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   <news:title>クラウド基盤の異常検知とランキングを学ぶ（Anomaly detecting and ranking of the cloud computing platform by multi-view learning）</news:title>
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   <news:title>グラフェン中のSi原子ダイナミクスの原子機構（Atomic mechanisms for the Si atom dynamics in graphene: chemical transformations at the edge and in the bulk）</news:title>
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   <news:title>Fixupによる初期化で正規化を不要にする残差学習（Fixup Initialization: Residual Learning Without Normalization）</news:title>
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   <news:title>エンドツーエンド学習のモジュール化（Modularization of End-to-End Learning: Case Study in Arcade Games）</news:title>
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   <news:title>対称損失を用いた汚れたラベルからの学習（On Symmetric Losses for Learning from Corrupted Labels）</news:title>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>無限地平マルコフ決定過程におけるQ学習の有効性（Q-learning with UCB Exploration is Sample Efficient for Infinite-Horizon MDP）</news:title>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>学習中にモデルを縮小して高速化する方法（PruneTrain: Fast Neural Network Training by Dynamic Sparse Model Reconfiguration）</news:title>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>ホテル認識を加速する大規模データセット（Hotels-50K: A Global Hotel Recognition Dataset）</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>転移学習による糖尿病網膜症分類の実用性評価（Evaluation of Transfer Learning for Classification of Diabetic Retinopathy）</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>二者間プライベート線形最小二乗法の実用的スキーム（A Practical Scheme for Two-Party Private Linear Least Squares）</news:title>
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    <news:language>ja</news:language>
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   <news:title>汎用ハードでのリアルタイム動画要約（Real-time Video Summarization on Commodity Hardware）</news:title>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>Point-Cloudから画像を生成する新手法の要点（Points2Pix: 3D Point-Cloud to Image Translation using conditional GANs）</news:title>
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   <news:genres>Blog</news:genres>
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  <lastmod>2026-07-30T21:50:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>無駄にされている統計情報を活用して分類器精度を改善する方法 (Money on the Table: Statistical information ignored by softmax can improve classifier accuracy)</news:title>
   <news:publication_date>2026-07-30T21:50:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-07-30T20:59:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>実用的なリプシッツ・バンディットに向けて（Towards Practical Lipschitz Bandits）</news:title>
   <news:publication_date>2026-07-30T20:59:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>ナノ構造非晶質シリコンの速度特性を決める原子スケール要因（Atomic-scale factors that control the rate capability of nanostructured amorphous Si for high-energy-density batteries）</news:title>
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   <news:genres>Blog</news:genres>
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  <lastmod>2026-07-30T20:58:24Z</lastmod>
  <news:news>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>自動運転のデータセット設計と検証の課題（CHALLENGES IN DESIGNING DATASETS AND VALIDATION FOR AUTONOMOUS DRIVING）</news:title>
   <news:publication_date>2026-07-30T20:58:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-07-30T20:57:48Z</lastmod>
  <news:news>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>多年にわたる24時間・週7日の起点結点需要推定（Estimating multi-year 24/7 origin-destination demand using high-granular multi-source traffic data）</news:title>
   <news:publication_date>2026-07-30T20:57:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-07-30T20:57:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>遷移金属上の吸着エネルギーを多層データで精度良く推定する方法（On Deriving Probabilistic Models for Adsorption Energy on Transition Metals using Multi-level ab initio and Experimental Data）</news:title>
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   <news:genres>Blog</news:genres>
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  <lastmod>2026-07-30T20:57:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>差分圧縮による分散学習（Distributed Learning with Compressed Gradient Differences）</news:title>
   <news:publication_date>2026-07-30T20:57:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-07-30T20:57:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>離散値時系列のクラスタリング（Clustering Discrete-Valued Time Series）</news:title>
   <news:publication_date>2026-07-30T20:57:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-07-30T20:06:07Z</lastmod>
  <news:news>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>ラベル無し動画から学ぶ動画表現学習（DistInit: Learning Video Representations Without a Single Labeled Video）</news:title>
   <news:publication_date>2026-07-30T20:06:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-07-30T20:05:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>単純な特徴で効率的に毒性を予測する（Efficient Toxicity Prediction via Simple Features Using Shallow Neural Networks and Decision Trees）</news:title>
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   <news:genres>Blog</news:genres>
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  <lastmod>2026-07-30T20:05:32Z</lastmod>
  <news:news>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>スペクトラムデータ汚染による敵対的ディープ学習（Spectrum Data Poisoning with Adversarial Deep Learning）</news:title>
   <news:publication_date>2026-07-30T20:05:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/717724</loc>
  <lastmod>2026-07-30T20:04:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>高効率四接合太陽電池の設計と評価（Novel High Efficiency Quadruple Junction Solar Cell with Current Matching and Optimized Quantum Efficiency）</news:title>
   <news:publication_date>2026-07-30T20:04:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/717722</loc>
  <lastmod>2026-07-30T20:04:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>特徴マップ注意による深層学習転移（DELTA: DEEP LEARNING TRANSFER USING FEATURE MAP WITH ATTENTION）</news:title>
   <news:publication_date>2026-07-30T20:04:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-07-30T20:04:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模画像・信号における分散畳み込み辞書学習（Distributed Convolutional Dictionary Learning (DiCoDiLe): Pattern Discovery in Large Images and Signals）</news:title>
   <news:publication_date>2026-07-30T20:04:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-07-30T20:04:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GAN生成画像とレタッチ検出の自動化（On Detecting GANs and Retouching based Synthetic Alterations）</news:title>
   <news:publication_date>2026-07-30T20:04:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
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  <loc>https://aibr.jp/archives/717716</loc>
  <lastmod>2026-07-30T19:11:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層的思考で捉える多エージェントの有限合理性（Modelling Bounded Rationality in Multi-Agent Interactions by Generalized Recursive Reasoning）</news:title>
   <news:publication_date>2026-07-30T19:11:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717714</loc>
  <lastmod>2026-07-30T19:11:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>段階的画像雨除去ネットワークの簡潔な基準（Progressive Image Deraining Networks: A Better and Simpler Baseline）</news:title>
   <news:publication_date>2026-07-30T19:11:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717712</loc>
  <lastmod>2026-07-30T19:11:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>磁気浮上ハプティックのためのCascade LSTMベース視覚・慣性航法（Cascade LSTM Based Visual-Inertial Navigation for Magnetic Levitation Haptic Interaction）</news:title>
   <news:publication_date>2026-07-30T19:11:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717710</loc>
  <lastmod>2026-07-30T19:09:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的再帰的推論（Probabilistic Recursive Reasoning, PR2）によるマルチエージェント強化学習の刷新（PROBABILISTIC RECURSIVE REASONING FOR MULTI-AGENT REINFORCEMENT LEARNING）</news:title>
   <news:publication_date>2026-07-30T19:09:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/717708</loc>
  <lastmod>2026-07-30T19:09:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>フル解像度Atrous Convolutional Neural Networkによる医用画像セグメンテーション（ACNN: a Full Resolution DCNN for Medical Image Segmentation）</news:title>
   <news:publication_date>2026-07-30T19:09:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/717706</loc>
  <lastmod>2026-07-30T19:09:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>カーネル誘導による暗黙的生成モデルの安定化（Kernel-Guided Training of Implicit Generative Models with Stability Guarantees）</news:title>
   <news:publication_date>2026-07-30T19:09:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/717704</loc>
  <lastmod>2026-07-30T18:16:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>選択的予測を統合した深層ニューラルネットワーク（SelectiveNet: A Deep Neural Network with an Integrated Reject Option）</news:title>
   <news:publication_date>2026-07-30T18:16:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717702</loc>
  <lastmod>2026-07-30T18:06:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>群進化に基づくブラックボックス攻撃の実像（A BLACK-BOX ATTACK ON NEURAL NETWORKS BASED ON SWARM EVOLUTIONARY ALGORITHM）</news:title>
   <news:publication_date>2026-07-30T18:06:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717700</loc>
  <lastmod>2026-07-30T18:06:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>心電図拍における重要な部分列の発見（Discovery of Important Subsequences in Electrocardiogram Beats Using the Nearest Neighbour Algorithm）</news:title>
   <news:publication_date>2026-07-30T18:06:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717698</loc>
  <lastmod>2026-07-30T18:05:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>平面に基づくクラスタリングの一般モデル（A general model for plane-based clustering with loss function）</news:title>
   <news:publication_date>2026-07-30T18:05:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717696</loc>
  <lastmod>2026-07-30T18:05:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>少光子パラメトリック発振器の量子ダイナミクス（Quantum dynamics of a few-photon parametric oscillator）</news:title>
   <news:publication_date>2026-07-30T18:05:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717694</loc>
  <lastmod>2026-07-30T18:05:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スパース進化的ディープラーニング：汎用PCで百万ニューロンを動かす（Sparse evolutionary Deep Learning with over one million artificial neurons on commodity hardware）</news:title>
   <news:publication_date>2026-07-30T18:05:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717692</loc>
  <lastmod>2026-07-30T18:04:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>行動ロバスト強化学習と連続制御への応用（Action Robust Reinforcement Learning and Applications in Continuous Control）</news:title>
   <news:publication_date>2026-07-30T18:04:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717690</loc>
  <lastmod>2026-07-30T17:12:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>データ駆動によるフォトニックシミュレーションの高速化（Data-driven acceleration of photonic simulations）</news:title>
   <news:publication_date>2026-07-30T17:12:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717688</loc>
  <lastmod>2026-07-30T17:12:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>柔軟な契約による大口電力需要学習（Learning Large Electrical Loads via Flexible Contracts with Commitment）</news:title>
   <news:publication_date>2026-07-30T17:12:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717686</loc>
  <lastmod>2026-07-30T17:12:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>重み付き動的ネットワークの非線形時系列リンク予測（GCN-GAN: A Non-linear Temporal Link Prediction Model for Weighted Dynamic Networks）</news:title>
   <news:publication_date>2026-07-30T17:12:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717684</loc>
  <lastmod>2026-07-30T17:11:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>注目領域に導かれるデータ拡張で細分類の精度を高める手法（See Better Before Looking Closer: Weakly Supervised Data Augmentation Network for Fine-Grained Visual Classification）</news:title>
   <news:publication_date>2026-07-30T17:11:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717682</loc>
  <lastmod>2026-07-30T17:11:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SDRとPESQを同時最適化するエンドツーエンド多目的デノイジング（End-to-End Multi-Task Denoising for Joint SDR and PESQ Optimization）</news:title>
   <news:publication_date>2026-07-30T17:11:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717680</loc>
  <lastmod>2026-07-30T17:10:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>制約された空力データセットからの高速ニューラル予測（Fast Neural Network Predictions from Constrained Aerodynamics Datasets）</news:title>
   <news:publication_date>2026-07-30T17:10:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717678</loc>
  <lastmod>2026-07-30T17:10:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>鞍点から抜け出す適応的勾配法（Escaping Saddle Points with Adaptive Gradient Methods）</news:title>
   <news:publication_date>2026-07-30T17:10:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717676</loc>
  <lastmod>2026-07-30T16:18:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>メタメトリック学習による少数ショット学習（Few-shot Learning with Meta Metric Learners）</news:title>
   <news:publication_date>2026-07-30T16:18:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717674</loc>
  <lastmod>2026-07-30T16:18:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフィカルモデルによる差分プライバシー下の推定と推論（Graphical-model based estimation and inference for differential privacy）</news:title>
   <news:publication_date>2026-07-30T16:18:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717672</loc>
  <lastmod>2026-07-30T16:17:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>漸進的ラベル蒸留による入力効率化（PROGRESSIVE LABEL DISTILLATION: LEARNING INPUT-EFFICIENT DEEP NEURAL NETWORKS）</news:title>
   <news:publication_date>2026-07-30T16:17:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717670</loc>
  <lastmod>2026-07-30T16:17:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>VQNet：量子-古典ハイブリッドニューラルネットワークのライブラリ（VQNet: Library for a Quantum-Classical Hybrid Neural Network）</news:title>
   <news:publication_date>2026-07-30T16:17:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717668</loc>
  <lastmod>2026-07-30T16:17:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>最適なk被覆充電問題（Optimal k-Coverage Charging Problem）</news:title>
   <news:publication_date>2026-07-30T16:17:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717666</loc>
  <lastmod>2026-07-30T16:16:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スタッキングと安定性（Stacking and Stability）</news:title>
   <news:publication_date>2026-07-30T16:16:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717664</loc>
  <lastmod>2026-07-30T16:16:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層的文書表現のための言語モデル事前学習（LANGUAGE MODEL PRE-TRAINING FOR HIERARCHICAL DOCUMENT REPRESENTATIONS）</news:title>
   <news:publication_date>2026-07-30T16:16:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717662</loc>
  <lastmod>2026-07-30T15:24:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ラベル無しデータとラベル有りデータによるアクティブラーニングの停止判断の比較 (The Use of Unlabeled Data versus Labeled Data for Stopping Active Learning for Text Classification)</news:title>
   <news:publication_date>2026-07-30T15:24:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717660</loc>
  <lastmod>2026-07-30T15:24:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>予測されたF値変化に基づくアクティブラーニングの停止法（Stopping Active Learning based on Predicted Change of F Measure for Text Classification）</news:title>
   <news:publication_date>2026-07-30T15:24:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717658</loc>
  <lastmod>2026-07-30T15:24:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DeepSZによるニューラルネットワーク圧縮の実務的示唆（DeepSZ: A Novel Framework to Compress Deep Neural Networks by Using Error-Bounded Lossy Compression）</news:title>
   <news:publication_date>2026-07-30T15:24:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717656</loc>
  <lastmod>2026-07-30T15:23:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非常に疎な高次元データの部分空間クラスタリング（Subspace Clustering of Very Sparse High-Dimensional Data）</news:title>
   <news:publication_date>2026-07-30T15:23:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717654</loc>
  <lastmod>2026-07-30T15:23:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散型適応モーメント推定法DADAMの要点（DADAM: A Consensus-based Distributed Adaptive Gradient Method for Online Optimization）</news:title>
   <news:publication_date>2026-07-30T15:23:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717652</loc>
  <lastmod>2026-07-30T15:22:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>限られた学習データでのブラックボックスAPI攻撃に対する生成対抗ネットワーク（Generative Adversarial Networks for Black-Box API Attacks with Limited Training Data）</news:title>
   <news:publication_date>2026-07-30T15:22:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717650</loc>
  <lastmod>2026-07-30T15:22:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動物と植物を形で見分ける視覚分類の研究（A study on general visual categorization of objects into animal and plant groups using global shape descriptors with a focus on category-specific deficits）</news:title>
   <news:publication_date>2026-07-30T15:22:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717648</loc>
  <lastmod>2026-07-30T14:31:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>柔軟な演算子埋め込みによるデータベース機械学習の効率化（Flexible Operator Embeddings via Deep Learning）</news:title>
   <news:publication_date>2026-07-30T14:31:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717646</loc>
  <lastmod>2026-07-30T14:24:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>変換に強いニューラル層の設計：Equivariant Transformer Networks（Equivariant Transformer Networks）</news:title>
   <news:publication_date>2026-07-30T14:24:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717644</loc>
  <lastmod>2026-07-30T14:23:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>意味のあるコード変更を学習する（On Learning Meaningful Code Changes via Neural Machine Translation）</news:title>
   <news:publication_date>2026-07-30T14:23:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717642</loc>
  <lastmod>2026-07-30T14:23:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>移動ソリトンに関するノート：平均曲率流の翻訳的解（Notes on translating solitons for Mean Curvature Flow）</news:title>
   <news:publication_date>2026-07-30T14:23:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717640</loc>
  <lastmod>2026-07-30T14:22:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>最適性が示すカーネル和分類器の統計的効率性（Optimality Implies Kernel Sum Classifiers are Statistically Efficient）</news:title>
   <news:publication_date>2026-07-30T14:22:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717638</loc>
  <lastmod>2026-07-30T14:22:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CT画像による脳室・脳実質・くも膜下腔の3次元自動分割がもたらす診断支援の革新（Automated Segmentation of CT Scans for Normal Pressure Hydrocephalus）</news:title>
   <news:publication_date>2026-07-30T14:22:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717636</loc>
  <lastmod>2026-07-30T14:22:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>相関推定の通信量限界（Communication Complexity of Estimating Correlations）</news:title>
   <news:publication_date>2026-07-30T14:22:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717634</loc>
