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   <news:title>CTからMRを合成する深層学習：ペアデータと非ペアデータを併用するアプローチ（Deep CT to MR Synthesis using Paired and Unpaired Data）</news:title>
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    <news:language>ja</news:language>
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   <news:title>パブロフ条件付けにおける行動安定性と個体差の拡張モデル（Behavior stability and individual differences in Pavlovian extended conditioning）</news:title>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>小天体周回の安定周期軌道の設計（Stable Periodic Orbits for Spacecrafts around Minor Celestial Bodies）</news:title>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>Deep Trustworthy Knowledge Tracing（Deep Trustworthy Knowledge Tracing）</news:title>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>活動的小惑星358Pの核の観測と回転性の検証（Nucleus of active asteroid 358P/Pan-STARRS (P/2012 T1)）</news:title>
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   <news:genres>Blog</news:genres>
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    <news:language>ja</news:language>
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   <news:title>GenAttack：勾配情報を使わない実践的ブラックボックス攻撃（GenAttack: Practical Black-box Attacks with Gradient-Free Optimization）</news:title>
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   <news:title>高次特徴空間を制約して知識を保持する継続学習（Keep and Learn: Continual Learning by Constraining the Latent Space for Knowledge Preservation in Neural Networks）</news:title>
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    <news:language>ja</news:language>
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   <news:title>深層畳み込みニューラルネットワークの普遍性（Universality of Deep Convolutional Neural Networks）</news:title>
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   <news:genres>Blog</news:genres>
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  <lastmod>2026-05-10T05:59:13Z</lastmod>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>オブジェクトレベル表現による少ショット画像分類（Object-Level Representation Learning for Few-Shot Image Classification）</news:title>
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   <news:genres>Blog</news:genres>
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  <lastmod>2026-05-10T05:07:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>深層畳み込みニューラルネットワークの一般化と最適化性能の理解（Understanding Generalization and Optimization Performance of Deep CNNs）</news:title>
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   <news:genres>Blog</news:genres>
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    <news:language>ja</news:language>
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   <news:title>CNN特徴マップの解像度を効率的に改善するマルチサンプリング（Improving the Resolution of CNN Feature Maps Efficiently with Multisampling）</news:title>
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   <news:genres>Blog</news:genres>
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  <lastmod>2026-05-10T05:07:03Z</lastmod>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>インスタンス認識型物体検出と決定点過程による重なり物体の改善（Learning Instance-Aware Object Detection Using Determinantal Point Processes）</news:title>
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   <news:genres>Blog</news:genres>
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  <lastmod>2026-05-10T05:06:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>超音波画像における深層敵対的文脈認識ランドマーク検出（Deep Adversarial Context-Aware Landmark Detection for Ultrasound Imaging）</news:title>
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   <news:genres>Blog</news:genres>
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  <lastmod>2026-05-10T05:06:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>Dual Policy Iteration（Dual Policy Iteration）</news:title>
   <news:publication_date>2026-05-10T05:06:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-05-10T05:06:03Z</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>局所的な次元で群を分ける手法の要点（Clustering by latent dimensions）</news:title>
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   <news:genres>Blog</news:genres>
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  <lastmod>2026-05-10T05:05:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>未来フレーム予測で学習したニューラルネットワークは生物学的ニューロン応答と知覚の重要特性を模倣する（A neural network trained to predict future video frames mimics critical properties of biological neuronal responses and perception）</news:title>
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   <news:genres>Blog</news:genres>
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  <lastmod>2026-05-10T04:15:06Z</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>EMRを解きほぐす可視解析と解釈可能なRNNの設計（RetainVis / RetainEX）</news:title>
   <news:publication_date>2026-05-10T04:15:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-05-10T04:14:52Z</lastmod>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>ユーザーを深く知る：複数Eコマースタスクからの普遍的ユーザー表現（Perceive Your Users in Depth: Learning Universal User Representations from Multiple E-commerce Tasks）</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>LHCにおけるRパリティ破れ超対称性探索の深層学習（Deep learning for the R-parity violating supersymmetry searches at the LHC）</news:title>
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   <news:genres>Blog</news:genres>
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  <lastmod>2026-05-10T04:14:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>大規模天文データにおける複雑天体の自動検出法（Identifying Complex Sources in Large Astronomical Data Using a Coarse-Grained Complexity Measure）</news:title>
   <news:publication_date>2026-05-10T04:14:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-05-10T04:14:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>パーシステンス図から平面グラフを再構築する手法（Learning Simplicial Complexes from Persistence Diagrams）</news:title>
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   <news:genres>Blog</news:genres>
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  <lastmod>2026-05-10T04:14:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>判別器特徴を再利用した潜在推定（Discriminator Feature-based Inference by Recycling the Discriminator of GANs）</news:title>
   <news:publication_date>2026-05-10T04:14:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-05-10T04:13:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的ニューラルネットワークの微分学習則への架け橋（From statistical inference to a differential learning rule for stochastic neural networks）</news:title>
   <news:publication_date>2026-05-10T04:13:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-05-10T03:22:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次元における戦略耐性線形回帰（Strategyproof Linear Regression in High Dimensions）</news:title>
   <news:publication_date>2026-05-10T03:22:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-05-10T03:21:33Z</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コンパクトで計算効率の高い表現（Compact and Computationally Efficient Representation of Deep Neural Networks）</news:title>
   <news:publication_date>2026-05-10T03:21:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/688463</loc>
  <lastmod>2026-05-10T03:21:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Batch Normalizationの指数的収束率（Exponential convergence rates for Batch Normalization）</news:title>
   <news:publication_date>2026-05-10T03:21:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
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 <url>
  <loc>https://aibr.jp/archives/688461</loc>
  <lastmod>2026-05-10T03:20:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>パルスと撮像を協調させるSPIN（A Synergized Pulsing‑Imaging Network）</news:title>
   <news:publication_date>2026-05-10T03:20:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/688459</loc>
  <lastmod>2026-05-10T03:20:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>変形の敵対的正則化による画像レジストレーション学習（Adversarial Deformation Regularization for Training Image Registration Neural Networks）</news:title>
   <news:publication_date>2026-05-10T03:20:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-05-10T03:20:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>法的文書検索における文書ベクトル埋め込みと深層学習（Legal Document Retrieval using Document Vector Embeddings and Deep Learning）</news:title>
   <news:publication_date>2026-05-10T03:20:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-05-10T03:20:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>余補完だが非補完なアーベル圏の構成（A COCOMPLETE BUT NOT COMPLETE ABELIAN CATEGORY）</news:title>
   <news:publication_date>2026-05-10T03:20:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
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  <loc>https://aibr.jp/archives/688453</loc>
  <lastmod>2026-05-10T02:29:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GAN全体を利用した敵対的サンプル防御（Defending Against Adversarial Samples Using An Entire GAN）</news:title>
   <news:publication_date>2026-05-10T02:29:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688451</loc>
  <lastmod>2026-05-10T02:29:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ロバスト強化学習のためのフィンガープリント方策最適化（Fingerprint Policy Optimisation for Robust Reinforcement Learning）</news:title>
   <news:publication_date>2026-05-10T02:29:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688449</loc>
  <lastmod>2026-05-10T02:28:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>気候変動が導く欧州の最適再生可能電力網の変化（The impact of climate change on a cost-optimal highly renewable European electricity network）</news:title>
   <news:publication_date>2026-05-10T02:28:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688447</loc>
  <lastmod>2026-05-10T02:28:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Fog-RANにおける異種遅延要件を持つIoT向け強化学習ベースの資源割当（Reinforcement Learning-based Resource Allocation in Fog RAN for IoT with Heterogeneous Latency Requirements）</news:title>
   <news:publication_date>2026-05-10T02:28:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688445</loc>
  <lastmod>2026-05-10T02:28:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Contextual Graph Markov Modelの解説（Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing）</news:title>
   <news:publication_date>2026-05-10T02:28:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688443</loc>
  <lastmod>2026-05-10T02:28:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>IoTによる省エネ建物管理の統合プラットフォーム（IoT for Green Building Management）</news:title>
   <news:publication_date>2026-05-10T02:28:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688441</loc>
  <lastmod>2026-05-10T02:27:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Anderson加速を用いた高速K平均クラスタリング (Fast K-Means Clustering with Anderson Acceleration)</news:title>
   <news:publication_date>2026-05-10T02:27:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688439</loc>
  <lastmod>2026-05-10T01:37:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>混雑場面における異常検知と局所化（Anomaly Detection and Localization in Crowded Scenes by Motion-field Shape Description and Similarity-based Statistical Learning）</news:title>
   <news:publication_date>2026-05-10T01:37:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688437</loc>
  <lastmod>2026-05-10T01:37:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>人間のバンディットフィードバックで学ぶ翻訳強化（Reliability and Learnability of Human Bandit Feedback for Sequence-to-Sequence Reinforcement Learning）</news:title>
   <news:publication_date>2026-05-10T01:37:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688435</loc>
  <lastmod>2026-05-10T01:36:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ソーシャルセンサーによる人身取引の把握と監視（Understanding and Monitoring Human Trafficking via Social Sensors: A Sociological Approach）</news:title>
   <news:publication_date>2026-05-10T01:36:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688433</loc>
  <lastmod>2026-05-10T01:36:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所情報基準による動的システムのモデル選択（A Local Information Criterion for Dynamical Systems）</news:title>
   <news:publication_date>2026-05-10T01:36:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688431</loc>
  <lastmod>2026-05-10T01:36:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>監視カメラ向けカスタム深層学習映像解析の実装と評価（Deployment of Customized Deep Learning based Video Analytics On Surveillance Cameras）</news:title>
   <news:publication_date>2026-05-10T01:36:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688429</loc>
  <lastmod>2026-05-10T01:36:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ワッサースタイン不確実性集合を用いたロバスト仮説検定（Robust Hypothesis Testing Using Wasserstein Uncertainty Sets）</news:title>
   <news:publication_date>2026-05-10T01:36:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688427</loc>
  <lastmod>2026-05-10T01:35:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>部分観測からの動的ネットワークモデル（Dynamic Network Model from Partial Observations）</news:title>
   <news:publication_date>2026-05-10T01:35:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688425</loc>
  <lastmod>2026-05-10T00:44:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層的に解釈可能な潜在表現を学ぶDTLC-GAN（Generative Adversarial Image Synthesis with Decision Tree Latent Controller）</news:title>
   <news:publication_date>2026-05-10T00:44:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688423</loc>
  <lastmod>2026-05-10T00:43:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>予測のための意味的説明（Semantic Explanations of Predictions）</news:title>
   <news:publication_date>2026-05-10T00:43:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688421</loc>
  <lastmod>2026-05-10T00:43:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>化学物質―疾病関係抽出における文字基盤単語埋め込みの有効性（Convolutional neural networks for chemical-disease relation extraction are improved with character-based word embeddings）</news:title>
