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   <news:title>Transformerを用いた変分半教師付きアスペクト項目感情分析（Variational Semi-supervised Aspect-term Sentiment Analysis via Transformer）</news:title>
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   <news:title>メタデータを使い分ける地図方程式（A Map Equation with Metadata: Varying the Role of Attributes in Community Detection）</news:title>
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   <news:title>車車間通信における効率的な情報伝播のための深層学習アプローチ（A Deep Learning Approach to Efficient Information Dissemination in Vehicular Floating Content）</news:title>
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   <news:title>地下多相流の縮約モデル化とDR-RNNによる高速近似（Reduced order modeling of subsurface multiphase flow models using deep residual recurrent neural networks）</news:title>
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   <news:title>双方向テレオペレーションによるスクーピング動作の深層学習（Deep Learning Scooping Motion using Bilateral Teleoperations）</news:title>
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    <news:name>AI Benchmark Research</news:name>
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   <news:title>UAVネットワークにおけるマルチエージェント強化学習による資源配分（Multi-Agent Reinforcement Learning Based Resource Allocation for UAV Networks）</news:title>
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   <news:title>マイクロヘキサキャビティピクセル検出器の動作と性能（Operation and Performance of Microhexcavity Pixel Detector in Gas Discharge and Avalanche Mode）</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>CatBoost: カテゴリ変数対応の勾配ブースティング（CatBoost: gradient boosting with categorical features）</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>離散領域におけるスパースガウス過程（Sparse Gaussian Processes on Discrete Domains）</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>中国電力システムの脱炭素における水力・蓄電・送電の役割（The role of hydro power, storage and transmission in the decarbonization of the Chinese power system）</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>G-SMOTEによる不均衡学習の高次元合成少数オーバーサンプリング（G-SMOTE: A GMM-BASED SYNTHETIC MINORITY OVERSAMPLING TECHNIQUE FOR IMBALANCED LEARNING）</news:title>
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   <news:title>ラベル比率から個別ラベルを復元するためのラベル伝播法（Label Propagation for Learning with Label Proportions）</news:title>
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   <news:genres>Blog</news:genres>
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    <news:name>AI Benchmark Research</news:name>
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   <news:title>外部情報のノイズを見抜く学習法（Learning to Discriminate Noises for Incorporating External Information in Neural Machine Translation）</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>人工ニューラルネットワークによる量子計算のエミュレーション（Emulating quantum computation with artificial neural networks）</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>3DコーンビームCTにおける歯科病変検出（Dental pathology detection in 3D cone-beam CT）</news:title>
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   <news:genres>Blog</news:genres>
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   <news:title>人間中心サイバーフィジカルシステムにおけるセグメンテーション解析（Segmentation Analysis in Human Centric Cyber-Physical Systems using Graphical Lasso）</news:title>
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   <news:genres>Blog</news:genres>
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   <news:title>合成画像の色空間適応で実画像セグメンテーションを改善する手法（Learning Color Space Adaptation from Synthetic to Real Images of Cirrus Clouds）</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>少数データで学習する音声分類器の訓練（TRAINING NEURAL AUDIO CLASSIFIERS WITH FEW DATA）</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: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>道路ネットワークにおけるマルチステップ速度予測（Multistep Speed Prediction on Traffic Networks: A Graph Convolutional Sequence-to-Sequence Learning Approach with Attention Mechanism）</news:title>
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   <news:genres>Blog</news:genres>
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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:title>車載向けフローティングコンテンツ管理に対する深層学習戦略（A Deep Learning Strategy for Vehicular Floating Content Management）</news:title>
   <news:publication_date>2026-06-27T05:55:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
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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:title>コードスイッチを学習するデータ増強法（LEARN TO CODE-SWITCH: DATA AUGMENTATION USING COPY MECHANISM ON LANGUAGE MODELING）</news:title>
   <news:publication_date>2026-06-27T05:55:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
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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:title>弱凸弱凹ミンマックス問題に対する一階収束理論（First-order Convergence Theory for Weakly-Convex-Weakly-Concave Min-max Problems）</news:title>
   <news:publication_date>2026-06-27T05:04:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-06-27T05:04:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DSFD: Dual Shot Face Detector（DSFD: Dual Shot Face Detector）</news:title>
   <news:publication_date>2026-06-27T05:04:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-06-27T05:03:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>サブワードでULMFiTを多層化する意義（Universal Language Model Fine-Tuning with Subword Tokenization for Polish）</news:title>
   <news:publication_date>2026-06-27T05:03:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/705346</loc>
  <lastmod>2026-06-27T05:03:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>PN接合の粒子シミュレーションにおけるポアソン方程式の深層学習による解法（Solving Poisson’s Equation using Deep Learning in Particle Simulation of PN Junction）</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>深層表現を活用したニューラル機械翻訳の改良（Exploiting Deep Representations for Neural Machine Translation）</news:title>
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   <news:genres>Blog</news:genres>
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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:title>粒子凝集型バイオセンサーの高スループット解析を可能にする深層学習とホログラフィー（Deep learning enables high-throughput analysis of particle-aggregation-based bio-sensors imaged using holography）</news:title>
   <news:publication_date>2026-06-27T05:03:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-06-27T05:03:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>視覚と触覚を「同時に学ぶ」ことで接触を伴う作業が劇的に効率化する（Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich Tasks）</news:title>
   <news:publication_date>2026-06-27T05:03:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/705338</loc>
  <lastmod>2026-06-27T04:11:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習された最適化器の訓練における病理の理解と是正（Understanding and correcting pathologies in the training of learned optimizers）</news:title>
   <news:publication_date>2026-06-27T04:11:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705336</loc>
  <lastmod>2026-06-27T04:11:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模知識ベースからの検索のためのテキスト埋め込み（Text Embeddings for Retrieval from a Large Knowledge Base）</news:title>
   <news:publication_date>2026-06-27T04:11:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705334</loc>
  <lastmod>2026-06-27T04:11:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オンライン映画知識ライブラリに基づくデータ駆動型ブロックバスター企画（Data-driven Blockbuster Planning on Online Movie Knowledge Library）</news:title>
   <news:publication_date>2026-06-27T04:11:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705332</loc>
  <lastmod>2026-06-27T04:10:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非凸・非滑らかなスパース最適化の適応的反復再重み付け法（Nonconvex and Nonsmooth Sparse Optimization via Adaptively Iterative Reweighted Methods）</news:title>
   <news:publication_date>2026-06-27T04:10:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705330</loc>
  <lastmod>2026-06-27T04:10:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ランダム化勾配ブースティング機械（Randomized Gradient Boosting Machine）</news:title>
   <news:publication_date>2026-06-27T04:10:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705328</loc>
  <lastmod>2026-06-27T04:10:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>長短期記憶を用いた時系列予測の深層学習的アプローチ（Deep Learning with Long Short-Term Memory for Time Series Prediction）</news:title>
   <news:publication_date>2026-06-27T04:10:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705326</loc>
  <lastmod>2026-06-27T04:09:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>窒化物半導体のバンドギャップ・バンド整合の機械学習予測（Band gap and band alignment prediction of nitride based semiconductors using machine learning）</news:title>
   <news:publication_date>2026-06-27T04:09:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705324</loc>
  <lastmod>2026-06-27T03:19:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ファッション属性検出のための深層学習モデル（A Deep-Learning-Based Fashion Attributes Detection Model）</news:title>
   <news:publication_date>2026-06-27T03:19:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705322</loc>
  <lastmod>2026-06-27T03:18:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>FewRelをめぐる実務的解説—大規模少ショット関係分類データセット（FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation）</news:title>
   <news:publication_date>2026-06-27T03:18:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705320</loc>
  <lastmod>2026-06-27T03:18:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>認知無線ベースのブロックチェーンネットワークにおける取引伝送とチャネル選択の共同最適化（JOINT TRANSACTION TRANSMISSION AND CHANNEL SELECTION IN COGNITIVE RADIO BASED BLOCKCHAIN NETWORKS: A DEEP REINFORCEMENT LEARNING APPROACH）</news:title>
   <news:publication_date>2026-06-27T03:18:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705318</loc>
  <lastmod>2026-06-27T03:18:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単語埋め込みの局所ホモロジー（Local Homology of Word Embeddings）</news:title>
   <news:publication_date>2026-06-27T03:18:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705316</loc>
  <lastmod>2026-06-27T03:18:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>平滑化された回帰とLQR制御のためのオンラインアルゴリズム（An Online Algorithm for Smoothed Regression and LQR Control）</news:title>
   <news:publication_date>2026-06-27T03:18:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705314</loc>
  <lastmod>2026-06-27T03:17:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>制約付きK平均クラスタリングへの2値最適化アプローチ（A Binary Optimization Approach for Constrained K-Means Clustering）</news:title>
   <news:publication_date>2026-06-27T03:17:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705312</loc>
  <lastmod>2026-06-27T03:17:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>エリア注意機構が変える注意の粒度（Area Attention）</news:title>
   <news:publication_date>2026-06-27T03:17:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705310</loc>
  <lastmod>2026-06-27T02:26:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>nGraph-HEによる同型暗号下での深層学習コンパイラ（nGraph-HE: A Graph Compiler for Deep Learning on Homomorphically Encrypted Data）</news:title>
   <news:publication_date>2026-06-27T02:26:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705308</loc>
  <lastmod>2026-06-27T02:26:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Fisherカーネルによるブラックボックス予測の解釈（Interpreting Black Box Predictions using Fisher Kernels）</news:title>
   <news:publication_date>2026-06-27T02:26:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705306</loc>
  <lastmod>2026-06-27T02:26:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>PoPPy: PyTorchベースの点過程ツールボックス（PoPPy: A Point Process Toolbox Based on PyTorch）</news:title>
   <news:publication_date>2026-06-27T02:26:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705304</loc>
  <lastmod>2026-06-27T02:25:48Z</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における意味表現の数学理論（A mathematical theory of semantic development in deep neural networks）</news:title>
   <news:publication_date>2026-06-27T02:25:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705302</loc>
  <lastmod>2026-06-27T02:25:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>心臓MRIからのエンドツーエンド診断とセグメンテーション学習（End-to-End Diagnosis and Segmentation Learning from Cardiac Magnetic Resonance Imaging）</news:title>
   <news:publication_date>2026-06-27T02:25:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705300</loc>
  <lastmod>2026-06-27T02:25:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>損失のある観測からの生成モデル再現（Reproducing AmbientGAN: Generative models from lossy measurements）</news:title>
   <news:publication_date>2026-06-27T02:25:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705298</loc>
  <lastmod>2026-06-27T02:25:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習ベースの逆問題解法：肺EITのシミュレーション研究（A Learning-Based Method for Solving Ill-Posed Nonlinear Inverse Problems: A Simulation Study of Lung EIT）</news:title>
   <news:publication_date>2026-06-27T02:25:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705296</loc>
  <lastmod>2026-06-27T01:33:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Autowarpによる時系列類似度の自動学習（Autowarp: Learning a Warping Distance from Unlabeled Time Series Using Sequence Autoencoders）</news:title>
   <news:publication_date>2026-06-27T01:33:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705294</loc>
  <lastmod>2026-06-27T01:32:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>重なり合う銀河のデブレンディングにおける分岐型生成対抗ネットワーク（Deblending galaxy superpositions with branched generative adversarial networks）</news:title>
   <news:publication_date>2026-06-27T01:32:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705292</loc>
  <lastmod>2026-06-27T01:32:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>モデルフリー階層的強化学習における表現学習（Learning Representations in Model-Free Hierarchical Reinforcement Learning）</news:title>
   <news:publication_date>2026-06-27T01:32:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705290</loc>
  <lastmod>2026-06-27T01:32:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>NestDNNによるリソース対応型マルチテナント端末上深層学習（NestDNN: Resource-Aware Multi-Tenant On-Device Deep Learning for Continuous Mobile Vision）</news:title>
   <news:publication_date>2026-06-27T01:32:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705288</loc>
  <lastmod>2026-06-27T01:31:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>最小二乗における早期停止の連続時間的考察 (A Continuous-Time View of Early Stopping for Least Squares)</news:title>
   <news:publication_date>2026-06-27T01:31:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705286</loc>
  <lastmod>2026-06-27T01:31:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非負値行列因子分解のモデル選択（Model Selection for Nonnegative Matrix Factorization by Support Union Recovery）</news:title>
   <news:publication_date>2026-06-27T01:31:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705284</loc>
  <lastmod>2026-06-27T01:31:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>成人の国勢調査データによる所得階層予測の統計的アプローチ（A Statistical Approach to Adult Census Income Level Prediction）</news:title>
   <news:publication_date>2026-06-27T01:31:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705282</loc>
  <lastmod>2026-06-27T00:40:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>低ランクテンソル分解の統計力学的分析（Statistical mechanics of low-rank tensor decomposition）</news:title>
   <news:publication_date>2026-06-27T00:40:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705280</loc>
  <lastmod>2026-06-27T00:40:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフ類似度の畳み込み的集合照合（Convolutional Set Matching for Graph Similarity）</news:title>
   <news:publication_date>2026-06-27T00:40:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705278</loc>
  <lastmod>2026-06-27T00:39:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>無効結果の価値――物理教育研究における「何も起きなかった」ことの意味（Nothing’s plenty: The significance of null results in physics education research）</news:title>
   <news:publication_date>2026-06-27T00:39:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705276</loc>
  <lastmod>2026-06-27T00:39:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Graph Laplacian Mixture Modelの解説（Graph Laplacian Mixture Model）</news:title>
   <news:publication_date>2026-06-27T00:39:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705274</loc>
  <lastmod>2026-06-27T00:38:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習で量子デバイスの計測を効率化する手法（Efficiently measuring a quantum device using machine learning）</news:title>
   <news:publication_date>2026-06-27T00:38:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705272</loc>
  <lastmod>2026-06-27T00:38:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>NormADに基づく多層スパイキングニューラルネットワークの学習（TRAINING MULTI-LAYER SPIKING NEURAL NETWORKS USING NORMAD BASED SPATIO-TEMPORAL ERROR BACKPROPAGATION）</news:title>
   <news:publication_date>2026-06-27T00:38:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705270</loc>
  <lastmod>2026-06-27T00:38:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>近似的な二乗輸送距離をほぼ線形時間で計算する手法（Approximating the Quadratic Transportation Metric in Near-Linear Time）</news:title>
   <news:publication_date>2026-06-27T00:38:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705268</loc>
  <lastmod>2026-06-26T23:47:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単層・多層フィードフォワードニューラルネットワークの近似に関する負の結果（NEGATIVE RESULTS FOR APPROXIMATION USING SINGLE LAYER AND MULTILAYER FEEDFORWARD NEURAL NETWORKS）</news:title>
   <news:publication_date>2026-06-26T23:47:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705266</loc>
  <lastmod>2026-06-26T23:47:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>レーザー照明画像のスペックルノイズ低減を学ぶ（DeepLSR: a deep learning approach for laser speckle reduction）</news:title>
   <news:publication_date>2026-06-26T23:47:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705264</loc>
  <lastmod>2026-06-26T23:46:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>感情認識の深層化と可視化：EmotionalDANによる顔ランドマーク統合学習（Classifying and Visualizing Emotions with Emotional DAN）</news:title>
   <news:publication_date>2026-06-26T23:46:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705262</loc>
  <lastmod>2026-06-26T23:45:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>クラスター環境で小型の星形成銀河が急速に消光するメカニズム（Compact star-forming galaxies preferentially quenched to become PSBs in z &amp;lt; 1 clusters）</news:title>
   <news:publication_date>2026-06-26T23:45:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705260</loc>
