</loc>
  <lastmod>2026-06-20T14:29:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>疎行列における部分集合選択（Subset Selection in Sparse Matrices）</news:title>
   <news:publication_date>2026-06-20T14:29:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702863</loc>
  <lastmod>2026-06-20T14:29:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層的再帰フィルタによる多フレーム対応DenseNet（Hierarchical Recurrent Filtering for Fully Convolutional DenseNets）</news:title>
   <news:publication_date>2026-06-20T14:29:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702861</loc>
  <lastmod>2026-06-20T13:37:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>割り当てフローの幾何学的数値積分（Geometric Numerical Integration of the Assignment Flow）</news:title>
   <news:publication_date>2026-06-20T13:37:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702859</loc>
  <lastmod>2026-06-20T13:37:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>古典中国語の文境界検出を変える一手（Sentence Segmentation for Classical Chinese Based on LSTM with Radical Embedding）</news:title>
   <news:publication_date>2026-06-20T13:37:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702857</loc>
  <lastmod>2026-06-20T13:37:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Sinkhorn発散のサンプル複雑性（Sample Complexity of Sinkhorn Divergences）</news:title>
   <news:publication_date>2026-06-20T13:37:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702855</loc>
  <lastmod>2026-06-20T13:36:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>T1強調画像から拡散MRIスカラー地図を生成するGANの研究（Generating diffusion MRI scalar maps from T1 weighted images using generative adversarial networks）</news:title>
   <news:publication_date>2026-06-20T13:36:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702853</loc>
  <lastmod>2026-06-20T13:36:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オッカムの剃刀をメメティック計算に適用する試み（Ockham’s Razor in Memetic Computing: Three Stage Optimal Memetic Exploration）</news:title>
   <news:publication_date>2026-06-20T13:36:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702851</loc>
  <lastmod>2026-06-20T13:36:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>井戸内抵抗率測定の逆問題に対する深層学習アプローチ（A Deep Learning Approach to the Inversion of Borehole Resistivity Measurements）</news:title>
   <news:publication_date>2026-06-20T13:36:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702849</loc>
  <lastmod>2026-06-20T13:35:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マゼラン橋の恒星運動が示す系の再構成（Proper motions of the Magellanic Bridge from VMC and Gaia DR2）</news:title>
   <news:publication_date>2026-06-20T13:35:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702846</loc>
  <lastmod>2026-06-20T12:44:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ベイズ予測モデルの局所解釈可能性とKL射影（Local Interpretable Model-agnostic Explanations of Bayesian Predictive Models via Kullback–Leibler Projections）</news:title>
   <news:publication_date>2026-06-20T12:44:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702844</loc>
  <lastmod>2026-06-20T12:44:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散最適化の加速手法と局所更新の効用（ACCELERATED DECENTRALIZED OPTIMIZATION WITH LOCAL UPDATES FOR SMOOTH AND STRONGLY CONVEX OBJECTIVES）</news:title>
   <news:publication_date>2026-06-20T12:44:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702842</loc>
  <lastmod>2026-06-20T12:43:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>IMMIGRATE：相互作用項を考慮したマージンベース特徴選択法 (IMMIGRATE: A Margin-based Feature Selection Method with Interaction Terms)</news:title>
   <news:publication_date>2026-06-20T12:43:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702840</loc>
  <lastmod>2026-06-20T12:43:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>リアルタイム単眼人間パフォーマンスキャプチャ（LiveCap: Real-time Human Performance Capture from Monocular Video）</news:title>
   <news:publication_date>2026-06-20T12:43:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702838</loc>
  <lastmod>2026-06-20T12:42:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ハイブリッド能動的推論（HYBRID ACTIVE INFERENCE）</news:title>
   <news:publication_date>2026-06-20T12:42:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702836</loc>
  <lastmod>2026-06-20T12:42:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文脈を使った意味曖昧性解消がニューラル機械翻訳を改善する（Integrating Weakly Supervised Word Sense Disambiguation into Neural Machine Translation）</news:title>
   <news:publication_date>2026-06-20T12:42:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702834</loc>
  <lastmod>2026-06-20T12:42:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>FingerVisionによる触覚センシングと滑り検出（FingerVision Tactile Sensor Design and Slip Detection Using Convolutional LSTM Network）</news:title>
   <news:publication_date>2026-06-20T12:42:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702832</loc>
  <lastmod>2026-06-20T11:51:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>美術作品における弱教師あり物体検出（Weakly Supervised Object Detection in Artworks）</news:title>
   <news:publication_date>2026-06-20T11:51:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702830</loc>
  <lastmod>2026-06-20T11:51:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>AIRNetによる3D医療画像の自己教師付きアフィン登録（AIRNet: Self-Supervised Affine Registration for 3D Medical Images using Neural Networks）</news:title>