   <news:publication_date>2026-05-10T00:43:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688419</loc>
  <lastmod>2026-05-10T00:43:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ベイズ的量子回路の要点解説（Bayesian Quantum Circuit）</news:title>
   <news:publication_date>2026-05-10T00:43:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688417</loc>
  <lastmod>2026-05-10T00:42:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>滑らかで強凸な関数に対する頑健な加速勾配法（Robust Accelerated Gradient Methods for Smooth Strongly Convex Functions）</news:title>
   <news:publication_date>2026-05-10T00:42:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688415</loc>
  <lastmod>2026-05-10T00:42:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>検証指標を直接最適化する重み付き学習（Metric-Optimized Example Weights）</news:title>
   <news:publication_date>2026-05-10T00:42:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688413</loc>
  <lastmod>2026-05-10T00:42:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>部分的注釈で特徴を分離する技術（Dual Swap Disentangling）</news:title>
   <news:publication_date>2026-05-10T00:42:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688411</loc>
  <lastmod>2026-05-09T23:50:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時系列データの欠損値を双方向で学習して埋める手法（BRITS: Bidirectional Recurrent Imputation for Time Series）</news:title>
   <news:publication_date>2026-05-09T23:50:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688409</loc>
  <lastmod>2026-05-09T23:49:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>制約を学習する対話的枠組みが示す、ラベル負担の軽減（Adversarial Constraint Learning for Structured Prediction）</news:title>
   <news:publication_date>2026-05-09T23:49:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688407</loc>
  <lastmod>2026-05-09T23:49:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>通信効率と差分プライバシーを両立する分散SGD（cpSGD: Communication-efficient and differentially-private distributed SGD）</news:title>
   <news:publication_date>2026-05-09T23:49:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688405</loc>
  <lastmod>2026-05-09T23:49:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>小さなラベルから学ぶ深層学習の拡張手法（Transductive Label Augmentation for Improved Deep Network Learning）</news:title>
   <news:publication_date>2026-05-09T23:49:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688403</loc>
  <lastmod>2026-05-09T23:47:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DPW-SDNetによるJPEG圧縮画像のソフトデコーディング (Dual Pixel-Wavelet Domain Deep CNNs for Soft Decoding of JPEG-Compressed Images)</news:title>
   <news:publication_date>2026-05-09T23:47:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688401</loc>
  <lastmod>2026-05-09T23:47:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>楽譜上の小さな記号を捉える深層ウォーターシェッド検出器（Deep Watershed Detector for Music Object Recognition）</news:title>
   <news:publication_date>2026-05-09T23:47:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688399</loc>
  <lastmod>2026-05-09T23:47:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層的表現学習による親族認証（Hierarchical Representation Learning for Kinship Verification）</news:title>
   <news:publication_date>2026-05-09T23:47:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688397</loc>
  <lastmod>2026-05-09T22:55:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文と問いの依存性を読む技術が示す実務価値（Dependent Gated Reading for Cloze-Style Question Answering）</news:title>
   <news:publication_date>2026-05-09T22:55:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688395</loc>
  <lastmod>2026-05-09T22:54:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>教師なし学習とSUREによる画像復元の再考（Unsupervised Learning with Stein’s Unbiased Risk Estimator）</news:title>
   <news:publication_date>2026-05-09T22:54:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688393</loc>
  <lastmod>2026-05-09T22:53:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>主観的空間・時間パターンの代数表現（Algebraic Expression of Subjective Spatial and Temporal Patterns）</news:title>
   <news:publication_date>2026-05-09T22:53:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688391</loc>
  <lastmod>2026-05-09T22:52:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>並列逐次パターンマイニングの総覧（A Survey of Parallel Sequential Pattern Mining）</news:title>
   <news:publication_date>2026-05-09T22:52:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688389</loc>
  <lastmod>2026-05-09T22:52:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>アイスホッケーにおける文脈対応型選手評価を実現する深層強化学習（Deep Reinforcement Learning in Ice Hockey for Context-Aware Player Evaluation）</news:title>
   <news:publication_date>2026-05-09T22:52:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688387</loc>
  <lastmod>2026-05-09T22:52:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層畳み込みガウス過程の較正（Calibrating Deep Convolutional Gaussian Processes）</news:title>
   <news:publication_date>2026-05-09T22:52:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688385</loc>
  <lastmod>2026-05-09T22:52:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>仮想教室の未来：既存機能で対面の限界を超える（The Future of Virtual Classroom: Using Existing Features to Move Beyond Traditional Classroom Limitations）</news:title>
   <news:publication_date>2026-05-09T22:52:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688383</loc>
  <lastmod>2026-05-09T22:00:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>バンド絶縁体のトポロジカル不変量を深層学習で学習する（Deep Learning Topological Invariants of Band Insulators）</news:title>
   <news:publication_date>2026-05-09T22:00:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688381</loc>
  <lastmod>2026-05-09T21:59:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>遠隔音声認識における自動コンテキスト窓構成（Automatic context window composition for distant speech recognition）</news:title>
   <news:publication_date>2026-05-09T21:59:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/688379</loc>
  <lastmod>2026-05-09T21:59:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>太陽フレアの典型的Mg IIスペクトルを機械学習で同定する（IDENTIFYING TYPICAL MG II FLARE SPECTRA USING MACHINE LEARNING）</news:title>
   <news:publication_date>2026-05-09T21:59:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688377</loc>
  <lastmod>2026-05-09T21:58:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ハイパーボリック空間での安定な測地線更新とPoincaré埋め込みへの応用 (Stable Geodesic Update on Hyperbolic Space and its Application to Poincaré Embeddings)</news:title>
   <news:publication_date>2026-05-09T21:58:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688375</loc>
  <lastmod>2026-05-09T21:58:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DAMA/LIBRA–phase2の独立検証的結果（First model independent results from DAMA/LIBRA–phase2）</news:title>
   <news:publication_date>2026-05-09T21:58:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688373</loc>
  <lastmod>2026-05-09T21:57:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スプレッドシートの「匂い」を組み合わせて不具合を予測する方法（Combining Spreadsheet Smells for Improved Fault Prediction）</news:title>
   <news:publication_date>2026-05-09T21:57:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688371</loc>
  <lastmod>2026-05-09T21:56:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所目標伝播に基づく生物学的動機付けアルゴリズム（Biologically Motivated Algorithms for Propagating Local Target Representations）</news:title>
   <news:publication_date>2026-05-09T21:56:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688369</loc>
  <lastmod>2026-05-09T21:05:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>航空画像・映像からの車両インスタンス分割（Vehicle Instance Segmentation from Aerial Image and Video Using a Multi-Task Learning Residual Fully Convolutional Network）</news:title>
   <news:publication_date>2026-05-09T21:05:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688367</loc>
  <lastmod>2026-05-09T21:05:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Apache SAMOA によるストリーム学習の大規模化（Large-Scale Learning from Data Streams with Apache SAMOA）</news:title>
   <news:publication_date>2026-05-09T21:05:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688365</loc>
  <lastmod>2026-05-09T21:04:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ワンレイヤーニューラルネットワークに基づく非線形帰納行列補完（Nonlinear Inductive Matrix Completion based on One-layer Neural Networks）</news:title>
   <news:publication_date>2026-05-09T21:04:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688363</loc>
  <lastmod>2026-05-09T21:03:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>FPGA上でのCNN推論高速化サーベイ（Accelerating CNN inference on FPGAs: A Survey）</news:title>
   <news:publication_date>2026-05-09T21:03:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688361</loc>
  <lastmod>2026-05-09T21:03:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>リウェイテッド・ウェイクスリープによる確率的制御フローモデル学習の再検討 (Revisiting Reweighted Wake-Sleep for Models with Stochastic Control Flow)</news:title>
   <news:publication_date>2026-05-09T21:03:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688359</loc>
  <lastmod>2026-05-09T21:02:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的勾配降下法の正則化的性質（On the Regularizing Property of Stochastic Gradient Descent）</news:title>
   <news:publication_date>2026-05-09T21:02:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688357</loc>
  <lastmod>2026-05-09T21:02:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>顔認識に強いL1-2D2PCANet（L1-2D2PCANet: A Deep Learning Network for Face Recognition）</news:title>
   <news:publication_date>2026-05-09T21:02:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688355</loc>
  <lastmod>2026-05-09T20:10:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>知識グラフ埋め込みからオントロジー埋め込みへ（From Knowledge Graph Embedding to Ontology Embedding?）</news:title>
   <news:publication_date>2026-05-09T20:10:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688353</loc>
  <lastmod>2026-05-09T20:10:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層学習の幾何学的理解（Geometric Understanding of Deep Learning）</news:title>
   <news:publication_date>2026-05-09T20:10:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688351</loc>
  <lastmod>2026-05-09T20:10:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>KDD Cup 99 の徹底前処理と機械学習による侵入検知性能評価（Intensive Preprocessing of KDD Cup 99 for Network Intrusion Classification Using Machine Learning Techniques）</news:title>
   <news:publication_date>2026-05-09T20:10:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688349</loc>
  <lastmod>2026-05-09T20:08:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>象とロバとコロネル・ブロット：政治的言説をデータで見る新しい枠組み（Elephants, Donkeys, and Colonel Blotto）</news:title>
   <news:publication_date>2026-05-09T20:08:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688347</loc>
  <lastmod>2026-05-09T20:08:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>球面上の多チャンネル疎ブラインドデコンボリューション（Multichannel Sparse Blind Deconvolution on the Sphere）</news:title>
   <news:publication_date>2026-05-09T20:08:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688345</loc>
  <lastmod>2026-05-09T20:08:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>明るいF型星を周回する膨張ホット・ジュピターの発見（NGTS-2b: An inflated hot-Jupiter transiting a bright F-dwarf）</news:title>
   <news:publication_date>2026-05-09T20:08:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688343</loc>
  <lastmod>2026-05-09T20:07:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>顔画像からの精密年齢推定とAttention LSTM（Fine-Grained Age Estimation in the Wild with Attention LSTM Networks）</news:title>
   <news:publication_date>2026-05-09T20:07:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688341</loc>
  <lastmod>2026-05-09T19:15:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ソースコード識別子の分割における双方向LSTMの応用（Splitting source code identifiers using Bidirectional LSTM Recurrent Neural Network）</news:title>
   <news:publication_date>2026-05-09T19:15:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688339</loc>
  <lastmod>2026-05-09T19:15:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>データ市場におけるモデルベース価格設定（Model-based Pricing for Machine Learning in a Data Marketplace）</news:title>
   <news:publication_date>2026-05-09T19:15:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688337</loc>
  <lastmod>2026-05-09T19:14:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>模倣と強化の組合せで速やかに学習する方法（Fast Policy Learning through Imitation and Reinforcement）</news:title>
   <news:publication_date>2026-05-09T19:14:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688335</loc>
  <lastmod>2026-05-09T19:13:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Huberのϵ汚染モデル下におけるロバスト非パラメトリック回帰（Robust Nonparametric Regression under Huber’s ϵ-contamination Model）</news:title>
   <news:publication_date>2026-05-09T19:13:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688333</loc>
  <lastmod>2026-05-09T19:13:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>畳み込み層の特異値の解析（The Singular Values of Convolutional Layers）</news:title>
   <news:publication_date>2026-05-09T19:13:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688331</loc>
  <lastmod>2026-05-09T19:13:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>地図種類の自動判別における深層畳み込みニューラルネットワークの実用性（Deep Convolutional Neural Networks for Map-Type Classification）</news:title>
   <news:publication_date>2026-05-09T19:13:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688329</loc>
  <lastmod>2026-05-09T19:12:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>半教師ありディープカーネル学習：予測分散を用いた回帰の実務的示唆 (Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive Variance)</news:title>
   <news:publication_date>2026-05-09T19:12:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688327</loc>
  <lastmod>2026-05-09T18:21:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>信頼できるIoTのための教師なし学習（Unsupervised Learning for Trustworthy IoT）</news:title>