  <lastmod>2026-07-30T13:30:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>過学習が起きる理由とオンライン学習の挙動（Generalisation dynamics of online learning in over-parameterised neural networks）</news:title>
   <news:publication_date>2026-07-30T13:30:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717632</loc>
  <lastmod>2026-07-30T13:30:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>都市の建設工事が生活の質に与える影響を機械学習で予測する手法（Leveraging Machine Learning Approaches to Predict the Impact of Construction Projects on Urban Quality of Life）</news:title>
   <news:publication_date>2026-07-30T13:30:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717630</loc>
  <lastmod>2026-07-30T13:29:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>太陽周期の急激な終焉が示す太陽内部の姿（What the Sudden Death of Solar Cycles Can Tell us About the Nature of the Solar Interior）</news:title>
   <news:publication_date>2026-07-30T13:29:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717628</loc>
  <lastmod>2026-07-30T13:28:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューラルネットでアーキタイプ空間を見つける方法（Finding Archetypal Spaces Using Neural Networks）</news:title>
   <news:publication_date>2026-07-30T13:28:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717626</loc>
  <lastmod>2026-07-30T13:28:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフェン—ダイアメン相転移の層依存性（Layer dependence of graphene-diamene phase transition in epitaxial and exfoliated few-layer graphene using machine learning）</news:title>
   <news:publication_date>2026-07-30T13:28:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717624</loc>
  <lastmod>2026-07-30T13:28:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>潜在意味パーツに基づくドメイン適応とゼロ・少数ショット認識のための分類器学習（Learning Classifiers for Domain Adaptation, Zero and Few-Shot Recognition Based on Learning Latent Semantic Parts）</news:title>
   <news:publication_date>2026-07-30T13:28:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717622</loc>
  <lastmod>2026-07-30T13:27:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>カルマンフィルタを用いたメタヒューリスティッククラスタリングの改良（A Kalman filtering induced heuristic optimization based partitional data clustering）</news:title>
   <news:publication_date>2026-07-30T13:27:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717620</loc>
  <lastmod>2026-07-30T12:36:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単語埋め込みの概観と実務的示唆（Word Embeddings: A Survey）</news:title>
   <news:publication_date>2026-07-30T12:36:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717618</loc>
  <lastmod>2026-07-30T12:36:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的非凸最適化におけるステップサイズのオンライン学習のための代理損失（Surrogate Losses for Online Learning of Stepsizes in Stochastic Non-Convex Optimization）</news:title>
   <news:publication_date>2026-07-30T12:36:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717616</loc>
  <lastmod>2026-07-30T12:35:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>測定誤差を伴うデータからの学習：報告漏れへの対処 (Learning Models from Data with Measurement Error: Tackling Underreporting)</news:title>
   <news:publication_date>2026-07-30T12:35:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717614</loc>
  <lastmod>2026-07-30T12:35:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>メモリ制約下で高速化したブースティング（Faster Boosting with Smaller Memory）</news:title>
   <news:publication_date>2026-07-30T12:35:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717612</loc>
  <lastmod>2026-07-30T12:34:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>離散化された勾配フローによる多様体学習（DISCRETIZED GRADIENT FLOW FOR MANIFOLD LEARNING）</news:title>
   <news:publication_date>2026-07-30T12:34:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717610</loc>
  <lastmod>2026-07-30T12:34:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>小規模データで学習する新しい損失関数：コサイン損失（Deep Learning on Small Datasets without Pre-Training using Cosine Loss）</news:title>
   <news:publication_date>2026-07-30T12:34:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717608</loc>
  <lastmod>2026-07-30T12:34:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>生物学的に実装可能な時系列逆伝播の代替（Biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets）</news:title>
   <news:publication_date>2026-07-30T12:34:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717606</loc>
  <lastmod>2026-07-30T11:43:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多様性に敏感な条件付き生成的敵対ネットワーク（DIVERSITY-SENSITIVE CONDITIONAL GENERATIVE ADVERSARIAL NETWORKS）</news:title>
   <news:publication_date>2026-07-30T11:43:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717604</loc>
  <lastmod>2026-07-30T11:33:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層ネットワークにおける線形領域の複雑度（Complexity of Linear Regions in Deep Networks）</news:title>
   <news:publication_date>2026-07-30T11:33:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717602</loc>
  <lastmod>2026-07-30T11:33:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>豊富な観測から潜在状態を復元して効率的探索を可能にする手法（Provably efficient RL with Rich Observations via Latent State Decoding）</news:title>
   <news:publication_date>2026-07-30T11:33:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717600</loc>
  <lastmod>2026-07-30T11:32:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>拡散変分オートエンコーダ（Diffusion Variational Autoencoders）</news:title>
   <news:publication_date>2026-07-30T11:32:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717598</loc>
  <lastmod>2026-07-30T11:32:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>運動学的ターゲット質量感度が示すもの（What does kinematical target mass sensitivity in DIS reveal about hadron structure?）</news:title>
   <news:publication_date>2026-07-30T11:32:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717596</loc>
  <lastmod>2026-07-30T11:32:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>集合上の関数表現の限界（On the Limitations of Representing Functions on Sets）</news:title>
   <news:publication_date>2026-07-30T11:32:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717594</loc>
  <lastmod>2026-07-30T11:32:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自己教師あり視覚表現学習の再考（Revisiting Self-Supervised Visual Representation Learning）</news:title>
   <news:publication_date>2026-07-30T11:32:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717592</loc>
  <lastmod>2026-07-30T10:40:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>LSTMとGRUの動的等方性と平均場理論（Dynamical Isometry and a Mean Field Theory of LSTMs and GRUs）</news:title>
   <news:publication_date>2026-07-30T10:40:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717590</loc>
  <lastmod>2026-07-30T10:40:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>FaceForensics++ による顔画像改ざん検出の標準化（FaceForensics++: Learning to Detect Manipulated Facial Images）</news:title>
   <news:publication_date>2026-07-30T10:40:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717588</loc>
  <lastmod>2026-07-30T10:40:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>教師なし前処理によるクロスバリデーションのバイアス（On the cross-validation bias due to unsupervised preprocessing）</news:title>
   <news:publication_date>2026-07-30T10:40:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717586</loc>
  <lastmod>2026-07-30T10:39:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オプション価格付けとインプライド・ボラティリティの高速推定（Pricing options and computing implied volatilities using neural networks）</news:title>
   <news:publication_date>2026-07-30T10:39:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717584</loc>
  <lastmod>2026-07-30T10:39:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>部分空間に強いワッサースタイン距離（Subspace Robust Wasserstein Distances）</news:title>
   <news:publication_date>2026-07-30T10:39:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717582</loc>
  <lastmod>2026-07-30T10:39:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Skip-GANomaly による異常検知の実務的理解（Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detection）</news:title>
   <news:publication_date>2026-07-30T10:39:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717580</loc>
  <lastmod>2026-07-30T10:38:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>変数分割を緩和した粗い勾配降下法によるスパース・バイナリ活性化ニューラルネットワークの収束（Convergence of a Relaxed Variable Splitting Coarse Gradient Descent Method for Learning Sparse Weight Binarized Activation Neural Networks）</news:title>
   <news:publication_date>2026-07-30T10:38:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717578</loc>
  <lastmod>2026-07-30T09:48:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散SDN制御における最適同期率の学習（Learning the Optimal Synchronization Rates in Distributed SDN Control Architectures）</news:title>
   <news:publication_date>2026-07-30T09:48:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717576</loc>
  <lastmod>2026-07-30T09:47:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自己教師ありで汎化を高めるメタ補助学習（Self-Supervised Generalisation with Meta Auxiliary Learning）</news:title>
   <news:publication_date>2026-07-30T09:47:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717574</loc>
  <lastmod>2026-07-30T09:46:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模分散ネットワークにおける通信効率の高い高速アルゴリズム（Exploring Fast and Communication-Efﬁcient Algorithms in Large-scale Distributed Networks）</news:title>
   <news:publication_date>2026-07-30T09:46:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717572</loc>
  <lastmod>2026-07-30T09:46:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>コアオントロジーに基づく用語クラスタリングの比較（Comparing of Term Clustering Frameworks for Modular Ontology Learning）</news:title>
   <news:publication_date>2026-07-30T09:46:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717570</loc>
  <lastmod>2026-07-30T09:46:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>車載安全通信のための深層学習による送信スケジューラ（Deep Learning-aided Application Scheduler for Vehicular Safety Communication）</news:title>
   <news:publication_date>2026-07-30T09:46:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717568</loc>
  <lastmod>2026-07-30T09:45:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深いピラミッドネットワークによる高密度3D点群再構築（Dense 3D Point Cloud Reconstruction Using a Deep Pyramid Network）</news:title>
   <news:publication_date>2026-07-30T09:45:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717566</loc>
  <lastmod>2026-07-30T09:45:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動的シミュレータにおけるベイジアン代替モデル学習（Bayesian surrogate learning in dynamic simulator-based regression problems）</news:title>
   <news:publication_date>2026-07-30T09:45:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717564</loc>
  <lastmod>2026-07-30T08:54:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>要約統計量の局所次元削減が切り開く尤度フリー推論の実用化（Local dimension reduction of summary statistics for likelihood-free inference）</news:title>
   <news:publication_date>2026-07-30T08:54:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717562</loc>
  <lastmod>2026-07-30T08:53:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>製造部品の表面亀裂検出のための撮像検査システム（Vision-based inspection system employing computer vision &amp;amp; neural networks for detection of fractures in manufactured components）</news:title>
   <news:publication_date>2026-07-30T08:53:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717560</loc>
  <lastmod>2026-07-30T08:53:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>アンサンブル多様性による敵対的堅牢性の向上（Improving Adversarial Robustness via Promoting Ensemble Diversity）</news:title>
   <news:publication_date>2026-07-30T08:53:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717558</loc>
  <lastmod>2026-07-30T08:53:16Z</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>2026-07-30T08:53:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717556</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>2026-07-30T08:52:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-07-30T08:52:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>最小限の深層ネットワークで形状学習とセグメンテーションを同時に行う（Joint shape learning and segmentation for medical images using a minimalistic deep network）</news:title>
   <news:publication_date>2026-07-30T08:52:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717552</loc>
  <lastmod>2026-07-30T08:51:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多ラベル分類におけるBayesメタ分類器とソフト混同行列分類器の比較（Bayes metaclassifier and Soft-confusion-matrix classifier in the task of multi-label classification）</news:title>
   <news:publication_date>2026-07-30T08:51:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-07-30T08:01:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>近似消失イデアルにおけるスプリアス消失問題（Spurious Vanishing Problem in Approximate Vanishing Ideal）</news:title>
   <news:publication_date>2026-07-30T08:01:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717548</loc>
  <lastmod>2026-07-30T08:00:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>音声の教師なし表現学習（Unsupervised speech representation learning using WaveNet autoencoders）</news:title>
   <news:publication_date>2026-07-30T08:00:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717546</loc>
  <lastmod>2026-07-30T08:00:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>印刷/スキャンと異種画像ソース下での顔モーフィング検出（Face morphing detection in the presence of printing/scanning and heterogeneous image sources）</news:title>
   <news:publication_date>2026-07-30T08:00:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717544</loc>
  <lastmod>2026-07-30T08:00:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的複合最適化の推定シーケンス（Estimate Sequences for Stochastic Composite Optimization: Variance Reduction, Acceleration, and Robustness to Noise）</news:title>
   <news:publication_date>2026-07-30T08:00:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717542</loc>
  <lastmod>2026-07-30T08:00:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ICETによる合金クラスター展開の実務化（ICET – A Python library for constructing and sampling alloy cluster expansions）</news:title>
   <news:publication_date>2026-07-30T08:00:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717540</loc>
  <lastmod>2026-07-30T07:59:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Bethe-Hessianの再考（Revisiting the Bethe-Hessian: Improved Community Detection in Sparse Heterogeneous Graphs）</news:title>
   <news:publication_date>2026-07-30T07:59:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717538</loc>
  <lastmod>2026-07-30T07:59:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的・敵対的環境を同時に最適化するセミバンディット手法（Beating Stochastic and Adversarial Semi-bandits Optimally and Simultaneously）</news:title>
   <news:publication_date>2026-07-30T07:59:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717536</loc>
  <lastmod>2026-07-30T07:08:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ノイズ下でのツリー構造ガウスグラフィカルモデルの頑健推定（Robust estimation of tree structured Gaussian Graphical Model）</news:title>
   <news:publication_date>2026-07-30T07:08:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717534</loc>
  <lastmod>2026-07-30T07:08:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>仮想条件付き生成対向ネットワーク（Virtual Conditional Generative Adversarial Networks）</news:title>
   <news:publication_date>2026-07-30T07:08:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717532</loc>
  <lastmod>2026-07-30T07:08:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>再帰型ニューラルネットワークによる高速チャネルの過渡シミュレーション高速化（Fast Transient Simulation of High-Speed Channels Using Recurrent Neural Network）</news:title>
   <news:publication_date>2026-07-30T07:08:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717530</loc>
  <lastmod>2026-07-30T07:07:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ロスレスなフェデレーテッド学習フレームワーク SecureBoost（SecureBoost: A Lossless Federated Learning Framework）</news:title>
   <news:publication_date>2026-07-30T07:07:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717528</loc>
  <lastmod>2026-07-30T07:07:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>YouTubeにおける誤解を招くメタデータ検出（Misleading Metadata Detection on YouTube）</news:title>
   <news:publication_date>2026-07-30T07:07:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717526</loc>
  <lastmod>2026-07-30T07:07:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散型ポリシー反復による協調型マルチエージェント方策の拡張近似（Distributed Policy Iteration for Scalable Approximation of Cooperative Multi-Agent Policies）</news:title>
   <news:publication_date>2026-07-30T07:07:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717524</loc>
  <lastmod>2026-07-30T07:07:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>海王星原始円盤の局在形成モデル（Model of Neptune’s protoplanetary disk forming in-situ its surviving regular satellites after Triton’s capture and comparison of the protoplanetary disks of the four gaseous giants）</news:title>
   <news:publication_date>2026-07-30T07:07:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717522</loc>
  <lastmod>2026-07-30T06:16:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層強化学習によるスピンダイナミクス制御（Manipulation of Spin Dynamics by Deep Reinforcement Learning Agent）</news:title>
   <news:publication_date>2026-07-30T06:16:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717520</loc>
  <lastmod>2026-07-30T06:16:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>敵対的損失における出力活性化関数の理論と実証研究（On Output Activation Functions for Adversarial Losses: A Theoretical Analysis via Variational Divergence Minimization and An Empirical Study on MNIST Classification）</news:title>
   <news:publication_date>2026-07-30T06:16:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717518</loc>
  <lastmod>2026-07-30T06:15:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>電荷を扱える機械学習ポテンシャルの進化──eSNAPが切り拓くリチウム窒化物の原子スケール挙動（An Electrostatic Spectral Neighbor Analysis Potential (eSNAP) for Lithium Nitride）</news:title>
   <news:publication_date>2026-07-30T06:15:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717516</loc>
  <lastmod>2026-07-30T06:14:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動的ポートフォリオ最適化のためのモデルベース深層強化学習（Model-based Deep Reinforcement Learning for Dynamic Portfolio Optimization）</news:title>
   <news:publication_date>2026-07-30T06:14:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717514</loc>
  <lastmod>2026-07-30T06:14:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>BioBERTによる医療文献の言語表現最適化（BioBERT: a pre-trained biomedical language representation model for biomedical text mining）</news:title>
   <news:publication_date>2026-07-30T06:14:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717512</loc>
  <lastmod>2026-07-30T06:14:36Z</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>2026-07-30T06:14:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717510</loc>
  <lastmod>2026-07-30T06:14:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>質問を減らして市場調査を拡大する方法（Ask less — Scale Market Research without Annoying Your Customers）</news:title>
   <news:publication_date>2026-07-30T06:14:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717508</loc>
  <lastmod>2026-07-30T05:22:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スクラブルにおける評価関数近似（Evaluation Function Approximation for Scrabble）</news:title>
   <news:publication_date>2026-07-30T05:22:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717506</loc>