   <news:publication_date>2026-06-20T11:51:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702828</loc>
  <lastmod>2026-06-20T11:50:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>μECoGデータに対する深層学習の応用と評価（Deep learning for μECoG decoding）</news:title>
   <news:publication_date>2026-06-20T11:50:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702826</loc>
  <lastmod>2026-06-20T11:49:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>波形直接操作による単一チャネル音声分離のエンドツーエンド手法（End-to-end Networks for Supervised Single-channel Speech Separation）</news:title>
   <news:publication_date>2026-06-20T11:49:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702824</loc>
  <lastmod>2026-06-20T11:49:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的最適化アルゴリズムの連続時間モデル（Continuous-time Models for Stochastic Optimization Algorithms）</news:title>
   <news:publication_date>2026-06-20T11:49:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702822</loc>
  <lastmod>2026-06-20T11:49:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GPdoemdによるモデル識別のための実験設計（GPdoemd: A Python Package for Design of Experiments for Model Discrimination）</news:title>
   <news:publication_date>2026-06-20T11:49:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702820</loc>
  <lastmod>2026-06-20T11:49:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>特徴量を用いたオンライン学習によるランキング（Online Learning to Rank with Features）</news:title>
   <news:publication_date>2026-06-20T11:49:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702818</loc>
  <lastmod>2026-06-20T10:57:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散削減のための微分可能な反対サンプリング（Differentiable Antithetic Sampling for Variance Reduction in Stochastic Variational Inference）</news:title>
   <news:publication_date>2026-06-20T10:57:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702816</loc>
  <lastmod>2026-06-20T10:57:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動的チャネル借用における干渉低減手法（Interference Declination for Dynamic Channel Borrowing Scheme in Wireless Networks）</news:title>
   <news:publication_date>2026-06-20T10:57:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702814</loc>
  <lastmod>2026-06-20T10:56:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>探索分散の動的調整によるPPO改良（PPO-CMA: Proximal Policy Optimization with Covariance Matrix Adaptation）</news:title>
   <news:publication_date>2026-06-20T10:56:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702812</loc>
  <lastmod>2026-06-20T10:55:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>最適解はどこへ行った？（Where Did My Optimum Go?: An Empirical Analysis of Gradient Descent Optimization in Policy Gradient Methods）</news:title>
   <news:publication_date>2026-06-20T10:55:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702810</loc>
  <lastmod>2026-06-20T10:55:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高速な超常磁性クラスタリング（Fast Super-Paramagnetic Clustering）</news:title>
   <news:publication_date>2026-06-20T10:55:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702808</loc>
  <lastmod>2026-06-20T10:55:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習によるシミュレーション最適化（Learning to Simulate）</news:title>
   <news:publication_date>2026-06-20T10:55:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702806</loc>
  <lastmod>2026-06-20T10:54:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単純勾配ペナルティµ-Wasserstein GANの局所安定性と性能（Local Stability and Performance of Simple Gradient Penalty µ-Wasserstein GAN）</news:title>
   <news:publication_date>2026-06-20T10:54:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702804</loc>
  <lastmod>2026-06-20T10:03:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次元ポアソン構造方程式モデルの学習（High-Dimensional Poisson Structural Equation Model Learning via ℓ1-Regularized Regression）</news:title>
   <news:publication_date>2026-06-20T10:03:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702802</loc>
  <lastmod>2026-06-20T10:03:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>補間された最近傍アルゴリズムの統計的最適性（Statistical Optimality of Interpolated Nearest Neighbor Algorithms）</news:title>
   <news:publication_date>2026-06-20T10:03:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702800</loc>
  <lastmod>2026-06-20T10:02:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>心電図のノイズ検出のための畳み込みニューラルネットワーク（Deep Convolutional Neural Networks for Noise Detection in ECGs）</news:title>
   <news:publication_date>2026-06-20T10:02:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702798</loc>
  <lastmod>2026-06-20T10:01:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>アベル2142の深層X線観測とプラズマ粘性の制約（A Deep X-Ray Look at Abell 2142 – Viscosity Constraints from Kelvin-Helmholtz Eddies, a Displaced Cool Peak that Makes a Warm Core, and a Possible Plasma Depletion Layer）</news:title>
   <news:publication_date>2026-06-20T10:01:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702796</loc>
  <lastmod>2026-06-20T10:01:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>少数サンプルから学習する脳の神経・認知アーキテクチャ (The neural and cognitive architecture for learning from a small sample)</news:title>