   <news:publication_date>2026-05-09T18:21:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688325</loc>
  <lastmod>2026-05-09T18:20:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>頭頸部がんに対する3次元放射線治療線量予測の新アーキテクチャ（Three-Dimensional Radiotherapy Dose Prediction on Head and Neck Cancer Patients with a Hierarchically Densely Connected U-net Deep Learning Architecture）</news:title>
   <news:publication_date>2026-05-09T18:20:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688323</loc>
  <lastmod>2026-05-09T18:19:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散確率的勾配トラッキング手法の要点（Distributed Stochastic Gradient Tracking Methods）</news:title>
   <news:publication_date>2026-05-09T18:19:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688321</loc>
  <lastmod>2026-05-09T18:19:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Reddit AMAにおける質問の有効性の研究（A Study of Question Effectiveness Using Reddit “Ask Me Anything” Threads）</news:title>
   <news:publication_date>2026-05-09T18:19:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688319</loc>
  <lastmod>2026-05-09T18:19:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>需要変動に反応するコンバージョン率予測（Reacting to Variations in Product Demand: An Application for Conversion Rate (CR) Prediction in Sponsored Search）</news:title>
   <news:publication_date>2026-05-09T18:19:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688317</loc>
  <lastmod>2026-05-09T18:18:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>プライバシーポリシーの曖昧さを機械で読む（Modeling Language Vagueness in Privacy Policies using Deep Neural Networks）</news:title>
   <news:publication_date>2026-05-09T18:18:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688315</loc>
  <lastmod>2026-05-09T18:17:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>質問注目型報酬による強化抽出型要約（Reinforced Extractive Summarization with Question-Focused Rewards）</news:title>
   <news:publication_date>2026-05-09T18:17:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688313</loc>
  <lastmod>2026-05-09T17:26:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>不確実性を考慮した大規模距離計量学習（Large-scale Distance Metric Learning with Uncertainty）</news:title>
   <news:publication_date>2026-05-09T17:26:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688311</loc>
  <lastmod>2026-05-09T17:26:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>エルゴディック推論：最適化による収束の加速（Ergodic Inference: Accelerate Convergence by Optimisation）</news:title>
   <news:publication_date>2026-05-09T17:26:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688309</loc>
  <lastmod>2026-05-09T17:25:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的ブロックモデルによる勾配コーディング（Gradient Coding via the Stochastic Block Model）</news:title>
   <news:publication_date>2026-05-09T17:25:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688307</loc>
  <lastmod>2026-05-09T17:25:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>少ない方が強い：腹部超音波画像の同時ビュー分類とランドマーク検出（Less is More: Simultaneous View Classification and Landmark Detection for Abdominal Ultrasound Images）</news:title>
   <news:publication_date>2026-05-09T17:25:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688305</loc>
  <lastmod>2026-05-09T17:24:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>安定な再帰モデルが示す実務的示唆（Stable Recurrent Models）</news:title>
   <news:publication_date>2026-05-09T17:24:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688303</loc>
  <lastmod>2026-05-09T17:24:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>メタラーニングで読み解くシンボリック回帰ベンチマークの実態（Analysing Symbolic Regression Benchmarks under a Meta-Learning Approach）</news:title>
   <news:publication_date>2026-05-09T17:24:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688301</loc>
  <lastmod>2026-05-09T17:24:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ゼロ次の確率的分散削減法が拓くブラックボックス最適化の高速化（Zeroth-Order Stochastic Variance Reduction for Nonconvex Optimization）</news:title>
   <news:publication_date>2026-05-09T17:24:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688299</loc>
  <lastmod>2026-05-09T16:33:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>生成的敵対的ネットワークで偽レビューを見抜く（Detecting Deceptive Reviews using Generative Adversarial Networks）</news:title>
   <news:publication_date>2026-05-09T16:33:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688297</loc>
  <lastmod>2026-05-09T16:32:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Self-Netによる継続学習と自己モデル化の実装（Self-Net: Lifelong Learning via Continual Self-Modeling）</news:title>
   <news:publication_date>2026-05-09T16:32:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688295</loc>
  <lastmod>2026-05-09T16:32:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>顔と体の形状が示す身長推定の可能性（What Face and Body Shapes Can Tell About Height）</news:title>
   <news:publication_date>2026-05-09T16:32:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688293</loc>
  <lastmod>2026-05-09T16:32:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>同時非対称テンソル分解の理論的保証（Guaranteed Simultaneous Asymmetric Tensor Decomposition via Orthogonalized Alternating Least Squares）</news:title>
   <news:publication_date>2026-05-09T16:32:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688291</loc>
  <lastmod>2026-05-09T16:31:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>テンソリアルニューラルネットワークの考え方と実務的意義 (Tensorial Neural Networks: Generalization of Neural Networks and Application to Model Compression)</news:title>
   <news:publication_date>2026-05-09T16:31:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688289</loc>
  <lastmod>2026-05-09T16:31:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>差分プライバシー対応のエンドツーエンドLDA（An end-to-end Differentially Private Latent Dirichlet Allocation Using a Spectral Algorithm）</news:title>
   <news:publication_date>2026-05-09T16:31:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688287</loc>
  <lastmod>2026-05-09T16:31:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>カリキュラム学習による音声感情認識の効率化（Curriculum Learning for Speech Emotion Recognition from Crowdsourced Labels）</news:title>
   <news:publication_date>2026-05-09T16:31:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688285</loc>
  <lastmod>2026-05-09T15:40:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>人間の意思決定の精度と公平性を高める方法 (Enhancing the Accuracy and Fairness of Human Decision Making)</news:title>
   <news:publication_date>2026-05-09T15:40:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688283</loc>
  <lastmod>2026-05-09T15:30:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ゼロショット双方向機械翻訳（Zero-Shot Dual Machine Translation）</news:title>
   <news:publication_date>2026-05-09T15:30:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688281</loc>
  <lastmod>2026-05-09T15:30:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>絵文字予測におけるアンサンブル学習とオーバーサンプリング（Duluth UROP at SemEval-2018 Task 2: Multilingual Emoji Prediction with Ensemble Learning and Oversampling）</news:title>
   <news:publication_date>2026-05-09T15:30:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688279</loc>
  <lastmod>2026-05-09T15:30:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自己模倣による多様な方策の学習（Learning Self-Imitating Diverse Policies）</news:title>
   <news:publication_date>2026-05-09T15:30:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688277</loc>
  <lastmod>2026-05-09T15:29:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模アジャイル開発における学習の実態（Learning in the Large - An Exploratory Study of Retrospectives in Large-Scale Agile Development）</news:title>
   <news:publication_date>2026-05-09T15:29:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688275</loc>
  <lastmod>2026-05-09T15:29:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>薄膜III–V量子ドット太陽電池の光捕捉強化（Light-Trapping Enhanced Thin-Film III-V Quantum Dot Solar Cells Fabricated by Epitaxial Lift-Off）</news:title>
   <news:publication_date>2026-05-09T15:29:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688273</loc>
  <lastmod>2026-05-09T15:28:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>天文学における転移学習を用いた銀河合体検出（Using transfer learning to detect galaxy mergers）</news:title>
   <news:publication_date>2026-05-09T15:28:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688271</loc>
  <lastmod>2026-05-09T14:38:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>起業家と投資家のマッチングツールと研究（Matching Startup Founders to Investors: a Tool and a Study）</news:title>
   <news:publication_date>2026-05-09T14:38:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688269</loc>
  <lastmod>2026-05-09T14:37:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>影響度最大化による制限付きボルツマンマシンの学習（Learning Restricted Boltzmann Machines via Influence Maximization）</news:title>
   <news:publication_date>2026-05-09T14:37:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688267</loc>
  <lastmod>2026-05-09T14:37:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>検証可能な学習者を学習済み検証器で訓練する（Training Verified Learners with Learned Verifiers）</news:title>
   <news:publication_date>2026-05-09T14:37:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688265</loc>
  <lastmod>2026-05-09T14:37:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模繊維検出と追跡の教師なし学習（Unsupervised Learning for Large-Scale Fiber Detection and Tracking in Microscopic Material Images）</news:title>
   <news:publication_date>2026-05-09T14:37:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688263</loc>
  <lastmod>2026-05-09T14:36:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>並列アーキテクチャとハイパーパラメータ探索の効率化（Parallel Architecture and Hyperparameter Search via Successive Halving and Classification）</news:title>
   <news:publication_date>2026-05-09T14:36:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688261</loc>
  <lastmod>2026-05-09T14:36:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スケーラブルで頑健なコミュニティ検出（Scalable and Robust Community Detection With Randomized Sketching）</news:title>
   <news:publication_date>2026-05-09T14:36:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688259</loc>
  <lastmod>2026-05-09T14:36:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スケール空間分解による内在画像変換（Intrinsic Image Transformation via Scale Space Decomposition）</news:title>
   <news:publication_date>2026-05-09T14:36:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688257</loc>
  <lastmod>2026-05-09T13:45:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>どれだけのRestricted Isometryが非凸行列復元に必要か（How Much Restricted Isometry is Needed In Nonconvex Matrix Recovery?）</news:title>
   <news:publication_date>2026-05-09T13:45:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688255</loc>
  <lastmod>2026-05-09T13:45:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>少量注釈で伝播するセグメンテーション（Few-Shot Segmentation Propagation with Guided Networks）</news:title>
   <news:publication_date>2026-05-09T13:45:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688253</loc>
  <lastmod>2026-05-09T13:45:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフオラクルモデル、下界、および並列確率的最適化のギャップ（Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization）</news:title>
   <news:publication_date>2026-05-09T13:45:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688251</loc>
  <lastmod>2026-05-09T13:44:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>皮膚病変領域分割の実務的進化：SLSDeepの要点と導入観点（SLSDeep: Skin Lesion Segmentation Based on Dilated Residual and Pyramid Pooling Networks）</news:title>
   <news:publication_date>2026-05-09T13:44:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688249</loc>
  <lastmod>2026-05-09T13:44:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチビュー学習における重み付き多数決をBregman発散最小化で学ぶ（Multiview Learning of Weighted Majority Vote by Bregman Divergence Minimization）</news:title>
   <news:publication_date>2026-05-09T13:44:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688247</loc>
  <lastmod>2026-05-09T13:44:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>COREclust: 複雑データの代表変数を頑健かつスケール可能に選ぶ手法（COREclust: a new package for a robust and scalable analysis of complex data）</news:title>
   <news:publication_date>2026-05-09T13:44:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688245</loc>
  <lastmod>2026-05-09T13:44:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>逐次指示を単一ステップ報酬観測で行動へ対応付ける（Situated Mapping of Sequential Instructions to Actions with Single-step Reward Observation）</news:title>
   <news:publication_date>2026-05-09T13:44:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688243</loc>
  <lastmod>2026-05-09T12:52:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>条件付き生成対向ネットワークによる乳房腫瘤セグメンテーションと形状分類（Conditional Generative Adversarial and Convolutional Networks for X-ray Breast Mass Segmentation and Shape Classification）</news:title>
   <news:publication_date>2026-05-09T12:52:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688241</loc>
  <lastmod>2026-05-09T12:52:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>感情の構造を探るマルチモーダル感情分析（Multimodal Sentiment Analysis To Explore the Structure of Emotions）</news:title>
   <news:publication_date>2026-05-09T12:52:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688239</loc>
  <lastmod>2026-05-09T12:52:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>FastICAにおけるエントロピー推定の落とし穴（On the Estimation of Entropy in the FastICA Algorithm）</news:title>
   <news:publication_date>2026-05-09T12:52:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688237</loc>
  <lastmod>2026-05-09T12:51:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>獲得関数を最大化する手法（Maximizing acquisition functions for Bayesian optimization）</news:title>