  <lastmod>2026-06-26T23:45:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>遅延する作業者（Straggler）を許容する分散学習の計算スケジューリング（Computation Scheduling for Distributed Machine Learning with Straggling Workers）</news:title>
   <news:publication_date>2026-06-26T23:45:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705258</loc>
  <lastmod>2026-06-26T23:45:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフ畳み込みエンコーダによる構造化データからのテキスト生成（Deep Graph Convolutional Encoders for Structured Data to Text Generation）</news:title>
   <news:publication_date>2026-06-26T23:45:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705256</loc>
  <lastmod>2026-06-26T23:45:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的代替学習によるグラデーション隠蔽への攻撃（Stochastic Substitute Training）</news:title>
   <news:publication_date>2026-06-26T23:45:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705254</loc>
  <lastmod>2026-06-26T22:53:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>メタ学習によるマルチタスク通信（META-LEARNING MULTI-TASK COMMUNICATION）</news:title>
   <news:publication_date>2026-06-26T22:53:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705252</loc>
  <lastmod>2026-06-26T22:53:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スパイキングニューラルネットワークによる低消費電力強化学習（Learning First-to-Spike Policies for Neuromorphic Control Using Policy Gradients）</news:title>
   <news:publication_date>2026-06-26T22:53:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705250</loc>
  <lastmod>2026-06-26T22:53:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>部分集合の好みによる能動ランキング（Active Ranking with Subset-wise Preferences）</news:title>
   <news:publication_date>2026-06-26T22:53:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705248</loc>
  <lastmod>2026-06-26T22:52:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>矮小銀河の低金属間質（Dwarf Galaxies: Their Low Metallicity Interstellar Medium）</news:title>
   <news:publication_date>2026-06-26T22:52:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705246</loc>
  <lastmod>2026-06-26T22:52:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>注文板データから価格予測へ──非定常性を克服する特徴設計（Using Deep Learning for price prediction by exploiting stationary limit order book features）</news:title>
   <news:publication_date>2026-06-26T22:52:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705244</loc>
  <lastmod>2026-06-26T22:52:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GhostVLADによる集合ベース顔認証の要点整理（GhostVLAD for set-based face recognition）</news:title>
   <news:publication_date>2026-06-26T22:52:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705242</loc>
  <lastmod>2026-06-26T22:52:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>λリターンとエクスペリエンスリプレイの調和（Reconciling λ-Returns with Experience Replay）</news:title>
   <news:publication_date>2026-06-26T22:52:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705240</loc>
  <lastmod>2026-06-26T22:01:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>リカレント深層学習で脳画像を読む（Analyzing Neuroimaging Data Through Recurrent Deep Learning Models）</news:title>
   <news:publication_date>2026-06-26T22:01:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705238</loc>
  <lastmod>2026-06-26T22:01:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>対話行為を段階的に獲得するロボット学習（Stepwise Acquisition of Dialogue Act Through Human-Robot Interaction）</news:title>
   <news:publication_date>2026-06-26T22:01:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705236</loc>
  <lastmod>2026-06-26T22:00:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>前処理選択とAutoMLパイプライン設計（Preprocessor Selection for Machine Learning Pipelines）</news:title>
   <news:publication_date>2026-06-26T22:00:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705234</loc>
  <lastmod>2026-06-26T22:00:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非線形時系列のクラスタリング：ベイズ非パラメトリックと粒子法の接合（Clustering Time Series with Nonlinear Dynamics: A Bayesian Non-Parametric and Particle-Based Approach）</news:title>
   <news:publication_date>2026-06-26T22:00:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705232</loc>
  <lastmod>2026-06-26T22:00:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ブランドはロゴだけでは語れない（Brand &amp;gt; Logo: Visual Analysis of Fashion Brands）</news:title>
   <news:publication_date>2026-06-26T22:00:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705230</loc>
  <lastmod>2026-06-26T21:59:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>方策勾配で学ぶ古典的プランニング戦略（Learning Classical Planning Strategies with Policy Gradient）</news:title>
   <news:publication_date>2026-06-26T21:59:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705228</loc>
  <lastmod>2026-06-26T21:59:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習で加速するライクリー・フリーなイベント再構成（Machine Learning Accelerated Likelihood-Free Event Reconstruction in Dark Matter Direct Detection）</news:title>
   <news:publication_date>2026-06-26T21:59:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705226</loc>
  <lastmod>2026-06-26T21:08:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DIREによる動的尤度フリー推論（Dynamic Likelihood-free Inference via Ratio Estimation）</news:title>
   <news:publication_date>2026-06-26T21:08:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705224</loc>
  <lastmod>2026-06-26T21:07:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>異種大規模データの統合とバッチ補正（Heterogeneous large datasets integration using Bayesian factor regression）</news:title>
   <news:publication_date>2026-06-26T21:07:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705222</loc>
  <lastmod>2026-06-26T21:07:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>暗黙モデルのための効率的ベイズ実験設計（Efficient Bayesian Experimental Design for Implicit Models）</news:title>
   <news:publication_date>2026-06-26T21:07:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705220</loc>
  <lastmod>2026-06-26T21:07:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチクラウド環境における異常検知と攻撃分類の機械学習（Machine Learning for Anomaly Detection and Categorization in Multi-cloud Environments）</news:title>
   <news:publication_date>2026-06-26T21:07:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705218</loc>
  <lastmod>2026-06-26T21:06:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>平面調和系の量子散逸：Maxwell–Chern–Simons理論（Quantum dissipation of planar harmonic systems: Maxwell-Chern-Simons theory）</news:title>
   <news:publication_date>2026-06-26T21:06:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705216</loc>
  <lastmod>2026-06-26T21:06:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>クラウドセキュリティにおける教師あり機械学習の適用可能性（Feasibility of Supervised Machine Learning for Cloud Security）</news:title>
   <news:publication_date>2026-06-26T21:06:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705214</loc>
  <lastmod>2026-06-26T21:06:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Juliaプログラムと機械学習モデルの自動完全コンパイルをCloud TPUへ（Compiling Julia to TPUs）</news:title>
   <news:publication_date>2026-06-26T21:06:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705212</loc>
  <lastmod>2026-06-26T20:15:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>語長を削減した深層ニューラルネットワーク推論（DEEP NEURAL NETWORK INFERENCE WITH REDUCED WORD LENGTH）</news:title>
   <news:publication_date>2026-06-26T20:15:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705210</loc>
  <lastmod>2026-06-26T20:14:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DropFilter: 畳み込み層のためのドロップアウト最適化（DropFilter: Dropout for Convolutions）</news:title>
   <news:publication_date>2026-06-26T20:14:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705208</loc>
  <lastmod>2026-06-26T20:14:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層依存型クロスプラットフォーム多視点特徴学習による施設カテゴリ推定（Hierarchy-Dependent Cross-Platform Multi-View Feature Learning for Venue Category Prediction）</news:title>
   <news:publication_date>2026-06-26T20:14:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705206</loc>
  <lastmod>2026-06-26T20:13:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>一筆書きで注目領域を守る仕組み――Self-Erasing Networkによる弱教師ありオブジェクト注意の改善 (Self-Erasing Network for Integral Object Attention)</news:title>
   <news:publication_date>2026-06-26T20:13:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705204</loc>
  <lastmod>2026-06-26T20:13:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DCSVMによる高速多クラス分類（DCSVM: FAST MULTI-CLASS CLASSIFICATION USING SUPPORT VECTOR MACHINES）</news:title>
   <news:publication_date>2026-06-26T20:13:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705202</loc>
  <lastmod>2026-06-26T20:13:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GANの訓練を安定化する混合ナッシュ均衡への接近（Finding Mixed Nash Equilibria of Generative Adversarial Networks）</news:title>
   <news:publication_date>2026-06-26T20:13:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705200</loc>
  <lastmod>2026-06-26T20:12:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>視点発見と理解のためのネットワーク手法（Viewpoint Discovery and Understanding in Social Networks）</news:title>
   <news:publication_date>2026-06-26T20:12:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705198</loc>
  <lastmod>2026-06-26T19:21:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>不確かな通信環境下での遠隔状態推定における最適スケジューリング学習（Learning Optimal Scheduling Policy for Remote State Estimation under Uncertain Channel Condition）</news:title>
   <news:publication_date>2026-06-26T19:21:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705196</loc>
  <lastmod>2026-06-26T19:21:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>幼児語彙を用いた大規模照応解決データセット PreCo（PreCo: A Large-scale Dataset in Preschool Vocabulary for Coreference Resolution）</news:title>
   <news:publication_date>2026-06-26T19:21:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705194</loc>
  <lastmod>2026-06-26T19:20:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>小売店向け果物・野菜識別に機械学習を使う試み（Fruit and Vegetable Identification Using Machine Learning for Retail Applications）</news:title>
   <news:publication_date>2026-06-26T19:20:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705192</loc>
  <lastmod>2026-06-26T19:19:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>都市交通における歩行者の行動・意図認識（Action and intention recognition of pedestrians in urban traffic）</news:title>
   <news:publication_date>2026-06-26T19:19:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705190</loc>
  <lastmod>2026-06-26T19:19:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>M87のイオン化ガスフィラメントの特性（Properties of the ionised gas filament of M87）</news:title>
   <news:publication_date>2026-06-26T19:19:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705188</loc>
  <lastmod>2026-06-26T19:19:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SING: 軽量な波形生成で音を合成する新流儀（SING: Symbol-to-Instrument Neural Generator）</news:title>
   <news:publication_date>2026-06-26T19:19:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705186</loc>
  <lastmod>2026-06-26T19:19:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>算術的操作法からみるSobolev–Jacobi多項式（Operational Methods in the Study of Sobolev-Jacobi Polynomials）</news:title>
   <news:publication_date>2026-06-26T19:19:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705184</loc>
  <lastmod>2026-06-26T18:27:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>フォトメトリック赤方偏移の確率密度関数の統計解析（Statistical analysis of probability density functions for photometric redshifts through the KiDS-ESO-DR3 galaxies）</news:title>
   <news:publication_date>2026-06-26T18:27:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705182</loc>
  <lastmod>2026-06-26T18:27:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時間とコーパスに敏感なエンティティ関連度の評価（Time-Aware and Corpus-Specific Entity Relatedness）</news:title>
   <news:publication_date>2026-06-26T18:27:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705180</loc>
  <lastmod>2026-06-26T18:27:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>視覚的意味再ランク付けによるテキストスポッティングの改善（Visual Semantic Re-ranker for Text Spotting）</news:title>
   <news:publication_date>2026-06-26T18:27:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705178</loc>
  <lastmod>2026-06-26T18:26:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>LoGAN：色を条件にロゴを生成するAI（LoGAN: Generating Logos with a Generative Adversarial Neural Network Conditioned on color）</news:title>
   <news:publication_date>2026-06-26T18:26:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705176</loc>
  <lastmod>2026-06-26T18:26:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>重み付きスキップ接続による原子表現の解析（Analysis of Atomistic Representations Using Weighted Skip-Connections）</news:title>
   <news:publication_date>2026-06-26T18:26:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705174</loc>
  <lastmod>2026-06-26T18:26:18Z</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自然言語推論における汎化力の検証（Testing the Generalization Power of Neural Network Models Across NLI Benchmarks）</news:title>
   <news:publication_date>2026-06-26T18:26:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705172</loc>
  <lastmod>2026-06-26T18:25:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ランダムフォレストのPAC-ベイズ境界に関する考察（On PAC-Bayesian Bounds for Random Forests）</news:title>
   <news:publication_date>2026-06-26T18:25:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705170</loc>
  <lastmod>2026-06-26T17:34:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>OCAPIS：Scalaで構築されたR向け序数データ処理パッケージ（OCAPIS: R package for Ordinal Classification And Preprocessing In Scala）</news:title>
   <news:publication_date>2026-06-26T17:34:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705168</loc>
  <lastmod>2026-06-26T17:34:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチUAVによるサイバーフィジカルシステム設計の課題と展望（Multi-UAV Design Challenges for Cyber-Physical Systems）</news:title>
   <news:publication_date>2026-06-26T17:34:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705166</loc>
  <lastmod>2026-06-26T17:34:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>領域選択型コスト効率的アクティブラーニングによるセマンティックセグメンテーション（CEREALS: Cost-Effective REgion-based Active Learning for Semantic Segmentation）</news:title>
   <news:publication_date>2026-06-26T17:34:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705164</loc>
  <lastmod>2026-06-26T17:33:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>適切な交通規制の自動発見（Finding Appropriate Traffic Regulations via Graph Convolutional Networks）</news:title>
   <news:publication_date>2026-06-26T17:33:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705162</loc>
  <lastmod>2026-06-26T17:33:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Cheeger変形による正のリッチ曲率の再考（Positive Ricci curvature through Cheeger deformations）</news:title>
   <news:publication_date>2026-06-26T17:33:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705160</loc>
  <lastmod>2026-06-26T17:33:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自然言語からの構造対応プログラム合成（Structure-Aware Program Synthesis from Natural Language）</news:title>
   <news:publication_date>2026-06-26T17:33:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705158</loc>
  <lastmod>2026-06-26T17:32:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単一画像からのSVBRDF推定とレンダリング認識型深層ネットワーク（Single-Image SVBRDF Capture with a Rendering-Aware Deep Network）</news:title>
   <news:publication_date>2026-06-26T17:32:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705156</loc>
  <lastmod>2026-06-26T16:42:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>一枚画像から光と色を分ける方法（Consistency-aware Shading Orders Selective Fusion for Intrinsic Image Decomposition）</news:title>
   <news:publication_date>2026-06-26T16:42:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705154</loc>
  <lastmod>2026-06-26T16:41:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層強化学習におけるセキュリティ問題と未解決の課題（The Faults in Our π∗s: Security Issues and Open Challenges in Deep Reinforcement Learning）</news:title>
   <news:publication_date>2026-06-26T16:41:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705152</loc>
  <lastmod>2026-06-26T16:41:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>フリジア語─オランダ語の混合音声に対する半教師あり音響モデル訓練（Semi-supervised acoustic model training for speech with code-switching）</news:title>
   <news:publication_date>2026-06-26T16:41:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705150</loc>
  <lastmod>2026-06-26T16:40:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SwitchNetによる散乱問題の順逆写像学習（SWITCHNET: A NEURAL NETWORK MODEL FOR FORWARD AND INVERSE SCATTERING PROBLEMS）</news:title>
   <news:publication_date>2026-06-26T16:40:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705148</loc>
  <lastmod>2026-06-26T16:40:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>二値関数の類似性を学習するグラフ埋め込み（Unsupervised Features Extraction for Binary Similarity Using Graph Embedding Neural Networks）</news:title>
   <news:publication_date>2026-06-26T16:40:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705146</loc>
  <lastmod>2026-06-26T16:39:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>サーバーレスで学ぶ線形代数の実行基盤（numpywren: Serverless Linear Algebra）</news:title>
   <news:publication_date>2026-06-26T16:39:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705144</loc>
  <lastmod>2026-06-26T16:39:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>部分的フィードバックと切替コスト下のオンライン学習（Online learning with feedback graphs and switching costs）</news:title>
   <news:publication_date>2026-06-26T16:39:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705142</loc>
  <lastmod>2026-06-26T15:47:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>順序的な希薄3Dデータからの顔認識と深層登録（Face Recognition from Sequential Sparse 3D Data via Deep Registration）</news:title>
   <news:publication_date>2026-06-26T15:47:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705140</loc>
  <lastmod>2026-06-26T15:47:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>パラメータ化された行動空間における階層的強化学習の提案（Hierarchical Approaches for Reinforcement Learning in Parameterized Action Space）</news:title>
   <news:publication_date>2026-06-26T15:47:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705138</loc>
  <lastmod>2026-06-26T15:47:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ナップサック付きバンディット問題の統一化（Unifying the stochastic and the adversarial Bandits with Knapsack）</news:title>
   <news:publication_date>2026-06-26T15:47:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705136</loc>
  <lastmod>2026-06-26T15:46:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>画像キャプションのニューラル合成パラダイム（A Neural Compositional Paradigm for Image Captioning）</news:title>