   <news:publication_date>2026-06-20T10:01:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702794</loc>
  <lastmod>2026-06-20T10:01:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>PET-CT画像から学ぶ特徴融合の共同学習（Co-Learning Feature Fusion Maps from PET-CT Images of Lung Cancer）</news:title>
   <news:publication_date>2026-06-20T10:01:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702792</loc>
  <lastmod>2026-06-20T10:00:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ピアツーピア貸付におけるワイド・アンド・ディープ学習（Wide and Deep Learning for Peer-to-Peer Lending）</news:title>
   <news:publication_date>2026-06-20T10:00:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702790</loc>
  <lastmod>2026-06-20T09:09:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>画像ベースの自律航空機による山火事監視と予測（Image-based Guidance of Autonomous Aircraft for Wildfire Surveillance and Prediction）</news:title>
   <news:publication_date>2026-06-20T09:09:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702788</loc>
  <lastmod>2026-06-20T09:08:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>最小二乗法のバイアス補正とボリューム再標本化（Correcting the bias in least squares regression with volume-rescaled sampling）</news:title>
   <news:publication_date>2026-06-20T09:08:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702786</loc>
  <lastmod>2026-06-20T09:08:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチチャネルUWB SAR画像のテンソル疎性学習による分類（Classifying Multi-channel UWB SAR Imagery via Tensor Sparsity Learning Techniques）</news:title>
   <news:publication_date>2026-06-20T09:08:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702784</loc>
  <lastmod>2026-06-20T09:07:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SIDISと新しいQCDプローブの展望（Novel experimental probes of QCD in SIDIS and e+e− annihilation）</news:title>
   <news:publication_date>2026-06-20T09:07:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702782</loc>
  <lastmod>2026-06-20T09:07:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>個人向けアウトフィット推薦のための深層ニューラルネットワーク（FashionNet: Personalized Outfit Recommendation with Deep Neural Network）</news:title>
   <news:publication_date>2026-06-20T09:07:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702780</loc>
  <lastmod>2026-06-20T09:07:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習タスクの動力学と到達可能性（Dynamics and Reachability of Learning Tasks）</news:title>
   <news:publication_date>2026-06-20T09:07:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702778</loc>
  <lastmod>2026-06-20T09:07:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>AutoLoss: 最適化スケジュールを自動で学習する枠組み（AutoLoss: Learning Discrete Schedules for Alternate Optimization）</news:title>
   <news:publication_date>2026-06-20T09:07:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702776</loc>
  <lastmod>2026-06-20T08:16:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率モデルの抽象化：論理的視点から (Abstracting Probabilistic Models: A Logical Perspective)</news:title>
   <news:publication_date>2026-06-20T08:16:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702774</loc>
  <lastmod>2026-06-20T08:16:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>協調推論の理論を一般化する（Generalizing the theory of cooperative inference）</news:title>
   <news:publication_date>2026-06-20T08:16:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702772</loc>
  <lastmod>2026-06-20T08:16:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>特徴の優先付けと正則化による標準精度と敵対的頑健性の改善（Feature Prioritization and Regularization Improve Standard Accuracy and Adversarial Robustness）</news:title>
   <news:publication_date>2026-06-20T08:16:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702770</loc>
  <lastmod>2026-06-20T08:15:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高解像度動画生成に向けたスライスド・ワッサースタインGANのプログレッシブ成長（Towards High Resolution Video Generation with Progressive Growing of Sliced Wasserstein GANs）</news:title>
   <news:publication_date>2026-06-20T08:15:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702768</loc>
  <lastmod>2026-06-20T08:15:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層的隠れマルコフモデルによるストリーミングセンサデータの行動認識（Activity Recognition using Hierarchical Hidden Markov Models on Streaming Sensor Data）</news:title>
   <news:publication_date>2026-06-20T08:15:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702766</loc>
  <lastmod>2026-06-20T08:14:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>極小フォトニック結晶共振器におけるQ/V比最適化（Maximizing the quality factor to mode volume ratio for ultra-small photonic crystal cavities）</news:title>
   <news:publication_date>2026-06-20T08:14:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702764</loc>
  <lastmod>2026-06-20T08:14:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ゼロショット技能合成とシミュレーション→現実転移の新手法（Zero-Shot Skill Composition and Simulation-to-Real Transfer by Learning Task Representations）</news:title>
   <news:publication_date>2026-06-20T08:14:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702762</loc>
  <lastmod>2026-06-20T07:23:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>シンプル音声コマンド認識に関する深層学習アプローチ（Deep Learning Approaches for Understanding Simple Speech Commands）</news:title>
   <news:publication_date>2026-06-20T07:23:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702760</loc>