   <news:publication_date>2026-05-09T12:51:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688235</loc>
  <lastmod>2026-05-09T12:51:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>計算的制約が招く敵対的事例の脆弱性（Adversarial examples from computational constraints）</news:title>
   <news:publication_date>2026-05-09T12:51:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688233</loc>
  <lastmod>2026-05-09T12:51:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>MRSIにおける代謝物定量を機械学習で行う意義（Quantification of Metabolites in Magnetic Resonance Spectroscopic Imaging using Machine Learning）</news:title>
   <news:publication_date>2026-05-09T12:51:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688231</loc>
  <lastmod>2026-05-09T12:51:12Z</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の8ビット学習のためのスケーラブル手法（Scalable Methods for 8-bit Training of Neural Networks）</news:title>
   <news:publication_date>2026-05-09T12:51:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688229</loc>
  <lastmod>2026-05-09T12:00:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>組み込み型プライバシー重視の音声理解プラットフォーム（Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces）</news:title>
   <news:publication_date>2026-05-09T12:00:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688227</loc>
  <lastmod>2026-05-09T11:59:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>英語→日本語機械翻訳のための再帰型ニューラルネットワークによる事前並べ替え（Recursive Neural Network Based Preordering for English-to-Japanese Machine Translation）</news:title>
   <news:publication_date>2026-05-09T11:59:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688225</loc>
  <lastmod>2026-05-09T11:58:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>個別影響推定（Personalized Influence Estimation Technique）</news:title>
   <news:publication_date>2026-05-09T11:58:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688223</loc>
  <lastmod>2026-05-09T11:58:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多層ネットワーク埋め込みのスケーラブル手法（Multi-Net: A Scalable Multiplex Network Embedding Framework）</news:title>
   <news:publication_date>2026-05-09T11:58:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688221</loc>
  <lastmod>2026-05-09T11:57:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>メタマテリアルの逆設計を自動化する生成モデル（A Generative Model for Inverse Design of Metamaterials）</news:title>
   <news:publication_date>2026-05-09T11:57:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688219</loc>
  <lastmod>2026-05-09T11:57:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ピラミッド注意ネットワークによるセマンティックセグメンテーションの改善（Pyramid Attention Network for Semantic Segmentation）</news:title>
   <news:publication_date>2026-05-09T11:57:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688217</loc>
  <lastmod>2026-05-09T11:56:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>生涯学習的アプローチによる脳MRIセグメンテーションの横断的適応（A Lifelong Learning Approach to Brain MR Segmentation Across Scanners and Protocols）</news:title>
   <news:publication_date>2026-05-09T11:56:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688215</loc>
  <lastmod>2026-05-09T11:05:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動的参照フレームを用いた予測窓の創出（Futuristic Classification with Dynamic Reference Frame Strategy）</news:title>
   <news:publication_date>2026-05-09T11:05:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688213</loc>
  <lastmod>2026-05-09T11:05:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文脈を考慮するニューラル機械翻訳が照応解析（anaphora resolution）を学習する仕組み（Context-Aware Neural Machine Translation Learns Anaphora Resolution）</news:title>
   <news:publication_date>2026-05-09T11:05:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688211</loc>
  <lastmod>2026-05-09T11:05:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Airbnbの新規掲載物件の価格予測手法（Unravelling Airbnb: Predicting Price for New Listing）</news:title>
   <news:publication_date>2026-05-09T11:05:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688209</loc>
  <lastmod>2026-05-09T11:04:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非調和キャビティQED領域における分子オプトメカニクス（Molecular optomechanics in the anharmonic cavity-QED regime using hybrid metal-dielectric cavity modes）</news:title>
   <news:publication_date>2026-05-09T11:04:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688207</loc>
  <lastmod>2026-05-09T11:03:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層学習を統計モデルに組み込む新手法：DeepGLMとDeepGLMM（Bayesian Deep Net GLM and GLMM）</news:title>
   <news:publication_date>2026-05-09T11:03:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688205</loc>
  <lastmod>2026-05-09T11:03:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>物体志向の動的予測モデルが示した一般化の道（Object-Oriented Dynamics Predictor）</news:title>
   <news:publication_date>2026-05-09T11:03:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688203</loc>
  <lastmod>2026-05-09T11:03:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>視覚的推論で答えるAI：仮想イメージによる質問応答（Think Visually: Question Answering through Virtual Imagery）</news:title>
   <news:publication_date>2026-05-09T11:03:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688201</loc>
  <lastmod>2026-05-09T10:11:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>通信効率を劇的に下げる二重量子化（Double Quantization for Communication-Efﬁcient Distributed Optimization）</news:title>
   <news:publication_date>2026-05-09T10:11:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688199</loc>
  <lastmod>2026-05-09T10:11:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Relation Networksを用いたソース表現の洗練（Refining Source Representations with Relation Networks for Neural Machine Translation）</news:title>
   <news:publication_date>2026-05-09T10:11:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688197</loc>
  <lastmod>2026-05-09T10:10:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>補間から回帰へ：再構成アプローチ（The Reconstruction Approach: From Interpolation to Regression）</news:title>
   <news:publication_date>2026-05-09T10:10:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688195</loc>
  <lastmod>2026-05-09T10:09:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ダブルディープ・スパシオアングラー学習によるライトフィールド顔認識（Double-Deep Spatio-Angular Learning for Light Field Based Face Recognition）</news:title>
   <news:publication_date>2026-05-09T10:09:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688193</loc>
  <lastmod>2026-05-09T10:09:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数パスを用いた確率的勾配降下法の統計的最適性（Statistical Optimality of Stochastic Gradient Descent on Hard Learning Problems through Multiple Passes）</news:title>
   <news:publication_date>2026-05-09T10:09:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688191</loc>
  <lastmod>2026-05-09T10:09:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>欧州通貨統合の歴史から学ぶ教訓（Lessons from the History of European EMU）</news:title>
   <news:publication_date>2026-05-09T10:09:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688189</loc>
  <lastmod>2026-05-09T10:08:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>水中魚種分類における畳み込みニューラルネットワークと深層学習（Underwater Fish Species Classification using Convolutional Neural Network and Deep Learning）</news:title>
   <news:publication_date>2026-05-09T10:08:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688187</loc>
  <lastmod>2026-05-09T09:16:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>変化する環境下で学習するスライディングウィンドウ手法（A Sliding-Window Algorithm for Markov Decision Processes with Arbitrarily Changing Rewards and Transitions）</news:title>
   <news:publication_date>2026-05-09T09:16:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688185</loc>
  <lastmod>2026-05-09T09:16:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スパイラル銀河NGC 1560の恒星ハローの検出（THE STELLAR HALO OF THE SPIRAL GALAXY NGC 1560）</news:title>
   <news:publication_date>2026-05-09T09:16:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688183</loc>
  <lastmod>2026-05-09T09:16:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模多変量オーンシュタイン＝ウーレンベック脳結合のベイズ推定（Bayesian estimation for large scale multivariate Ornstein-Uhlenbeck model of brain connectivity）</news:title>
   <news:publication_date>2026-05-09T09:16:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688181</loc>
  <lastmod>2026-05-09T09:16:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的手法で降下保証を与えるICAアルゴリズム（Stochastic algorithms with descent guarantees for ICA）</news:title>
   <news:publication_date>2026-05-09T09:16:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688179</loc>
  <lastmod>2026-05-09T09:16:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>再構成可能なテラヘルツ四分の一波長板によるヘリシティ切替（Reconfigurable terahertz quarter-wave plate for helicity switching based on Babinet inversion of anisotropic checkerboard metasurface）</news:title>
   <news:publication_date>2026-05-09T09:16:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688177</loc>
  <lastmod>2026-05-09T09:15:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>構造役割を保つネットワーク埋め込み（struc2gauss: Structural Role Preserving Network Embedding via Gaussian Embedding）</news:title>
   <news:publication_date>2026-05-09T09:15:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688175</loc>
  <lastmod>2026-05-09T09:15:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習ユニット状態認識のためのマルチチャネルデータ融合（Learning Unit State Recognition Based on Multi-channel Data Fusion）</news:title>
   <news:publication_date>2026-05-09T09:15:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688173</loc>
  <lastmod>2026-05-09T08:24:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Zenoによる分散SGDの疑いベース耐故障性（Zeno: Distributed Stochastic Gradient Descent with Suspicion-based Fault-tolerance）</news:title>
   <news:publication_date>2026-05-09T08:24:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688171</loc>
  <lastmod>2026-05-09T08:24:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>酩酊顔のデータセットによる酔っぱらい検知（DIF: Dataset of Perceived Intoxicated Faces for Drunk Person Identification）</news:title>
   <news:publication_date>2026-05-09T08:24:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688169</loc>
  <lastmod>2026-05-09T08:24:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>心電気生理モデルの応答面が不連続な場合のガウス過程エミュレーション（Gaussian process emulation for discontinuous response surfaces with applications for cardiac electrophysiology models）</news:title>
   <news:publication_date>2026-05-09T08:24:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688167</loc>
  <lastmod>2026-05-09T08:23:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ラベル伝搬を学習する：少数ショット学習のための推論ネットワーク（Learning to Propagate Labels: Transductive Propagation Network for Few-Shot Learning）</news:title>
   <news:publication_date>2026-05-09T08:23:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688165</loc>
  <lastmod>2026-05-09T08:23:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>順序付き近傍グラフのカーネル（KONG: Kernels for ordered-neighborhood graphs）</news:title>
   <news:publication_date>2026-05-09T08:23:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688163</loc>
  <lastmod>2026-05-09T08:23:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ランダム射影と適格トレースを組み合わせたLSTDの有限サンプル解析（Finite Sample Analysis of LSTD with Random Projections and Eligibility Traces）</news:title>
   <news:publication_date>2026-05-09T08:23:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
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 <url>
  <loc>https://aibr.jp/archives/688161</loc>
  <lastmod>2026-05-09T08:22:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>環境音分類のためのマスク付き条件付きニューラルネットワーク（Masked Conditional Neural Networks for Environmental Sound Classification）</news:title>
   <news:publication_date>2026-05-09T08:22:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688159</loc>
  <lastmod>2026-05-09T07:31:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SOSAによるセンサー観測の軽量語彙（SOSA: A Lightweight Ontology for Sensors, Observations, Samples, and Actuators）</news:title>
   <news:publication_date>2026-05-09T07:31:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688157</loc>
  <lastmod>2026-05-09T07:22:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Virtual-Taobaoによる仮想化学習基盤の事業的インパクト（Virtual-Taobao: Virtualizing Real-world Online Retail Environment for Reinforcement Learning）</news:title>
   <news:publication_date>2026-05-09T07:22:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688155</loc>
  <lastmod>2026-05-09T07:22:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ライフロングドメイン単語埋め込みとメタラーニング（Lifelong Domain Word Embedding via Meta-Learning）</news:title>
   <news:publication_date>2026-05-09T07:22:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688153</loc>
  <lastmod>2026-05-09T07:22:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習に基づく安全な最適経路計画（Safe learning-based optimal motion planning for automated driving）</news:title>
   <news:publication_date>2026-05-09T07:22:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688151</loc>
  <lastmod>2026-05-09T07:21:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ランダムビニング特徴を用いた大規模スペクトルクラスタリングの高速化（Scalable Spectral Clustering Using Random Binning Features）</news:title>
   <news:publication_date>2026-05-09T07:21:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688149</loc>
  <lastmod>2026-05-09T07:21:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチドメイン芸術画像から学ぶ任意のスタイル転送（Learning from Multi-domain Artistic Images for Arbitrary Style Transfer）</news:title>
   <news:publication_date>2026-05-09T07:21:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688147</loc>
  <lastmod>2026-05-09T07:21:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフの“翻訳”で稀な事象を予測する（Deep Graph Translation）</news:title>