   <news:publication_date>2026-06-26T15:46:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705134</loc>
  <lastmod>2026-06-26T15:46:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>トピック表現：トピックモデルでより代表的な単語を見つける（Topic representation: finding more representative words in topic models）</news:title>
   <news:publication_date>2026-06-26T15:46:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705132</loc>
  <lastmod>2026-06-26T15:45:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>冗長性が原因だった：敵対的事例を「1ビット」で理解する（ONE BIT MATTERS: UNDERSTANDING ADVERSARIAL EXAMPLES AS THE ABUSE OF REDUNDANCY）</news:title>
   <news:publication_date>2026-06-26T15:45:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705130</loc>
  <lastmod>2026-06-26T15:45:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>点群を用いた場所認識にCNNを応用する手法（Point-cloud-based Place Recognition using CNN）</news:title>
   <news:publication_date>2026-06-26T15:45:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705128</loc>
  <lastmod>2026-06-26T14:54:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>生体内血栓力学の計測（IN VIVO MEASUREMENT OF BLOOD CLOT MECHANICS FROM COMPUTATIONAL FLUID DYNAMICS BASED ON INTRAVITAL MICROSCOPY IMAGES）</news:title>
   <news:publication_date>2026-06-26T14:54:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705126</loc>
  <lastmod>2026-06-26T14:53:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>クラウドソーシング予測のための深層ニューラルランキング（Deep Neural Ranking for Crowdsourced Geopolitical Event Forecasting）</news:title>
   <news:publication_date>2026-06-26T14:53:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705124</loc>
  <lastmod>2026-06-26T14:53:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スパース化されたDNNの敵対的耐性の向上（Sparse DNNs with Improved Adversarial Robustness）</news:title>
   <news:publication_date>2026-06-26T14:53:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <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>
  </news:news>
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 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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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>
   </news:publication>
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   <news:publication_date>2026-06-26T14:52:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-06-26T14:52:32Z</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>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705112</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>2026-06-26T14:01:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705110</loc>
  <lastmod>2026-06-26T14:00:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>OCTベースのモーション補償のためのシステムパラメータ較正に向けた二経路3D CNN（Two-path 3D CNNs for calibration of system parameters for OCT-based motion compensation）</news:title>
   <news:publication_date>2026-06-26T14:00:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705108</loc>
  <lastmod>2026-06-26T14:00:10Z</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>2026-06-26T14:00:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705106</loc>
  <lastmod>2026-06-26T13:59:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>完全アテンション型情報検索器（A Fully Attention-Based Information Retriever）</news:title>
   <news:publication_date>2026-06-26T13:59:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705104</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>プロクルステスとクラシカルスケーリングの摂動境界とその応用（Perturbation Bounds for Procrustes, Classical Scaling, and Trilateration, with Applications to Manifold Learning）</news:title>
   <news:publication_date>2026-06-26T13:59:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705102</loc>
  <lastmod>2026-06-26T13:59:26Z</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>2026-06-26T13:59:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705100</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>2026-06-26T13:08:06Z</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>
   </news:publication>
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   <news:publication_date>2026-06-26T13:07:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/705096</loc>
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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>
  <loc>https://aibr.jp/archives/705094</loc>
  <lastmod>2026-06-26T13:07:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/705092</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:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/705090</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>2026-06-26T13:06:51Z</news:publication_date>
   <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>
   </news:publication>
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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: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>
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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>
  <loc>https://aibr.jp/archives/705078</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>深層特徴に基づく皮膚鏡画像の類似検索の診断精度（Diagnostic Accuracy of Content Based Dermatoscopic Image Retrieval with Deep Classification Features）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/705076</loc>
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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:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/705074</loc>
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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>
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   <news:publication_date>2026-06-26T11:22:00Z</news:publication_date>
   <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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   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/705068</loc>
  <lastmod>2026-06-26T11:21:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-06-26T11:21:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/705066</loc>
  <lastmod>2026-06-26T11:20:38Z</lastmod>
  <news:news>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-06-26T11:20:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/705064</loc>
  <lastmod>2026-06-26T11:20:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>Nタイムスライス動的チェーンイベントグラフの性質（Properties of an N Time-Slice Dynamic Chain Event Graph）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/705062</loc>
  <lastmod>2026-06-26T11:20:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SGDで得られる最終モデルの最適性（Optimality of the Final Model Found via Stochastic Gradient Descent）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/705060</loc>
  <lastmod>2026-06-26T11:20:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>RNNにおけるドロップアウトの効果検証（An Exploration of Dropout with RNNs for Natural Language Inference）</news:title>
   <news:publication_date>2026-06-26T11:20:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/705058</loc>
  <lastmod>2026-06-26T10:28:26Z</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>2026-06-26T10:28:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/705056</loc>
  <lastmod>2026-06-26T10:28:12Z</lastmod>
  <news:news>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-06-26T10:28:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/705054</loc>
  <lastmod>2026-06-26T10:27:59Z</lastmod>
  <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>
  <loc>https://aibr.jp/archives/705052</loc>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-06-26T10:27:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/705050</loc>
  <lastmod>2026-06-26T10:27:36Z</lastmod>
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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>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705048</loc>
  <lastmod>2026-06-26T10:27:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-06-26T10:27:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705046</loc>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模連続学習を神経回路風に解く—オンラインクラスタリングと階層的予測符号化（A neuro-inspired architecture for unsupervised continual learning based on online clustering and hierarchical predictive coding）</news:title>
   <news:publication_date>2026-06-26T10:27:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705044</loc>
  <lastmod>2026-06-26T09:36:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>車両の縦横同時制御を深層学習で実現する（Coupled Longitudinal and Lateral Control of a Vehicle using Deep Learning）</news:title>
   <news:publication_date>2026-06-26T09:36:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705042</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>脳腫瘍画像の類似検索を多タスク学習で改善する手法（BRAIN TUMOR IMAGE RETRIEVAL VIA MULTITASK LEARNING）</news:title>
   <news:publication_date>2026-06-26T09:35:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705040</loc>
  <lastmod>2026-06-26T09:35:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ensmallenによるC++最適化の実務入門（ensmallen: a flexible C++ library for efficient function optimization）</news:title>
   <news:publication_date>2026-06-26T09:35:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705038</loc>
  <lastmod>2026-06-26T09:34:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>インタープリタブルモデルの安定性評価（Assessing the Stability of Interpretable Models）</news:title>
   <news:publication_date>2026-06-26T09:34:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705036</loc>
  <lastmod>2026-06-26T09:34:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単一撮影レントゲンから仮想デュアルエネルギー画像を生成する（Generation of Virtual Dual Energy Images from Standard Single-Shot Radiographs using Multi-scale and Conditional Adversarial Network）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705034</loc>
  <lastmod>2026-06-26T09:34:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ノイズ下の敵対的オンライン学習の基礎と示唆（Adversarial Online Learning with noise）</news:title>
   <news:publication_date>2026-06-26T09:34:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705032</loc>
  <lastmod>2026-06-26T09:34:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>歴史文書のベースライン検出（Baseline Detection in Historical Documents using Convolutional U-Nets）</news:title>
   <news:publication_date>2026-06-26T09:34:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705030</loc>
  <lastmod>2026-06-26T08:42:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>量子アニーリングを学習的に活用する探索法（Quantum Annealing Learning Search for solving QUBO problems）</news:title>
   <news:publication_date>2026-06-26T08:42:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705028</loc>
  <lastmod>2026-06-26T08:42:46Z</lastmod>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>モデルフリー強化学習におけるロバスト性の回復（Recovering Robustness in Model-Free Reinforcement Learning）</news:title>
   <news:publication_date>2026-06-26T08:42:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705026</loc>
  <lastmod>2026-06-26T08:42:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>医学生におけるゲーミフィケーションが自主学習に与える影響の評価（Assessing the Impact of Gamification on Self-Directed Learning in Medical Students）</news:title>
   <news:publication_date>2026-06-26T08:42:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705024</loc>
  <lastmod>2026-06-26T08:41:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>会話型ソーシャルエージェント「Ruuh」の設計と示唆（Ruuh: A Deep Learning Based Conversational Social Agent）</news:title>
   <news:publication_date>2026-06-26T08:41:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705022</loc>
  <lastmod>2026-06-26T08:41:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>BioSentVec：バイオ医療テキストのための文埋め込み作成（BioSentVec: creating sentence embeddings for biomedical texts）</news:title>
   <news:publication_date>2026-06-26T08:41:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705020</loc>
  <lastmod>2026-06-26T08:41:15Z</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>2026-06-26T08:41:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705018</loc>
  <lastmod>2026-06-26T08:40:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>不確かさ推定を伴う暗黙的モデルによるIVIMイメージング（IMPLICIT MODELING WITH UNCERTAINTY ESTIMATION FOR INTRAVOXEL INCOHERENT MOTION IMAGING）</news:title>
   <news:publication_date>2026-06-26T08:40:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705016</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>2026-06-26T07:49:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705014</loc>
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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:publication_date>2026-06-26T07:46:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705012</loc>
  <lastmod>2026-06-26T07:46:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多剤併用による副作用を知識グラフで予測する（Knowledge Graph Completion to Predict Polypharmacy Side Effects）</news:title>
   <news:publication_date>2026-06-26T07:46:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705010</loc>
  <lastmod>2026-06-26T07:45:29Z</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>2026-06-26T07:45:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705008</loc>
  <lastmod>2026-06-26T07:45:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ハザード比の解釈に潜む微妙さ（Subtleties in the interpretation of hazard ratios）</news:title>
   <news:publication_date>2026-06-26T07:45:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705006</loc>
  <lastmod>2026-06-26T07:45:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>協調を生む強化学習：GCPNを用いたマルチエージェントActor‑Criticの要点解説（MULTI‑AGENT ACTOR‑CRITIC WITH GENERATIVE COOPERATIVE POLICY NETWORK）</news:title>
   <news:publication_date>2026-06-26T07:45:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705004</loc>
  <lastmod>2026-06-26T07:44:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフ畳み込み強化学習の要点（Graph Convolutional Reinforcement Learning）</news:title>
   <news:publication_date>2026-06-26T07:44:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705002</loc>
  <lastmod>2026-06-26T06:53:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフ上の深層学習による固有表現の曖昧性解消（Named Entity Disambiguation using Deep Learning on Graphs）</news:title>
   <news:publication_date>2026-06-26T06:53:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/705000</loc>
  <lastmod>2026-06-26T06:46:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>バックプロパゲーションでスパース変換を学ぶ（Learning sparse transformations through backpropagation）</news:title>
   <news:publication_date>2026-06-26T06:46:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704998</loc>
  <lastmod>2026-06-26T06:45:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>合成符号化カプセルネットワークとK-Meansルーティングによるテキスト分類（Compositional Coding Capsule Network with K-Means Routing for Text Classification）</news:title>
   <news:publication_date>2026-06-26T06:45:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704996</loc>
  <lastmod>2026-06-26T06:45:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>有向グラフのためのノード表現学習（Node Representation Learning for Directed Graphs）</news:title>
   <news:publication_date>2026-06-26T06:45:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704994</loc>
  <lastmod>2026-06-26T06:44:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>検閲された需要予測のアンサンブル手法（Ensemble Method for Censored Demand Prediction）</news:title>
   <news:publication_date>2026-06-26T06:44:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704992</loc>
  <lastmod>2026-06-26T06:44:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>超平面の配置による多クラス分類の最適化（Optimal arrangements of hyperplanes for multiclass classification）</news:title>
   <news:publication_date>2026-06-26T06:44:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704990</loc>
  <lastmod>2026-06-26T06:44:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>莫高窟の古代壁画の年代推定（Dating Ancient Paintings of Mogao Grottoes Using Deeply Learnt Visual Codes）</news:title>
   <news:publication_date>2026-06-26T06:44:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704988</loc>
  <lastmod>2026-06-26T05:52:52Z</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>2026-06-26T05:52:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704986</loc>
  <lastmod>2026-06-26T05:52:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>過学習とパラメータのジャミング転移（A jamming transition from under- to over-parametrization affects generalization in deep learning）</news:title>
   <news:publication_date>2026-06-26T05:52:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704984</loc>
  <lastmod>2026-06-26T05:52:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフ分類のためのシンプルなベースラインアルゴリズム（A Simple Baseline Algorithm for Graph Classification）</news:title>
   <news:publication_date>2026-06-26T05:52:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/704982</loc>
  <lastmod>2026-06-26T05:51:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>平均値に基づくリアルタイム計画探索（Mean-based Heuristic Search for Real-Time Planning）</news:title>
   <news:publication_date>2026-06-26T05:51:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704980</loc>
  <lastmod>2026-06-26T05:51:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>計画問題における有用なマクロアクションの発見（Mining useful Macro-actions in Planning）</news:title>
   <news:publication_date>2026-06-26T05:51:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704978</loc>
  <lastmod>2026-06-26T05:51:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DNNを使った音質評価最適化（DNN-based Source Enhancement to Increase Objective Sound Quality Assessment Score）</news:title>
   <news:publication_date>2026-06-26T05:51:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704976</loc>
  <lastmod>2026-06-26T05:51:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層生成モデルは自分の無知を知っているか（DO DEEP GENERATIVE MODELS KNOW WHAT THEY DON’T KNOW?）</news:title>
   <news:publication_date>2026-06-26T05:51:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704974</loc>
  <lastmod>2026-06-26T04:59:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>リスク感応型強化学習の方策勾配探索（Risk-Sensitive Reinforcement Learning via Policy Gradient Search）</news:title>
   <news:publication_date>2026-06-26T04:59:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704972</loc>
  <lastmod>2026-06-26T04:59:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>音響異常検知におけるオートエンコーダとネイマン・ピアソン補題の応用（Unsupervised Detection of Anomalous Sound based on Deep Learning and the Neyman-Pearson Lemma）</news:title>