  <lastmod>2026-07-30T05:22:25Z</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>2026-07-30T05:22:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717504</loc>
  <lastmod>2026-07-30T05:22:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>プライバシーと精度の両立を目指す再構成的敵対ネットワーク（Reconstructive Adversarial Network）</news:title>
   <news:publication_date>2026-07-30T05:22:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717502</loc>
  <lastmod>2026-07-30T05:21:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Almost Boltzmann Exploration（Almost Boltzmann Exploration）</news:title>
   <news:publication_date>2026-07-30T05:21:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717500</loc>
  <lastmod>2026-07-30T05:21:16Z</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>2026-07-30T05:21:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717498</loc>
  <lastmod>2026-07-30T05:21:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Deep Multimodality Modelによるマルチタスク・マルチビュー学習の実務的意義（Deep Multimodality Model for Multi-task Multi-view Learning）</news:title>
   <news:publication_date>2026-07-30T05:21:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717496</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>直交統計学習（Orthogonal Statistical Learning）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717494</loc>
  <lastmod>2026-07-30T04:29:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多エージェント間通信ゲームにおける言語の自発的現象（Emergent Linguistic Phenomena in Multi-Agent Communication Games）</news:title>
   <news:publication_date>2026-07-30T04:29:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717492</loc>
  <lastmod>2026-07-30T04:28:48Z</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>2026-07-30T04:28:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <lastmod>2026-07-30T04:28:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>医用画像解析における代理教師学習（SURROGATE SUPERVISION FOR MEDICAL IMAGE ANALYSIS: EFFECTIVE DEEP LEARNING FROM LIMITED QUANTITIES OF LABELED DATA）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単一クラス畳み込みニューラルネットワーク（One-Class Convolutional Neural Network）</news:title>
   <news:publication_date>2026-07-30T04:28:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717484</loc>
  <lastmod>2026-07-30T04:27:49Z</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>2026-07-30T04:27:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717482</loc>
  <lastmod>2026-07-30T04:27:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>電磁気学教育のための電場シミュレータ（Python script used as simulator for the teaching of electric field in electromagnetism course）</news:title>
   <news:publication_date>2026-07-30T04:27:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717480</loc>
  <lastmod>2026-07-30T03:35:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スペクトラルクラスタリングに公正性制約を組み込む理論的保証（Guarantees for Spectral Clustering with Fairness Constraints）</news:title>
   <news:publication_date>2026-07-30T03:35:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717478</loc>
  <lastmod>2026-07-30T03:35:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>任意サンプリングを用いたSAGAの一般化（SAGA with Arbitrary Sampling）</news:title>
   <news:publication_date>2026-07-30T03:35:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717476</loc>
  <lastmod>2026-07-30T03:34:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>パリレン系メモリスタで多段階抵抗変化を実現する研究（Parylene Based Memristive Devices with Multilevel Resistive Switching for Neuromorphic Applications）</news:title>
   <news:publication_date>2026-07-30T03:34:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717474</loc>
  <lastmod>2026-07-30T03:34:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>助けるマルチアームバンディット（The Assistive Multi-Armed Bandit）</news:title>
   <news:publication_date>2026-07-30T03:34:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717472</loc>
  <lastmod>2026-07-30T03:33:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>特徴抽出と方策学習の分離がもたらす効果（Decoupling Feature Extraction from Policy Learning: Assessing Benefits of State Representation Learning in Goal Based Robotics）</news:title>
   <news:publication_date>2026-07-30T03:33:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717470</loc>
  <lastmod>2026-07-30T03:33:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>公平性リスク測度（Fairness Risk Measures）</news:title>
   <news:publication_date>2026-07-30T03:33:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717468</loc>
  <lastmod>2026-07-30T03:33:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習による四足ロボットの俊敏な運動技能獲得（Learning Agile and Dynamic Motor Skills for Legged Robots）</news:title>
   <news:publication_date>2026-07-30T03:33:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717466</loc>
  <lastmod>2026-07-30T02:41:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>リアルタイム呼吸運動予測に向けたLSTMニューラルネットワークの試み（Towards Real-Time Respiratory Motion Prediction based on Long Short-Term Memory Neural Networks）</news:title>
   <news:publication_date>2026-07-30T02:41:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717464</loc>
  <lastmod>2026-07-30T02:32:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>独立に獲得可能な報酬関数の学習（Learning Independently-Obtainable Reward Functions）</news:title>
   <news:publication_date>2026-07-30T02:32:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717462</loc>
  <lastmod>2026-07-30T02:32:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューラルネットワークのアブレーション研究入門（Ablation Studies in Artificial Neural Networks）</news:title>
   <news:publication_date>2026-07-30T02:32:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717460</loc>
  <lastmod>2026-07-30T02:31:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動的計測スケジューリングによるイベント予測（Dynamic Measurement Scheduling for Event Forecasting Using Deep RL）</news:title>
   <news:publication_date>2026-07-30T02:31:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717458</loc>
  <lastmod>2026-07-30T02:31:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>建設現場で走る自律ロボットのための軽量リアルタイム画面分割（Real-time Scene Segmentation Using a Light Deep Neural Network Architecture for Autonomous Robot Navigation on Construction Sites）</news:title>
   <news:publication_date>2026-07-30T02:31:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717456</loc>
  <lastmod>2026-07-30T02:31:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>フェアなk中心クラスタリングによるデータ要約（Fair k-Center Clustering for Data Summarization）</news:title>
   <news:publication_date>2026-07-30T02:31:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717454</loc>
  <lastmod>2026-07-30T02:31:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>太陽フレア大気の逆問題を解く可逆ニューラルネットワーク（RADYNVERSION: Learning to Invert a Solar Flare Atmosphere with Invertible Neural Networks）</news:title>
   <news:publication_date>2026-07-30T02:31:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717452</loc>
  <lastmod>2026-07-30T01:40:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>AutoShuffleNetによる順列行列の自動学習（AutoShuffleNet: Learning Permutation Matrices via an Exact Lipschitz Continuous Penalty in Deep Convolutional Neural Networks）</news:title>
   <news:publication_date>2026-07-30T01:40:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717450</loc>
  <lastmod>2026-07-30T01:39:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ヒンディー語—英語ニュースの並列コーパス自動生成の試み（Automatic Parallel Corpus Creation for Hindi-English News Translation Task）</news:title>
   <news:publication_date>2026-07-30T01:39:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717448</loc>
  <lastmod>2026-07-30T01:39:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>地震波データから井戸ログ特性を推定する再帰型ニューラルネットワークの応用（Petrophysical Property Estimation from Seismic Data Using Recurrent Neural Networks）</news:title>
   <news:publication_date>2026-07-30T01:39:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717446</loc>
  <lastmod>2026-07-30T01:38:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習型アンサッツの近似能力（Approximating power of machine-learning ansatz for quantum many-body states）</news:title>
   <news:publication_date>2026-07-30T01:38:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717444</loc>
  <lastmod>2026-07-30T01:38:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューラル線形バンディットと忘却対策（Neural Linear Bandits: Overcoming Catastrophic Forgetting through Likelihood Matching）</news:title>
   <news:publication_date>2026-07-30T01:38:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717442</loc>
  <lastmod>2026-07-30T01:38:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単純スケーリングとSNR適応を用いた学習済み信念伝播デコーディング（Learned Belief-Propagation Decoding with Simple Scaling and SNR Adaptation）</news:title>
   <news:publication_date>2026-07-30T01:38:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717440</loc>
  <lastmod>2026-07-30T01:38:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフ熱混合モデルによる信号分離とグラフ推定（GRAPH HEAT MIXTURE MODEL LEARNING）</news:title>
   <news:publication_date>2026-07-30T01:38:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717438</loc>
  <lastmod>2026-07-30T00:46:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>過学習しない過剰パラメータ化二層ネットの最適化と一般化の精密解析 (Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks)</news:title>
   <news:publication_date>2026-07-30T00:46:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717436</loc>
  <lastmod>2026-07-30T00:46:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非凸・非滑らか最適化における摂動付き近接降下法（Perturbed Proximal Descent to Escape Saddle Points for Non-convex and Non-smooth Objective Functions）</news:title>
   <news:publication_date>2026-07-30T00:46:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717434</loc>
  <lastmod>2026-07-30T00:45:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>TAP方程式のメモリーフリー動力学（Memory-free dynamics for the TAP equations of Ising models）</news:title>
   <news:publication_date>2026-07-30T00:45:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717432</loc>
  <lastmod>2026-07-30T00:45:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>層の幅が最適化にもたらす決定的影響（Width Provably Matters in Optimization for Deep Linear Neural Networks）</news:title>
   <news:publication_date>2026-07-30T00:45:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717430</loc>
  <lastmod>2026-07-30T00:44:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>解釈可能な因果保証付きモデルの学習（Learning Interpretable Models with Causal Guarantees）</news:title>
   <news:publication_date>2026-07-30T00:44:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717428</loc>
  <lastmod>2026-07-30T00:44:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GstLALによるコンパクトバイナリ合体探索手法（The GstLAL Search Analysis Methods for Compact Binary Mergers）</news:title>
   <news:publication_date>2026-07-30T00:44:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717426</loc>
  <lastmod>2026-07-30T00:44:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>堅牢性と精度の理論的トレードオフ（Theoretically Principled Trade-off between Robustness and Accuracy）</news:title>
   <news:publication_date>2026-07-30T00:44:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717424</loc>
  <lastmod>2026-07-29T23:53:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的変換による一般化監督学習の枠組み（GENERAL SUPERVISION VIA PROBABILISTIC TRANSFORMATIONS）</news:title>
   <news:publication_date>2026-07-29T23:53:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717422</loc>
  <lastmod>2026-07-29T23:44:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>有限ビット量化下の非パラメトリック推論（Nonparametric Inference under B-bits Quantization）</news:title>
   <news:publication_date>2026-07-29T23:44:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717420</loc>
  <lastmod>2026-07-29T23:44:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動的意思決定における公平性アルゴリズム（Algorithms for Fairness in Sequential Decision Making）</news:title>
   <news:publication_date>2026-07-29T23:44:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717418</loc>
  <lastmod>2026-07-29T23:44:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散性IM/DDチャネルに対する双方向RNNを用いたエンドツーエンド最適化伝送（End-to-end optimized transmission over dispersive intensity-modulated channels using bidirectional recurrent neural networks）</news:title>
   <news:publication_date>2026-07-29T23:44:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717416</loc>
  <lastmod>2026-07-29T23:43:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ほぼ確定的な力学系における方策勾配推定のサンプル複雑度（Sample Complexity of Estimating the Policy Gradient for Nearly Deterministic Dynamical Systems）</news:title>
   <news:publication_date>2026-07-29T23:43:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717414</loc>
  <lastmod>2026-07-29T23:43:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>プログラム合成によるニューロシンボリック生成モデルの学習 (Learning Neurosymbolic Generative Models via Program Synthesis)</news:title>
   <news:publication_date>2026-07-29T23:43:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717412</loc>
  <lastmod>2026-07-29T23:43:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>セミ・アンマーク学習: 超希薄ラベルによるクラスタリングと分類（Semi-Unsupervised Learning: Clustering and Classifying using Ultra-Sparse Labels）</news:title>
   <news:publication_date>2026-07-29T23:43:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717410</loc>
  <lastmod>2026-07-29T22:51:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>全天サーベイで遠隔太陽系天体を深く探す方法（Enabling Deep All-Sky Searches of Outer Solar System Objects）</news:title>
   <news:publication_date>2026-07-29T22:51:52Z</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>
   <news:title>Nearest Neighbor探索のための学習型空間分割（Learning Space Partitions for Nearest Neighbor Search）</news:title>
   <news:publication_date>2026-07-29T22:51:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </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>2026-07-29T22:51:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-07-29T22:50:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散学習によるアーティキュレーテッド移動ロボットの分散制御ポリシー学習（Distributed Learning of Decentralized Control Policies for Articulated Mobile Robots）</news:title>
   <news:publication_date>2026-07-29T22:50:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-07-29T22:50:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチレゾリューション解析による地震データ解釈の進化（Multiresolution analysis and learning for computational seismic interpretation）</news:title>
   <news:publication_date>2026-07-29T22:50:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717400</loc>
  <lastmod>2026-07-29T22:50:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>参照ベースの変分オートエンコーダによる分離表現学習（Learning Disentangled Representations with Reference-Based Variational Autoencoders）</news:title>
   <news:publication_date>2026-07-29T22:50:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717398</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>深宇宙の熱放射中性子星を深掘りする（A deep XMM-Newton look on the thermally emitting isolated neutron star RX J1605.3+3249）</news:title>
   <news:publication_date>2026-07-29T22:49:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>テンソルポメロンと低-x深部非弾性散乱（The Tensor Pomeron and Low-x Deep Inelastic Scattering）</news:title>
   <news:publication_date>2026-07-29T21:58:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717394</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>Elastic Net最適化の曲率活用による加速（Curvature-Exploiting Acceleration of Elastic Net Computations）</news:title>
   <news:publication_date>2026-07-29T21:58:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717392</loc>
  <lastmod>2026-07-29T21:57:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>補助情報の学習を伴うライキング比経験過程（Raking-ratio empirical process with auxiliary information learning）</news:title>
   <news:publication_date>2026-07-29T21:57:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717390</loc>
  <lastmod>2026-07-29T21:57:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数都市から学ぶメタラーニングによる時空間予測（Learning from Multiple Cities: A Meta-Learning Approach for Spatial-Temporal Prediction）</news:title>
   <news:publication_date>2026-07-29T21:57:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717388</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>2026-07-29T21:56:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/717386</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>最大エントロピー生成器によるエネルギーベースモデルの学習（Maximum Entropy Generators for Energy-Based Models）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717384</loc>
  <lastmod>2026-07-29T21:56:25Z</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>2026-07-29T21:56:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/717382</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>Feudal Multi-Agent Hierarchiesによる協調学習の階層化（Feudal Multi-Agent Hierarchies for Cooperative Reinforcement Learning）</news:title>
   <news:publication_date>2026-07-29T21:05:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>通信学習によるマルチエージェント方策の分散化（Decentralization of Multiagent Policies by Learning What to Communicate）</news:title>
   <news:publication_date>2026-07-29T21:04:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>忘れずに探索と活用を両立する方法（Never Forget: Balancing Exploration and Exploitation via Learning Optical Flow）</news:title>
   <news:publication_date>2026-07-29T21:03:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </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/717374</loc>
  <lastmod>2026-07-29T21:03:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>潜在空間の変換が変える生成と復元の精度（On the Transformation of Latent Space in Autoencoders）</news:title>
   <news:publication_date>2026-07-29T21:03:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717372</loc>
  <lastmod>2026-07-29T21:02:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率空間上の変分勾配流で学ぶ深層生成モデル（Deep Generative Learning via Variational Gradient Flow）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717370</loc>
  <lastmod>2026-07-29T21:02:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>完全分散型の個別化モデルと協力グラフの共同学習（Fully Decentralized Joint Learning of Personalized Models and Collaboration Graphs）</news:title>
   <news:publication_date>2026-07-29T21:02:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717368</loc>
  <lastmod>2026-07-29T20:10:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模類似検索と学習画像圧縮の二段階手法（Multi-Layer Sparse Ternary Codes for Similarity Search and Learned Image Compression）</news:title>
   <news:publication_date>2026-07-29T20:10:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717366</loc>
  <lastmod>2026-07-29T20:10:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>表形式データの文字レベルCNNによる意味分類（Semantic Classification of Tabular Datasets via Character-Level Convolutional Neural Networks）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717364</loc>
  <lastmod>2026-07-29T20:09:58Z</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>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717362</loc>
  <lastmod>2026-07-29T20:09:33Z</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>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717360</loc>
  <lastmod>2026-07-29T20:09:24Z</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>2026-07-29T20:09:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717358</loc>