  <lastmod>2026-06-20T07:14:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>シフト付き内積類似度によるグラフ埋め込みの改良（Graph Embedding with Shifted Inner Product Similarity and Its Improved Approximation Capability）</news:title>
   <news:publication_date>2026-06-20T07:14:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702758</loc>
  <lastmod>2026-06-20T07:14:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次元データにおける予測的射影法による特徴選択（Projective Inference in High-dimensional Problems: Prediction and Feature Selection）</news:title>
   <news:publication_date>2026-06-20T07:14:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702756</loc>
  <lastmod>2026-06-20T07:13:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>プライバシーを守りつつ複数社で学習する方法（Privacy-Preserving Multiparty Learning For Logistic Regression）</news:title>
   <news:publication_date>2026-06-20T07:13:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702754</loc>
  <lastmod>2026-06-20T07:13:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SNIP：初期化時1回の剪定で学習コストを下げる手法（SNIP: Single-Shot Network Pruning Based on Connection Sensitivity）</news:title>
   <news:publication_date>2026-06-20T07:13:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702752</loc>
  <lastmod>2026-06-20T07:13:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Isingモデルの超解像と畳み込みニューラルネットワークによる拡張（Super-resolving the Ising model with convolutional neural networks）</news:title>
   <news:publication_date>2026-06-20T07:13:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702750</loc>
  <lastmod>2026-06-20T07:12:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>視覚と言語理解から推論を切り離すNS-VQA（Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding）</news:title>
   <news:publication_date>2026-06-20T07:12:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702748</loc>
  <lastmod>2026-06-20T06:21:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューラルネットワークの適正サイズ算出法（A Practical Approach to Sizing Neural Networks）</news:title>
   <news:publication_date>2026-06-20T06:21:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702746</loc>
  <lastmod>2026-06-20T06:21:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マイクロサービス向けクラスタリングベース異常検知（Clustering-based Anomaly Detection for microservices）</news:title>
   <news:publication_date>2026-06-20T06:21:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702744</loc>
  <lastmod>2026-06-20T06:21:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>メタ学習による教師なし学習（UNSUPERVISED LEARNING VIA META-LEARNING）</news:title>
   <news:publication_date>2026-06-20T06:21:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702742</loc>
  <lastmod>2026-06-20T06:20:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>離散決定的点過程のマルコフ性（Markov Properties of Discrete Determinantal Point Processes）</news:title>
   <news:publication_date>2026-06-20T06:20:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702740</loc>
  <lastmod>2026-06-20T06:20:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>異方性ガウスSVMによる最適学習（Optimal Learning with Anisotropic Gaussian SVMs）</news:title>
   <news:publication_date>2026-06-20T06:20:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702738</loc>
  <lastmod>2026-06-20T06:19:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>低変位ランクを用いた圧縮変換の学習（Learning Compressed Transforms with Low Displacement Rank）</news:title>
   <news:publication_date>2026-06-20T06:19:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702736</loc>
  <lastmod>2026-06-20T06:19:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>無秩序媒質における電子相関の機械学習（Machine learning electron correlation in a disordered medium）</news:title>
   <news:publication_date>2026-06-20T06:19:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702734</loc>
  <lastmod>2026-06-20T05:28:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次元・非微分可能学習問題に対する近似Leave-One-Out推定（Approximate Leave-One-Out for High-Dimensional Non-Differentiable Learning Problems）</news:title>
   <news:publication_date>2026-06-20T05:28:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702732</loc>
  <lastmod>2026-06-20T05:17:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深い線形ニューラルネットワークに対する勾配降下の収束解析（A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks）</news:title>
   <news:publication_date>2026-06-20T05:17:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702730</loc>
  <lastmod>2026-06-20T05:17:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>プログレッシブ特徴融合ネットワークによる実世界画像のデハジング（Progressive Feature Fusion Network for Realistic Image Dehazing）</news:title>
   <news:publication_date>2026-06-20T05:17:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702728</loc>
  <lastmod>2026-06-20T05:16:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>低線量胸部CTからの心血管死亡予測を深層学習で直接行う（Direct Prediction of Cardiovascular Mortality from Low-dose Chest CT using Deep Learning）</news:title>
   <news:publication_date>2026-06-20T05:16:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702726</loc>
  <lastmod>2026-06-20T05:16:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>キャラクター用遷移生成の再帰遷移ネットワーク（Recurrent Transition Networks for Character Locomotion）</news:title>