   <news:publication_date>2026-06-26T04:59:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704970</loc>
  <lastmod>2026-06-26T04:58:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>心房瘢痕の自動分割を可能にするグラフカットと学習ポテンシャル（Atrial scar segmentation via potential learning in the graph-cut framework）</news:title>
   <news:publication_date>2026-06-26T04:58:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704968</loc>
  <lastmod>2026-06-26T04:58:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Bregmanコード発散の概念と応用（The Bregman Chord Divergence）</news:title>
   <news:publication_date>2026-06-26T04:58:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704966</loc>
  <lastmod>2026-06-26T04:58:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ホロニック制御アーキテクチャの進化とIndustry 4.0への接続（Evolution of holonic control architectures towards Industry 4.0: A short overview）</news:title>
   <news:publication_date>2026-06-26T04:58:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704964</loc>
  <lastmod>2026-06-26T04:58:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>運動イメージEEGの高精度デコードを可能にする多重ウェーブレット時頻度因果性とブーステッドConvNets（Boosted Convolutional Neural Networks for Motor Imagery EEG Decoding with Multiwavelet-based Time-Frequency Conditional Granger Causality Analysis）</news:title>
   <news:publication_date>2026-06-26T04:58:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704962</loc>
  <lastmod>2026-06-26T04:58:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>シーン変化検出のための畳み込みシアミーズ距離学習（Learning to Measure Changes: Fully Convolutional Siamese Metric Networks for Scene Change Detection）</news:title>
   <news:publication_date>2026-06-26T04:58:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704960</loc>
  <lastmod>2026-06-26T04:06:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GreedyAC：条件付きクロスエントロピーで方針改善を行う新手法（GREEDY ACTOR-CRITIC: A NEW CONDITIONAL CROSS-ENTROPY METHOD FOR POLICY IMPROVEMENT）</news:title>
   <news:publication_date>2026-06-26T04:06:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704958</loc>
  <lastmod>2026-06-26T04:06:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>重みの直交性を活かす訓練法の効果検証（Can We Gain More from Orthogonality Regularizations in Training Deep CNNs?）</news:title>
   <news:publication_date>2026-06-26T04:06:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704956</loc>
  <lastmod>2026-06-26T04:06:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ノームレンジ分割が変えるLSHベースのMIPS（Norm-Range Partition: A Universal Catalyst for LSH based Maximum Inner Product Search）</news:title>
   <news:publication_date>2026-06-26T04:06:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704954</loc>
  <lastmod>2026-06-26T04:05:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューロンのバーストとトニック発火に基づく一般学習システム（A general learning system based on neuron bursting and tonic firing）</news:title>
   <news:publication_date>2026-06-26T04:05:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704952</loc>
  <lastmod>2026-06-26T04:05:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ワンショット画像セグメンテーションを導く類似度ガイダンス（SG-One: Similarity Guidance Network for One-Shot Semantic Segmentation）</news:title>
   <news:publication_date>2026-06-26T04:05:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704950</loc>
  <lastmod>2026-06-26T04:05:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多クラス可加モデルの公理的解釈性（Axiomatic Interpretability for Multiclass Additive Models）</news:title>
   <news:publication_date>2026-06-26T04:05:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704948</loc>
  <lastmod>2026-06-26T04:05:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>状態空間モデルのための確率勾配MCMC（Stochastic Gradient MCMC for State Space Models）</news:title>
   <news:publication_date>2026-06-26T04:05:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704946</loc>
  <lastmod>2026-06-26T03:13:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ComNet：専門知識と深層学習を組み合わせたOFDM受信機（ComNet: Combination of Deep Learning and Expert Knowledge in OFDM Receivers）</news:title>
   <news:publication_date>2026-06-26T03:13:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704944</loc>
  <lastmod>2026-06-26T03:13:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>トピックのスパース性を捉える新しいニューラル手法（Sparsemax and Relaxed Wasserstein for Topic Sparsity）</news:title>
   <news:publication_date>2026-06-26T03:13:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704942</loc>
  <lastmod>2026-06-26T03:12:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>音声で種を認識する機械学習の実践（OUR PRACTICE OF USING MACHINE LEARNING TO RECOGNIZE SPECIES BY VOICE）</news:title>
   <news:publication_date>2026-06-26T03:12:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704940</loc>
  <lastmod>2026-06-26T03:12:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>カーネルとレンジ空間から学ぶ（Learning from the Kernel and the Range Space）</news:title>
   <news:publication_date>2026-06-26T03:12:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704938</loc>
  <lastmod>2026-06-26T03:11:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>サイドチャネルでニューラルネットワークを丸裸にする方法（CSI Neural Network: Using Side-channels to Recover Your Artificial Neural Network Information）</news:title>
   <news:publication_date>2026-06-26T03:11:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704936</loc>
  <lastmod>2026-06-26T03:11:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>映像から場所を特定する技術の要点（Where is this? Video geolocation based on neural network features）</news:title>
   <news:publication_date>2026-06-26T03:11:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704934</loc>
  <lastmod>2026-06-26T03:11:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>モノラル前処理による堅牢な音声認識の検討（INVESTIGATION OF MONAURAL FRONT-END PROCESSING FOR ROBUST ASR WITHOUT RETRAINING OR JOINT-TRAINING）</news:title>
   <news:publication_date>2026-06-26T03:11:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704932</loc>
  <lastmod>2026-06-26T02:19:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>辞書学習に基づく頑健なオンライン分類手法とウェアラブルセンサ応用（A Method for Robust Online Classification using Dictionary Learning）</news:title>
   <news:publication_date>2026-06-26T02:19:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704930</loc>
  <lastmod>2026-06-26T02:19:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>弱ラベル下の音声イベント検出における5種のMultiple Instance Learningプーリング関数比較（A COMPARISON OF FIVE MULTIPLE INSTANCE LEARNING POOLING FUNCTIONS FOR SOUND EVENT DETECTION WITH WEAK LABELING）</news:title>
   <news:publication_date>2026-06-26T02:19:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704928</loc>
  <lastmod>2026-06-26T02:19:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>代表性の基準を探る：マサチューセッツ州における共和党の不振（Locating the Representational Baseline: Republicans in Massachusetts）</news:title>
   <news:publication_date>2026-06-26T02:19:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704926</loc>
  <lastmod>2026-06-26T02:18:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非線形学習と進化ゲームにおける学習優位性（Nonlinear learning and learning advantages in evolutionary games）</news:title>
   <news:publication_date>2026-06-26T02:18:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704924</loc>
  <lastmod>2026-06-26T02:18:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>皮膚科医レベルのダーモスコピー皮膚がん分類（Dermatologist Level Dermoscopy Skin Cancer Classification Using Different Deep Learning Convolutional Neural Networks Algorithms）</news:title>
   <news:publication_date>2026-06-26T02:18:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704922</loc>
  <lastmod>2026-06-26T02:18:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>インラインホログラムから深層学習で粒子体積を再構築する方法（Digital holographic particle volume reconstruction using a deep neural network）</news:title>
   <news:publication_date>2026-06-26T02:18:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704920</loc>
  <lastmod>2026-06-26T02:17:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>不規則観察と病勢進行を考慮した患者サブタイピング（Patient Subtyping with Disease Progression and Irregular Observation Trajectories）</news:title>
   <news:publication_date>2026-06-26T02:17:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704918</loc>
  <lastmod>2026-06-26T01:26:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>RLgraph：深層強化学習のためのモジュラー計算グラフ（RLgraph: Modular Computation Graphs for Deep Reinforcement Learning）</news:title>
   <news:publication_date>2026-06-26T01:26:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704916</loc>
  <lastmod>2026-06-26T01:26:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深さと非線形性はResNetにおいて悪い局所最小値を生まない（Depth with nonlinearity creates no bad local minima in ResNets）</news:title>
   <news:publication_date>2026-06-26T01:26:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704914</loc>
  <lastmod>2026-06-26T01:26:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>部分観測マルチエージェント環境におけるアクター・クリティック最適化（Actor-Critic Policy Optimization in Partially Observable Multiagent Environments）</news:title>
   <news:publication_date>2026-06-26T01:26:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704912</loc>
  <lastmod>2026-06-26T01:25:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>セミグループ値メトリック空間とRamsey理論の接続（Semigroup-valued Metric Spaces and Ramsey Theory）</news:title>
   <news:publication_date>2026-06-26T01:25:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704910</loc>
  <lastmod>2026-06-26T01:25:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非漸近的かつ鋭い下側尾部確率の下界（On the Non-asymptotic and Sharp Lower Tail Bounds of Random Variables）</news:title>
   <news:publication_date>2026-06-26T01:25:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704908</loc>
  <lastmod>2026-06-26T01:25:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ハードディスクの残存寿命予測における特徴正規化とLSTM応用の仕組み（Mechanisms for Integrated Feature Normalization and Remaining Useful Life Estimation Using LSTMs Applied to Hard-Disks）</news:title>
   <news:publication_date>2026-06-26T01:25:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704906</loc>
  <lastmod>2026-06-26T01:25:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>AT-TPCの軌跡分類における機械学習手法（Machine Learning Methods for Track Classification in the AT-TPC）</news:title>
   <news:publication_date>2026-06-26T01:25:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704904</loc>
  <lastmod>2026-06-26T00:33:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多観測サーベイを横断する変光星分類の深層学習（Deep multi-survey classification of variable stars）</news:title>
   <news:publication_date>2026-06-26T00:33:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704902</loc>
  <lastmod>2026-06-26T00:33:45Z</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学習の並列制御と実行スケジューリング（Runtime Concurrency Control and Operation Scheduling for High Performance Neural Network Training）</news:title>
   <news:publication_date>2026-06-26T00:33:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704900</loc>
  <lastmod>2026-06-26T00:33:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>3D表面上のスペクトル変換ネットワークによる非剛体形状解析（Learning Spectral Transform Network on 3D Surface for Non-rigid Shape Analysis）</news:title>
   <news:publication_date>2026-06-26T00:33:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704898</loc>
  <lastmod>2026-06-26T00:32:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>インターネットアドレス空間の経路認識型分割によるCDNのサーバー選定最適化（Routing-Aware Partitioning of the Internet Address Space for Server Ranking in CDNs）</news:title>
   <news:publication_date>2026-06-26T00:32:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704896</loc>
  <lastmod>2026-06-26T00:32:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>教示を通じて逆強化学習エージェントを育てる—特徴とデモンストレーションで教える方法（Teaching Inverse Reinforcement Learners via Features and Demonstrations）</news:title>
   <news:publication_date>2026-06-26T00:32:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704894</loc>
  <lastmod>2026-06-26T00:32:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CNNを用いた株式市場予測の実践的枠組み（CNNPred: CNN-based stock market prediction using several data sources）</news:title>
   <news:publication_date>2026-06-26T00:32:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704892</loc>
  <lastmod>2026-06-26T00:32:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動的指数族モデルの学習―因果・側方依存性を持つニューロモルフィック計算のために (Training Dynamic Exponential Family Models with Causal and Lateral Dependencies for Generalized Neuromorphic Computing)</news:title>
   <news:publication_date>2026-06-26T00:32:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704890</loc>
  <lastmod>2026-06-25T23:40:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>情報ボトルネックによる非2進LDPC復号（Decoding of Non-Binary LDPC Codes Using the Information Bottleneck Method）</news:title>
   <news:publication_date>2026-06-25T23:40:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704888</loc>
  <lastmod>2026-06-25T23:40:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ラベルノイズ除去による単調性分類の改善（Label Noise Filtering Techniques to Improve Monotonic Classification）</news:title>
   <news:publication_date>2026-06-25T23:40:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704886</loc>
  <lastmod>2026-06-25T23:39:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高解像度常微分方程式による加速現象の理解（Understanding the Acceleration Phenomenon via High-Resolution Differential Equations）</news:title>
   <news:publication_date>2026-06-25T23:39:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704884</loc>
  <lastmod>2026-06-25T23:39:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動的平均拡散とランダム座標更新（DYNAMIC AVERAGE DIFFUSION WITH RANDOMIZED COORDINATE UPDATES）</news:title>
   <news:publication_date>2026-06-25T23:39:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704882</loc>
  <lastmod>2026-06-25T23:39:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ディープラーニングが変えたアナログ→デジタル変換の世界（Analog-to-digital Conversion Revolutionized by Deep Learning）</news:title>
   <news:publication_date>2026-06-25T23:39:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704880</loc>
  <lastmod>2026-06-25T23:38:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>組み込み機器における深層学習モデル圧縮の適用判断（To Compress, or Not to Compress: Characterizing Deep Learning Model Compression for Embedded Inference）</news:title>
   <news:publication_date>2026-06-25T23:38:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704878</loc>
  <lastmod>2026-06-25T23:38:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Instagram上のフーカー（ウォーターパイプ）画像の自動識別（Automated identification of hookahs (waterpipes) on Instagram: an application in feature extraction using Convolutional Neural Network and Support Vector Machine classification）</news:title>
   <news:publication_date>2026-06-25T23:38:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704876</loc>
  <lastmod>2026-06-25T22:47:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多チャンネルポリソムノグラフィーからの睡眠覚醒検出（Sleep Arousal Detection from Polysomnography using the Scattering Transform and Recurrent Neural Networks）</news:title>
   <news:publication_date>2026-06-25T22:47:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704874</loc>
  <lastmod>2026-06-25T22:46:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>異種多数コア向け3D NoC設計を学習で自動化する研究（Learning-based Application-Agnostic 3D NoC Design for Heterogeneous Manycore Systems）</news:title>
   <news:publication_date>2026-06-25T22:46:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704872</loc>
  <lastmod>2026-06-25T22:46:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>連続領域のDPPからの多項式時間MCMC法（A Polynomial Time MCMC Method for Sampling from Continuous DPPs）</news:title>
   <news:publication_date>2026-06-25T22:46:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704870</loc>
  <lastmod>2026-06-25T22:45:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単語ペア埋め込みによる跨文推論の改善（pair2vec: Compositional Word-Pair Embeddings for Cross-Sentence Inference）</news:title>
   <news:publication_date>2026-06-25T22:45:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704868</loc>
  <lastmod>2026-06-25T22:45:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>光学/近赤外アフターグロウにおける縁が明るいジェットの証拠（Evidence for a Bright-Edged Jet in the Optical/NIR Afterglow of GRB 160625B）</news:title>
   <news:publication_date>2026-06-25T22:45:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704866</loc>
  <lastmod>2026-06-25T22:45:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>供給網におけるサービス障害予測のデータモデル (Data models for service failure prediction in supply-chain networks)</news:title>
   <news:publication_date>2026-06-25T22:45:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704864</loc>
  <lastmod>2026-06-25T22:44:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>層ごとの最適ビット幅でモデルを圧縮する新手法（Differentiable Fine-grained Quantization for Deep Neural Network Compression）</news:title>
   <news:publication_date>2026-06-25T22:44:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704862</loc>
  <lastmod>2026-06-25T21:53:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ペアワイズ選好集約を効率化するHybrid-MST（Hybrid-MST: A Hybrid Active Sampling Strategy for Pairwise Preference Aggregation）</news:title>
   <news:publication_date>2026-06-25T21:53:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704860</loc>
  <lastmod>2026-06-25T21:53:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習が脳活動と結合性を異なる形で再編成する（Learning differentially reorganizes brain activity and connectivity）</news:title>
   <news:publication_date>2026-06-25T21:53:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704858</loc>
  <lastmod>2026-06-25T21:52:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>データ駆動タイトフレームによるクライオEM画像のノイズ除去とコンフォメーショナル分類（Data-Driven Tight Frame for Cryo-EM Image Denoising and Conformational Classification）</news:title>
   <news:publication_date>2026-06-25T21:52:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704856</loc>
  <lastmod>2026-06-25T21:52:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Interactive Reinforcement Learningエージェントの自己説明手法（Autonomous Self-Explanation of Behavior for Interactive Reinforcement Learning Agents）</news:title>
   <news:publication_date>2026-06-25T21:52:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704854</loc>
  <lastmod>2026-06-25T21:52:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習における公平性の最前線（The Frontiers of Fairness in Machine Learning）</news:title>