   <news:publication_date>2026-05-09T07:21:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688145</loc>
  <lastmod>2026-05-09T06:30:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散Cartesianべきグラフ分割によるグラフオン推定（Distributed Cartesian Power Graph Segmentation for Graphon Estimation）</news:title>
   <news:publication_date>2026-05-09T06:30:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688143</loc>
  <lastmod>2026-05-09T06:30:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>部分パートに基づく構造的サポート相関フィルタによる視覚追跡（Part-based Visual Tracking via Structural Support Correlation Filter）</news:title>
   <news:publication_date>2026-05-09T06:30:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688141</loc>
  <lastmod>2026-05-09T06:30:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>生体的報酬によるリスク回避型強化学習（Visceral Machines: Risk-Aversion in Reinforcement Learning with Intrinsic Physiological Rewards）</news:title>
   <news:publication_date>2026-05-09T06:30:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688139</loc>
  <lastmod>2026-05-09T06:29:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>通信効率化のための遅延集約勾配（Lazily Aggregated Gradient: LAG）</news:title>
   <news:publication_date>2026-05-09T06:29:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688137</loc>
  <lastmod>2026-05-09T06:29:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>より効率的な確率的分散学習：収束の高速化とスパース通信（Towards More Efficient Stochastic Decentralized Learning: Faster Convergence and Sparse Communication）</news:title>
   <news:publication_date>2026-05-09T06:29:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688135</loc>
  <lastmod>2026-05-09T06:29:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>事後サンプリングに基づくミオピック実験設計（Myopic Bayesian Design of Experiments via Posterior Sampling and Probabilistic Programming）</news:title>
   <news:publication_date>2026-05-09T06:29:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688133</loc>
  <lastmod>2026-05-09T06:28:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>調理状態認識のためのInceptionアーキテクチャ応用（Cooking State Recognition from Images Using Inception Architecture）</news:title>
   <news:publication_date>2026-05-09T06:28:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688131</loc>
  <lastmod>2026-05-09T05:37:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Meta Transfer Learning for Facial Emotion Recognition（Meta Transfer Learning for Facial Emotion Recognition）</news:title>
   <news:publication_date>2026-05-09T05:37:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688129</loc>
  <lastmod>2026-05-09T05:37:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層機能辞書による3Dモデルの一貫した意味構造学習（Deep Functional Dictionaries: Learning Consistent Semantic Structures on 3D Models from Functions）</news:title>
   <news:publication_date>2026-05-09T05:37:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688127</loc>
  <lastmod>2026-05-09T05:37:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Twitter上の乳がん治療経験の感情分析が示すもの（A Sentiment Analysis of Breast Cancer Treatment Experiences and Healthcare Perceptions Across Twitter）</news:title>
   <news:publication_date>2026-05-09T05:37:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688125</loc>
  <lastmod>2026-05-09T05:36:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>反復停止による非パラメトリック検定の最適化（Early Stopping for Nonparametric Testing）</news:title>
   <news:publication_date>2026-05-09T05:36:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688123</loc>
  <lastmod>2026-05-09T05:36:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>決定境界のトポロジカルデータ解析（Topological Data Analysis of Decision Boundaries with Application to Model Selection）</news:title>
   <news:publication_date>2026-05-09T05:36:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688121</loc>
  <lastmod>2026-05-09T05:36:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ヤンヤン地下研究所での高感度アルファ粒子検出器の初期性能（Initial performance of the high sensitivity alpha particle detector at the Yangyang underground laboratory）</news:title>
   <news:publication_date>2026-05-09T05:36:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688119</loc>
  <lastmod>2026-05-09T05:36:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>並列計算で使える機械数の限界（How Many Machines Can We Use in Parallel Computing for Kernel Ridge Regression?）</news:title>
   <news:publication_date>2026-05-09T05:36:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688117</loc>
  <lastmod>2026-05-09T04:45:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Polynomially Coded Regressionによるストラグラー対策と分散学習の効率化（Polynomially Coded Regression: Optimal Straggler Mitigation via Data Encoding）</news:title>
   <news:publication_date>2026-05-09T04:45:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688115</loc>
  <lastmod>2026-05-09T04:45:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>フォトニックニューラルネットワークの現場学習法（Training of photonic neural networks through in situ backpropagation）</news:title>
   <news:publication_date>2026-05-09T04:45:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688113</loc>
  <lastmod>2026-05-09T04:44:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューラルネットワークの自動検証—安全性を担保するための現状と展望（Automated Verification of Neural Networks: Advances, Challenges and Perspectives）</news:title>
   <news:publication_date>2026-05-09T04:44:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688111</loc>
  <lastmod>2026-05-09T04:43:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>フェルミオン-ボソン相互作用系のデジタル量子計算（Digital quantum computation of fermion-boson interacting systems）</news:title>
   <news:publication_date>2026-05-09T04:43:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688109</loc>
  <lastmod>2026-05-09T04:42:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>予測のための確率的推論をメタ学習する枠組み（Meta-Learning Probabilistic Inference for Prediction）</news:title>
   <news:publication_date>2026-05-09T04:42:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688107</loc>
  <lastmod>2026-05-09T04:42:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ロバストな遠隔教師あり学習による関係抽出の強化（Robust Distant Supervision Relation Extraction via Deep Reinforcement Learning）</news:title>
   <news:publication_date>2026-05-09T04:42:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688105</loc>
  <lastmod>2026-05-09T04:42:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DSGANに学ぶ遠隔監督(Relation Extraction)のノイズ対処（DSGAN: Generative Adversarial Training for Distant Supervision Relation Extraction）</news:title>
   <news:publication_date>2026-05-09T04:42:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688103</loc>
  <lastmod>2026-05-09T03:51:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチタスクDPPを活用した推薦の本質（Multi-Task Determinantal Point Processes for Recommendation）</news:title>
   <news:publication_date>2026-05-09T03:51:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688101</loc>
  <lastmod>2026-05-09T03:50:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Fairness GANによる公平なデータ生成（Fairness GAN）</news:title>
   <news:publication_date>2026-05-09T03:50:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688099</loc>
  <lastmod>2026-05-09T03:49:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時系列からの構造学習と誤検出制御 (Structure Learning from Time Series with False Discovery Control)</news:title>
   <news:publication_date>2026-05-09T03:49:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688097</loc>
  <lastmod>2026-05-09T03:48:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>テキスト結合ネットワークの拡張：Diffusion Mapsによる埋め込み（Diffusion Maps for Textual Network Embedding）</news:title>
   <news:publication_date>2026-05-09T03:48:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688095</loc>
  <lastmod>2026-05-09T03:48:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>列生成によるブール決定ルール学習（Boolean Decision Rules via Column Generation）</news:title>
   <news:publication_date>2026-05-09T03:48:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688093</loc>
  <lastmod>2026-05-09T03:47:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>VIPERSによる銀河分類の再定義（The VIMOS Public Extragalactic Redshift Survey: The complexity of galaxy populations revealed with unsupervised machine-learning algorithms）</news:title>
   <news:publication_date>2026-05-09T03:47:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688091</loc>
  <lastmod>2026-05-09T03:47:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所H0測定における宇宙分散の影響（The impact of the cosmic variance on H0 on cosmological analyses）</news:title>
   <news:publication_date>2026-05-09T03:47:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688089</loc>
  <lastmod>2026-05-09T02:56:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>共通メンバーシップ攻撃（Co-Membership Attacks）に関する解説</news:title>
   <news:publication_date>2026-05-09T02:56:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688087</loc>
  <lastmod>2026-05-09T02:55:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>カルシウムイメージングから学ぶ脳活動の動力学：結合van der PolとLSTMのハイブリッド（Learning Brain Dynamics from Calcium Imaging with Coupled van der Pol and LSTM）</news:title>
   <news:publication_date>2026-05-09T02:55:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688085</loc>
  <lastmod>2026-05-09T02:54:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>低ランク行列回帰における特異部分空間の信頼領域（Confidence Region of Singular Subspaces for Low-rank Matrix Regression）</news:title>
   <news:publication_date>2026-05-09T02:54:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688083</loc>
  <lastmod>2026-05-09T02:54:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>反事実的公平性の下での因果モデルの統合（Pooling of Causal Models under Counterfactual Fairness via Causal Judgement Aggregation）</news:title>
   <news:publication_date>2026-05-09T02:54:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688081</loc>
  <lastmod>2026-05-09T02:54:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学力分布を用いた教育格差の可視化（Measure of gap and inequalities in basic education students proficiencies）</news:title>
   <news:publication_date>2026-05-09T02:54:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688079</loc>
  <lastmod>2026-05-09T02:54:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高速ニューラル機械翻訳の実装手法（Fast Neural Machine Translation Implementation）</news:title>
   <news:publication_date>2026-05-09T02:54:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688077</loc>
  <lastmod>2026-05-09T02:53:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>行動から読み解く「内部モデル」の推定法（Inverse Rational Control: Inferring What You Think from How You Forage）</news:title>
   <news:publication_date>2026-05-09T02:53:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688075</loc>
  <lastmod>2026-05-09T02:01:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>z≈3の双AGN系の宿主銀河を捉える — HAWK-I+GRAALによる観測の重要性（A cosmic dance at z ∼3: Detecting the host galaxies of the dual AGN system LBQS 0302−0019 and Jil with HAWK-I+GRAAL）</news:title>
   <news:publication_date>2026-05-09T02:01:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688073</loc>
  <lastmod>2026-05-09T02:01:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>H I自己吸収（HISA）温度の較正とリーゲル–クラッチャー雲の温度測定（Calibrating the HISA temperature: Measuring the temperature of the Riegel–Crutcher cloud）</news:title>
   <news:publication_date>2026-05-09T02:01:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688071</loc>
  <lastmod>2026-05-09T02:00:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>銀河の温暖中性中間層のHI 21cm吸収による検出 (Detection of the Galactic Warm Neutral Medium in HI 21cm absorption)</news:title>
   <news:publication_date>2026-05-09T02:00:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688069</loc>
  <lastmod>2026-05-09T02:00:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ステレオ拡大：マルチプレーン画像を用いたビュー合成学習（Stereo Magnification: Learning view synthesis using multiplane images）</news:title>
   <news:publication_date>2026-05-09T02:00:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688067</loc>
  <lastmod>2026-05-09T01:59:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単純語埋め込みモデルの再評価（Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms）</news:title>
   <news:publication_date>2026-05-09T01:59:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688065</loc>
  <lastmod>2026-05-09T01:59:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Competitive Collaborationによる無監督での深度・カメラ動作・オプティカルフロー・動き分割の同時学習（Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation）</news:title>
   <news:publication_date>2026-05-09T01:59:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688063</loc>
  <lastmod>2026-05-09T01:59:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>レッドシフト広帯吸収線クエーサーの機械学習による発見（Redshifted broad absorption line quasars found via machine-learned spectral similarity）</news:title>
   <news:publication_date>2026-05-09T01:59:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688061</loc>
  <lastmod>2026-05-09T01:07:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>メタグラディエント強化学習の要点（Meta-Gradient Reinforcement Learning）</news:title>
   <news:publication_date>2026-05-09T01:07:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688059</loc>
  <lastmod>2026-05-09T01:07:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>基礎fMRIから自閉症治療反応を予測する手法（Prediction of Autism Treatment Response from Baseline fMRI using Random Forests and Tree Bagging）</news:title>
   <news:publication_date>2026-05-09T01:07:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688057</loc>
  <lastmod>2026-05-09T01:07:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Implicit Autoencoders（Implicit Autoencoders）</news:title>
   <news:publication_date>2026-05-09T01:07:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688055</loc>