  <lastmod>2026-07-29T20:09:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>プロジェクト横断の欠陥予測における転移学習とクラス不均衡の統合手法（Transfer-Learning Oriented Class Imbalance Learning for Cross-Project Defect Prediction）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717356</loc>
  <lastmod>2026-07-29T20:09:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>XGBoostとベイズ最適化で高精度な事業リスク判定を実現する（A XGBOOST RISK MODEL VIA FEATURE SELECTION AND BAYESIAN HYPER-PARAMETER OPTIMIZATION）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717354</loc>
  <lastmod>2026-07-29T19:17:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>タグベース推薦システムをプロファイル注入攻撃から守る（Securing Tag-based recommender systems against profile injection attacks）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-07-29T19:17:08Z</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>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-07-29T19:16:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Deep Lyapunov Function: 自動安定性解析の要点（Deep Lyapunov Function: Automatic Stability Analysis for Dynamical Systems）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>空間を再構築することで点群に対する自己教師あり深層学習（Self-Supervised Deep Learning on Point Clouds by Reconstructing Space）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>有限メモリ下での多腕バンディットに関する後悔最小化の要点（Regret Minimisation in Multi-Armed Bandits Using Bounded Arm Memory）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 <url>
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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>
   <news:title>CycleGANを用いたOCT機器間の画像変動低減と網膜液体セグメンテーション改善（USING CYCLEGANS FOR EFFECTIVELY REDUCING IMAGE VARIABILITY ACROSS OCT DEVICES AND IMPROVING RETINAL FLUID SEGMENTATION）</news:title>
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   <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: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:genres>Blog</news:genres>
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 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深絞り工程におけるゼロショット学習の応用（A Zero-Shot Learning application in Deep Drawing process using Hyper-Process Model）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>情報鮮度（Age of Information）を減らす強化学習アプローチ（Reinforcement Learning to Minimize Age of Information）</news:title>
   <news:publication_date>2026-07-29T18:20:52Z</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>
   <news:title>多階層文脈による深い推論で顕著領域検出を高精度化する（Deep Reasoning with Multi-Scale Context for Salient Object Detection）</news:title>
   <news:publication_date>2026-07-29T18:19:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Jensenの力が照らす皮質非同期状態の統計力学（Jensen’s force and the statistical mechanics of cortical asynchronous states）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/717324</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>交差エントロピーと低ランク特徴が招く敵対的事例の本質（Cross-Entropy Loss and Low-Rank Features Have Responsibility for Adversarial Examples）</news:title>
   <news:publication_date>2026-07-29T17:28:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/717322</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>グローバルな2変量相互作用を不確かさ付きで学習する手法（Learning Global Pairwise Interactions with Bayesian Neural Networks）</news:title>
   <news:publication_date>2026-07-29T17:28:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717320</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>取得関数の局所最適化に関する考察（On Local Optimizers of Acquisition Functions in Bayesian Optimization）</news:title>
   <news:publication_date>2026-07-29T17:27:28Z</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>
   <news:title>RCT要旨からのPICO要素抽出（Extracting PICO elements from RCT abstracts using 1-2gram analysis and multitask classification）</news:title>
   <news:publication_date>2026-07-29T17:27:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 <url>
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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>2026-07-29T17:26:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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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>
   <news:title>機械学習とビッグデータが変える無線通信の視点（When Machine Learning Meets Big Data: A Wireless Communication Perspective）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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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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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>組合せQ学習による『ドゥーディーズー（Dou Di Zhu）』攻略（Combinational Q-Learning for Dou Di Zhu）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/717294</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>
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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>
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  <lastmod>2026-07-29T15:38:18Z</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>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717288</loc>
  <lastmod>2026-07-29T15:38:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所内容のベクトル表現と局所運動の行列表現によるV1のシンプル細胞学習 (Learning V1 Simple Cells with Vector Representation of Local Content and Matrix Representation of Local Motion)</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューラルネットワークにおける伝統的およびヘビーテール自己正則化（Traditional and Heavy-Tailed Self Regularization in Neural Network Models）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/717280</loc>
  <lastmod>2026-07-29T14:35:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模バッチでLSTMを高速学習する方法（Large-Batch Training for LSTM and Beyond）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/717278</loc>
  <lastmod>2026-07-29T14:34:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>軸受（ベアリング）故障診断における深層学習の包括的レビュー（Deep Learning Algorithms for Bearing Fault Diagnostics – A Comprehensive Review）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/717276</loc>
  <lastmod>2026-07-29T14:34:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ノード信頼度を組み込むグラフ畳み込みネットワーク（Confidence-based Graph Convolutional Networks for Semi-Supervised Learning）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/717274</loc>
  <lastmod>2026-07-29T14:34:20Z</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>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>緊急時ツイートから位置参照を抽出する深層学習（Location reference identification from tweets during emergencies: A deep learning approach）</news:title>
   <news:publication_date>2026-07-29T13:42:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717268</loc>
  <lastmod>2026-07-29T13:42:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>映画を用いたトポグラフィック独立成分分析の可視化（Visualizing Topographic Independent Component Analysis with Movies）</news:title>
   <news:publication_date>2026-07-29T13:42:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717266</loc>
  <lastmod>2026-07-29T13:41:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次元ロバストM推定：任意の汚染と重い裾（High Dimensional Robust M-Estimation: Arbitrary Corruption and Heavy Tails）</news:title>
   <news:publication_date>2026-07-29T13:41:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717264</loc>
  <lastmod>2026-07-29T13:40:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習が導く無線ネットワークの最適化（Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks）</news:title>
   <news:publication_date>2026-07-29T13:40:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717262</loc>
  <lastmod>2026-07-29T13:40:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>軌道正規化勾配による分散最適化（Trajectory Normalized Gradients for Distributed Optimization）</news:title>
   <news:publication_date>2026-07-29T13:40:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717260</loc>
  <lastmod>2026-07-29T13:40:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SARと光学リモートセンシング画像の相互変換（Reciprocal Translation between SAR and Optical Remote Sensing Images with Cascaded-Residual Adversarial Networks）</news:title>
   <news:publication_date>2026-07-29T13:40:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717258</loc>
  <lastmod>2026-07-29T13:39:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>領域分解と組み立てに基づく物体検出（Object Detection based on Region Decomposition and Assembly）</news:title>
   <news:publication_date>2026-07-29T13:39:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717256</loc>
  <lastmod>2026-07-29T12:48:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自律走行車が介入し人へ返すべき時（When is it right and good for an intelligent autonomous vehicle to take over control (and hand it back)?)</news:title>
   <news:publication_date>2026-07-29T12:48:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717254</loc>
  <lastmod>2026-07-29T12:48:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>半教師付き画像間翻訳の実践的意義（Semi-Supervised Image-to-Image Translation）</news:title>
   <news:publication_date>2026-07-29T12:48:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717252</loc>
  <lastmod>2026-07-29T12:48:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>外れ値を含むk中心クラスタリングに対する貪欲法とコアセット構築（Greedy Strategy Works for k-Center Clustering with Outliers and Coreset Construction）</news:title>
   <news:publication_date>2026-07-29T12:48:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717250</loc>
  <lastmod>2026-07-29T12:47:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Bottom-up Broadcast Neural Networkによる音楽ジャンル分類の再定義（Bottom-up Broadcast Neural Network For Music Genre Classification）</news:title>
   <news:publication_date>2026-07-29T12:47:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717248</loc>
  <lastmod>2026-07-29T12:47:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>画像と特徴の相乗的適応による医用画像のクロスモダリティ適応（Synergistic Image and Feature Adaptation: Towards Cross-Modality Domain Adaptation for Medical Image Segmentation）</news:title>
   <news:publication_date>2026-07-29T12:47:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717246</loc>
  <lastmod>2026-07-29T12:47:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ストリーミング音楽における逐次スキップ予測とFew-shot学習（Sequential Skip Prediction with Few-shot in Streamed Music Contents）</news:title>
   <news:publication_date>2026-07-29T12:47:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717244</loc>
  <lastmod>2026-07-29T12:46:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ICU内死亡予測の可視化可能な深層学習（ISeeU: Visually interpretable deep learning for mortality prediction inside the ICU）</news:title>
   <news:publication_date>2026-07-29T12:46:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717242</loc>
  <lastmod>2026-07-29T11:55:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自律レーシングにおける学習モデル予測制御のアプローチ（Learning How to Autonomously Race a Car: a Predictive Control Approach）</news:title>
   <news:publication_date>2026-07-29T11:55:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717240</loc>
  <lastmod>2026-07-29T11:54:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>赤い方形星雲から伸びる高コリメーションジェット（A Highly Collimated Jet from the Red Square Nebula, MWC 922）</news:title>
   <news:publication_date>2026-07-29T11:54:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717238</loc>
  <lastmod>2026-07-29T11:54:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>データ幾何から生成・整合する手法（Generating and Aligning from Data Geometries with Generative Adversarial Networks）</news:title>
   <news:publication_date>2026-07-29T11:54:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717236</loc>
  <lastmod>2026-07-29T11:53:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>正則化された線形オートエンコーダの損失地形（Loss Landscapes of Regularized Linear Autoencoders）</news:title>
   <news:publication_date>2026-07-29T11:53:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717234</loc>
  <lastmod>2026-07-29T11:53:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>木構造アンサンブルによる異常検知の実効性（Effectiveness of Tree-based Ensembles for Anomaly Discovery: Insights, Batch and Streaming Active Learning）</news:title>
   <news:publication_date>2026-07-29T11:53:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717232</loc>
  <lastmod>2026-07-29T11:53:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>完全確率的プライマル・デュアルアルゴリズム（A Fully Stochastic Primal-Dual Algorithm）</news:title>
   <news:publication_date>2026-07-29T11:53:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717230</loc>
  <lastmod>2026-07-29T11:53:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>近似勾配符号化の根本限界（Fundamental Limits of Approximate Gradient Coding）</news:title>
   <news:publication_date>2026-07-29T11:53:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717228</loc>
  <lastmod>2026-07-29T11:01:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>メタ強化学習からの因果推論（Causal Reasoning from Meta-reinforcement Learning）</news:title>
   <news:publication_date>2026-07-29T11:01:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717226</loc>
  <lastmod>2026-07-29T11:01:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CNNの層ごとの独立学習で訓練の「更新ロック」を解く（Decoupled Greedy Learning of CNNs）</news:title>
   <news:publication_date>2026-07-29T11:01:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717224</loc>
  <lastmod>2026-07-29T11:01:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>生成的敵対ネットワークが作る映像の時間的生成手法（LEARNING TO NAVIGATE IMAGE MANIFOLDS INDUCED BY GENERATIVE ADVERSARIAL NETWORKS FOR UNSUPERVISED VIDEO GENERATION）</news:title>
   <news:publication_date>2026-07-29T11:01:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717222</loc>
  <lastmod>2026-07-29T11:00:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>フラクタル拡張によるスケーラブルな現実的推薦データセット（Scalable Realistic Recommendation Datasets through Fractal Expansions）</news:title>
   <news:publication_date>2026-07-29T11:00:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717220</loc>
  <lastmod>2026-07-29T11:00:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>真実的なデータサイエンス（Veridical Data Science）</news:title>
   <news:publication_date>2026-07-29T11:00:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717218</loc>
  <lastmod>2026-07-29T11:00:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ソーシャル不安の時空間分析ツール（A Tool for Spatio-Temporal Analysis of Social Anxiety with Twitter Data）</news:title>
   <news:publication_date>2026-07-29T11:00:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717216</loc>
  <lastmod>2026-07-29T11:00:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文脈付きバンディットの探索をメタ学習する手法（Meta-Learning for Contextual Bandit Exploration）</news:title>
   <news:publication_date>2026-07-29T11:00:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717214</loc>
  <lastmod>2026-07-29T10:08:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ハイパーグラフ畳み込みとハイパーグラフ注意（Hypergraph Convolution and Hypergraph Attention）</news:title>
   <news:publication_date>2026-07-29T10:08:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717212</loc>
  <lastmod>2026-07-29T10:08:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>TransferTransfoによる会話生成の転移学習（TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents）</news:title>
   <news:publication_date>2026-07-29T10:08:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717210</loc>
  <lastmod>2026-07-29T10:07:46Z</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強化学習競技の意義と設計（The Multi-Agent Reinforcement Learning in MalmÖ (MARLÖ) Competition）</news:title>
   <news:publication_date>2026-07-29T10:07:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717208</loc>
  <lastmod>2026-07-29T10:06:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>強化学習におけるディスティレーションとPPOの共演（Distillation Strategies for Proximal Policy Optimization）</news:title>
   <news:publication_date>2026-07-29T10:06:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717206</loc>
  <lastmod>2026-07-29T10:06:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフニューラルネットワークによるSDN向けネットワークモデル化と最適化の可能性（Unveiling the potential of Graph Neural Networks for network modeling and optimization in SDN）</news:title>
   <news:publication_date>2026-07-29T10:06:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717204</loc>
  <lastmod>2026-07-29T10:06:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Sitatapatra: 敵対的サンプルの移植を阻む仕組み（SITATAPATRA: BLOCKING THE TRANSFER OF ADVERSARIAL SAMPLES）</news:title>
   <news:publication_date>2026-07-29T10:06:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717202</loc>
  <lastmod>2026-07-29T10:06:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模マルチモーダルデータと分離可能リスク評価による1年死亡率予測（A Large-scale Multimodal Study for Predicting Mortality Risk Using Minimal and Low Parameter Models and Separable Risk Assessment）</news:title>
   <news:publication_date>2026-07-29T10:06:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717200</loc>
  <lastmod>2026-07-29T09:15:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非可逆な勝負を学び続ける仕組み（Open-ended Learning in Symmetric Zero-sum Games）</news:title>
   <news:publication_date>2026-07-29T09:15:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717198</loc>
  <lastmod>2026-07-29T09:13:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-07-29T09:13:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717196</loc>
  <lastmod>2026-07-29T09:05:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-07-29T09:05:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深度からRGBへのドメイン変換（Domain Translation with Conditional GANs: from Depth to RGB Face-to-Face）</news:title>
   <news:publication_date>2026-07-29T09:04:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-07-29T09:04:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>共有データの鍵漏洩対策を見直す（Revisiting Shared Data Protection Against Key Exposure）</news:title>
   <news:publication_date>2026-07-29T09:04:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時系列予測のための独立ベイズフィルタ学習（Recurrent Neural Filters: Learning Independent Bayesian Filtering Steps for Time Series Prediction）</news:title>
   <news:publication_date>2026-07-29T09:03:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-07-29T09:03:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ガウス過程の平均関数をメタ学習する（Meta-Learning Mean Functions for Gaussian Processes）</news:title>
   <news:publication_date>2026-07-29T09:03:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717186</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>質問包含（Question Entailment）に基づく質問応答アプローチ（A QUESTION-ENTAILMENT APPROACH TO QUESTION ANSWERING）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>パラメータ化量子回路による生成モデルの堅牢な実装（Robust Implementation of Generative Modeling with Parametrized Quantum Circuits）</news:title>
   <news:publication_date>2026-07-29T08:11:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717182</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>協調オンライン学習：隣人と情報を共有して学ぶ仕組み（Cooperative Online Learning: Keeping your Neighbors Updated）</news:title>
   <news:publication_date>2026-07-29T08:11:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717180</loc>
  <lastmod>2026-07-29T08:11:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>協調するAIを学ぶ——非定常な協働環境での学習戦略（Learning to Collaborate in Markov Decision Processes）</news:title>