   <news:publication_date>2026-06-25T21:52:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704852</loc>
  <lastmod>2026-06-25T21:51:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>アウトラインを用いた階層的テキスト生成（Hierarchical Text Generation using an Outline）</news:title>
   <news:publication_date>2026-06-25T21:51:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704850</loc>
  <lastmod>2026-06-25T21:51:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複合ラベルを用いたニューラル形態素タグ付け（Modeling Composite Labels for Neural Morphological Tagging）</news:title>
   <news:publication_date>2026-06-25T21:51:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704848</loc>
  <lastmod>2026-06-25T21:00:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ランダムフォレストで記述するレイノルズ応力テンソルのデータ駆動モデリング（Data-Driven Modelling of the Reynolds Stress Tensor using Random Forests with Invariance）</news:title>
   <news:publication_date>2026-06-25T21:00:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704846</loc>
  <lastmod>2026-06-25T21:00:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多様なデータからの集合学習による現場対応型エンティティ分類（Collective Learning From Diverse Datasets for Entity Typing in the Wild）</news:title>
   <news:publication_date>2026-06-25T21:00:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704844</loc>
  <lastmod>2026-06-25T20:59:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GANに基づく顔画像のセマンティック修復の改良技術（Improved Techniques for GAN based Facial Inpainting）</news:title>
   <news:publication_date>2026-06-25T20:59:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704842</loc>
  <lastmod>2026-06-25T20:58:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動き推定と補償で駆動するニューラルネットワークによる映像補間と強調（MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Interpolation and Enhancement）</news:title>
   <news:publication_date>2026-06-25T20:58:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704840</loc>
  <lastmod>2026-06-25T20:58:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ボルツマンマシンの自由エネルギーの厳密論点（Free energies of Boltzmann Machines: self-averaging, annealed and replica symmetric approximations in the thermodynamic limit）</news:title>
   <news:publication_date>2026-06-25T20:58:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704838</loc>
  <lastmod>2026-06-25T20:58:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>地球内部の超イオン性水素の発見と意義（Superionic hydrogen in Earth’s deep interior）</news:title>
   <news:publication_date>2026-06-25T20:58:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704836</loc>
  <lastmod>2026-06-25T20:58:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>属性対応協調フィルタリングの俯瞰と分類（Atribute-aware Collaborative Filtering: Survey and Classification）</news:title>
   <news:publication_date>2026-06-25T20:58:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704834</loc>
  <lastmod>2026-06-25T20:06:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>BCR-Netによる非標準ウェーブレット形式に基づくニューラルネットワーク（BCR-Net: a neural network based on the nonstandard wavelet form）</news:title>
   <news:publication_date>2026-06-25T20:06:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704832</loc>
  <lastmod>2026-06-25T20:06:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分布ロバスト最適化による均一性能学習（Learning Models with Uniform Performance via Distributionally Robust Optimization）</news:title>
   <news:publication_date>2026-06-25T20:06:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704830</loc>
  <lastmod>2026-06-25T20:06:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>OCTデータで学習した深層学習による眼底写真からの緑内障定量評価（FROM MACHINE TO MACHINE: AN OCT-TRAINED DEEP LEARNING ALGORITHM FOR OBJECTIVE QUANTIFICATION OF GLAUCOMATOUS DAMAGE IN FUNDUS PHOTOGRAPHS）</news:title>
   <news:publication_date>2026-06-25T20:06:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704828</loc>
  <lastmod>2026-06-25T20:05:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>探索負担の定量化とフリーライディングの不公平性（Quantifying the Burden of Exploration and the Unfairness of Free Riding）</news:title>
   <news:publication_date>2026-06-25T20:05:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704826</loc>
  <lastmod>2026-06-25T20:05:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>MMLSparkが変えた大規模機械学習の実務導入像（MMLSpark: Unifying Machine Learning Ecosystems at Massive Scales）</news:title>
   <news:publication_date>2026-06-25T20:05:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704824</loc>
  <lastmod>2026-06-25T20:05:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時間的近接性が導く属性類似性（Temporal Proximity induces Attributes Similarity）</news:title>
   <news:publication_date>2026-06-25T20:05:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704822</loc>
  <lastmod>2026-06-25T20:04:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>連続値ドメインにおけるGaussianネットワークの新しいスコアリング基準（Renormalized Normalized Maximum Likelihood and Three-Part Code Criteria For Learning Gaussian Networks）</news:title>
   <news:publication_date>2026-06-25T20:04:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704820</loc>
  <lastmod>2026-06-25T19:13:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Twitterにおけるトルコ語の固有表現認識の半教師あり埋め込み手法（Named Entity Recognition on Twitter for Turkish using Semi-supervised Learning with Word Embeddings）</news:title>
   <news:publication_date>2026-06-25T19:13:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704818</loc>
  <lastmod>2026-06-25T19:13:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>予測と制御のためのコップマン固有関数の最適構築（Optimal construction of Koopman eigenfunctions for prediction and control）</news:title>
   <news:publication_date>2026-06-25T19:13:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704816</loc>
  <lastmod>2026-06-25T19:12:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>素材認識に基づく3D局所記述子学習（Learning Material-Aware Local Descriptors for 3D Shapes）</news:title>
   <news:publication_date>2026-06-25T19:12:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704814</loc>
  <lastmod>2026-06-25T19:12:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ロジスティック回帰の条件数解析が示す実務的示唆（Condition Number Analysis of Logistic Regression, and its Implications for Standard First-Order Solution Methods）</news:title>
   <news:publication_date>2026-06-25T19:12:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704812</loc>
  <lastmod>2026-06-25T19:12:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Ocean Tensor Packageの要点と経営視点での意味（The Ocean Tensor Package）</news:title>
   <news:publication_date>2026-06-25T19:12:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704810</loc>
  <lastmod>2026-06-25T19:11:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SL2MFによるがんにおける合成致死性の予測（SL2MF: Predicting Synthetic Lethality in Human Cancers via Logistic Matrix Factorization）</news:title>
   <news:publication_date>2026-06-25T19:11:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704808</loc>
  <lastmod>2026-06-25T19:11:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>6自由度惑星着陸のための深層強化学習（Deep Reinforcement Learning for Six Degree-of-Freedom Planetary Powered Descent and Landing）</news:title>
   <news:publication_date>2026-06-25T19:11:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704806</loc>
  <lastmod>2026-06-25T18:20:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>対話から人物像を学習する注意型メモリネットワーク（Learning Personas from Dialogue with Attentive Memory Networks）</news:title>
   <news:publication_date>2026-06-25T18:20:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704804</loc>
  <lastmod>2026-06-25T18:20:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>モデル不確実性推定による安全な強化学習（Safe Reinforcement Learning with Model Uncertainty Estimates）</news:title>
   <news:publication_date>2026-06-25T18:20:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704802</loc>
  <lastmod>2026-06-25T18:19:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ゲシュタルト理論から読み解く深層畳み込みネットワーク（Understanding Deep Convolutional Networks through Gestalt Theory）</news:title>
   <news:publication_date>2026-06-25T18:19:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704800</loc>
  <lastmod>2026-06-25T18:18:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチタスク学習を活用した公平な分類の実現（Taking Advantage of Multitask Learning for Fair Classification）</news:title>
   <news:publication_date>2026-06-25T18:18:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704798</loc>
  <lastmod>2026-06-25T18:18:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分子最適化を深層強化学習で行う（Optimization of Molecules via Deep Reinforcement Learning）</news:title>
   <news:publication_date>2026-06-25T18:18:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704796</loc>
  <lastmod>2026-06-25T18:18:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>日常活動の音声認識を大規模埋め込みで学習する（Audio-Based Activities of Daily Living (ADL) Recognition with Large-Scale Acoustic Embeddings from Online Videos）</news:title>
   <news:publication_date>2026-06-25T18:18:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704794</loc>
  <lastmod>2026-06-25T18:18:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ケーブルニュースにおける比喩的暴力の分類のためのニューラルネットワーク（A neural network to classify metaphorical violence on cable news）</news:title>
   <news:publication_date>2026-06-25T18:18:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704792</loc>
  <lastmod>2026-06-25T17:26:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>社会的影響を内発的動機付けとするマルチエージェント深層強化学習（Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning）</news:title>
   <news:publication_date>2026-06-25T17:26:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704790</loc>
  <lastmod>2026-06-25T17:26:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ボランティアコンピューティングにおけるエネルギー浪費削減に機械学習を使う（Using Machine Learning to reduce the energy wasted in Volunteer Computing Environments）</news:title>
   <news:publication_date>2026-06-25T17:26:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704788</loc>
  <lastmod>2026-06-25T17:26:06Z</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活性化に対するサブセットスキャニング（Subset Scanning Over Neural Network Activations）</news:title>
   <news:publication_date>2026-06-25T17:26:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704786</loc>
  <lastmod>2026-06-25T17:25:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CLEVERの拡張：ニューラルネットワーク堅牢性評価アルゴリズムの拡張（ON EXTENSIONS OF CLEVER: A NEURAL NETWORK ROBUSTNESS EVALUATION ALGORITHM）</news:title>
   <news:publication_date>2026-06-25T17:25:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704784</loc>
  <lastmod>2026-06-25T17:25:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>OGLEサーベイで発見された高赤方偏移クエasar二件（Discovery of two quasars at z = 5 from the OGLE Survey）</news:title>
   <news:publication_date>2026-06-25T17:25:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704782</loc>
  <lastmod>2026-06-25T17:25:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ランキング評価における母集団・経験的PR曲線の扱い（Population and Empirical PR Curves to Assess Ranking Algorithms）</news:title>
   <news:publication_date>2026-06-25T17:25:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704780</loc>
  <lastmod>2026-06-25T17:25:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高速で頑健な複数ColorChecker検出法（Fast and Robust Multiple ColorChecker Detection using Deep Convolutional Neural Networks）</news:title>
   <news:publication_date>2026-06-25T17:25:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704778</loc>
  <lastmod>2026-06-25T16:33:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>部分的非同期システム向けモデル並列近接確率的勾配法（A Model Parallel Proximal Stochastic Gradient Algorithm for Partially Asynchronous Systems）</news:title>
   <news:publication_date>2026-06-25T16:33:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704776</loc>
  <lastmod>2026-06-25T16:33:07Z</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バイアス・バリアンスの再考（A Modern Take on the Bias-Variance Tradeoff in Neural Networks）</news:title>
   <news:publication_date>2026-06-25T16:33:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704774</loc>
  <lastmod>2026-06-25T16:32:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単一参照拡張による夜間航空画像の都市検出（Detecting cities in aerial night-time images by learning structural invariants using single reference augmentation）</news:title>
   <news:publication_date>2026-06-25T16:32:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704772</loc>
  <lastmod>2026-06-25T16:32:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>離れた区間から成る固有表現を学習する方法（Learning to Recognize Discontiguous Entities）</news:title>
   <news:publication_date>2026-06-25T16:32:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704770</loc>
  <lastmod>2026-06-25T16:31:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>消費者の購買行動が示す概念組織（Conceptual Organization is Revealed by Consumer Activity Patterns）</news:title>
   <news:publication_date>2026-06-25T16:31:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704768</loc>
  <lastmod>2026-06-25T16:31:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>積演算を活性化関数として活用する手法（Leveraging Product as an Activation Function in Deep Networks）</news:title>
   <news:publication_date>2026-06-25T16:31:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704766</loc>
  <lastmod>2026-06-25T16:31:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>限られた試行で自律的に歩行を獲得する腱駆動肢の学習（Autonomous Functional Locomotion in a Tendon-Driven Limb via Limited Experience）</news:title>
   <news:publication_date>2026-06-25T16:31:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704764</loc>
  <lastmod>2026-06-25T15:40:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>弱い専門家を増幅して強い学習者を監督する（Supervising strong learners by amplifying weak experts）</news:title>
   <news:publication_date>2026-06-25T15:40:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704762</loc>
  <lastmod>2026-06-25T15:39:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的データアソシエーションのための深層人物再識別（Deep Person Re-identification for Probabilistic Data Association in Multiple Pedestrian Tracking）</news:title>
   <news:publication_date>2026-06-25T15:39:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704760</loc>
  <lastmod>2026-06-25T15:39:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Lomax delegate racingによる生存分析の新展開（Nonparametric Bayesian Lomax delegate racing for survival analysis with competing risks）</news:title>
   <news:publication_date>2026-06-25T15:39:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704758</loc>
  <lastmod>2026-06-25T15:38:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>公正さは誰のためか—ナッシュ福祉積による再定義（Fairness for Whom? Critically Reframing Fairness with Nash Welfare Product）</news:title>
   <news:publication_date>2026-06-25T15:38:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704756</loc>
  <lastmod>2026-06-25T15:38:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>勾配ターゲット伝搬が示した学習則の一般化（Gradient Target Propagation）</news:title>
   <news:publication_date>2026-06-25T15:38:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704754</loc>
  <lastmod>2026-06-25T15:38:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散医療データベースにおけるフェデレーテッドラーニングの実用性（Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Data）</news:title>
   <news:publication_date>2026-06-25T15:38:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704752</loc>
  <lastmod>2026-06-25T15:38:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非線形積分微分作用素の回帰とニューラルネットワーク（Nonlinear integro–differential operator regression with neural networks）</news:title>
   <news:publication_date>2026-06-25T15:38:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704750</loc>
  <lastmod>2026-06-25T14:47:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ベイズ距離クラスタリング（Bayesian Distance Clustering）</news:title>
   <news:publication_date>2026-06-25T14:47:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704748</loc>
  <lastmod>2026-06-25T14:46:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ngVLAによる太陽系科学の可能性（Potential for Solar System Science with the ngVLA）</news:title>
   <news:publication_date>2026-06-25T14:46:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704746</loc>
  <lastmod>2026-06-25T14:46:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習でナノフォトニクス設計空間を全方位マッピングする（Mapping the global design space of nanophotonic components using machine learning pattern recognition）</news:title>
   <news:publication_date>2026-06-25T14:46:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704744</loc>
  <lastmod>2026-06-25T14:45:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>項目単位の個別プライバシーを実現する推薦技術（Probabilistic Matrix Factorization with Personalized Differential Privacy）</news:title>
   <news:publication_date>2026-06-25T14:45:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704742</loc>
  <lastmod>2026-06-25T14:45:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>低線量CT画像からの全死因死亡予測をめぐるハイブリッド深層ニューラルネットワーク（HYBRID DEEP NEURAL NETWORKS FOR ALL-CAUSE MORTALITY PREDICTION FROM LDCT IMAGES）</news:title>
   <news:publication_date>2026-06-25T14:45:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704740</loc>
  <lastmod>2026-06-25T14:45:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Transfer LearningとMulti-agent Learningの比較 — 高速道路交通における分散意思決定の検討（Transfer Learning versus Multi-agent Learning regarding Distributed Decision-Making in Highway Traffic）</news:title>
   <news:publication_date>2026-06-25T14:45:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704738</loc>
  <lastmod>2026-06-25T14:45:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>最小制約のReverse Monte Carloが示す原子構造の信頼性（Assessing the Reliability of Minimally Constrained Reverse Monte Carlo Simulations for Model Metallic Liquids）</news:title>
   <news:publication_date>2026-06-25T14:45:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704727</loc>
  <lastmod>2026-06-25T13:53:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高速セグメンテーションの改善—教師-生徒学習による高速化と精度向上（Improving Fast Segmentation With Teacher-student Learning）</news:title>
   <news:publication_date>2026-06-25T13:53:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704725</loc>
  <lastmod>2026-06-25T13:45:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>KM3NeT/ARCAによる点状ニュートリノ源感度の評価（Sensitivity of the KM3NeT/ARCA neutrino telescope to point-like neutrino sources）</news:title>
   <news:publication_date>2026-06-25T13:45:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704723</loc>