  <lastmod>2026-05-09T01:06:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>光浸漬（Light Soaking）でJSCが低下する原因：EV+0.98 eVトラップの役割（Role of EV+0.98 eV trap in light soaking-induced short circuit current instability in CIGS solar cells）</news:title>
   <news:publication_date>2026-05-09T01:06:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688053</loc>
  <lastmod>2026-05-09T01:06:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>レイヤー単位のニューロン共有によるマルチタスク圧縮（Multi-Task Zipping via Layer-wise Neuron Sharing）</news:title>
   <news:publication_date>2026-05-09T01:06:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688051</loc>
  <lastmod>2026-05-09T01:06:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層性を活かす注意機構の再定義：Hyperbolic Attention Networks（Hyperbolic Attention Networks）</news:title>
   <news:publication_date>2026-05-09T01:06:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688049</loc>
  <lastmod>2026-05-09T01:05:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>バンディット問題におけるブートストラップの新知見（New Insights into Bootstrapping for Bandits）</news:title>
   <news:publication_date>2026-05-09T01:05:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688047</loc>
  <lastmod>2026-05-09T00:14:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層ニューラルネットワークにおけるエントロピーと相互情報量の定量化（Entropy and mutual information in models of deep neural networks）</news:title>
   <news:publication_date>2026-05-09T00:14:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688045</loc>
  <lastmod>2026-05-09T00:03:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>技術サポート文書から手順を抽出する方法（Mining Procedures from Technical Support Documents）</news:title>
   <news:publication_date>2026-05-09T00:03:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688043</loc>
  <lastmod>2026-05-09T00:03:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多タスク・コックス過程における効率的推論（Efficient Inference in Multi-task Cox Process Models）</news:title>
   <news:publication_date>2026-05-09T00:03:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688041</loc>
  <lastmod>2026-05-09T00:02:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Local SGDによる分散学習の通信最適化（Local SGD Converges Fast and Communicates Little）</news:title>
   <news:publication_date>2026-05-09T00:02:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688039</loc>
  <lastmod>2026-05-09T00:02:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチ時間分解能とマルチレベル特徴を組み合わせた環境音分類（Environmental Sound Classification Based on Multi-temporal Resolution Convolutional Neural Network Combining with Multi-level Features）</news:title>
   <news:publication_date>2026-05-09T00:02:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688037</loc>
  <lastmod>2026-05-09T00:02:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>精密なスパイク時刻を用いた学習：Liquid State Machineの新しいデコーディングアルゴリズム（Learning with precise spike times: A new decoding algorithm for liquid state machines）</news:title>
   <news:publication_date>2026-05-09T00:02:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688035</loc>
  <lastmod>2026-05-09T00:01:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>地理的Hidden Markov Treeによる洪水範囲推定（Geographical Hidden Markov Tree for Flood Extent Mapping）</news:title>
   <news:publication_date>2026-05-09T00:01:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688033</loc>
  <lastmod>2026-05-08T23:10:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>MobiFace：モバイル環境における顔追跡のための大規模データセット（MobiFace: A Novel Dataset for Mobile Face Tracking in the Wild）</news:title>
   <news:publication_date>2026-05-08T23:10:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688031</loc>
  <lastmod>2026-05-08T23:10:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>同字異形文字（Homoglyph）攻撃の検出に関するSiameseニューラルネットワークの提案（Detecting Homoglyph Attacks with a Siamese Neural Network）</news:title>
   <news:publication_date>2026-05-08T23:10:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688029</loc>
  <lastmod>2026-05-08T23:09:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>継続学習評価の再設計がもたらす本質的な変化（Towards Robust Evaluations of Continual Learning）</news:title>
   <news:publication_date>2026-05-08T23:09:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688027</loc>
  <lastmod>2026-05-08T23:08:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Fenchel-Young損失による分類器学習の新枠組み（Learning Classifiers with Fenchel-Young Losses: Generalized Entropies, Margins, and Algorithms）</news:title>
   <news:publication_date>2026-05-08T23:08:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688025</loc>
  <lastmod>2026-05-08T23:08:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>クロスドメイン分離のための画像間翻訳（Image-to-image translation for cross-domain disentanglement）</news:title>
   <news:publication_date>2026-05-08T23:08:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688023</loc>
  <lastmod>2026-05-08T23:08:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マージン付きデータからの凸多面体学習（Learning convex polyhedra with margin*）</news:title>
   <news:publication_date>2026-05-08T23:08:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688021</loc>
  <lastmod>2026-05-08T23:08:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所近似による動的システムの効率的符号化（Efficient Encoding of Dynamical Systems through Local Approximations）</news:title>
   <news:publication_date>2026-05-08T23:08:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688019</loc>
  <lastmod>2026-05-08T22:16:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GANによる深層ニューラルネットワークのパラメータ同時自動最適化（Autonomously and Simultaneously Refining Deep Neural Network Parameters by Generative Adversarial Networks）</news:title>
   <news:publication_date>2026-05-08T22:16:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688017</loc>
  <lastmod>2026-05-08T22:16:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>視覚的関係事実学習によるVQA改善（R-VQA: Learning Visual Relation Facts with Semantic Attention for Visual Question Answering）</news:title>
   <news:publication_date>2026-05-08T22:16:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688015</loc>
  <lastmod>2026-05-08T22:16:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>データ拡張と学習を同時最適化する手法（Jointly Optimize Data Augmentation and Network Training: Adversarial Data Augmentation in Human Pose Estimation）</news:title>
   <news:publication_date>2026-05-08T22:16:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688013</loc>
  <lastmod>2026-05-08T22:15:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>全結合再構成層を持つ深層残差ネットワークによる単一画像超解像（Deep Residual Networks with a Fully Connected Reconstruction Layer for Single Image Super-Resolution）</news:title>
   <news:publication_date>2026-05-08T22:15:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688011</loc>
  <lastmod>2026-05-08T22:14:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Interventionによる因果モデルの学習と検証（Learning and Testing Causal Models with Interventions）</news:title>
   <news:publication_date>2026-05-08T22:14:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688009</loc>
  <lastmod>2026-05-08T22:14:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>メタラーニングとエピソディックリコールが切り開く再発タスクの学習（Meta-Learning with Episodic Recall）</news:title>
   <news:publication_date>2026-05-08T22:14:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688007</loc>
  <lastmod>2026-05-08T22:14:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>適応的辞書学習によるGPR地雷分類の高速化と実地適応（Dictionary Learning for Adaptive GPR Landmine Classification）</news:title>
   <news:publication_date>2026-05-08T22:14:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688005</loc>
  <lastmod>2026-05-08T21:22:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>IDEASによるセキュリティ分析の再設計（Forming IDEAS: Interactive Data Exploration &amp;amp; Analysis System – Configurable Visual Analytics for Cyber Security Analysts）</news:title>
   <news:publication_date>2026-05-08T21:22:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688003</loc>
  <lastmod>2026-05-08T21:21:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>LF-Netによる局所特徴学習（LF-Net: Learning Local Features from Images）</news:title>
   <news:publication_date>2026-05-08T21:21:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688001</loc>
  <lastmod>2026-05-08T21:21:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>電磁脳信号のための多変量畳み込みスパースコーディング（Multivariate Convolutional Sparse Coding for Electromagnetic Brain Signals）</news:title>
   <news:publication_date>2026-05-08T21:21:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687999</loc>
  <lastmod>2026-05-08T21:20:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Regularized Nonlinear Acceleration の要点と実務への示唆（Online Regularized Nonlinear Acceleration）</news:title>
   <news:publication_date>2026-05-08T21:20:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687997</loc>
  <lastmod>2026-05-08T21:20:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>異文化が出会うとき――クロスカルチュラル・ナレッジスペースの設計（When Cultures Meet: Modelling Cross-Cultural Knowledge Spaces）</news:title>
   <news:publication_date>2026-05-08T21:20:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687995</loc>
  <lastmod>2026-05-08T21:19:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>結合マルチリンガル空間での平行データのフィルタリングと抽出（Filtering and Mining Parallel Data in a Joint Multilingual Space）</news:title>
   <news:publication_date>2026-05-08T21:19:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687993</loc>
  <lastmod>2026-05-08T21:19:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>不確実性を考慮した注意機構による信頼性向上（Uncertainty-Aware Attention for Reliable Interpretation and Prediction）</news:title>
   <news:publication_date>2026-05-08T21:19:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687991</loc>
  <lastmod>2026-05-08T20:27:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>無線共存管理における強化学習ベースの資源配分（Resource Allocation for a Wireless Coexistence Management System Based on Reinforcement Learning）</news:title>
   <news:publication_date>2026-05-08T20:27:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687989</loc>
  <lastmod>2026-05-08T20:27:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習中の干渉と前進の一歩（One step back, two steps forward: interference and learning in recurrent neural networks）</news:title>
   <news:publication_date>2026-05-08T20:27:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687987</loc>
  <lastmod>2026-05-08T20:27:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SOSELETOによる転移学習とノイズラベル対策の統一的アプローチ（SOSELETO: A Unified Approach to Transfer Learning and Training with Noisy Labels）</news:title>
   <news:publication_date>2026-05-08T20:27:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687985</loc>
  <lastmod>2026-05-08T20:26:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>N次元ベクトルニューロンの汎用バックプロパゲーション（Backpropagation with N-D Vector-Valued Neurons Using Arbitrary Bilinear Products）</news:title>
   <news:publication_date>2026-05-08T20:26:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687983</loc>
  <lastmod>2026-05-08T20:26:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>入門プログラミングにおける問題類似度の測定（Measuring Item Similarity in Introductory Programming: Python and Robot Programming Case Studies）</news:title>
   <news:publication_date>2026-05-08T20:26:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687981</loc>
  <lastmod>2026-05-08T20:25:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Residual Networksを「変形の流れ」として読む — ResNetと微分同相写像の関係（Residual Networks as Geodesic Flows of Diffeomorphisms）</news:title>
   <news:publication_date>2026-05-08T20:25:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687979</loc>
  <lastmod>2026-05-08T20:25:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>連続行動空間におけるAlpha Zeroの拡張（A0C: Alpha Zero in Continuous Action Space）</news:title>
   <news:publication_date>2026-05-08T20:25:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687977</loc>
  <lastmod>2026-05-08T19:34:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>偶然の深宇宙観測領域バイアスが示す注意点（Accidental deep field bias in CMB T and SNe z correlation）</news:title>
   <news:publication_date>2026-05-08T19:34:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687975</loc>
  <lastmod>2026-05-08T19:33:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>酸化物界面における絶縁状態の渦励起（Vortex excitations in the Insulating State of an Oxide Interface）</news:title>
   <news:publication_date>2026-05-08T19:33:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687973</loc>
  <lastmod>2026-05-08T19:32:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Primal-Dual Wasserstein GANの要点と事業活用の示唆（Primal-Dual Wasserstein GAN）</news:title>
   <news:publication_date>2026-05-08T19:32:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687971</loc>
  <lastmod>2026-05-08T19:32:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模論文・技術文書を機械的に読み解く仕組み（Corpus Conversion Service: A Machine Learning Platform to Ingest Documents at Scale）</news:title>
   <news:publication_date>2026-05-08T19:32:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687969</loc>
  <lastmod>2026-05-08T19:32:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>System APIに基づくAndroidランサムウェア検出の有効性（On the Effectiveness of System API-Related Information for Android Ransomware Detection）</news:title>
   <news:publication_date>2026-05-08T19:32:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687967</loc>
  <lastmod>2026-05-08T19:32:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>関連学習における可解釈性と合成性を高める共同訓練型オートエンコーダ（Interpretable and Compositional Relation Learning by Joint Training with an Autoencoder）</news:title>
   <news:publication_date>2026-05-08T19:32:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687965</loc>