   <news:publication_date>2026-06-20T05:16:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702724</loc>
  <lastmod>2026-06-20T05:16:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>回転・磁場を持つ恒星の放射層における波動（WAVES IN THE RADIATIVE ZONES OF ROTATING, MAGNETIZED STARS）</news:title>
   <news:publication_date>2026-06-20T05:16:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702722</loc>
  <lastmod>2026-06-20T05:16:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>エピソディック好奇心と到達可能性（Episodic Curiosity Through Reachability）</news:title>
   <news:publication_date>2026-06-20T05:16:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702720</loc>
  <lastmod>2026-06-20T04:24:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>凸クラスタリング：モデル、理論的保証と効率的アルゴリズム（Convex Clustering: Model, Theoretical Guarantee and Efficient Algorithm）</news:title>
   <news:publication_date>2026-06-20T04:24:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702718</loc>
  <lastmod>2026-06-20T04:24:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Adamの収束と力学的振る舞いの解明（Convergence and Dynamical Behavior of the Adam Algorithm for Non-Convex Stochastic Optimization）</news:title>
   <news:publication_date>2026-06-20T04:24:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702716</loc>
  <lastmod>2026-06-20T04:24:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>概念ドリフトを時系列として扱うべき理由（Concept-drifting Data Streams are Time Series; The Case for Continuous Adaptation）</news:title>
   <news:publication_date>2026-06-20T04:24:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702714</loc>
  <lastmod>2026-06-20T04:24:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>見ているものを印刷する脳信号解析（Brain2Object: Printing Your Mind from Brain Signals with Spatial Correlation Embedding）</news:title>
   <news:publication_date>2026-06-20T04:24:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702712</loc>
  <lastmod>2026-06-20T04:23:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単語ベクトル結合モデルに基づく言語進化樹の構築（Building a language evolution tree based on word vector combination model）</news:title>
   <news:publication_date>2026-06-20T04:23:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702710</loc>
  <lastmod>2026-06-20T04:23:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ウィースフェラー・レーマンをニューラルに拡張する手法（Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks）</news:title>
   <news:publication_date>2026-06-20T04:23:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702708</loc>
  <lastmod>2026-06-20T04:23:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>イタリア語におけるイベント検出を深層学習で改善する（Italian Event Detection Goes Deep Learning）</news:title>
   <news:publication_date>2026-06-20T04:23:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702706</loc>
  <lastmod>2026-06-20T03:32:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>敵対的に頑健な学習の一般化境界の改善（Improved Generalization Bounds for Adversarially Robust Learning）</news:title>
   <news:publication_date>2026-06-20T03:32:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702704</loc>
  <lastmod>2026-06-20T03:31:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>畳み込み空間伝播ネットワークによる深度学習（Learning Depth with Convolutional Spatial Propagation Network）</news:title>
   <news:publication_date>2026-06-20T03:31:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702702</loc>
  <lastmod>2026-06-20T03:31:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>XBARTによる高速化されたベイジアン加法回帰木（XBART: Accelerated Bayesian Additive Regression Trees）</news:title>
   <news:publication_date>2026-06-20T03:31:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702700</loc>
  <lastmod>2026-06-20T03:31:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>より細分化された学習で汎用表現をつくる（Learning Finer-class Networks for Universal Representations）</news:title>
   <news:publication_date>2026-06-20T03:31:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702698</loc>
  <lastmod>2026-06-20T03:30:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>異言語間で否定の影響範囲を検出するニューラル手法（Neural networks for cross-lingual negation scope detection）</news:title>
   <news:publication_date>2026-06-20T03:30:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702696</loc>
  <lastmod>2026-06-20T03:30:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>周辺監視問題の適応的方策（Adaptive Policies for Perimeter Surveillance Problems）</news:title>
   <news:publication_date>2026-06-20T03:30:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702694</loc>
  <lastmod>2026-06-20T03:30:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>フォトニック結晶ファイバーを用いたUVソリトンダイナミクスとラマン強化超広帯域生成（UV soliton dynamics and Raman-enhanced super-continuum generation in photonic crystal fiber）</news:title>
   <news:publication_date>2026-06-20T03:30:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702692</loc>
  <lastmod>2026-06-20T02:39:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スワップション戦略のための機械学習ベースの推薦システム（A Machine Learning-based Recommendation System for Swaptions Strategies）</news:title>
   <news:publication_date>2026-06-20T02:39:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702690</loc>