  <lastmod>2026-06-25T13:44:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>実世界ネットワークとモデル生成ネットワークの構造比較（Network Classification Based Structural Analysis of Real Networks and their Model-Generated Counterparts）</news:title>
   <news:publication_date>2026-06-25T13:44:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704721</loc>
  <lastmod>2026-06-25T13:43:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>畳み込み層の推論高速化と圧縮を両立する辞書化セントロイド法（CNN inference acceleration using dictionary of centroids）</news:title>
   <news:publication_date>2026-06-25T13:43:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704719</loc>
  <lastmod>2026-06-25T13:43:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>リアルタイムニューラル入力方式の実用化（REAL-TIME NEURAL-BASED INPUT METHOD）</news:title>
   <news:publication_date>2026-06-25T13:43:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704717</loc>
  <lastmod>2026-06-25T13:43:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ピアノとオーケストラのMIDIを結ぶデータベースと自動編曲への応用（A Database Linking Piano and Orchestral MIDI Scores with Application to Automatic Projective Orchestration）</news:title>
   <news:publication_date>2026-06-25T13:43:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704715</loc>
  <lastmod>2026-06-25T13:42:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>経験モーメントとクリストッフェル関数によるデータ解析（Data analysis from empirical moments and the Christoffel function）</news:title>
   <news:publication_date>2026-06-25T13:42:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704713</loc>
  <lastmod>2026-06-25T12:51:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模高解像度セマンティック変化検出のためのマルチタスク学習（Multitask Learning for Large-scale Semantic Change Detection）</news:title>
   <news:publication_date>2026-06-25T12:51:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704711</loc>
  <lastmod>2026-06-25T12:51:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多スペクトル衛星画像を用いた都市変化検出（URBAN CHANGE DETECTION FOR MULTISPECTRAL EARTH OBSERVATION USING CONVOLUTIONAL NEURAL NETWORKS）</news:title>
   <news:publication_date>2026-06-25T12:51:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704709</loc>
  <lastmod>2026-06-25T12:50:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>完全畳み込みサイアミーズネットワークによる変化検出（FULLY CONVOLUTIONAL SIAMESE NETWORKS FOR CHANGE DETECTION）</news:title>
   <news:publication_date>2026-06-25T12:50:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704707</loc>
  <lastmod>2026-06-25T12:49:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>構造生物学におけるシミュレーションと機械学習の出会い (Simulations meet Machine Learning in Structural Biology)</news:title>
   <news:publication_date>2026-06-25T12:49:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704705</loc>
  <lastmod>2026-06-25T12:49:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>銀河団質量推定における機械学習応用の実務的意義（An application of machine learning techniques to galaxy cluster mass estimation using the MACSIS simulations）</news:title>
   <news:publication_date>2026-06-25T12:49:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704703</loc>
  <lastmod>2026-06-25T12:49:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>依存構造を手がかりにした効率的な固有表現抽出（Efficient Dependency-Guided Named Entity Recognition）</news:title>
   <news:publication_date>2026-06-25T12:49:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704701</loc>
  <lastmod>2026-06-25T12:49:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>特権情報を用いた学習と敵対的識別モダリティ蒸留（Learning with Privileged Information via Adversarial Discriminative Modality Distillation）</news:title>
   <news:publication_date>2026-06-25T12:49:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704699</loc>
  <lastmod>2026-06-25T11:57:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CVABS: 共通ベクトルアプローチによる動体分割（CVABS: Moving Object Segmentation with Common Vector Approach for Videos）</news:title>
   <news:publication_date>2026-06-25T11:57:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704697</loc>
  <lastmod>2026-06-25T11:57:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ScratchDetによるスクラッチ学習の再評価（ScratchDet: Training Single-Shot Object Detectors from Scratch）</news:title>
   <news:publication_date>2026-06-25T11:57:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704695</loc>
  <lastmod>2026-06-25T11:57:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ディラック液体における電子相互作用の場の理論的研究（Field theoretic study of electron-electron interaction effects in Dirac liquids）</news:title>
   <news:publication_date>2026-06-25T11:57:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704693</loc>
  <lastmod>2026-06-25T11:56:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模グラフニューラルネットワークの効率的計算に向けて (Towards Efficient Large-Scale Graph Neural Network Computing)</news:title>
   <news:publication_date>2026-06-25T11:56:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704691</loc>
  <lastmod>2026-06-25T11:55:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>同時翻訳の新しい枠組みと待機戦略の提案（STACL: Simultaneous Translation with Implicit Anticipation and Controllable Latency using Prefix-to-Prefix Framework）</news:title>
   <news:publication_date>2026-06-25T11:55:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704689</loc>
  <lastmod>2026-06-25T11:55:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>成人サイズの3Dプリントオープンヒューマノイド（NimbRo-OP2X: Adult-sized Open-source 3D Printed Humanoid Robot）</news:title>
   <news:publication_date>2026-06-25T11:55:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704687</loc>
  <lastmod>2026-06-25T11:55:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>コーパス品質がニューラル機械翻訳に与える影響（Impact of Corpora Quality on Neural Machine Translation）</news:title>
   <news:publication_date>2026-06-25T11:55:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704685</loc>
  <lastmod>2026-06-25T11:04:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>PyDCIによる分布対応インデクシングの再実装と新規実験（REVISITING DISTRIBUTIONAL CORRESPONDENCE INDEXING: A PYTHON REIMPLEMENTATION AND NEW EXPERIMENTS）</news:title>
   <news:publication_date>2026-06-25T11:04:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704683</loc>
  <lastmod>2026-06-25T10:59:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高スケーラビリティで省エネな人工ニューロン（A highly scalable and energy-efficient artificial neuron using an Ovonic Threshold Switch）</news:title>
   <news:publication_date>2026-06-25T10:59:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704681</loc>
  <lastmod>2026-06-25T10:58:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>QBDTによるシステム的不確かさを組み込むブースティング決定木（QBDT, a new boosting decision tree method with systematical uncertainties into training for High Energy Physics）</news:title>
   <news:publication_date>2026-06-25T10:58:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704679</loc>
  <lastmod>2026-06-25T10:58:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>呼び出し駆動型ニューラル近似計算と多クラス判別器・複数近似器の組合せ（Invocation-driven Neural Approximate Computing with a Multiclass-Classifier and Multiple Approximators）</news:title>
   <news:publication_date>2026-06-25T10:58:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704677</loc>
  <lastmod>2026-06-25T10:58:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数グラフを同時に構築するためのPANDAm手法（AdaPtive Noisy Data Augmentation (PANDA) for Simultaneous Construction of Multiple Graph Models）</news:title>
   <news:publication_date>2026-06-25T10:58:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704675</loc>
  <lastmod>2026-06-25T10:58:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Generative Low-Shot Network Expansion（Generative Low-Shot Network Expansion）</news:title>
   <news:publication_date>2026-06-25T10:58:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704673</loc>
  <lastmod>2026-06-25T10:57:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>サリエンシー誘導型深層ネットワークによる弱教師あり画像セグメンテーション（Saliency Guided Deep Network for Weakly-Supervised Image Segmentation）</news:title>
   <news:publication_date>2026-06-25T10:57:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704671</loc>
  <lastmod>2026-06-25T10:07:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オンライン信頼性・パフォーマンスデータを用いた悪質ウェブドメイン識別（Malicious Web Domain Identification using Online Credibility and Performance Data by Considering the Class Imbalance Issue）</news:title>
   <news:publication_date>2026-06-25T10:07:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704669</loc>
  <lastmod>2026-06-25T10:06:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>超ピクセル単位の雲検出と階層融合CNN（Super-pixel cloud detection using Hierarchical Fusion CNN）</news:title>
   <news:publication_date>2026-06-25T10:06:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704667</loc>
  <lastmod>2026-06-25T10:06:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ngVLAによる宇宙機テレメトリ受信の可能性（Spacecraft Telecommunications）</news:title>
   <news:publication_date>2026-06-25T10:06:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704665</loc>
  <lastmod>2026-06-25T10:06:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習済みMLPにおける交換可能性とカーネル不変性（Exchangeability and Kernel Invariance in Trained MLPs）</news:title>
   <news:publication_date>2026-06-25T10:06:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704663</loc>
  <lastmod>2026-06-25T10:05:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>トーンマップされたHDR画像の品質評価（QUALITY ASSESSMENT FOR TONE-MAPPED HDR IMAGES USING MULTI-SCALE AND MULTI-LAYER INFORMATION）</news:title>
   <news:publication_date>2026-06-25T10:05:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704661</loc>
  <lastmod>2026-06-25T10:05:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Multi-Domain Pose Networkによるマルチ人物姿勢推定と追跡の改善（Multi-Domain Pose Network for Multi-Person Pose Estimation and Tracking）</news:title>
   <news:publication_date>2026-06-25T10:05:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704659</loc>
  <lastmod>2026-06-25T10:05:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>積み重ねオートエンコーダーによるオンライン状態監視の自動特徴抽出法（A STACKED AUTOENCODER NEURAL NETWORK BASED AUTOMATED FEATURE EXTRACTION METHOD FOR ANOMALY DETECTION IN ON-LINE CONDITION MONITORING）</news:title>
   <news:publication_date>2026-06-25T10:05:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704657</loc>
  <lastmod>2026-06-25T09:14:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層構造を使った転移特徴と射影学習によるゼロショット学習の革新（Transferrable Feature and Projection Learning with Class Hierarchy for Zero-Shot Learning）</news:title>
   <news:publication_date>2026-06-25T09:14:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704655</loc>
  <lastmod>2026-06-25T09:14:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>セマンティック特徴合成と競合学習によるゼロ/フューショット学習（Zero and Few Shot Learning with Semantic Feature Synthesis and Competitive Learning）</news:title>
   <news:publication_date>2026-06-25T09:14:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704653</loc>
  <lastmod>2026-06-25T09:14:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>微粒度オブジェクトの3D姿勢注釈改善（Improving Annotation for 3D Pose Dataset of Fine-Grained Object Categories）</news:title>
   <news:publication_date>2026-06-25T09:14:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704651</loc>
  <lastmod>2026-06-25T09:13:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ヘテロスケダスティックPCAのためのアルゴリズムと最適性（Heteroskedastic PCA: Algorithm, Optimality, and Applications）</news:title>
   <news:publication_date>2026-06-25T09:13:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704649</loc>
  <lastmod>2026-06-25T09:12:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>シーケンスト置換サンプリングによる学習の探索性向上（SEQUENCED-REPLACEMENT SAMPLING FOR DEEP LEARNING）</news:title>
   <news:publication_date>2026-06-25T09:12:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704647</loc>
  <lastmod>2026-06-25T09:12:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ドメイン不変射影学習によるゼロショット認識の要点（Domain-Invariant Projection Learning for Zero-Shot Recognition）</news:title>
   <news:publication_date>2026-06-25T09:12:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704645</loc>
  <lastmod>2026-06-25T09:12:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多層トランスフォーム学習が画像ノイズ処理を変える（LEARNING MULTI-LAYER TRANSFORM MODELS）</news:title>
   <news:publication_date>2026-06-25T09:12:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704643</loc>
  <lastmod>2026-06-25T08:21:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高解像度組織クリアリングデータにおける位置合わせ手法の比較（A Comparative Analysis of Registration Tools: Traditional vs Deep Learning Approach on High Resolution Tissue Cleared Data）</news:title>
   <news:publication_date>2026-06-25T08:21:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704641</loc>
  <lastmod>2026-06-25T08:21:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DeepXSによる粒子生成断面積の高速近似（DeepXS: Fast approximation of MSSM electroweak cross sections at NLO）</news:title>
   <news:publication_date>2026-06-25T08:21:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704639</loc>
  <lastmod>2026-06-25T08:21:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>通信頻度を動的に変えて学習時間と誤差を両立する手法（Adaptive Communication Strategies to Achieve the Best Error-Runtime Trade-off in Local-Update SGD）</news:title>
   <news:publication_date>2026-06-25T08:21:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704637</loc>
  <lastmod>2026-06-25T08:20:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>眼底写真でOCT由来の糖尿病性黄斑浮腫グレードを予測する深層学習（Predicting optical coherence tomography-derived diabetic macular edema grades from fundus photographs using deep learning）</news:title>
   <news:publication_date>2026-06-25T08:20:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704635</loc>
  <lastmod>2026-06-25T08:20:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>教師なし異常データ空間の仕様化（Unsupervised Anomalous Data Space Specification）</news:title>
   <news:publication_date>2026-06-25T08:20:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704633</loc>
  <lastmod>2026-06-25T08:20:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ソースコードにおけるオープンボキャブラリ学習とグラフ構造キャッシュ（Open Vocabulary Learning on Source Code with a Graph–Structured Cache）</news:title>
   <news:publication_date>2026-06-25T08:20:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704631</loc>
  <lastmod>2026-06-25T08:19:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>バイアフィン分類器のパラメータ冗長性削減（Reduction of Parameter Redundancy in Biaffine Classifiers with Symmetric and Circulant Weight Matrices）</news:title>
   <news:publication_date>2026-06-25T08:19:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704629</loc>
  <lastmod>2026-06-25T07:28:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>サンプル無しで入力文法を学ぶ方法（Sample-Free Learning of Input Grammars for Comprehensive Software Fuzzing）</news:title>
   <news:publication_date>2026-06-25T07:28:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704627</loc>
  <lastmod>2026-06-25T07:27:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルウェア検出における敵対的事例の探究 (Exploring Adversarial Examples in Malware Detection)</news:title>
   <news:publication_date>2026-06-25T07:27:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704625</loc>
  <lastmod>2026-06-25T07:20:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層学習を含む自律システムの合成検証（Compositional Verification for Autonomous Systems with Deep Learning Components）</news:title>
   <news:publication_date>2026-06-25T07:20:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704623</loc>
  <lastmod>2026-06-25T07:19:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的プリマル・デュアルQ学習の実務的解説（Stochastic Primal-Dual Q-Learning）</news:title>
   <news:publication_date>2026-06-25T07:19:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704621</loc>
  <lastmod>2026-06-25T07:19:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CURE-OR: 現実と非現実をまたぐ物体認識の堅牢性検証データセット（CURE-OR: Challenging Unreal and Real Environments for Object Recognition）</news:title>
   <news:publication_date>2026-06-25T07:19:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704619</loc>
  <lastmod>2026-06-25T07:17:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>処方行動の社会的学習が抗生物質使用の集団最適を促す（Social learning of prescribing behavior can promote population optimum of antibiotic use）</news:title>
   <news:publication_date>2026-06-25T07:17:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704617</loc>
  <lastmod>2026-06-25T07:17:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>糖尿病性網膜症スクリーニングにおける深層学習と人間グレーダーの比較（Deep Learning vs. Human Graders for Classifying Severity Levels of Diabetic Retinopathy in a Real-World Nationwide Screening Program）</news:title>
   <news:publication_date>2026-06-25T07:17:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704615</loc>
  <lastmod>2026-06-25T06:26:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Optimal TransportとMMDをつなぐSinkhorn Divergences（Interpolating between Optimal Transport and MMD using Sinkhorn Divergences）</news:title>
   <news:publication_date>2026-06-25T06:26:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
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 <url>
  <loc>https://aibr.jp/archives/704613</loc>
  <lastmod>2026-06-25T06:25:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>無限因子型有限状態機械によるブラインド多元ユーザチャネル推定（Infinite Factorial Finite State Machine for Blind Multiuser Channel Estimation）</news:title>
   <news:publication_date>2026-06-25T06:25:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704611</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>BabyAI：人間をループに含めた基礎言語学習のサンプル効率化（BABYAI: A Platform to Study the Sample Efficiency of Grounded Language Learning）</news:title>
   <news:publication_date>2026-06-25T06:25:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704609</loc>
  <lastmod>2026-06-25T06:24:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>チャネル注目を連鎖させたMRI復元ネットワークの要点解説（MRI RECONSTRUCTION VIA CASCADED CHANNEL-WISE ATTENTION NETWORK）</news:title>
   <news:publication_date>2026-06-25T06:24:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704607</loc>
  <lastmod>2026-06-25T06:24:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>メモリ制約下の分位回帰（Quantile Regression Under Memory Constraint）</news:title>
   <news:publication_date>2026-06-25T06:24:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704605</loc>
  <lastmod>2026-06-25T06:24:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>実践における公平なケーキ分配（Fair Cake-Cutting in Practice）</news:title>