   <news:publication_date>2026-07-29T08:11:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717178</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>直交行列のためのハミルトニアンモンテカルロ（Hamiltonian Monte-Carlo for Orthogonal Matrices）</news:title>
   <news:publication_date>2026-07-29T08:11:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717176</loc>
  <lastmod>2026-07-29T08:10:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>下からの物体検出：極点と中心点を結ぶ（Bottom-up Object Detection by Grouping Extreme and Center Points）</news:title>
   <news:publication_date>2026-07-29T08:10:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717174</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>2026-07-29T08:10:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717172</loc>
  <lastmod>2026-07-29T07:19:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news: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>
   <news:title>重大事象に強い時系列差分学習の設計（Robust Temporal Difference Learning for Critical Domains）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>一晩で星の変動を描く発想（Characterizing Variable Stars in a Single Night with LSST）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717162</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>近似活性化を用いたメモリ効率的バックプロパゲーション（Backprop with Approximate Activations for Memory-efficient Network Training）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </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>
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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>
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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>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>軽量暗号で実現するプライベート分散機械学習（PD-ML-Lite: Private Distributed Machine Learning from Lightweight Cryptography）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717152</loc>
  <lastmod>2026-07-29T06:24:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>縮約モデルと生成モデルを組み合わせた重要度サンプリング推定器の提案（Coupling the Reduced-Order Model and the Generative Model for an Importance Sampling Estimator）</news:title>
   <news:publication_date>2026-07-29T06:24:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717150</loc>
  <lastmod>2026-07-29T06:23:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CTCModelによるKerasでの時系列ラベリング拡張（CTCModel: a Keras Model for Connectionist Temporal Classification）</news:title>
   <news:publication_date>2026-07-29T06:23:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次元変数の交互作用検出に向けたスパース主ヘッセ行列法（High-Dimensional Interactions Detection with Sparse Principal Hessian Matrix）</news:title>
   <news:publication_date>2026-07-29T06:23:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717146</loc>
  <lastmod>2026-07-29T06:23:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>データ希少な侵入検知を強化する深層敵対的学習とデータ拡張（Deep Adversarial Learning in Intrusion Detection: A Data Augmentation Enhanced Framework）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717144</loc>
  <lastmod>2026-07-29T05:31:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>IoT時代の無線通信における機械学習の総覧（Machine Learning for Wireless Communications in the Internet of Things: A Comprehensive Survey）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717142</loc>
  <lastmod>2026-07-29T05:31:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>フリーエネルギー原理が脳について教えること（What does the free energy principle tell us about the brain?）</news:title>
   <news:publication_date>2026-07-29T05:31:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717140</loc>
  <lastmod>2026-07-29T05:31:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>エンドツーエンド自然言語生成の最前線評価（Evaluating the State-of-the-Art of End-to-End Natural Language Generation: The E2E NLG Challenge）</news:title>
   <news:publication_date>2026-07-29T05:31:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717138</loc>
  <lastmod>2026-07-29T05:30:44Z</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>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717136</loc>
  <lastmod>2026-07-29T05:30:35Z</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>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717134</loc>
  <lastmod>2026-07-29T05:30:20Z</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地震データの補間と雑音除去（Interpolation and Denoising of Seismic Data using Convolutional Neural Networks）</news:title>
   <news:publication_date>2026-07-29T05:30:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717132</loc>
  <lastmod>2026-07-29T05:29:43Z</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>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-07-29T04:38:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自然言語ベースの制約なしサービス合成（Unrestricted Natural Language-based Service Composition through Sentence Embeddings）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
  </news:news>
 </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: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>
   <news:title>中国語感情分析のための音韻強化テキスト表現と強化学習（Phonetic-enriched Text Representation for Chinese Sentiment Analysis with Reinforcement Learning）</news:title>
   <news:publication_date>2026-07-29T04:37:38Z</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>
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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>
   <news:title>船舶自動操縦のための強化学習：サンプル効率とモデル予測制御の統合（Reinforcement Learning Boat Autopilot: A Sample-efficient and Model Predictive Control based Approach）</news:title>
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   <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: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:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>価値関数最適化における信頼領域とカルマンフィルタの融合（Trust Region Value Optimization using Kalman Filtering）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>ベイズネットワークとハイブリッド量子古典学習による因果解明（Bayesian Networks based Hybrid Quantum-Classical Machine Learning Approach to Elucidate Gene Regulatory Pathways）</news:title>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
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   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>肺結節診断の進化：古典的手法から深層学習支援の意思決定へ（Evolving the pulmonary nodules diagnosis from classical approaches to deep learning-aided decision support）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ホモモルフィックセンシングの実務的インパクト（Homomorphic Sensing）</news:title>
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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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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ピーク制約付きマルコフ決定過程の強化学習（Reinforcement Learning of Markov Decision Processes with Peak Constraints）</news:title>
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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>
   <news:title>学習表現を伴うランダムフォレストによるセマンティックセグメンテーション（Random Forest with Learned Representations for Semantic Segmentation）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>アスペクトに着目した評価予測モデル（AspeRa: Aspect-based Rating Prediction Model）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news: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>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>構造的スパース正則化によるフィルタ剪定で小型ConvNetへ（Towards Compact ConvNets via Structure-Sparsity Regularized Filter Pruning）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/717076</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>若い星の深部を覗くX線観測の示唆（A deep X-ray view of the Class I YSO Elias 29 with XMM-Newton and NuSTAR）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 </url>
 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 </url>
 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 <url>
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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>2026-07-29T00:01:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <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>
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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>
   <news:title>パッチ群と残差の共同スパース符号化による画像圧縮センシング復元（Joint group and residual sparse coding for image compressive sensing）</news:title>
   <news:publication_date>2026-07-29T00:00:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>二段階学習に基づく自動作文採点（Automated Essay Scoring based on Two-Stage Learning）</news:title>
   <news:publication_date>2026-07-28T23:59:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>漸近制約を用いたニューラル誘導シンボリック回帰（Neural-Guided Symbolic Regression with Asymptotic Constraints）</news:title>
   <news:publication_date>2026-07-28T23:08:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </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>2026-07-28T23:07:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-07-28T23:07:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-07-28T23:07:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-07-28T23:06:50Z</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>2026-07-28T23:06:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-07-28T23:06:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-07-28T22:15:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-07-28T22:15:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-07-28T22:14:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-07-28T22:14:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <lastmod>2026-07-28T22:14:03Z</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>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ラマン分光と深層学習で迅速に病原菌を同定する方法（Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GANの分解と合成による生成モデルのモジュール化（COMPOSITION AND DECOMPOSITION OF GANS）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>低計算量な非パラメトリックベイズによるオンライン予測と普遍的保証（Low-Complexity Nonparametric Bayesian Online Prediction with Universal Guarantees）</news:title>
   <news:publication_date>2026-07-28T21:22:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単語ベクトルにおけるバイアスの緩和手法（Attenuating Bias in Word Vectors）</news:title>
   <news:publication_date>2026-07-28T21:22:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717014</loc>
  <lastmod>2026-07-28T21:22:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>量子コンピュータ上での固有状態と熱平衡状態の効率的決定（Determining eigenstates and thermal states on a quantum computer using quantum imaginary time evolution）</news:title>
   <news:publication_date>2026-07-28T21:22:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717012</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>2026-07-28T21:20:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717010</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>SARAHを用いた有限和滑らか最適化（Finite-Sum Smooth Optimization with SARAH）</news:title>
   <news:publication_date>2026-07-28T21:20:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717008</loc>
  <lastmod>2026-07-28T21:20:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Encoder-Decoder CNNの幾何学的理解（Understanding Geometry of Encoder-Decoder CNNs）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717006</loc>
  <lastmod>2026-07-28T21:20:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>衝突検査から学習するコンフィギュレーション空間信念モデル（Learning Configuration Space Belief Model from Collision Checks for Motion Planning）</news:title>
   <news:publication_date>2026-07-28T21:20:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717004</loc>
  <lastmod>2026-07-28T20:29:04Z</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>2026-07-28T20:29:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717002</loc>
  <lastmod>2026-07-28T20:28:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単一ディープ反事実後悔最小化（Single Deep Counterfactual Regret Minimization）</news:title>
   <news:publication_date>2026-07-28T20:28:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717000</loc>
  <lastmod>2026-07-28T20:27:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>音声認識におけるSelf-AttentionによるCTCネットワーク（SELF-ATTENTION NETWORKS FOR CONNECTIONIST TEMPORAL CLASSIFICATION IN SPEECH RECOGNITION）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </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>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/716996</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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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習が通信分野に教えてくれること（What Can Machine Learning Teach Us about Communications）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </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>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 </url>
 <url>
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  <news:news>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 </url>
 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-07-28T19:33:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716980</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>2026-07-28T19:33:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-07-28T19:33:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>謎めいた（ほぼ）暗黒銀河 Coma P の距離測定と恒星母集団（THE ENIGMATIC (ALMOST) DARK GALAXY COMA P: DISTANCE MEASUREMENT AND STELLAR POPULATIONS FROM HST IMAGING）</news:title>
   <news:publication_date>2026-07-28T19:33:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
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 <url>
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  <lastmod>2026-07-28T18:41:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ICLabelによるEEG独立成分分類の自動化（ICLabel: An automated electroencephalographic independent component classifier, dataset, and website）</news:title>
   <news:publication_date>2026-07-28T18:41:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-07-28T18:41:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>転倒から自律回復する四足歩行ロボット制御（Robust Recovery Controller for a Quadrupedal Robot using Deep Reinforcement Learning）</news:title>
   <news:publication_date>2026-07-28T18:41:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716972</loc>
  <lastmod>2026-07-28T18:41:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DQNターゲットの多段階強化学習理解（Understanding Multi-Step Deep Reinforcement Learning: A Systematic Study of the DQN Target）</news:title>
   <news:publication_date>2026-07-28T18:41:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716970</loc>
  <lastmod>2026-07-28T18:40:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>制約付きトポロジ設計最適化のための新しいCGAN技術（A New CGAN Technique for Constrained Topology Design Optimization）</news:title>
   <news:publication_date>2026-07-28T18:40:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716968</loc>
  <lastmod>2026-07-28T18:40:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>正則化重み付きチェビシェフ近似による支持サイズ推定（Regularized Weighted Chebyshev Approximations for Support Estimation）</news:title>
   <news:publication_date>2026-07-28T18:40:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716966</loc>
  <lastmod>2026-07-28T18:40:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非漸近解析によるフラクショナル・ランジュバン・モンテカルロの示唆（Non-Asymptotic Analysis of Fractional Langevin Monte Carlo for Non-Convex Optimization）</news:title>
   <news:publication_date>2026-07-28T18:40:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716964</loc>
  <lastmod>2026-07-28T18:39:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ベイズ加法回帰木と汎用BARTモデル（Bayesian additive regression trees and the General BART model）</news:title>
   <news:publication_date>2026-07-28T18:39:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716962</loc>
  <lastmod>2026-07-28T17:48:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数グラフの構造不変表現を学ぶ（Multiple Graph Adversarial Learning）</news:title>
   <news:publication_date>2026-07-28T17:48:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716960</loc>
  <lastmod>2026-07-28T17:30:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフ観測に対する特徴ベクトル型RBFネットワークの正確な再定式化（An Exact Reformulation of Feature-Vector-based Radial-Basis-Function Networks for Graph-based Observations）</news:title>
   <news:publication_date>2026-07-28T17:30:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716958</loc>
  <lastmod>2026-07-28T17:30:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>歩行者属性認識の総説（Pedestrian Attribute Recognition: A Survey）</news:title>
   <news:publication_date>2026-07-28T17:30:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716956</loc>
  <lastmod>2026-07-28T17:30:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>個々の経験と情報伝達による集団学習と採餌行動（Collective learning from individual experiences and information transfer during group foraging）</news:title>
   <news:publication_date>2026-07-28T17:30:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716954</loc>
  <lastmod>2026-07-28T17:29:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的モメンタム法の加速線形収束（Accelerated Linear Convergence of Stochastic Momentum Methods in Wasserstein Distances）</news:title>
   <news:publication_date>2026-07-28T17:29:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716952</loc>
  <lastmod>2026-07-28T17:29:06Z</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>2026-07-28T17:29:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716950</loc>
  <lastmod>2026-07-28T17:28:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層学習と部分語単位による詩生成（Deep learning and sub-word-unit approach in written art generation）</news:title>
   <news:publication_date>2026-07-28T17:28:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716948</loc>
  <lastmod>2026-07-28T16:37:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Multiple Sclerosisの病変と脳構造を同時に分割する深層学習（Simultaneous lesion and brain segmentation in Multiple Sclerosis using deep neural networks）</news:title>
   <news:publication_date>2026-07-28T16:37:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716946</loc>
  <lastmod>2026-07-28T16:36:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>通信負荷を抑える新手法 CAMR（Coded Aggregated MapReduce）</news:title>
   <news:publication_date>2026-07-28T16:36:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716944</loc>
  <lastmod>2026-07-28T16:36:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層学習における連結なサブレベル集合（On Connected Sublevel Sets in Deep Learning）</news:title>
   <news:publication_date>2026-07-28T16:36:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716942</loc>
  <lastmod>2026-07-28T16:35:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>屋内空間のレジアビリティ（判読性）を定量化する手法（Quantifying Legibility of Indoor Spaces Using Deep Convolutional Neural Networks: Case Studies in Train Stations）</news:title>
   <news:publication_date>2026-07-28T16:35:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716940</loc>
  <lastmod>2026-07-28T16:35:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>長期依存性を扱うための非飽和型再帰ユニット（Towards Non-saturating Recurrent Units for Modelling Long-term Dependencies）</news:title>
   <news:publication_date>2026-07-28T16:35:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716938</loc>
  <lastmod>2026-07-28T16:34:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的近接点法の非漸近収束率の更新（New nonasymptotic convergence rates of stochastic proximal point algorithm for stochastic convex optimization）</news:title>