  <lastmod>2026-06-20T02:39:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>疎アフィン制約を伴うマルチエージェント最適化のための近接拡散戦略 (A Proximal Diffusion Strategy for Multi-Agent Optimization with Sparse Affine Constraints)</news:title>
   <news:publication_date>2026-06-20T02:39:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702688</loc>
  <lastmod>2026-06-20T02:38:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>腫瘍セグメンテーションと転移学習を用いた生存予測（Survival prediction using ensemble tumor segmentation and transfer learning）</news:title>
   <news:publication_date>2026-06-20T02:38:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702686</loc>
  <lastmod>2026-06-20T02:37:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多峰性を狙い撃ちする探索基準の提案（Infill Criterion for Multimodal Model-Based Optimisation）</news:title>
   <news:publication_date>2026-06-20T02:37:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702684</loc>
  <lastmod>2026-06-20T02:37:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>モンテカルロ依存性推定（Monte Carlo Dependency Estimation）</news:title>
   <news:publication_date>2026-06-20T02:37:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702682</loc>
  <lastmod>2026-06-20T02:37:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Zooming Networkが変える長文理解の効率化（Zooming Network）</news:title>
   <news:publication_date>2026-06-20T02:37:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702680</loc>
  <lastmod>2026-06-20T02:37:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>胸部X線画像における解剖学的構造のセグメンテーション改善（Improving the Segmentation of Anatomical Structures in Chest Radiographs using U-Net with an ImageNet Pre-trained Encoder）</news:title>
   <news:publication_date>2026-06-20T02:37:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702678</loc>
  <lastmod>2026-06-20T01:46:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ドメイン外依存構文解析のための半教師あり手法（SEMI-SUPERVISED METHODS FOR OUT-OF-DOMAIN DEPENDENCY PARSING）</news:title>
   <news:publication_date>2026-06-20T01:46:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702676</loc>
  <lastmod>2026-06-20T01:45:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチリンガルSeq2Seq音声認識の転移学習と言語モデルの効果（MULTILINGUAL SEQUENCE-TO-SEQUENCE SPEECH RECOGNITION: ARCHITECTURE, TRANSFER LEARNING, AND LANGUAGE MODELING）</news:title>
   <news:publication_date>2026-06-20T01:45:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702674</loc>
  <lastmod>2026-06-20T01:45:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DeepNISによる非線形電磁逆散乱の新展開（DeepNIS: Deep Neural Network for Nonlinear Electromagnetic Inverse Scattering）</news:title>
   <news:publication_date>2026-06-20T01:45:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702672</loc>
  <lastmod>2026-06-20T01:44:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>画像から動画へ――アドバーサリアルなピクセルレベルドメイン適応による動画物体検出の学習（Unsupervised Adversarial Visual Level Domain Adaptation for Learning Video Object Detectors from Images）</news:title>
   <news:publication_date>2026-06-20T01:44:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702670</loc>
  <lastmod>2026-06-20T01:44:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ドメイン間での物理運動転移によるニューラル慣性追跡（Transferring Physical Motion Between Domains for Neural Inertial Tracking）</news:title>
   <news:publication_date>2026-06-20T01:44:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702668</loc>
  <lastmod>2026-06-20T01:44:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフのグラフに対する二重畳み込みニューラルネットワーク（Dual Convolutional Neural Network for Graph of Graphs Link Prediction）</news:title>
   <news:publication_date>2026-06-20T01:44:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702666</loc>
  <lastmod>2026-06-20T01:44:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>KMTNetによる低表面輝度撮像の最適戦略（KMTNET Nearby Galaxy Survey I: Optimal Strategy for Low Surface Brightness Imaging with KMTNet）</news:title>
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 <url>
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 <url>
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    <news:name>AI Benchmark Research</news:name>
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 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 <url>
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    <news:name>AI Benchmark Research</news:name>
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 <url>
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    <news:name>AI Benchmark Research</news:name>
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 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-06-20T00:50:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 </url>
 <url>
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  <news:news>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 </url>
 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 </url>
 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>作業指向の手運動リターゲティングによる巧緻な操作模倣（Task-Oriented Hand Motion Retargeting for Dexterous Manipulation Imitation）</news:title>
   <news:publication_date>2026-06-19T20:26:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-06-19T20:25:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>事前定義した類似度に基づくkクラスタ数の最適決定（Determining Optimal Number of k-Clusters based on Predefined Level-of-Similarity）</news:title>