   <news:publication_date>2026-06-25T06:24:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704603</loc>
  <lastmod>2026-06-25T06:24:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次元予測モデリングにおける群変数の影響除去（Removing the influence of a group variable in high-dimensional predictive modelling）</news:title>
   <news:publication_date>2026-06-25T06:24:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704601</loc>
  <lastmod>2026-06-25T05:33:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習による熱力学と特徴抽出（Thermodynamics and Feature Extraction by Machine Learning）</news:title>
   <news:publication_date>2026-06-25T05:33:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704599</loc>
  <lastmod>2026-06-25T05:32:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>検索スニペットの“読む場所”をモデル化する研究（Micro-Browsing Models for Search Snippets）</news:title>
   <news:publication_date>2026-06-25T05:32:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704597</loc>
  <lastmod>2026-06-25T05:32:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>空力解析における深層学習の実用性と限界（Deep Learning Methods for Reynolds-Averaged Navier-Stokes Simulations of Airfoil Flows）</news:title>
   <news:publication_date>2026-06-25T05:32:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704595</loc>
  <lastmod>2026-06-25T05:32:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>宇宙再電離史における21cm信号への機械学習応用（Machine Learning Applied to the Reionization History of the Universe in the 21 cm Signal）</news:title>
   <news:publication_date>2026-06-25T05:32:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704593</loc>
  <lastmod>2026-06-25T05:31:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動画トレーラーの時間配列を読む推薦モデル（Convolutional Collaborative Filter Network for Video Based Recommendation Systems）</news:title>
   <news:publication_date>2026-06-25T05:31:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704591</loc>
  <lastmod>2026-06-25T05:31:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>4D-STEMデータを可視化する曼荼羅的学習（Manifold Learning of Four-dimensional Scanning Transmission Electron Microscopy）</news:title>
   <news:publication_date>2026-06-25T05:31:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704589</loc>
  <lastmod>2026-06-25T05:31:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>アンプチュードロイドの最深カット解析（Cutting Deep Into The Amplituhedron）</news:title>
   <news:publication_date>2026-06-25T05:31:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704587</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>修正ボルツマン法による熱QCD媒質の深いLPM領域における分裂頂点のモデル化（A modified-Boltzmann approach for modeling the hot QCD medium-induced splitting vertices in the deep LPM region）</news:title>
   <news:publication_date>2026-06-25T04:39:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704585</loc>
  <lastmod>2026-06-25T04:39:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>重要状態を見せて信頼を適切に構築する方法（Establishing Appropriate Trust via Critical States）</news:title>
   <news:publication_date>2026-06-25T04:39:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704583</loc>
  <lastmod>2026-06-25T04:38:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>勾配合意を最適化目的に据えたメタラーニング（Gradient Agreement as an Optimization Objective for Meta-Learning）</news:title>
   <news:publication_date>2026-06-25T04:38:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704581</loc>
  <lastmod>2026-06-25T04:38:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>行列ランク検定の非適応サンプル最適化（Non-adaptive Sample-Optimal Testing of Matrix Rank）</news:title>
   <news:publication_date>2026-06-25T04:38:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704579</loc>
  <lastmod>2026-06-25T04:38:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>古典的バンディットアルゴリズムを構造化バンディットへ翻訳する統一手法（A Unified Approach to Translate Classical Bandit Algorithms to the Structured Bandit Setting）</news:title>
   <news:publication_date>2026-06-25T04:38:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704577</loc>
  <lastmod>2026-06-25T04:38:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高レベル意味情報を利用した参照なし画像品質評価（Exploiting High-Level Semantics for No-Reference Image Quality Assessment of Realistic Blur Images）</news:title>
   <news:publication_date>2026-06-25T04:38:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704575</loc>
  <lastmod>2026-06-25T04:38:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>過去からのオンライン調整による高速深層強化学習（Fast deep reinforcement learning using online adjustments from the past）</news:title>
   <news:publication_date>2026-06-25T04:38:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704573</loc>
  <lastmod>2026-06-25T03:46:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>レーダーにおける深層逆センサーモデリング（Probably Unknown: Deep Inverse Sensor Modelling In Radar）</news:title>
   <news:publication_date>2026-06-25T03:46:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704571</loc>
  <lastmod>2026-06-25T03:46:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>TensorFlow上での秘密保持機械学習（Private Machine Learning in TensorFlow using Secure Computation）</news:title>
   <news:publication_date>2026-06-25T03:46:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704569</loc>
  <lastmod>2026-06-25T03:46:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>双線形適応一般化ベクトル近似メッセージパッシング（Bilinear Adaptive Generalized Vector Approximate Message Passing）</news:title>
   <news:publication_date>2026-06-25T03:46:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704567</loc>
  <lastmod>2026-06-25T03:45:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>知識伝達敵対的ネットワークによる教師−生徒学習の再定義（Knowledge Transfer Adversarial Network）</news:title>
   <news:publication_date>2026-06-25T03:45:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704565</loc>
  <lastmod>2026-06-25T03:45:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>勾配降下法の一次・二次変種を統一的に理解する（FIRST-ORDER AND SECOND-ORDER VARIANTS OF THE GRADIENT DESCENT IN A UNIFIED FRAMEWORK）</news:title>
   <news:publication_date>2026-06-25T03:45:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704563</loc>
  <lastmod>2026-06-25T03:45:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>空撮画像におけるサリエンスバイアス損失（Salience Biased Loss for Object Detection in Aerial Images）</news:title>
   <news:publication_date>2026-06-25T03:45:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704561</loc>
  <lastmod>2026-06-25T03:44:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自律走行EVによる配車最適化と近似動的計画法（Approximate Dynamic Programming for Planning a Ride-Sharing System using Autonomous Fleets of Electric Vehicles）</news:title>
   <news:publication_date>2026-06-25T03:44:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704559</loc>
  <lastmod>2026-06-25T02:53:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DeepLens: 単一画像から生成する浅い被写界深度（DeepLens: Shallow Depth Of Field From A Single Image）</news:title>
   <news:publication_date>2026-06-25T02:53:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704557</loc>
  <lastmod>2026-06-25T02:53:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層モデルの変分ベイズにおける良好な初期化（Good Initializations of Variational Bayes for Deep Models）</news:title>
   <news:publication_date>2026-06-25T02:53:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704555</loc>
  <lastmod>2026-06-25T02:53:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ノイズあり回折パターンからの辞書学習位相回復（Dictionary Learning Phase Retrieval from Noisy Diffraction Patterns）</news:title>
   <news:publication_date>2026-06-25T02:53:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704553</loc>
  <lastmod>2026-06-25T02:52:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>矮小銀河の星形成履歴を復元する新手法（A new method to derive star formation histories in dwarf galaxies）</news:title>
   <news:publication_date>2026-06-25T02:52:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704551</loc>
  <lastmod>2026-06-25T02:52:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ヘリウム濃厚な準矮星O型星の可視・紫外スペクトルに対する定量的NLTE解析（A quantitative NLTE analysis of visual and ultraviolet spectra of four helium-rich subdwarf O stars）</news:title>
   <news:publication_date>2026-06-25T02:52:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704549</loc>
  <lastmod>2026-06-25T02:52:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>平均後悔率を最小化する集合の探索（Finding Average Regret Ratio Minimizing Set in Database）</news:title>
   <news:publication_date>2026-06-25T02:52:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704547</loc>
  <lastmod>2026-06-25T02:51:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ディスコースの文体装飾を学ぶ（Discourse Embellishment Using a Deep Encoder-Decoder Network）</news:title>
   <news:publication_date>2026-06-25T02:51:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704545</loc>
  <lastmod>2026-06-25T02:00:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>不完全な通信の数学理論とエネルギー効率性（A mathematical theory of imperfect communication: Energy efficiency considerations in multi-level coding）</news:title>
   <news:publication_date>2026-06-25T02:00:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704543</loc>
  <lastmod>2026-06-25T01:59:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層ReLUネットワークの適応性と次元の呪い回避（ADAPTIVITY OF DEEP RELU NETWORK FOR LEARNING IN BESOV AND MIXED SMOOTH BESOV SPACES）</news:title>
   <news:publication_date>2026-06-25T01:59:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704541</loc>
  <lastmod>2026-06-25T01:59:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スマホ撮影画像における光学フォント認識と本人証明書偽造検出への応用（Optical Font Recognition in Smartphone-Captured Images, and its Applicability for ID Forgery Detection）</news:title>
   <news:publication_date>2026-06-25T01:59:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704539</loc>
  <lastmod>2026-06-25T01:58:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>変分ノイズ対比推定の実務的意義（Variational Noise-Contrastive Estimation）</news:title>
   <news:publication_date>2026-06-25T01:58:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704537</loc>
  <lastmod>2026-06-25T01:58:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単一イオン量子ビットの高速・高忠実読み出しを機械学習で実現する（Fast and High-Fidelity Readout of Single Trapped-Ion Qubit via Machine Learning Methods）</news:title>
   <news:publication_date>2026-06-25T01:58:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704535</loc>
  <lastmod>2026-06-25T01:58:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>粒子衝突データの「雑音」除去が変わる——グラフニューラルネットワークによるパイルアップ緩和（Pileup mitigation at the Large Hadron Collider with Graph Neural Networks）</news:title>
   <news:publication_date>2026-06-25T01:58:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704533</loc>
  <lastmod>2026-06-25T01:58:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>浅潜から学ぶ深層学習：水中物体検出のためのソナー画像生成と訓練 (Deep Learning from Shallow Dives: Sonar Image Generation and Training for Underwater Object Detection)</news:title>
   <news:publication_date>2026-06-25T01:58:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704531</loc>
  <lastmod>2026-06-25T01:06:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>カテゴリ別把持のための形状空間レジストレーションを通じた姿勢シナジー学習（Learning Postural Synergies for Categorical Grasping through Shape Space Registration）</news:title>
   <news:publication_date>2026-06-25T01:06:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704529</loc>
  <lastmod>2026-06-25T01:05:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>LeukoNet: DCTベースのCNNによるB-ALL細胞分類（LEUKONET: DCT-BASED CNN ARCHITECTURE FOR THE CLASSIFICATION OF NORMAL VERSUS LEUKEMIC BLASTS IN B-ALL CANCER）</news:title>
   <news:publication_date>2026-06-25T01:05:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704527</loc>
  <lastmod>2026-06-25T01:05:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>観測誤差が因果探索に与える影響と上界推定による補正手法（An Upper Bound for Random Measurement Error in Causal Discovery）</news:title>
   <news:publication_date>2026-06-25T01:05:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704525</loc>
  <lastmod>2026-06-25T01:04:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層的マルチラベル分類における決定戦略と局所適合率（HIERLPR: DECISION MAKING IN HIERARCHICAL MULTI-LABEL CLASSIFICATION WITH LOCAL PRECISION RATES）</news:title>
   <news:publication_date>2026-06-25T01:04:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704523</loc>
  <lastmod>2026-06-25T01:04:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>S-Net: JPEG圧縮アーティファクト削減のためのスケーラブルCNN（S-Net: A Scalable Convolutional Neural Network for JPEG Compression Artifact Reduction）</news:title>
   <news:publication_date>2026-06-25T01:04:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704521</loc>
  <lastmod>2026-06-25T01:04:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>教師なしニューラル文簡易化の実践（Unsupervised Neural Text Simplification）</news:title>
   <news:publication_date>2026-06-25T01:04:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704519</loc>
  <lastmod>2026-06-25T01:04:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>畳み込みニューラルネットワークによる乱流混合燃焼モデル化（Modelling turbulent premixed flames using convolutional neural networks）</news:title>
   <news:publication_date>2026-06-25T01:04:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704517</loc>
  <lastmod>2026-06-25T00:13:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ランダム化ベンチマーキングからのスピンキュービットノイズ分光（Spin-qubit noise spectroscopy from randomized benchmarking by supervised learning）</news:title>
   <news:publication_date>2026-06-25T00:13:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704515</loc>
  <lastmod>2026-06-25T00:11:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スマートシティにおけるリアルタイム高精細大気質センシングネットワーク（Real-Time Fine-Grained Air Quality Sensing Networks in Smart City: Design, Implementation and Optimization）</news:title>
   <news:publication_date>2026-06-25T00:11:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704513</loc>
  <lastmod>2026-06-25T00:09:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>入力変数の影響を情報理論で解く：エントロピック変数射影による機械学習説明（Explaining machine learning models using entropic variable projection）</news:title>
   <news:publication_date>2026-06-25T00:09:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704511</loc>
  <lastmod>2026-06-25T00:08:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>合成画像から実世界の視線推定へ橋を架ける手法（Unsupervised Domain Adaptation for Learning Eye Gaze from a Million Synthetic Images: An Adversarial Approach）</news:title>
   <news:publication_date>2026-06-25T00:08:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704509</loc>
  <lastmod>2026-06-25T00:07:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次元におけるロバストスパース縮約ランク回帰（Robust Sparse Reduced Rank Regression in High Dimensions）</news:title>
   <news:publication_date>2026-06-25T00:07:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704507</loc>
  <lastmod>2026-06-25T00:07:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>クラス均衡自己学習によるセマンティックセグメンテーションのドメイン適応（Domain Adaptation for Semantic Segmentation via Class-Balanced Self-Training）</news:title>
   <news:publication_date>2026-06-25T00:07:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704505</loc>
  <lastmod>2026-06-25T00:07:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深海面波におけるソリトンと極端事象の共存（Coexistence of solitons and extreme events in deep water surface waves）</news:title>
   <news:publication_date>2026-06-25T00:07:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704503</loc>
  <lastmod>2026-06-24T23:15:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>暗号化トラフィック分類における深層学習の概観（Deep Learning for Encrypted Traffic Classification: An Overview）</news:title>
   <news:publication_date>2026-06-24T23:15:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704501</loc>
  <lastmod>2026-06-24T23:15:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>WikiHow: 大規模テキスト要約データセットの重要性（WikiHow: A Large Scale Text Summarization Dataset）</news:title>
   <news:publication_date>2026-06-24T23:15:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704499</loc>
  <lastmod>2026-06-24T23:14:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>部分観測環境におけるポリシー勾配：近似と収束（Policy Gradient in Partially Observable Environments: Approximation and Convergence）</news:title>
   <news:publication_date>2026-06-24T23:14:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704497</loc>
  <lastmod>2026-06-24T23:14:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>テスト時拡張を用いた畳み込みニューラルネットワークによる自動脳腫瘍セグメンテーション (Automatic Brain Tumor Segmentation using Convolutional Neural Networks with Test-Time Augmentation)</news:title>
   <news:publication_date>2026-06-24T23:14:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704495</loc>
  <lastmod>2026-06-24T23:13:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>幾何光学極限におけるレンズ化重力波の分類を機械学習で行う（Classifying Lensed Gravitational Waves in the Geometrical Optics Limit with Machine Learning）</news:title>
   <news:publication_date>2026-06-24T23:13:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704493</loc>
  <lastmod>2026-06-24T23:13:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>光ナノ構造の最適設計を強化学習で探る（Finding the best design parameters for optical nanostructures using reinforcement learning）</news:title>
   <news:publication_date>2026-06-24T23:13:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704491</loc>
  <lastmod>2026-06-24T23:13:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>畳み込みニューラルネットワークの可視化と解釈（Analyzing and Interpreting Convolutional Neural Networks in NLP）</news:title>
   <news:publication_date>2026-06-24T23:13:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704489</loc>
  <lastmod>2026-06-24T22:22:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>通信・ネットワークにおける深層強化学習の応用（Applications of Deep Reinforcement Learning in Communications and Networking: A Survey）</news:title>
   <news:publication_date>2026-06-24T22:22:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704487</loc>
  <lastmod>2026-06-24T22:22:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自己組織化テンソル構造によるマルチビュークラスタリング（A Self-Organizing Tensor Architecture for Multi-View Clustering）</news:title>
   <news:publication_date>2026-06-24T22:22:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704485</loc>
  <lastmod>2026-06-24T22:21:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>物理教育における計算導入を促す要因の特定（Identifying features predictive of faculty integrating computation into physics courses）</news:title>
   <news:publication_date>2026-06-24T22:21:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704483</loc>