   <news:publication_date>2026-07-28T16:34:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716936</loc>
  <lastmod>2026-07-28T16:34:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフィカルモデルの同定検定の下限（Lower bounds for testing graphical models: colorings and antiferromagnetic Ising models）</news:title>
   <news:publication_date>2026-07-28T16:34:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716934</loc>
  <lastmod>2026-07-28T15:43:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Top-Kワークロードにおけるホット・コールド階層配置の最適化（Adapting The Secretary Hiring Problem for Optimal Hot-Cold Tier Placement under Top-K Workloads）</news:title>
   <news:publication_date>2026-07-28T15:43:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716932</loc>
  <lastmod>2026-07-28T15:43:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>更新を絞ることで長期依存を扱う：Gaussian-gated LSTMの概観（REDUCING STATE UPDATES VIA GAUSSIAN-GATED LSTMS）</news:title>
   <news:publication_date>2026-07-28T15:43:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716930</loc>
  <lastmod>2026-07-28T15:42:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自動特徴量エンジニアリングと選択を提供するautofeatライブラリ（The autofeat Python Library for Automated Feature Engineering and Selection）</news:title>
   <news:publication_date>2026-07-28T15:42:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716928</loc>
  <lastmod>2026-07-28T15:42:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>クロスリンガル言語モデル事前学習（Cross-lingual Language Model Pretraining）</news:title>
   <news:publication_date>2026-07-28T15:42:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716926</loc>
  <lastmod>2026-07-28T15:42:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数の動的グラフをオンライン推定する手法（Online Estimation of Multiple Dynamic Graphs in Pattern Sequences）</news:title>
   <news:publication_date>2026-07-28T15:42:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716924</loc>
  <lastmod>2026-07-28T15:41:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>セントロメア衛星DNAを識別するdna-brnn（Identifying centromeric satellites with dna-brnn）</news:title>
   <news:publication_date>2026-07-28T15:41:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716922</loc>
  <lastmod>2026-07-28T15:41:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>モーダルクラスタリングの漸近理論と帯域幅選択（Modal clustering asymptotics with applications to bandwidth selection）</news:title>
   <news:publication_date>2026-07-28T15:41:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716920</loc>
  <lastmod>2026-07-28T14:50:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ドライバーの注意散漫検知に向けた畳み込みニューラルネットワークのアンサンブル（Driver Distraction Identification with an Ensemble of Convolutional Neural Networks）</news:title>
   <news:publication_date>2026-07-28T14:50:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716918</loc>
  <lastmod>2026-07-28T14:43:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>教師なし学習に基づく深度推定が支援するVisual SLAMアプローチ (Unsupervised Learning-based Depth Estimation aided Visual SLAM Approach)</news:title>
   <news:publication_date>2026-07-28T14:43:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716916</loc>
  <lastmod>2026-07-28T14:42:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>銀河合体と星形成の位置づけ（Deep Learning for Galaxy Mergers in the Galaxy Main Sequence）</news:title>
   <news:publication_date>2026-07-28T14:42:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716914</loc>
  <lastmod>2026-07-28T14:41:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>最小ペナルティとスロープヒューリスティクス（Minimal penalties and the slope heuristics）</news:title>
   <news:publication_date>2026-07-28T14:41:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716912</loc>
  <lastmod>2026-07-28T14:40:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高速で高精度かつ軽量な超解像を自動設計する手法（Fast, Accurate and Lightweight Super-Resolution with Neural Architecture Search）</news:title>
   <news:publication_date>2026-07-28T14:40:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716910</loc>
  <lastmod>2026-07-28T14:40:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ネットワークをまたいだノード分類の新展開（Network Together: Node Classification via Cross-Network Deep Network Embedding）</news:title>
   <news:publication_date>2026-07-28T14:40:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716908</loc>
  <lastmod>2026-07-28T14:40:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>IoTにおけるゼロインタラクション型セキュリティの危険性（Perils of Zero-Interaction Security in the Internet of Things）</news:title>
   <news:publication_date>2026-07-28T14:40:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716906</loc>
  <lastmod>2026-07-28T13:48:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>LAMOST銀河分光サーベイによる64万弱の赤色巨星の年齢と質量（Ages and Masses of 0.64 million Red Giant Branch stars from the LAMOST Galactic Spectroscopic Survey）</news:title>
   <news:publication_date>2026-07-28T13:48:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716904</loc>
  <lastmod>2026-07-28T13:47:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模時系列グラフにおける異常検知のスケーラブル手法（Anomaly detection in the dynamics of web and social networks）</news:title>
   <news:publication_date>2026-07-28T13:47:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716902</loc>
  <lastmod>2026-07-28T13:47:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>NGC 6752における主系列下部での複数集団の検出（The HST Large Programme on NGC 6752. II. Multiple populations at the bottom of the main sequence probed in NIR）</news:title>
   <news:publication_date>2026-07-28T13:47:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716900</loc>
  <lastmod>2026-07-28T13:46:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深紫外弱パルスの特性評価（Characterization of weak deep UV pulses using cross-phase modulation scans）</news:title>
   <news:publication_date>2026-07-28T13:46:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716898</loc>
  <lastmod>2026-07-28T13:46:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DF-SLAMが示した「小さな深層モジュールで実用的に強化する」道（DF-SLAM: A Deep-Learning Enhanced Visual SLAM System）</news:title>
   <news:publication_date>2026-07-28T13:46:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716896</loc>
  <lastmod>2026-07-28T13:46:13Z</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>2026-07-28T13:46:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716894</loc>
  <lastmod>2026-07-28T13:45:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>データから学ぶリーマン多様体上の高速で頑健な最短経路（Fast and Robust Shortest Paths on Manifolds Learned from Data）</news:title>
   <news:publication_date>2026-07-28T13:45:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716892</loc>
  <lastmod>2026-07-28T12:54:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>直腸がんセグメンテーションにおけるモデル分散低減の実践（Reducing Model Variance in Rectal Cancer Segmentation）</news:title>
   <news:publication_date>2026-07-28T12:54:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716890</loc>
  <lastmod>2026-07-28T12:54:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>転移とハイブリッドが出会う：テキストを用いたクロスドメイン協調フィルタリングの統合的手法（Transfer Meets Hybrid: A Synthetic Approach for Cross-Domain Collaborative Filtering with Text）</news:title>
   <news:publication_date>2026-07-28T12:54:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716888</loc>
  <lastmod>2026-07-28T12:54:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CAE-ADMMによる暗黙的ビットレート最適化（CAE-ADMM: IMPLICIT BITRATE OPTIMIZATION VIA ADMM-BASED PRUNING IN COMPRESSIVE AUTOENCODERS）</news:title>
   <news:publication_date>2026-07-28T12:54:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716886</loc>
  <lastmod>2026-07-28T12:52:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>教師あり学習と深層学習による測光赤方偏移解析（Photometric Redshift Analysis using Supervised Learning Algorithms and Deep Learning）</news:title>
   <news:publication_date>2026-07-28T12:52:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716884</loc>
  <lastmod>2026-07-28T12:52:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単一動画デモンストレーションからの模倣学習の探求（Towards Learning to Imitate from a Single Video Demonstration）</news:title>
   <news:publication_date>2026-07-28T12:52:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716882</loc>
  <lastmod>2026-07-28T12:52:41Z</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>2026-07-28T12:52:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716880</loc>
  <lastmod>2026-07-28T12:52:15Z</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>2026-07-28T12:52:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716878</loc>
  <lastmod>2026-07-28T12:00:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>エネルギー混乱を用いた敵対的距離学習によるゼロショット画像検索とクラスタリングの実務的示唆（Energy Confused Adversarial Metric Learning for Zero-Shot Image Retrieval and Clustering）</news:title>
   <news:publication_date>2026-07-28T12:00:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716876</loc>
  <lastmod>2026-07-28T11:49:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチユーザセルラーネットワークにおける電力割当の深層強化学習アプローチ (Power Allocation in Multi-User Cellular Networks: Deep Reinforcement Learning Approaches)</news:title>
   <news:publication_date>2026-07-28T11:49:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716874</loc>
  <lastmod>2026-07-28T11:49:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自然言語から高解像度3Dモデルを生成する手法（Generation High resolution 3D model from natural language by Generative Adversarial Network）</news:title>
   <news:publication_date>2026-07-28T11:49:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716872</loc>
  <lastmod>2026-07-28T11:48:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層ニューラルネットワークの感度解析（Sensitivity Analysis of Deep Neural Networks）</news:title>
   <news:publication_date>2026-07-28T11:48:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716870</loc>
  <lastmod>2026-07-28T11:48:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>テキスト分類を一変させる「普遍ルール」攻撃（Universal Rules for Fooling Deep Neural Networks based Text Classification）</news:title>
   <news:publication_date>2026-07-28T11:48:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716868</loc>
  <lastmod>2026-07-28T11:48:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>線形化マルチサンプリングによる微分可能な画像変換（Linearized Multi-Sampling for Differentiable Image Transformation）</news:title>
   <news:publication_date>2026-07-28T11:48:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716866</loc>
  <lastmod>2026-07-28T11:48:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>感情を指定できる対話生成の敵対的学習（An Adversarial Approach to High-Quality, Sentiment-Controlled Neural Dialogue Generation）</news:title>
   <news:publication_date>2026-07-28T11:48:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716864</loc>
  <lastmod>2026-07-28T10:57:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>四元数ベースのニューラルネットで人間の動作を扱う（Modeling Human Motion with Quaternion-based Neural Networks）</news:title>
   <news:publication_date>2026-07-28T10:57:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716862</loc>
  <lastmod>2026-07-28T10:56:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習データ適応カーネルとしてのニューラルネットワーク訓練（Training Neural Networks as Learning Data-adaptive Kernels: Provable Representation and Approximation Benefits）</news:title>
   <news:publication_date>2026-07-28T10:56:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716860</loc>
  <lastmod>2026-07-28T10:56:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高赤方偏移活動銀河核（AGN）におけるX線遮蔽と銀河環境の役割（The X-ray emission of z &amp;gt; 2.5 active galactic nuclei can be obscured by their host galaxies）</news:title>
   <news:publication_date>2026-07-28T10:56:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716858</loc>
  <lastmod>2026-07-28T10:56:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>不確実性下の教えと学び（Teaching and learning in uncertainty）</news:title>
   <news:publication_date>2026-07-28T10:56:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716856</loc>
  <lastmod>2026-07-28T10:55:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>予測インデックスによる継続的な物理設計最適化（Predictive Indexing）</news:title>
   <news:publication_date>2026-07-28T10:55:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716854</loc>
  <lastmod>2026-07-28T10:55:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニュートリノ研究における「誤り」からの学び（Neutrino Mistakes: Wrong tracks and Hints, Hopes and Failures）</news:title>
   <news:publication_date>2026-07-28T10:55:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716852</loc>
  <lastmod>2026-07-28T10:55:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>堅牢な角度ベースの局所特徴量学習（Robust Angular Local Descriptor Learning）</news:title>
   <news:publication_date>2026-07-28T10:55:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716850</loc>
  <lastmod>2026-07-28T10:04:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>細胞核検出のための事前情報を使った正則化深層学習（Prior Information Guided Regularized Deep Learning for Cell Nucleus Detection）</news:title>
   <news:publication_date>2026-07-28T10:04:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716848</loc>
  <lastmod>2026-07-28T10:04:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Partition Pruning：並列化を意識したニューラルネットワークの剪定（Partition Pruning: Parallelization-Aware Pruning for Deep Neural Networks）</news:title>
   <news:publication_date>2026-07-28T10:04:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716846</loc>
  <lastmod>2026-07-28T10:03:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>銀河形態分類における機械学習と深層学習の比較（Machine and Deep Learning Applied to Galaxy Morphology - A Comparative Study）</news:title>
   <news:publication_date>2026-07-28T10:03:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716844</loc>
  <lastmod>2026-07-28T10:02:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Spatial Broadcast Decoderによる表現分離の簡潔な設計（Spatial Broadcast Decoder: A Simple Architecture for Learning Disentangled Representations in VAEs）</news:title>
   <news:publication_date>2026-07-28T10:02:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716842</loc>
  <lastmod>2026-07-28T10:02:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>金属‑メタロイドガラスの局所構造が玻璃転移を制御する（Local Structure Controlling the Glass Transition in a Prototype Metal‑Metalloid Glass）</news:title>
   <news:publication_date>2026-07-28T10:02:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716840</loc>
  <lastmod>2026-07-28T10:02:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>MIMIC-CXR-JPG：胸部X線の大規模公開データベースがもたらす変化（MIMIC-CXR-JPG, A LARGE PUBLICLY AVAILABLE DATABASE OF LABELED CHEST RADIOGRAPHS）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716838</loc>
  <lastmod>2026-07-28T10:01:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CheXpertが拓く胸部X線画像解析の実用化（CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison）</news:title>
   <news:publication_date>2026-07-28T10:01:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716836</loc>
  <lastmod>2026-07-28T09:10:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ラベルの粒度がCNN画像分類に与える影響（Understanding the Impact of Label Granularity on CNN-based Image Classification）</news:title>
   <news:publication_date>2026-07-28T09:10:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716834</loc>
  <lastmod>2026-07-28T09:10:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ツイートにおける細粒度位置認識とリンクの深層パイプライン（DLocRL: A Deep Learning Pipeline for Fine-Grained Location Recognition and Linking in Tweets）</news:title>
   <news:publication_date>2026-07-28T09:10:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716832</loc>
  <lastmod>2026-07-28T09:09:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的強化学習に関する短いサーベイ（A Short Survey on Probabilistic Reinforcement Learning）</news:title>
   <news:publication_date>2026-07-28T09:09:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716830</loc>
  <lastmod>2026-07-28T09:09:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>内視鏡的顕微画像の教師なし超解像を可能にした手法の要点（Adversarial training with cycle consistency for unsuper-resolution in endomicroscopy）</news:title>
   <news:publication_date>2026-07-28T09:09:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716828</loc>
  <lastmod>2026-07-28T09:09:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>誤り訂正を用いたニューラル系列予測（Error-Correcting Neural Sequence Prediction）</news:title>
   <news:publication_date>2026-07-28T09:09:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716826</loc>
  <lastmod>2026-07-28T09:09:12Z</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を説明するための教師なし学習（Unsupervised Learning of Neural Networks to Explain Neural Networks）</news:title>
   <news:publication_date>2026-07-28T09:09:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716824</loc>
  <lastmod>2026-07-28T09:09:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>任意グラフ上における分散ネステロフ勾配法（Distributed Nesterov Gradient Methods over Arbitrary Graphs）</news:title>
   <news:publication_date>2026-07-28T09:09:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716822</loc>
  <lastmod>2026-07-28T08:17:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ネットワーク移植（Network Transplanting）</news:title>
   <news:publication_date>2026-07-28T08:17:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716820</loc>
  <lastmod>2026-07-28T08:17:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>最適化における推定解の許容性（Admissibility of solution estimators for stochastic optimization）</news:title>
   <news:publication_date>2026-07-28T08:17:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716818</loc>
  <lastmod>2026-07-28T08:17:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフプーリングとハイブリッド畳み込みによるテキスト表現の革新（Learning Graph Pooling and Hybrid Convolutional Operations for Text Representations）</news:title>
   <news:publication_date>2026-07-28T08:17:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716816</loc>
  <lastmod>2026-07-28T08:16:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>sEMGを用いたジェスチャー認識のドメイン適応とRNNによる実装（Domain Adaptation for sEMG-based Gesture Recognition with Recurrent Neural Networks）</news:title>
   <news:publication_date>2026-07-28T08:16:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716814</loc>
  <lastmod>2026-07-28T08:16:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>カスタムハードウェアのための深層ニューラルネットワーク近似（Deep Neural Network Approximation for Custom Hardware: Where We’ve Been, Where We’re Going）</news:title>
   <news:publication_date>2026-07-28T08:16:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716812</loc>
  <lastmod>2026-07-28T08:16:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>二重仮想光子によるη′遷移形状因子から学べること（What can be learned from the transition form factor γ*γ*→η′: feasibility study）</news:title>
   <news:publication_date>2026-07-28T08:16:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716810</loc>
  <lastmod>2026-07-28T08:15:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>潜在変数による交絡と因果の見分け方（We Are Not Your Real Parents: Telling Causal from Confounded using MDL）</news:title>
   <news:publication_date>2026-07-28T08:15:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716808</loc>
  <lastmod>2026-07-28T07:25:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>血管内超音波画像における管腔と外膜弾性板の分割（Segmentation of Lumen and External Elastic Laminae in Intravascular Ultrasound Images using Ultrasonic Backscattering Physics Initialized Multiscale Random Walks）</news:title>