   <news:publication_date>2026-06-19T20:25:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702590</loc>
  <lastmod>2026-06-19T20:25:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>重み正規化された深層ニューラルネットワークの理解（Understanding Weight Normalized Deep Neural Networks with Rectified Linear Units）</news:title>
   <news:publication_date>2026-06-19T20:25:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702588</loc>
  <lastmod>2026-06-19T20:24:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>重み付きシグモイドゲートによる活性化関数の提案（Weighted Sigmoid Gate Unit for an Activation Function of Deep Neural Network）</news:title>
   <news:publication_date>2026-06-19T20:24:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702586</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>重なりを扱うニューラルセグメンタルハイパーグラフ（Neural Segmental Hypergraphs for Overlapping Mention Recognition）</news:title>
   <news:publication_date>2026-06-19T20:24:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702584</loc>
  <lastmod>2026-06-19T20:24:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単一画像超解像におけるチャネル再校正の威力（An Effective Single-Image Super-Resolution Model Using Squeeze-and-Excitation Networks）</news:title>
   <news:publication_date>2026-06-19T20:24:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702582</loc>
  <lastmod>2026-06-19T20:24:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>人間中心の自動運転システム：効果的な共有自律の原則（Human-Centered Autonomous Vehicle Systems: Principles of Effective Shared Autonomy）</news:title>
   <news:publication_date>2026-06-19T20:24:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702580</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-06-19T19:32:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702578</loc>
  <lastmod>2026-06-19T19:32:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ASTに基づく深層学習による悪性PowerShell検出（AST-Based Deep Learning for Detecting Malicious PowerShell）</news:title>
   <news:publication_date>2026-06-19T19:32:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702576</loc>
  <lastmod>2026-06-19T19:32:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層生成モデルにおける「スプリアス（偽）サンプル」は欠陥か、機能か（Spurious samples in deep generative models: bug or feature?）</news:title>
   <news:publication_date>2026-06-19T19:32:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702574</loc>
  <lastmod>2026-06-19T19:31:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>一意的な森の因数分解（Unambiguous Forest Factorization）</news:title>
   <news:publication_date>2026-06-19T19:31:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702572</loc>
  <lastmod>2026-06-19T19:31:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>McTorch：PyTorchのための多様体最適化ライブラリ（McTorch, a manifold optimization library for deep learning）</news:title>
   <news:publication_date>2026-06-19T19:31:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702570</loc>
  <lastmod>2026-06-19T19:31:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ネストしたメンション認識の遷移基礎モデル（A Neural Transition-based Model for Nested Mention Recognition）</news:title>
   <news:publication_date>2026-06-19T19:31:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702568</loc>
  <lastmod>2026-06-19T19:30:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>音声に基づくアーティスト識別の拡張（Disambiguating Music Artists at Scale with Audio Metric Learning）</news:title>
   <news:publication_date>2026-06-19T19:30:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-06-19T18:39:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>離散化ニューラルネットワークのためのRelaxed Quantization（Relaxed Quantization for Discretized Neural Networks）</news:title>
   <news:publication_date>2026-06-19T18:39:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702564</loc>
  <lastmod>2026-06-19T18:39:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-06-19T18:39:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702562</loc>
  <lastmod>2026-06-19T18:39:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>人工ニューラルネットワークの位相的探索（Topological exploration of artificial neuronal network dynamics）</news:title>
   <news:publication_date>2026-06-19T18:39:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702560</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>センサ運動的不変性から学ぶロボットの空間認識（Learning agent’s spatial configuration from sensorimotor invariants）</news:title>
   <news:publication_date>2026-06-19T18:38:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702558</loc>
  <lastmod>2026-06-19T18:38:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>店舗棚上の商品認識のための深層学習パイプライン（A deep learning pipeline for product recognition on store shelves）</news:title>
   <news:publication_date>2026-06-19T18:38:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702556</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-06-19T18:38:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702554</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自然勾配とヘシアンフリーの統合による系列訓練最適化（Combining Natural Gradient with Hessian Free Methods for Sequence Training）</news:title>
   <news:publication_date>2026-06-19T18:37:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702552</loc>