  <lastmod>2026-06-24T22:20:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>EdgeSpeechNets：エッジ端末向け高効率音声認識ネットワーク（EdgeSpeechNets: Highly Efficient Deep Neural Networks for Speech Recognition on the Edge）</news:title>
   <news:publication_date>2026-06-24T22:20:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704481</loc>
  <lastmod>2026-06-24T22:20:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチバンド銀河形態分類の転移学習（Multiband galaxy morphologies for CLASH: a convolutional neural network transferred from CANDELS）</news:title>
   <news:publication_date>2026-06-24T22:20:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704479</loc>
  <lastmod>2026-06-24T22:20:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次元シンプレックス学習によるアンミキシング問題の再考（On Statistical Learning of Simplices: Unmixing Problem Revisited）</news:title>
   <news:publication_date>2026-06-24T22:20:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704477</loc>
  <lastmod>2026-06-24T22:20:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ノイズの多いデータの分散kクラスタリング（Distributed k-Clustering for Data with Heavy Noise）</news:title>
   <news:publication_date>2026-06-24T22:20:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704475</loc>
  <lastmod>2026-06-24T21:28:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>核反応におけるクォーク・グルーオンの硬散乱とスケーリング（Hard Breakup and Spin in QCD）</news:title>
   <news:publication_date>2026-06-24T21:28:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704473</loc>
  <lastmod>2026-06-24T21:28:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>心音記録の正常/異常分類をめぐる実務的視点（Classification of Normal/Abnormal Heart Sound Recordings based on Multi-Domain Features and Back Propagation Neural Network）</news:title>
   <news:publication_date>2026-06-24T21:28:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704471</loc>
  <lastmod>2026-06-24T21:27:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Wasserstein変換（The Wasserstein Transform）</news:title>
   <news:publication_date>2026-06-24T21:27:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704469</loc>
  <lastmod>2026-06-24T21:27:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>小さなReLUネットワークは強力な記憶装置である（Small ReLU networks are powerful memorizers: a tight analysis of memorization capacity）</news:title>
   <news:publication_date>2026-06-24T21:27:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704467</loc>
  <lastmod>2026-06-24T21:27:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>周期性に着目した並列時系列予測アルゴリズム（A Periodicity-based Parallel Time Series Prediction Algorithm in Cloud Computing Environments）</news:title>
   <news:publication_date>2026-06-24T21:27:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704465</loc>
  <lastmod>2026-06-24T21:26:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模PPIネットワークにおけるマルチソース学習を用いた並列タンパク質コミュニティ検出（Parallel Protein Community Detection in Large-scale PPI Networks Based on Multi-source Learning）</news:title>
   <news:publication_date>2026-06-24T21:26:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704463</loc>
  <lastmod>2026-06-24T21:26:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチエージェント完全分散価値関数学習と線形収束の保証（Multi-Agent Fully Decentralized Value Function Learning with Linear Convergence Rates）</news:title>
   <news:publication_date>2026-06-24T21:26:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704461</loc>
  <lastmod>2026-06-24T20:35:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>生成モデルにおけるオブジェクト構成性の検証（INVESTIGATING OBJECT COMPOSITIONALITY IN GENERATIVE ADVERSARIAL NETWORKS）</news:title>
   <news:publication_date>2026-06-24T20:35:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704459</loc>
  <lastmod>2026-06-24T20:35:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ビッグデータマイニングとクラウドコンピューティングに基づく疾病診断・治療推奨システム（A Disease Diagnosis and Treatment Recommendation System Based on Big Data Mining and Cloud Computing）</news:title>
   <news:publication_date>2026-06-24T20:35:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704457</loc>
  <lastmod>2026-06-24T20:34:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>信頼できないネットワーク上の分散学習（Distributed Learning over Unreliable Networks）</news:title>
   <news:publication_date>2026-06-24T20:34:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704455</loc>
  <lastmod>2026-06-24T20:33:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>脳構造の体積的形状表現を学習する畳み込みオートエンコーダ法（A Convolutional Autoencoder Approach to Learn Volumetric Shape Representations for Brain Structures）</news:title>
   <news:publication_date>2026-06-24T20:33:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704453</loc>
  <lastmod>2026-06-24T20:33:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>PepCVAEによる抗菌ペプチド設計の半教師あり生成（PepCVAE: Semi-Supervised Targeted Design of Antimicrobial Peptide Molecules）</news:title>
   <news:publication_date>2026-06-24T20:33:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704451</loc>
  <lastmod>2026-06-24T20:33:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模畳み込みニューラルネットワークのための二層並列学習アーキテクチャ（A Bi-layered Parallel Training Architecture for Large-scale Convolutional Neural Networks）</news:title>
   <news:publication_date>2026-06-24T20:33:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704449</loc>
  <lastmod>2026-06-24T20:33:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>UCR時系列アーカイブの拡張と実務的示唆（The UCR Time Series Archive）</news:title>
   <news:publication_date>2026-06-24T20:33:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704447</loc>
  <lastmod>2026-06-24T19:42:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>人と教えることでロボットが学ぶ映像物体分割（Video Object Segmentation using Teacher-Student Adaptation in a Human Robot Interaction (HRI) Setting）</news:title>
   <news:publication_date>2026-06-24T19:42:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704445</loc>
  <lastmod>2026-06-24T19:41:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>mass-Peak Patch法による高速ダークマターハローカタログ生成（The mass-Peak Patch algorithm for fast generation of deep all-sky dark matter halo catalogues and its N-Body validation）</news:title>
   <news:publication_date>2026-06-24T19:41:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704443</loc>
  <lastmod>2026-06-24T19:41:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ゼロ次近接法による非凸非滑らか制約最適化の実務的示唆（A Proximal Zeroth-Order Algorithm for Nonconvex Nonsmooth Problems）</news:title>
   <news:publication_date>2026-06-24T19:41:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704441</loc>
  <lastmod>2026-06-24T19:40:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深い線形ネットワークの損失地形を代数幾何学の視点で見る（The Loss Surface Of Deep Linear Networks Viewed Through The Algebraic Geometry Lens）</news:title>
   <news:publication_date>2026-06-24T19:40:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704439</loc>
  <lastmod>2026-06-24T19:40:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>銀河団のX線質量推定に対する深層学習アプローチ（A Deep Learning Approach to Galaxy Cluster X-ray Masses）</news:title>
   <news:publication_date>2026-06-24T19:40:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704437</loc>
  <lastmod>2026-06-24T19:40:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>RIn-Close CVC2による数値データのビクラスタ列挙の効率化（RIn-Close CVC2: an even more efficient enumerative algorithm for biclustering of numerical datasets）</news:title>
   <news:publication_date>2026-06-24T19:40:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704435</loc>
  <lastmod>2026-06-24T19:40:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深共晶溶媒がリン脂質単分子層の構造に与える影響のベイズ的解析（Bayesian determination of the effect of a deep eutectic solvent on the structure of lipid monolayers）</news:title>
   <news:publication_date>2026-06-24T19:40:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704433</loc>
  <lastmod>2026-06-24T18:49:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>部分最小二乗による深層ネットワークの剪定（Pruning Deep Networks using Partial Least Squares）</news:title>
   <news:publication_date>2026-06-24T18:49:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704431</loc>
  <lastmod>2026-06-24T18:49:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>プライベートデータ商用化に対する逆張り契約設計（Adversarial Contract Design for Private Data Commercialization）</news:title>
   <news:publication_date>2026-06-24T18:49:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704429</loc>
  <lastmod>2026-06-24T18:48:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>注意機構の解析：ニューラル機械翻訳における語義曖昧性の検討（An Analysis of Attention Mechanisms: The Case of Word Sense Disambiguation in Neural Machine Translation）</news:title>
   <news:publication_date>2026-06-24T18:48:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704427</loc>
  <lastmod>2026-06-24T18:47:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>年齢不変顔認識のための直交深層特徴分解（Orthogonal Deep Features Decomposition for Age-Invariant Face Recognition）</news:title>
   <news:publication_date>2026-06-24T18:47:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704425</loc>
  <lastmod>2026-06-24T18:47:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ユニタリー不変な低ランク誘導ノルムの近接写像計算の効率化（Efficient Proximal Mapping Computation for Unitarily Invariant Low-Rank Inducing Norms）</news:title>
   <news:publication_date>2026-06-24T18:47:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704423</loc>
  <lastmod>2026-06-24T18:47:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ミルザハニの地震流に関する研究（Mirzakhani’s Work on Earthquake Flow）</news:title>
   <news:publication_date>2026-06-24T18:47:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704421</loc>
  <lastmod>2026-06-24T18:47:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>サブグラディエントの図的収束と弱凸最適化（Graphical Convergence of Subgradients in Nonconvex Optimization and Learning）</news:title>
   <news:publication_date>2026-06-24T18:47:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704419</loc>
  <lastmod>2026-06-24T17:55:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>経路ベースの非線形拡張率とラグランジュ不確実性の指標（Measures of Path-Based Nonlinear Expansion Rates and Lagrangian Uncertainty in Stochastic Flows）</news:title>
   <news:publication_date>2026-06-24T17:55:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704417</loc>
  <lastmod>2026-06-24T17:55:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>乳幼児の自由運動から学ぶ3D身体形状と追跡（Learning and Tracking the 3D Body Shape of Freely Moving Infants from RGB-D sequences）</news:title>
   <news:publication_date>2026-06-24T17:55:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704415</loc>
  <lastmod>2026-06-24T17:55:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>知識ベース補完の評価を問い直す（On Evaluating Embedding Models for Knowledge Base Completion）</news:title>
   <news:publication_date>2026-06-24T17:55:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704413</loc>
  <lastmod>2026-06-24T17:54:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ホログラフィックPomeronによる高エネルギー散乱の解析（DIS at small x and hadron-hadron scattering at high energies via the holographic Pomeron exchange）</news:title>
   <news:publication_date>2026-06-24T17:54:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704411</loc>
  <lastmod>2026-06-24T17:54:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ReLUネットワークの証明可能な堅牢性の向上（Provable Robustness of ReLU networks via Maximization of Linear Regions）</news:title>
   <news:publication_date>2026-06-24T17:54:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704409</loc>
  <lastmod>2026-06-24T17:54:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>胸部X線における骨抑制と肺領域分割はいつ病変分類を改善するか（WHEN DOES BONE SUPPRESSION AND LUNG FIELD SEGMENTATION IMPROVE CHEST X-RAY DISEASE CLASSIFICATION?）</news:title>
   <news:publication_date>2026-06-24T17:54:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704407</loc>
  <lastmod>2026-06-24T17:53:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフ編集距離とトリプレットネットワークを組み合わせたオフライン署名認証（Offline Signature Verification by Combining Graph Edit Distance and Triplet Networks）</news:title>
   <news:publication_date>2026-06-24T17:53:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704405</loc>
  <lastmod>2026-06-24T17:03:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>矮小銀河レオAにおける星団と若い恒星集団（Star clusters and young populations in the dwarf irregular galaxy Leo A）</news:title>
   <news:publication_date>2026-06-24T17:03:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704403</loc>
  <lastmod>2026-06-24T17:02:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>矮小銀河レオAの明るい赤色星が示すもの（Bright-red stars in the dwarf irregular galaxy Leo A）</news:title>
   <news:publication_date>2026-06-24T17:02:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/704401</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>階層的モーメント法の実務的意義（Hierarchical Methods of Moments）</news:title>
   <news:publication_date>2026-06-24T17:02:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/704399</loc>
  <lastmod>2026-06-24T17:01:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マゼラン系の三次元構造をOGLE変光星で解く（OGLE-ing the Magellanic System: Three-Dimensional Structure）</news:title>
   <news:publication_date>2026-06-24T17:01:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/704397</loc>
  <lastmod>2026-06-24T17:01:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>地震の頻度―大きさ分布を一般化する非対称ラプラス混合モデル（Generalized Earthquake Frequency-Magnitude Distribution Described by Asymmetric Laplace Mixture Modelling）</news:title>
   <news:publication_date>2026-06-24T17:01:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/704395</loc>
  <lastmod>2026-06-24T17:01:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>染色不変なCNNの学習戦略（STRATEGIES FOR TRAINING STAIN INVARIANT CNNS）</news:title>
   <news:publication_date>2026-06-24T17:01:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
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 <url>
  <loc>https://aibr.jp/archives/704393</loc>
  <lastmod>2026-06-24T17:01:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CADモデルのリバースエンジニアリング（Reverse engineering of CAD models via clustering and approximate implicitization）</news:title>
   <news:publication_date>2026-06-24T17:01:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
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 <url>
  <loc>https://aibr.jp/archives/704391</loc>
  <lastmod>2026-06-24T16:09:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>MRI取得差を吸収する表現の学習（LEARNING AN MR ACQUISITION-INVARIANT REPRESENTATION USING SIAMESE NEURAL NETWORKS）</news:title>
   <news:publication_date>2026-06-24T16:09:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/704389</loc>
  <lastmod>2026-06-24T16:08:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>観測データのバイアスを是正する敵対的重み付け（Adversarial Balancing for Causal Inference）</news:title>
   <news:publication_date>2026-06-24T16:08:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
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 <url>
  <loc>https://aibr.jp/archives/704387</loc>
  <lastmod>2026-06-24T15:59:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>注視点のHMM推定誤差と必要サンプル数（EMHMM Simulation Study）</news:title>
   <news:publication_date>2026-06-24T15:59:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704385</loc>
  <lastmod>2026-06-24T15:59:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>肺気腫の範囲を定量化する学習：どのようなラベルが必要か（Learning to quantify emphysema extent: What labels do we need?）</news:title>
   <news:publication_date>2026-06-24T15:59:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704383</loc>
  <lastmod>2026-06-24T15:59:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>並列時系列ニューラル符号化ネットワーク（Parallel Temporal Neural Coding Network）</news:title>
   <news:publication_date>2026-06-24T15:59:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704381</loc>
  <lastmod>2026-06-24T15:58:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>表情認識における一貫性制約（Coherence Constraints in Facial Expression Recognition）</news:title>
   <news:publication_date>2026-06-24T15:58:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704379</loc>
  <lastmod>2026-06-24T15:58:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>温熱ネットワークのデータ駆動型同定（Data-driven identification of a thermal network in multi-zone building）</news:title>
   <news:publication_date>2026-06-24T15:58:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704377</loc>
  <lastmod>2026-06-24T15:06:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>部分的バイオメトリック特徴の認識（Recognizing Partial Biometric Patterns）</news:title>
   <news:publication_date>2026-06-24T15:06:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704375</loc>
  <lastmod>2026-06-24T15:05:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>鉄道事故の記述解析に深層学習を用いる意義（Analysis of Railway Accidents’ Narratives Using Deep Learning）</news:title>
   <news:publication_date>2026-06-24T15:05:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704373</loc>
  <lastmod>2026-06-24T15:05:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>格子上のハイブリッド物質波—マイクロ波ソリトン（Hybrid matter-wave - microwave solitons on the lattice）</news:title>
   <news:publication_date>2026-06-24T15:05:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704371</loc>
  <lastmod>2026-06-24T15:04:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>一般化ゼロショット学習のための合成空間学習（Learning the Compositional Spaces for Generalized Zero-shot Learning）</news:title>
   <news:publication_date>2026-06-24T15:04:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704369</loc>
  <lastmod>2026-06-24T15:04:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>コンテキスト付きバンディットにおける単純後悔最小化（Simple Regret Minimization for Contextual Bandits）</news:title>
   <news:publication_date>2026-06-24T15:04:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704367</loc>
  <lastmod>2026-06-24T15:04:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>セファイドとRRラエイの時系列解析が示す新たな物理制約（Time-series analyses of Cepheid and RR Lyrae variables）</news:title>
   <news:publication_date>2026-06-24T15:04:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704365</loc>
  <lastmod>2026-06-24T15:04:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>漸進的重みプルーニングによる深層ニューラルネットワークの高率圧縮（PROGRESSIVE WEIGHT PRUNING OF DEEP NEURAL NETWORKS USING ADMM）</news:title>
   <news:publication_date>2026-06-24T15:04:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
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