   <news:publication_date>2026-07-28T07:25:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716806</loc>
  <lastmod>2026-07-28T07:25:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SUMNetによる超音波ボリュームの高速器官セグメンテーション（SUMNET: FULLY CONVOLUTIONAL MODEL FOR FAST SEGMENTATION OF ANATOMICAL STRUCTURES IN ULTRASOUND VOLUMES）</news:title>
   <news:publication_date>2026-07-28T07:25:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716804</loc>
  <lastmod>2026-07-28T07:24:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>並列コンテキストバンディットによる無線ハンドオーバ最適化（Parallel Contextual Bandits in Wireless Handover Optimization）</news:title>
   <news:publication_date>2026-07-28T07:24:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716802</loc>
  <lastmod>2026-07-28T07:24:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習可能なCOPE特徴抽出器による音声表現学習（Learning sound representations using trainable COPE feature extractors）</news:title>
   <news:publication_date>2026-07-28T07:24:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716800</loc>
  <lastmod>2026-07-28T07:24:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>メッセージ伝播法による非ストコスティック量子アニーリングの古典シミュレーション（Message-passing algorithm of quantum annealing with nonstoquastic Hamiltonian）</news:title>
   <news:publication_date>2026-07-28T07:24:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716798</loc>
  <lastmod>2026-07-28T07:24:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>位相符号のニューラルデコーダ（Neural Decoder for Topological Codes using Pseudo-Inverse of Parity Check Matrix）</news:title>
   <news:publication_date>2026-07-28T07:24:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716796</loc>
  <lastmod>2026-07-28T07:23:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>クラウドでのDNN推論を共有インフラにする意義（No DNN Left Behind: Improving Inference in the Cloud with Multi-Tenancy）</news:title>
   <news:publication_date>2026-07-28T07:23:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716794</loc>
  <lastmod>2026-07-28T06:33:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>知覚を取り込む対話型で見つける敵対的事例（Perception-in-the-Loop Adversarial Examples）</news:title>
   <news:publication_date>2026-07-28T06:33:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716792</loc>
  <lastmod>2026-07-28T06:32:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スペクトログラムと深層残差で読み解く音声ステガノ分析（Spec-ResNet: A General Audio Steganalysis Scheme based on a Deep Residual Network for Spectrograms）</news:title>
   <news:publication_date>2026-07-28T06:32:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716790</loc>
  <lastmod>2026-07-28T06:32:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>バイアス補正キャリブレーションを用いた最尤法はラベルシフト適応で強力なベースラインである（Maximum Likelihood Label Shift with Bias-Corrected Calibration）</news:title>
   <news:publication_date>2026-07-28T06:32:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716788</loc>
  <lastmod>2026-07-28T06:31:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>鞍点回避のための決定論的勾配法（A Deterministic Gradient-Based Approach to Avoid Saddle Points）</news:title>
   <news:publication_date>2026-07-28T06:31:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716786</loc>
  <lastmod>2026-07-28T06:31:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>読み、観て、動く—動画内の自然言語記述の時間的基準付けのための強化学習 (Read, Watch, and Move: Reinforcement Learning for Temporally Grounding Natural Language Descriptions in Videos)</news:title>
   <news:publication_date>2026-07-28T06:31:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716784</loc>
  <lastmod>2026-07-28T06:31:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ロスレス特徴反射と重み付き構造損失による顕著領域検出（Salient Object Detection with Lossless Feature Reflection and Weighted Structural Loss）</news:title>
   <news:publication_date>2026-07-28T06:31:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716782</loc>
  <lastmod>2026-07-28T06:30:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>全結合層が画像分類用畳み込みニューラルネットワークの性能に与える影響（Impact of Fully Connected Layers on Performance of Convolutional Neural Networks for Image Classification）</news:title>
   <news:publication_date>2026-07-28T06:30:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716780</loc>
  <lastmod>2026-07-28T05:39:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>極性符号に基づくストラッグラー耐性サーバーレス計算（Straggler Resilient Serverless Computing Based on Polar Codes）</news:title>
   <news:publication_date>2026-07-28T05:39:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716778</loc>
  <lastmod>2026-07-28T05:38:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>広告オークションにおけるインセンティブ互換性のオンライン測定（Online Learning for Measuring Incentive Compatibility in Ad Auctions）</news:title>
   <news:publication_date>2026-07-28T05:38:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716776</loc>
  <lastmod>2026-07-28T05:38:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文書作成時刻推定のための注意機構付き深層モデル（AD3: Attentive Deep Document Dater）</news:title>
   <news:publication_date>2026-07-28T05:38:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716774</loc>
  <lastmod>2026-07-28T05:37:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層レベルセット：3D形状推定のための暗黙的表面表現 (Deep Level Sets: Implicit Surface Representations for 3D Shape Inference)</news:title>
   <news:publication_date>2026-07-28T05:37:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716772</loc>
  <lastmod>2026-07-28T05:37:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高スループット植物表現型解析のためのガウス過程を用いた能動学習（Active Learning with Gaussian Processes for High Throughput Phenotyping）</news:title>
   <news:publication_date>2026-07-28T05:37:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716770</loc>
  <lastmod>2026-07-28T05:36:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>不均衡データ分類のための動的カリキュラム学習（Dynamic Curriculum Learning for Imbalanced Data Classification）</news:title>
   <news:publication_date>2026-07-28T05:36:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-07-28T05:36:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>安全制御器の学習ベース合成 (Learning-Based Synthesis of Safety Controllers)</news:title>
   <news:publication_date>2026-07-28T05:36:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/716766</loc>
  <lastmod>2026-07-28T04:44:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>画像ベースのマルウェア分類における転移学習 (Transfer Learning for Image-Based Malware Classification)</news:title>
   <news:publication_date>2026-07-28T04:44:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
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 <url>
  <loc>https://aibr.jp/archives/716764</loc>
  <lastmod>2026-07-28T04:36:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文字列画像の合成による認識データ生成（Generating Text Sequence Images for Recognition）</news:title>
   <news:publication_date>2026-07-28T04:36:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716762</loc>
  <lastmod>2026-07-28T04:36:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ハイブリッド粗密分類による頭部姿勢推定（Hybrid Coarse-Fine Classification for Head Pose Estimation）</news:title>
   <news:publication_date>2026-07-28T04:36:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716760</loc>
  <lastmod>2026-07-28T04:35:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層的疎行列分解で加速するスパース近似ガウス学習（Accelerated scale bridging with sparsely approximated Gaussian learning）</news:title>
   <news:publication_date>2026-07-28T04:35:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716758</loc>
  <lastmod>2026-07-28T04:35:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>超深層ニューラルネットワークのGPUメモリ効率化による学習加速（AccUDNN: A GPU Memory Efficient Accelerator for Training Ultra-deep Neural Networks）</news:title>
   <news:publication_date>2026-07-28T04:35:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716756</loc>
  <lastmod>2026-07-28T04:34:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>プロファイルベースプライバシー：局所プライバシーの新たな定義（Profile-Based Privacy for Locally Private Computations）</news:title>
   <news:publication_date>2026-07-28T04:34:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716754</loc>
  <lastmod>2026-07-28T04:34:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Androidアプリのマルウェア比較分析（A Comparative Analysis of Android Malware）</news:title>
   <news:publication_date>2026-07-28T04:34:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716752</loc>
  <lastmod>2026-07-28T03:43:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ℓpノルム回帰の反復再精練法（Iterative Refinement for ℓp-norm Regression）</news:title>
   <news:publication_date>2026-07-28T03:43:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716750</loc>
  <lastmod>2026-07-28T03:42:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>手書き数式認識におけるパターン生成戦略（Pattern Generation Strategies for Improving Recognition of Handwritten Mathematical Expressions）</news:title>
   <news:publication_date>2026-07-28T03:42:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716748</loc>
  <lastmod>2026-07-28T03:41:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ヒト中心の転移学習説明と知識グラフの活用（Human-centric Transfer Learning Explanation via Knowledge Graph）</news:title>
   <news:publication_date>2026-07-28T03:41:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716746</loc>
  <lastmod>2026-07-28T03:41:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層学習によるリアルタイム路上駐車占有予測（A deep learning approach to real-time parking occupancy prediction in spatio-temporal networks incorporating multiple spatio-temporal data sources）</news:title>
   <news:publication_date>2026-07-28T03:41:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716744</loc>
  <lastmod>2026-07-28T03:41:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>テンソライズド論理プログラミング言語が切り開く「宣言的モデリング×大規模数値計算」の道（A tensorized logic programming language for large-scale data）</news:title>
   <news:publication_date>2026-07-28T03:41:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716742</loc>
  <lastmod>2026-07-28T03:40:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>シード駆動型ジオソーシャルデータ抽出（Seed-Driven Geo-Social Data Extraction）</news:title>
   <news:publication_date>2026-07-28T03:40:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716740</loc>
  <lastmod>2026-07-28T03:39:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>円柱周りの風圧を機械学習で予測する（Predicting wind pressures around circular cylinders using machine learning techniques）</news:title>
   <news:publication_date>2026-07-28T03:39:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716738</loc>
  <lastmod>2026-07-28T02:48:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>解釈可能な畳み込みネットワークのための普遍論理演算子（A Universal Logic Operator for Interpretable Deep Convolution Networks）</news:title>
   <news:publication_date>2026-07-28T02:48:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716736</loc>
  <lastmod>2026-07-28T02:48:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Pointerネットワークの生成品質を向上させるガイド付き注意（Improving generation quality of pointer networks via guided attention）</news:title>
   <news:publication_date>2026-07-28T02:48:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716734</loc>
  <lastmod>2026-07-28T02:48:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>フル畳み込みネットワークの最適化地形の可視化（Visualized Insights into the Optimization Landscape of Fully Convolutional Networks）</news:title>
   <news:publication_date>2026-07-28T02:48:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716732</loc>
  <lastmod>2026-07-28T02:47:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時系列データに基づく説明可能な故障予測（Explainable Failure Predictions with RNN Classifiers based on Time Series Data）</news:title>
   <news:publication_date>2026-07-28T02:47:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716730</loc>
  <lastmod>2026-07-28T02:47:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層生成モデルにおけるデータ補間の位相的落とし穴と密度正則化による解決（Data Interpolations in Deep Generative Models under Non-Simply-Connected Manifold Topology）</news:title>
   <news:publication_date>2026-07-28T02:47:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716728</loc>
  <lastmod>2026-07-28T02:47:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>代表置換（Representative Substitution）による単一方向の重要性の理解（Understanding the Importance of Single Directions via Representative Substitution）</news:title>
   <news:publication_date>2026-07-28T02:47:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716726</loc>
  <lastmod>2026-07-28T02:46:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層リカレントファクターモデルが示す解釈可能な非線形・時変多因子アプローチ（Deep Recurrent Factor Model: Interpretable Non-Linear and Time-Varying Multi-Factor Model）</news:title>
   <news:publication_date>2026-07-28T02:46:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716724</loc>
  <lastmod>2026-07-28T01:55:54Z</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・インフォメーション・フロー（On Network Science and Mutual Information for Explaining Deep Neural Networks）</news:title>
   <news:publication_date>2026-07-28T01:55:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716722</loc>
  <lastmod>2026-07-28T01:55:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>生成器ネットワークからのスパース符号化とAnd-Or文法の誘導（Inducing Sparse Coding and And-Or Grammar from Generator Network）</news:title>
   <news:publication_date>2026-07-28T01:55:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716720</loc>
  <lastmod>2026-07-28T01:54:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習の解釈性と信頼性を定量化する手法（Quantifying Interpretability and Trust in Machine Learning Systems）</news:title>
   <news:publication_date>2026-07-28T01:54:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716718</loc>
  <lastmod>2026-07-28T01:54:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>重み付き特徴帰属の統合手法（Towards Aggregating Weighted Feature Attributions）</news:title>
   <news:publication_date>2026-07-28T01:54:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716716</loc>
  <lastmod>2026-07-28T01:54:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>混合分布をデータに当てはめるチュートリアル（Fitting A Mixture Distribution to Data: Tutorial）</news:title>
   <news:publication_date>2026-07-28T01:54:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716714</loc>
  <lastmod>2026-07-28T01:54:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>注意機構による深層特徴の解析（Deep Features Analysis with Attention Networks）</news:title>
   <news:publication_date>2026-07-28T01:54:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716712</loc>
  <lastmod>2026-07-28T01:53:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時系列データの意味構造を可視化する手法（Visualizing Semantic Structures of Sequential Data by Learning Temporal Dependencies）</news:title>
   <news:publication_date>2026-07-28T01:53:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716707</loc>
  <lastmod>2026-07-28T01:02:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>X線画像での器用な外科用器具の位置特定手法（Localizing dexterous surgical tools in X-ray for image-based navigation）</news:title>
   <news:publication_date>2026-07-28T01:02:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716705</loc>
  <lastmod>2026-07-28T01:02:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所誤差信号によるニューラルネットワーク訓練（Training Neural Networks with Local Error Signals）</news:title>
   <news:publication_date>2026-07-28T01:02:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716703</loc>
  <lastmod>2026-07-28T01:02:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次元における小さな空隙を持つ決定的点集合の構成（DETERMINISTIC CONSTRUCTIONS OF HIGH-DIMENSIONAL SETS WITH SMALL DISPERSION）</news:title>
   <news:publication_date>2026-07-28T01:02:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716701</loc>
  <lastmod>2026-07-28T01:01:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>順序情報を保つニューラルネットの序列回帰（Rank Consistent Ordinal Regression for Neural Networks）</news:title>
   <news:publication_date>2026-07-28T01:01:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716699</loc>
  <lastmod>2026-07-28T01:01:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>PHONELABの四年間から得た教訓（Lessons from Four Years of PHONELAB Experimentation）</news:title>
   <news:publication_date>2026-07-28T01:01:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716697</loc>
  <lastmod>2026-07-28T01:01:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>事例埋め込みによる教師なしユーザー識別連携（Unsupervised User Identity Linkage via Factoid Embedding）</news:title>
   <news:publication_date>2026-07-28T01:01:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716695</loc>
  <lastmod>2026-07-28T01:01:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>バッチ効果の除去にGANを使う意義と実装（Removal of Batch Effects Using Generative Adversarial Networks）</news:title>
   <news:publication_date>2026-07-28T01:01:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716693</loc>
  <lastmod>2026-07-28T00:10:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>量子状態の可制御な伝送を深層強化学習で（Coherent Transport of Quantum States by Deep Reinforcement Learning）</news:title>
   <news:publication_date>2026-07-28T00:10:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716691</loc>
  <lastmod>2026-07-28T00:02:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CommunityGANによる重複コミュニティ検出の革新（CommunityGAN: Community Detection with Generative Adversarial Nets）</news:title>
   <news:publication_date>2026-07-28T00:02:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716689</loc>
  <lastmod>2026-07-28T00:01:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Mixed Formal Learning の解き方：透明性と少数例学習を同時に実現する道筋（Mixed Formal Learning）</news:title>
   <news:publication_date>2026-07-28T00:01:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716687</loc>
  <lastmod>2026-07-28T00:01:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>生体病理の連続体をモデル化する手法（Modeling the Biological Pathology Continuum with HSIC-regularized Wasserstein Auto-encoders）</news:title>
   <news:publication_date>2026-07-28T00:01:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716685</loc>
  <lastmod>2026-07-28T00:00:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分子力場を高精度で学習する手法の実用化（Molecular Force Fields with Gradient-Domain Machine Learning）</news:title>
   <news:publication_date>2026-07-28T00:00:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716683</loc>
  <lastmod>2026-07-28T00:00:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>重複するコミュニティ構造における協力性の検討（Investigating Cooperativity of Overlapping Community Structures in Social Networks）</news:title>
   <news:publication_date>2026-07-28T00:00:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716681</loc>
  <lastmod>2026-07-27T23:59:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層注意ハイブリッドニューラルネットワークによる文書分類（Hierarchical Attentional Hybrid Neural Networks for Document Classification）</news:title>
   <news:publication_date>2026-07-27T23:59:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716679</loc>
  <lastmod>2026-07-27T23:08:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>蓄積ビット幅の最適化がもたらす低精度学習の革新（Accumulation Bit-Width Scaling for Ultra-Low Precision Training of Deep Networks）</news:title>
   <news:publication_date>2026-07-27T23:08:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716677</loc>