  <lastmod>2026-05-08T19:31:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>潜在因子とその結合性を同時に学習する統一確率モデル（A Unified Probabilistic Model for Learning Latent Factors and Their Connectivities from High-Dimensional Data）</news:title>
   <news:publication_date>2026-05-08T19:31:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687963</loc>
  <lastmod>2026-05-08T18:39:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自動化されたECEIデータ前処理：機械学習による異常信号の自動識別（An Automatic Data Cleaning Procedure for Electron Cyclotron Emission Imaging on EAST Tokamak Using Machine Learning Algorithm）</news:title>
   <news:publication_date>2026-05-08T18:39:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687961</loc>
  <lastmod>2026-05-08T18:39:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>最適輸送を用いた過剰パラメータ化モデルの勾配降下法の大域収束（On the Global Convergence of Gradient Descent for Over-parameterized Models using Optimal Transport）</news:title>
   <news:publication_date>2026-05-08T18:39:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687959</loc>
  <lastmod>2026-05-08T18:39:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>AVIDによる視覚的異常検知の敵対的学習（Adversarial Visual Irregularity Detection）</news:title>
   <news:publication_date>2026-05-08T18:39:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687957</loc>
  <lastmod>2026-05-08T18:37:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Laplacian Networks：ニューラルネットワークにおけるクラス境界の平滑化を制御する正則化（Laplacian Networks: Bounding Indicator Function Smoothness for Neural Networks）</news:title>
   <news:publication_date>2026-05-08T18:37:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687955</loc>
  <lastmod>2026-05-08T18:37:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スタッキングによる分子原子化エネルギー予測の精度改善（Stacked Generalization Approach to Improve Prediction of Molecular Atomization Energies）</news:title>
   <news:publication_date>2026-05-08T18:37:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687953</loc>
  <lastmod>2026-05-08T18:36:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>潜在変数を扱う安定化仕様探索法（Stable specification search in structural equation model with latent variables）</news:title>
   <news:publication_date>2026-05-08T18:36:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687951</loc>
  <lastmod>2026-05-08T18:35:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>衛星画像に対する高速多段階物体検出の手法（You Only Look Twice: Rapid Multi-Scale Object Detection In Satellite Imagery）</news:title>
   <news:publication_date>2026-05-08T18:35:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687949</loc>
  <lastmod>2026-05-08T17:43:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフ中の単一コミュニティ探索（Searching for a Single Community in a Graph）</news:title>
   <news:publication_date>2026-05-08T17:43:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687947</loc>
  <lastmod>2026-05-08T17:43:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>KNOB-SynC: 非パラメトリック重複度に基づくシンシティアルクラスタリング（Kernel-estimated Nonparametric Overlap-Based Syncytial Clustering）</news:title>
   <news:publication_date>2026-05-08T17:43:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687945</loc>
  <lastmod>2026-05-08T17:42:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>データ拡張ポリシーの自動探索（AutoAugment: Learning Augmentation Strategies from Data）</news:title>
   <news:publication_date>2026-05-08T17:42:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687943</loc>
  <lastmod>2026-05-08T17:42:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>VisemeNet: 音声駆動のアニメーター向けスピーチアニメーション（VisemeNet: Audio-Driven Animator-Centric Speech Animation）</news:title>
   <news:publication_date>2026-05-08T17:42:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687941</loc>
  <lastmod>2026-05-08T17:41:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動的アドバイザベースアンサンブル（Dynamic Advisor-Based Ensemble (dynABE): Case study in stock trend prediction of critical metal companies）</news:title>
   <news:publication_date>2026-05-08T17:41:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687939</loc>
  <lastmod>2026-05-08T17:41:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散クラスタリングと外れ値検出の実務的アルゴリズム（A Practical Algorithm for Distributed Clustering and Outlier Detection）</news:title>
   <news:publication_date>2026-05-08T17:41:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687937</loc>
  <lastmod>2026-05-08T17:41:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>潜在空間探索と敵対的損失によるクロスドメイン画像生成（Cross Domain Image Generation through Latent Space Exploration with Adversarial Loss）</news:title>
   <news:publication_date>2026-05-08T17:41:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687935</loc>
  <lastmod>2026-05-08T16:49:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>なぜジョニーはパスワードを安全に保存できないのか（Why Johnny Can’t Store Passwords Securely? A Usability Evaluation of Bouncycastle Password Hashing）</news:title>
   <news:publication_date>2026-05-08T16:49:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687933</loc>
  <lastmod>2026-05-08T16:49:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチレベル深層カスケード木によるCVR予測（Multi-Level Deep Cascade Trees for Conversion Rate Prediction in Recommendation System）</news:title>
   <news:publication_date>2026-05-08T16:49:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687931</loc>
  <lastmod>2026-05-08T16:49:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>連続型非単調サブモジュラ最適化の最適アルゴリズム（Optimal Algorithms for Continuous Non-monotone Submodular and DR-Submodular Maximization）</news:title>
   <news:publication_date>2026-05-08T16:49:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687929</loc>
  <lastmod>2026-05-08T16:48:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>リソース制約下のIoTで大規模DNNを動かすローカル量子化（Deploy Large-Scale Deep Neural Networks in Resource Constrained IoT Devices with Local Quantization Region）</news:title>
   <news:publication_date>2026-05-08T16:48:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687927</loc>
  <lastmod>2026-05-08T16:48:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>VisualBackPropを用いた学習における特権情報の活用（VisualBackProp for learning using privileged information with CNNs）</news:title>
   <news:publication_date>2026-05-08T16:48:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687925</loc>
  <lastmod>2026-05-08T16:48:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>構造制約付き階層クラスタリング（Hierarchical Clustering with Structural Constraints）</news:title>
   <news:publication_date>2026-05-08T16:48:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687923</loc>
  <lastmod>2026-05-08T16:47:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非凸学習における非同期SGDの収束を制御する手法（Taming Convergence for Asynchronous Stochastic Gradient Descent with Unbounded Delay in Non-Convex Learning）</news:title>
   <news:publication_date>2026-05-08T16:47:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687921</loc>
  <lastmod>2026-05-08T15:56:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>エントリーワイズℓpノルム低ランク近似の実用的アルゴリズム（Simple and practical algorithms for ℓp-norm low-rank approximation）</news:title>
   <news:publication_date>2026-05-08T15:56:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687919</loc>
  <lastmod>2026-05-08T15:56:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>詳細な部品分割のための複雑な関係を組み込んだ深層構造予測モデル（Complex Relations in a Deep Structured Prediction Model for Fine Image Segmentation）</news:title>
   <news:publication_date>2026-05-08T15:56:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687917</loc>
  <lastmod>2026-05-08T15:55:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Deep Reinforcement Learningを用いたSeq2Seqモデルの強化（Deep Reinforcement Learning for Sequence-to-Sequence Models）</news:title>
   <news:publication_date>2026-05-08T15:55:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687915</loc>
  <lastmod>2026-05-08T15:54:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模グラフとゼロノイズ極限に関する半教師付き学習の理論的限界（LARGE DATA AND ZERO NOISE LIMITS OF GRAPH-BASED SEMI-SUPERVISED LEARNING ALGORITHMS）</news:title>
   <news:publication_date>2026-05-08T15:54:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687913</loc>
  <lastmod>2026-05-08T15:54:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>特徴に基づく生成モデルを用いたDyna計画（Dyna Planning using a Feature Based Generative Model）</news:title>
   <news:publication_date>2026-05-08T15:54:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687911</loc>
  <lastmod>2026-05-08T15:54:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>不変表現と敵対的学習を超える情報理論的アプローチ（Invariant Representations without Adversarial Training）</news:title>
   <news:publication_date>2026-05-08T15:54:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687909</loc>
  <lastmod>2026-05-08T15:54:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>慎重な深層学習（Cautious Deep Learning）</news:title>
   <news:publication_date>2026-05-08T15:54:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687907</loc>
  <lastmod>2026-05-08T15:02:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>二段階部分空間トラストリージョンによる深層ニューラルネットの訓練（A Two-Stage Subspace Trust Region Approach for Deep Neural Network Training）</news:title>
   <news:publication_date>2026-05-08T15:02:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687905</loc>
  <lastmod>2026-05-08T14:56:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>LSTMに内在する暗黙の言語モデルがOCRを変える（Implicit Language Model in LSTM for OCR）</news:title>
   <news:publication_date>2026-05-08T14:56:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687903</loc>
  <lastmod>2026-05-08T14:56:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>適応型確率的勾配ランジュバン力学（Adaptive Stochastic Gradient Langevin Dynamics）</news:title>
   <news:publication_date>2026-05-08T14:56:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687901</loc>
  <lastmod>2026-05-08T14:55:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>状態空間モデルのスケーラブルなベイズ学習と変分推論＋SMCサンプラー（Scalable Bayesian Learning for State Space Models using Variational Inference with SMC Samplers）</news:title>
   <news:publication_date>2026-05-08T14:55:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687899</loc>
  <lastmod>2026-05-08T14:54:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>補間とCNNを組み合わせたハイブリッド超解像法（A hybrid approach of interpolations and CNN to obtain super-resolution）</news:title>
   <news:publication_date>2026-05-08T14:54:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687897</loc>
  <lastmod>2026-05-08T14:54:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層能動学習による異常検知の転換（Deep Active Learning for Anomaly Detection）</news:title>
   <news:publication_date>2026-05-08T14:54:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687895</loc>
  <lastmod>2026-05-08T14:54:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>タスク依存適応メトリクスによるFew-Shot学習の前進（TADAM: Task dependent adaptive metric for improved few-shot learning）</news:title>
   <news:publication_date>2026-05-08T14:54:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687893</loc>
  <lastmod>2026-05-08T14:03:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>電子の経路を生成するモデル（A Generative Model for Electron Paths）</news:title>
   <news:publication_date>2026-05-08T14:03:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687891</loc>
  <lastmod>2026-05-08T14:02:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>日常動作「注ぐ」動作の予測と学習（Pouring Sequence Prediction using Recurrent Neural Network）</news:title>
   <news:publication_date>2026-05-08T14:02:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687889</loc>
  <lastmod>2026-05-08T14:02:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ロバスト適応制御に関する後悔境界の新展開（Regret Bounds for Robust Adaptive Control of the Linear Quadratic Regulator）</news:title>
   <news:publication_date>2026-05-08T14:02:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687887</loc>
  <lastmod>2026-05-08T14:01:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>顔画像の任意属性を選択的に匿名化する手法（Anonymizing k-Facial Attributes via Adversarial Perturbations）</news:title>
   <news:publication_date>2026-05-08T14:01:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687885</loc>
  <lastmod>2026-05-08T14:01:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Predictive Local Smoothnessによる確率的勾配法の適応学習率（Predictive Local Smoothness for Stochastic Gradient Methods）</news:title>
   <news:publication_date>2026-05-08T14:01:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687883</loc>
  <lastmod>2026-05-08T14:01:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>敵対的勾配の正則化によるニューラルネットワークの堅牢化（Towards Robust Training of Neural Networks by Regularizing Adversarial Gradients）</news:title>
   <news:publication_date>2026-05-08T14:01:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687881</loc>
  <lastmod>2026-05-08T14:01:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>モダリティ間の合意に基づく半教師あり分類（Semi-supervised classification by reaching consensus among modalities）</news:title>
   <news:publication_date>2026-05-08T14:01:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687879</loc>
  <lastmod>2026-05-08T13:09:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非定常環境における文脈付きバンディット学習（Learning Contextual Bandits in a Non-stationary Environment）</news:title>
   <news:publication_date>2026-05-08T13:09:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687877</loc>
  <lastmod>2026-05-08T13:09:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マーク付き時点過程の深層強化学習（Deep Reinforcement Learning of Marked Temporal Point Processes）</news:title>
   <news:publication_date>2026-05-08T13:09:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687875</loc>