  <lastmod>2026-06-19T17:46:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オンライン毒性検出の機械学習スイート（Machine Learning Suites for Online Toxicity Detection）</news:title>
   <news:publication_date>2026-06-19T17:46:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702550</loc>
  <lastmod>2026-06-19T17:46:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次元多標本比較の非パラメトリック手法（A Nonparametric Approach to High-dimensional k-sample Comparison Problems）</news:title>
   <news:publication_date>2026-06-19T17:46:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702548</loc>
  <lastmod>2026-06-19T17:46:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>知覚の基礎付け：感覚運動連関への発達的アプローチ (Grounding Perception: A Developmental Approach to Sensorimotor Contingencies)</news:title>
   <news:publication_date>2026-06-19T17:46:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702546</loc>
  <lastmod>2026-06-19T17:45:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-06-19T17:45:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702544</loc>
  <lastmod>2026-06-19T17:45:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>エンドエフェクタ配置空間の内部表現学習（Learning an internal representation of the end-effector configuration space）</news:title>
   <news:publication_date>2026-06-19T17:45:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702542</loc>
  <lastmod>2026-06-19T17:45:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>新領域のNLU向けアクティブラーニング（Active Learning for New Domains in Natural Language Understanding）</news:title>
   <news:publication_date>2026-06-19T17:45:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702540</loc>
  <lastmod>2026-06-19T17:45:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>集合特徴を学習可能なプーリング層としてのSet Aggregation Network（Set Aggregation Network as a Trainable Pooling Layer）</news:title>
   <news:publication_date>2026-06-19T17:45:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702538</loc>
  <lastmod>2026-06-19T16:53:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ダークマター探索の新時代（A New Era in the Quest for Dark Matter）</news:title>
   <news:publication_date>2026-06-19T16:53:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702536</loc>
  <lastmod>2026-06-19T16:46:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>アグノスティック回帰における有界サンプル圧縮の前進（Agnostic Sample Compression Schemes for Regression）</news:title>
   <news:publication_date>2026-06-19T16:46:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702534</loc>
  <lastmod>2026-06-19T16:45:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>実稼働HPCクラスターにおける熱モデル同定の堅牢化とデータ選択（Robust identification of thermal models for in-production High-Performance-Computing clusters with machine learning-based data selection）</news:title>
   <news:publication_date>2026-06-19T16:45:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702532</loc>
  <lastmod>2026-06-19T16:45:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習ベースによる完全自動の短軸心臓MRI層間動き補正法（A Comprehensive Approach for Learning-based Fully-Automated Inter-slice Motion Correction for Short-Axis Cine Cardiac MR Image Stacks）</news:title>
   <news:publication_date>2026-06-19T16:45:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702530</loc>
  <lastmod>2026-06-19T16:44:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Safe RuleFitによる最適スパースルール学習（Safe RuleFit: Learning Optimal Sparse Rule Model by Meta Safe Screening）</news:title>
   <news:publication_date>2026-06-19T16:44:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702528</loc>
  <lastmod>2026-06-19T16:44:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>扱いやすい経験的尤度を用いたABC法（An Easy-to-Use Empirical Likelihood ABC Method）</news:title>
   <news:publication_date>2026-06-19T16:44:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/702526</loc>
  <lastmod>2026-06-19T16:44:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>LN-CASS：スパイク・アンド・スラブの連続近似による同時推定と変数選択（Simultaneous Parameter Estimation and Variable Selection via the LN-CASS Prior）</news:title>
   <news:publication_date>2026-06-19T16:44:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-06-19T15:52:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DeepImageSpam: 画像スパム検出における深層学習の実践（DeepImageSpam: Deep Learning based Image Spam Detection）</news:title>
   <news:publication_date>2026-06-19T15:52:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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  <loc>https://aibr.jp/archives/702520</loc>
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   <news:publication_date>2026-06-19T15:51:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/702518</loc>
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    <news:name>AI Benchmark Research</news:name>
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   <news:publication_date>2026-06-19T15:50:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/702516</loc>
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  <news:news>
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    <news:name>AI Benchmark Research</news:name>
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   <news:genres>Blog</news:genres>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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