  <lastmod>2026-07-27T22:58:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ReLUとSGDの収束と量子化SGD（Fitting ReLUs via SGD and Quantized SGD）</news:title>
   <news:publication_date>2026-07-27T22:58:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716675</loc>
  <lastmod>2026-07-27T22:57:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>リアルタイム向けセマンティックセグメンテーションデコーダの設計（Design of Real-time Semantic Segmentation Decoder for Automated Driving）</news:title>
   <news:publication_date>2026-07-27T22:57:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716673</loc>
  <lastmod>2026-07-27T22:56:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>過学習のメカニズムと回避法（Overfitting Mechanism and Avoidance in Deep Neural Networks）</news:title>
   <news:publication_date>2026-07-27T22:56:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716671</loc>
  <lastmod>2026-07-27T22:56:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>適応型人工知能Q&amp;amp;Aプラットフォーム（Adaptive Artificial Intelligent Q&amp;amp;A Platform）</news:title>
   <news:publication_date>2026-07-27T22:56:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716669</loc>
  <lastmod>2026-07-27T22:56:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自己対戦で学ぶ逆合成計画（Learning retrosynthetic planning through self-play）</news:title>
   <news:publication_date>2026-07-27T22:56:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716667</loc>
  <lastmod>2026-07-27T22:55:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>物理的に安全な強化学習のための監督下学習（Towards Physically Safe Reinforcement Learning under Supervision）</news:title>
   <news:publication_date>2026-07-27T22:55:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716665</loc>
  <lastmod>2026-07-27T22:04:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>増加する削減係数を用いたコード化BKWとシービングの漸近計算量（The Asymptotic Complexity of Coded-BKW with Sieving Using Increasing Reduction Factors）</news:title>
   <news:publication_date>2026-07-27T22:04:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716663</loc>
  <lastmod>2026-07-27T22:04:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>顔のテクスチャと形状を同時生成するGAN（Synthesizing facial photometries and corresponding geometries using generative adversarial networks）</news:title>
   <news:publication_date>2026-07-27T22:04:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716661</loc>
  <lastmod>2026-07-27T22:03:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>MOROCO: モルドバとルーマニアの方言コーパス（MOROCO: The Moldavian and Romanian Dialectal Corpus）</news:title>
   <news:publication_date>2026-07-27T22:03:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716659</loc>
  <lastmod>2026-07-27T22:03:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層学習によるミリ波大規模MIMOのハイブリッドプレコーディング（Deep-Learning-based Millimeter-Wave Massive MIMO for Hybrid Precoding）</news:title>
   <news:publication_date>2026-07-27T22:03:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716657</loc>
  <lastmod>2026-07-27T22:03:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的感度のための事前期待値計算（A Pre-Expectation Calculus for Probabilistic Sensitivity）</news:title>
   <news:publication_date>2026-07-27T22:03:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716655</loc>
  <lastmod>2026-07-27T22:03:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>塩と胡椒ノイズ（Salt-and-Pepper Noise）を除去するための改良フィルタ提案（Image De-Noising For Salt and Pepper Noise by Introducing New Enhanced Filter）</news:title>
   <news:publication_date>2026-07-27T22:03:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716653</loc>
  <lastmod>2026-07-27T22:02:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>画像ノイズ除去アルゴリズムの比較と評価（A Comparative Study of Image Denoising Algorithms）</news:title>
   <news:publication_date>2026-07-27T22:02:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716651</loc>
  <lastmod>2026-07-27T21:11:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>筆跡を問わないオフライン署名認証におけるアンサンブル学習（Writer Independent Offline Signature Recognition Using Ensemble Learning）</news:title>
   <news:publication_date>2026-07-27T21:11:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716649</loc>
  <lastmod>2026-07-27T21:03:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>圧縮センシングと深層学習の比較（Deep learning versus `1-minimization for compressed sensing photoacoustic tomography）</news:title>
   <news:publication_date>2026-07-27T21:03:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716647</loc>
  <lastmod>2026-07-27T21:03:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>RobotriX：超写実的で大規模な屋内ロボット視覚データセット（The RobotriX: An eXtremely Photorealistic and Very-Large-Scale Indoor Dataset of Sequences with Robot Trajectories and Interactions）</news:title>
   <news:publication_date>2026-07-27T21:03:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716645</loc>
  <lastmod>2026-07-27T21:02:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>周波数原理が示す深層ニューラルネットワークの挙動（FREQUENCY PRINCIPLE: FOURIER ANALYSIS SHEDS LIGHT ON DEEP NEURAL NETWORKS）</news:title>
   <news:publication_date>2026-07-27T21:02:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716643</loc>
  <lastmod>2026-07-27T21:02:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多言語音声からの普遍的エンドツーエンド感情認識（Towards Universal End-to-End Affect Recognition from Multilingual Speech by ConvNets）</news:title>
   <news:publication_date>2026-07-27T21:02:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716641</loc>
  <lastmod>2026-07-27T21:01:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>限定視野光音響断層撮像における切り捨て特異値の深層学習（Deep Learning of truncated singular values for limited view photoacoustic tomography）</news:title>
   <news:publication_date>2026-07-27T21:01:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716639</loc>
  <lastmod>2026-07-27T21:01:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>光音響画像再構成における深層学習（Photoacoustic image reconstruction via deep learning）</news:title>
   <news:publication_date>2026-07-27T21:01:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716637</loc>
  <lastmod>2026-07-27T20:10:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>チャネル分割と逐次融合ネットワークによる単一MR画像の超解像（Single MR Image Super-Resolution via Channel Splitting and Serial Fusion Network）</news:title>
   <news:publication_date>2026-07-27T20:10:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716635</loc>
  <lastmod>2026-07-27T20:10:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>データマイニングで見るテロ予測と対策（A Conjoint Application of Data Mining Techniques for Analysis of Global Terrorist Attacks）</news:title>
   <news:publication_date>2026-07-27T20:10:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716633</loc>
  <lastmod>2026-07-27T20:10:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>繰り返し学習のための畳み込み忘却曲線モデル（Convolution Forgetting Curve Model for Repeated Learning）</news:title>
   <news:publication_date>2026-07-27T20:10:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716631</loc>
  <lastmod>2026-07-27T20:08:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深い時間周波数表現と段階的決定融合による心電図分類（Deep Time-Frequency Representation and Progressive Decision Fusion for ECG Classification）</news:title>
   <news:publication_date>2026-07-27T20:08:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716629</loc>
  <lastmod>2026-07-27T20:08:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ワイヤレスエッジキャッシュにおけるコンテンツ人気予測のオンライン学習モデル（Online Learning Models for Content Popularity Prediction In Wireless Edge Caching）</news:title>
   <news:publication_date>2026-07-27T20:08:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716627</loc>
  <lastmod>2026-07-27T20:08:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>クラス間分離で特徴を学ぶ深層表現学習による画像クラスタリング（Deep Representation Learning Characterized by Inter-class Separation for Image Clustering）</news:title>
   <news:publication_date>2026-07-27T20:08:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716625</loc>
  <lastmod>2026-07-27T20:08:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>核ノルム正則化多変量線形回帰のチューニングパラメータ選定規則（Tuning parameter selection rules for nuclear norm regularized multivariate linear regression）</news:title>
   <news:publication_date>2026-07-27T20:08:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716623</loc>
  <lastmod>2026-07-27T19:16:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Lifelong Federated Reinforcement Learning（Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems）</news:title>
   <news:publication_date>2026-07-27T19:16:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716615</loc>
  <lastmod>2026-07-27T19:08:51Z</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3D再構築を生成モデルで学ぶ（Learning single-image 3D reconstruction by generative modelling of shape, pose and shading）</news:title>
   <news:publication_date>2026-07-27T19:08:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716613</loc>
  <lastmod>2026-07-27T19:08:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>フェイクニュース対策の全体像と実務への示唆（Combating Fake News: A Survey on Identification and Mitigation Techniques）</news:title>
   <news:publication_date>2026-07-27T19:08:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716611</loc>
  <lastmod>2026-07-27T19:07:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Foothill関数によるエッジ向けニューラルネット正則化（Foothill: A Quasiconvex Regularization for Edge Computing of Deep Neural Networks）</news:title>
   <news:publication_date>2026-07-27T19:07:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716609</loc>
  <lastmod>2026-07-27T19:06:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次元スパース共分散行列の差分プライバシー推定（Differentially Private High Dimensional Sparse Covariance Matrix Estimation）</news:title>
   <news:publication_date>2026-07-27T19:06:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716607</loc>
  <lastmod>2026-07-27T19:06:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模な離散連続混合旅行行動データの生成モデル枠組み（A bi-partite generative model framework for analyzing and simulating large scale multiple discrete-continuous travel behaviour data）</news:title>
   <news:publication_date>2026-07-27T19:06:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716605</loc>
  <lastmod>2026-07-27T19:06:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Closネットワークを使った小規模ニューラルネットワークの高精度化（Machine Learning with Clos Networks）</news:title>
   <news:publication_date>2026-07-27T19:06:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716603</loc>
  <lastmod>2026-07-27T18:14:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチソース領域注意ネットワークによるリモートセンシングの微細分類の進展（Multisource Region Attention Network for Fine-Grained Object Recognition in Remote Sensing Imagery）</news:title>
   <news:publication_date>2026-07-27T18:14:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716601</loc>
  <lastmod>2026-07-27T18:14:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>小さなデータで高精度を実現した地震波位相検出の実務的手法（Deep learning for seismic phase detection and picking in the aftershock zone of 2008 Mw7.9 Wenchuan Earthquake）</news:title>
   <news:publication_date>2026-07-27T18:14:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716599</loc>
  <lastmod>2026-07-27T18:13:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スリムLSTMの実用性と導入示唆（Slim LSTM NETWORKS: LSTM 6 and LSTM C6）</news:title>
   <news:publication_date>2026-07-27T18:13:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716597</loc>
  <lastmod>2026-07-27T18:12:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>低金属量銀河におけるCO発光の抑制と高いG/D比の示唆（Suppressed CO emission and high G/D ratios in z=2 galaxies with sub-solar gas-phase metallicity）</news:title>
   <news:publication_date>2026-07-27T18:12:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716595</loc>
  <lastmod>2026-07-27T18:12:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>画像における頑健な異常検知（Robust Anomaly Detection in Images using Adversarial Autoencoders）</news:title>
   <news:publication_date>2026-07-27T18:12:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716593</loc>
  <lastmod>2026-07-27T18:12:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>事象の地平線を持たない空間における一般化されたノーヘア定理（Generalized no-hair theorems without horizons）</news:title>
   <news:publication_date>2026-07-27T18:12:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716591</loc>
  <lastmod>2026-07-27T18:12:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>舗装ひび割れ検出のための特徴ピラミッドと階層的ブースティングネットワーク（Feature Pyramid and Hierarchical Boosting Network for Pavement Crack Detection）</news:title>
   <news:publication_date>2026-07-27T18:12:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716589</loc>
  <lastmod>2026-07-27T17:20:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Spatial Pyramid Attentive Pooling による画像生成の改良（Learning Spatial Pyramid Attentive Pooling in Image Synthesis and Image-to-Image Translation）</news:title>
   <news:publication_date>2026-07-27T17:20:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716587</loc>
  <lastmod>2026-07-27T17:20:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模生物データの有効次元推定（Estimating the effective dimension of large biological datasets using Fisher separability analysis）</news:title>
   <news:publication_date>2026-07-27T17:20:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716585</loc>
  <lastmod>2026-07-27T17:19:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>物理制約を組み込んだラベル不要の深層学習による高次元サロゲートモデルと不確実性定量化（Physics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Data）</news:title>
   <news:publication_date>2026-07-27T17:19:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716583</loc>
  <lastmod>2026-07-27T17:18:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>最適輸送による系列間学習の改善（IMPROVING SEQUENCE-TO-SEQUENCE LEARNING VIA OPTIMAL TRANSPORT）</news:title>
   <news:publication_date>2026-07-27T17:18:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716581</loc>
  <lastmod>2026-07-27T17:17:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>陽子内部の光子分布を探る新手法（Probing the photonic content of the proton using photon-induced dilepton production in p + Pb collisions at the LHC）</news:title>
   <news:publication_date>2026-07-27T17:17:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716579</loc>
  <lastmod>2026-07-27T17:17:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>情報密度に応じて深さを変えるLSTM（DA-LSTM: A Long Short-Term Memory with Depth Adaptive to Non-uniform Information Flow in Sequential Data）</news:title>
   <news:publication_date>2026-07-27T17:17:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716577</loc>
  <lastmod>2026-07-27T17:16:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オンライントレーニングで学ぶ実用的自動最適化（On-line Application Autotuning Exploiting Ensemble Models）</news:title>
   <news:publication_date>2026-07-27T17:16:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716575</loc>
  <lastmod>2026-07-27T16:25:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Reduced Normal Formのベイズネット最適実装（Optimized Realization of Bayesian Networks in Reduced Normal Form using Latent Variable Model）</news:title>
   <news:publication_date>2026-07-27T16:25:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716573</loc>
  <lastmod>2026-07-27T16:25:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>リプレイバッファを用いたオンポリシー信頼領域方策最適化（ON-POLICY TRUST REGION POLICY OPTIMISATION WITH REPLAY BUFFERS）</news:title>
   <news:publication_date>2026-07-27T16:25:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716571</loc>
  <lastmod>2026-07-27T16:24:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>手書き文字の超解像のための生成的敵対分類器（Generative Adversarial Classifier for Handwriting Characters Super-Resolution）</news:title>
   <news:publication_date>2026-07-27T16:24:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716569</loc>
  <lastmod>2026-07-27T16:23:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>タンパク質の機能分類における機械学習比較分析（Protein Classification using Machine Learning and Statistical Techniques: A Comparative Analysis）</news:title>
   <news:publication_date>2026-07-27T16:23:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716567</loc>
  <lastmod>2026-07-27T16:23:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>指数重み付けによるニューラルネットワークの頑健な透かし（Robust Watermarking of Neural Network with Exponential Weighting）</news:title>
   <news:publication_date>2026-07-27T16:23:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716565</loc>
  <lastmod>2026-07-27T16:23:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>バックボーンは一度に全部学習できない：事前学習ネットワークへのロールバックによる人物再識別（Backbone Can Not be Trained at Once: Rolling Back to Pre-trained Network for Person Re-identiﬁcation）</news:title>
   <news:publication_date>2026-07-27T16:23:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716563</loc>
  <lastmod>2026-07-27T16:22:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>触覚データをグラフで扱う把持安定性予測（TactileGCN: A Graph Convolutional Network for Predicting Grasp Stability with Tactile Sensors）</news:title>
   <news:publication_date>2026-07-27T16:22:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716561</loc>
  <lastmod>2026-07-27T15:31:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非凸長方行列の補完手法と実運用への示唆（Nonconvex Rectangular Matrix Completion via Gradient Descent without ℓ2,∞Regularization）</news:title>
   <news:publication_date>2026-07-27T15:31:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716559</loc>
  <lastmod>2026-07-27T15:31:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数手がかりとスイッチャー認識分類によるマルチオブジェクト追跡（Multi-Object Tracking with Multiple Cues and Switcher-Aware Classification）</news:title>
   <news:publication_date>2026-07-27T15:31:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716557</loc>
  <lastmod>2026-07-27T15:31:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>コールドスタートのプレイリスト推薦を統一的に扱う多タスク学習（Cold-start Playlist Recommendation with Multitask Learning）</news:title>
   <news:publication_date>2026-07-27T15:31:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716555</loc>
  <lastmod>2026-07-27T15:30:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SuperNNovaによる光度データのみでの超新星分類（SuperNNova: an open-source framework for Bayesian, Neural Network based supernova classification）</news:title>
   <news:publication_date>2026-07-27T15:30:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716553</loc>
  <lastmod>2026-07-27T15:29:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>エッジ強調損失を用いた動的MRイメージングのためのカスケード残差密集ネットワーク（Cascaded Residual Dense Networks for Dynamic MR Imaging with Edge-enhanced Loss Constraint）</news:title>
   <news:publication_date>2026-07-27T15:29:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716551</loc>
  <lastmod>2026-07-27T15:29:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単一細胞データの大爆発と深層学習の切り札（Single cell data explosion: Deep learning to the rescue）</news:title>
   <news:publication_date>2026-07-27T15:29:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716549</loc>
  <lastmod>2026-07-27T15:28:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>バイオ医療文献における半教師あり変分オートエンコーダによる関係抽出（Exploring Semi-supervised Variational Autoencoders for Biomedical Relation Extraction）</news:title>
   <news:publication_date>2026-07-27T15:28:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
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