  <lastmod>2026-05-08T13:08:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>語彙含意の評価を変えた方向性ネットワーク（Scoring Lexical Entailment with a Supervised Directional Similarity Network）</news:title>
   <news:publication_date>2026-05-08T13:08:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687873</loc>
  <lastmod>2026-05-08T13:07:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DeepToF によるオフ・ザ・シェルフToFカメラのリアルタイムMPI補正（DeepToF: Off-the-Shelf Real-Time Correction of Multipath Interference in Time-of-Flight Imaging）</news:title>
   <news:publication_date>2026-05-08T13:07:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687871</loc>
  <lastmod>2026-05-08T13:07:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層学習による通信アルゴリズムの自動発見（Communication Algorithms via Deep Learning）</news:title>
   <news:publication_date>2026-05-08T13:07:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687869</loc>
  <lastmod>2026-05-08T13:07:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>変分法で学ぶグローバーの量子探索アルゴリズム（Variationally Learning Grover’s Quantum Search Algorithm）</news:title>
   <news:publication_date>2026-05-08T13:07:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687867</loc>
  <lastmod>2026-05-08T13:07:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>作業記憶ネットワーク：メモリネットワークに関係推論モジュールを付与する試み（Working Memory Networks: Augmenting Memory Networks with a Relational Reasoning Module）</news:title>
   <news:publication_date>2026-05-08T13:07:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687865</loc>
  <lastmod>2026-05-08T12:14:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>入力空間と重み空間のスムージングによる半教師あり学習の改良（Input and Weight Space Smoothing for Semi-supervised Learning）</news:title>
   <news:publication_date>2026-05-08T12:14:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687863</loc>
  <lastmod>2026-05-08T12:14:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SNIPERによる効率的なマルチスケール学習（SNIPER: Efficient Multi-Scale Training）</news:title>
   <news:publication_date>2026-05-08T12:14:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687861</loc>
  <lastmod>2026-05-08T12:13:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>最小超球面エネルギーに向けた学習（Learning towards Minimum Hyperspherical Energy）</news:title>
   <news:publication_date>2026-05-08T12:13:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687859</loc>
  <lastmod>2026-05-08T12:12:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文脈依存拘束問題をリアルタイムに解く学習手法（Learning to Optimize Contextually Constrained Problems）</news:title>
   <news:publication_date>2026-05-08T12:12:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687857</loc>
  <lastmod>2026-05-08T12:12:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>リザバーコンピューティングを用いたデータからの流体変数の機械学習推定 (Machine-learning inference of fluid variables from data using reservoir computing)</news:title>
   <news:publication_date>2026-05-08T12:12:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687855</loc>
  <lastmod>2026-05-08T12:12:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>部分観測マルコフ決定過程におけるデータ効率的モデル学習のための変分推論（Variational Inference for Data-Efﬁcient Model Learning in POMDPs）</news:title>
   <news:publication_date>2026-05-08T12:12:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687853</loc>
  <lastmod>2026-05-08T12:11:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>エミュレータネットワークによる尤度なし推論の実務的意義（Likelihood-free inference with emulator networks）</news:title>
   <news:publication_date>2026-05-08T12:11:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687851</loc>
  <lastmod>2026-05-08T11:20:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ヘテロジニアスなチームに対する強化学習とPALO境界（Reinforcement Learning for Heterogeneous Teams with PALO Bounds）</news:title>
   <news:publication_date>2026-05-08T11:20:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687849</loc>
  <lastmod>2026-05-08T11:19:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>物体認識から学ぶ照明推定（Learning Illuminant Estimation from Object Recognition）</news:title>
   <news:publication_date>2026-05-08T11:19:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687847</loc>
  <lastmod>2026-05-08T11:19:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散環境下における協調オンライン学習（Collective Online Learning via Decentralized Gaussian Processes in Massive Multi-Agent Systems）</news:title>
   <news:publication_date>2026-05-08T11:19:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687845</loc>
  <lastmod>2026-05-08T11:18:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>同心円状リザバー（Concentric ESN: Assessing the Effect of Modularity in Cycle Reservoirs）</news:title>
   <news:publication_date>2026-05-08T11:18:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687843</loc>
  <lastmod>2026-05-08T11:18:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時間方向の情報流を改善するHighway State Gating（Highway State Gating for Recurrent Highway Networks: improving information flow through time）</news:title>
   <news:publication_date>2026-05-08T11:18:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687841</loc>
  <lastmod>2026-05-08T11:18:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>部分観測（Partial Monitoring）における敵対的ゲームの完全分類（Cleaning up the neighborhood: A full classification for adversarial partial monitoring）</news:title>
   <news:publication_date>2026-05-08T11:18:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687839</loc>
  <lastmod>2026-05-08T11:18:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>光通信における深層ニューラルネットワークの応用（On the use of deep neural networks in optical communications）</news:title>
   <news:publication_date>2026-05-08T11:18:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687837</loc>
  <lastmod>2026-05-08T10:26:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Cramer-Wold AutoEncoder（Cramer-Wold AutoEncoder）</news:title>
   <news:publication_date>2026-05-08T10:26:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687835</loc>
  <lastmod>2026-05-08T10:26:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>肝臓病変の軽量化セグメンテーション手法（Segmentation of Liver Lesions with Reduced Complexity Deep Models）</news:title>
   <news:publication_date>2026-05-08T10:26:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687833</loc>
  <lastmod>2026-05-08T10:26:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>事象の地平線近傍における内在的構造の検出（Detection of Intrinsic Source Structure at ∼3 Schwarzschild Radii with Millimeter-VLBI Observations of Sagittarius A*）</news:title>
   <news:publication_date>2026-05-08T10:26:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687831</loc>
  <lastmod>2026-05-08T10:26:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>乗法重み法による共同PAC学習の厳密境界（Tight Bounds for Collaborative PAC Learning via Multiplicative Weights）</news:title>
   <news:publication_date>2026-05-08T10:26:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687829</loc>
  <lastmod>2026-05-08T10:25:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非対称木に強いモンテカルロ木探索の改良（Monte Carlo Tree Search for Asymmetric Trees）</news:title>
   <news:publication_date>2026-05-08T10:25:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687827</loc>
  <lastmod>2026-05-08T10:25:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>潜在変数を含む構造化予測モデルの学習とガウス摂動（Learning latent variable structured prediction models with Gaussian perturbations）</news:title>
   <news:publication_date>2026-05-08T10:25:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687825</loc>
  <lastmod>2026-05-08T10:25:04Z</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訓練の統一フレームワーク（A Unified Framework for Training Neural Networks）</news:title>
   <news:publication_date>2026-05-08T10:25:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687823</loc>
  <lastmod>2026-05-08T09:34:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>交互的ランダム化ブロック座標降下法が示す最小滑らかさ独立の可能性（Alternating Randomized Block Coordinate Descent）</news:title>
   <news:publication_date>2026-05-08T09:34:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687821</loc>
  <lastmod>2026-05-08T09:33:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ドロップアウトの境界を押し広げる（Pushing the Bounds of Dropout）</news:title>
   <news:publication_date>2026-05-08T09:33:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687819</loc>
  <lastmod>2026-05-08T09:33:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数顔画像からの被写体単位属性推定（Attributes in Multiple Facial Images）</news:title>
   <news:publication_date>2026-05-08T09:33:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687817</loc>
  <lastmod>2026-05-08T09:33:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>MNIST上での最初の敵対的ロバストニューラルネットワークに向けて（TOWARDS THE FIRST ADVERSARIALLY ROBUST NEURAL NETWORK MODEL ON MNIST）</news:title>
   <news:publication_date>2026-05-08T09:33:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687815</loc>
  <lastmod>2026-05-08T09:32:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ロボットを使ったモバイル学習プラットフォームの導入が変えるプログラミング教育（Introducing an innovative robot-based mobile platform for programming learning）</news:title>
   <news:publication_date>2026-05-08T09:32:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687813</loc>
  <lastmod>2026-05-08T09:32:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ASRを特徴抽出器として用いた音声感情認識—転移学習の実践的示唆（ASR-based Features for Emotion Recognition: A Transfer Learning Approach）</news:title>
   <news:publication_date>2026-05-08T09:32:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687811</loc>
  <lastmod>2026-05-08T09:31:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CT密度から深層学習で推定する吸収線量（Deep Learning Estimation of Absorbed Dose for Nuclear Medicine Diagnostics）</news:title>
   <news:publication_date>2026-05-08T09:31:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687809</loc>
  <lastmod>2026-05-08T08:41:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>無意識表象が明かす意識の仕組み（Why the Brain Knows More than We Do: Non-Conscious Representations and Their Role in the Construction of Conscious Experience）</news:title>
   <news:publication_date>2026-05-08T08:41:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687807</loc>
  <lastmod>2026-05-08T08:40:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>欠損特徴量と欠損ラベルを伴うマルチラベル分類の行列共同補完（Matrix Co-completion for Multi-label Classification with Missing Features and Labels）</news:title>
   <news:publication_date>2026-05-08T08:40:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687805</loc>
  <lastmod>2026-05-08T08:40:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スパース性を活かした高速後悔率（Fast-Rate）保証の実効的なオンラインアルゴリズム（EFFICIENT ONLINE ALGORITHMS FOR FAST-RATE REGRET BOUNDS UNDER SPARSITY）</news:title>
   <news:publication_date>2026-05-08T08:40:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687803</loc>
  <lastmod>2026-05-08T08:40:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>光電容積脈波からの血圧トレンドと夜間ディップ推定（Estimating blood pressure trends and the nocturnal dip from photoplethysmography）</news:title>
   <news:publication_date>2026-05-08T08:40:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687801</loc>
  <lastmod>2026-05-08T08:39:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的リーマン部分多様体学習とWGPLVM（Probabilistic Riemannian submanifold learning with wrapped Gaussian process latent variable models）</news:title>
   <news:publication_date>2026-05-08T08:39:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687799</loc>
  <lastmod>2026-05-08T08:39:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>RDF埋め込みによるオントロジー整合変化の分類（RDF2Vec-based Classification of Ontology Alignment Changes）</news:title>
   <news:publication_date>2026-05-08T08:39:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687797</loc>
  <lastmod>2026-05-08T08:39:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Gene Ontologyの注釈進化に基づくネガティブ例選定の分析（Analysis of Novel Annotations in the Gene Ontology for Boosting the Selection of Negative Examples）</news:title>
   <news:publication_date>2026-05-08T08:39:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687795</loc>
  <lastmod>2026-05-08T07:48:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>構造化データの最適輸送とグラフ応用（Optimal Transport for structured data with application on graphs）</news:title>
   <news:publication_date>2026-05-08T07:48:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687793</loc>
  <lastmod>2026-05-08T07:48:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ハイパーボリックニューラルネットワークの本質と実務応用（Hyperbolic Neural Networks）</news:title>
   <news:publication_date>2026-05-08T07:48:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687791</loc>
  <lastmod>2026-05-08T07:47:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>トウモロコシのハプロイド種子識別を高精度化する波長選択型LSTM-CNN（Maize Haploid Identification via LSTM-CNN and Hyperspectral Imaging Technology）</news:title>
   <news:publication_date>2026-05-08T07:47:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687789</loc>
  <lastmod>2026-05-08T07:47:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所パッチの周波数分布を推定して行う画像復元（Image Restoration by Estimating Frequency Distribution of Local Patches）</news:title>
   <news:publication_date>2026-05-08T07:47:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687787</loc>
  <lastmod>2026-05-08T07:47:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Excitation Dropoutによるニューラルネットの可塑性促進（Excitation Dropout: Encouraging Plasticity in Deep Neural Networks）</news:title>
   <news:publication_date>2026-05-08T07:47:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687785</loc>
  <lastmod>2026-05-08T07:46:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューラルネットワークによるアンサンブル天気予報の事後処理（Neural networks for post-processing ensemble weather forecasts）</news:title>
   <news:publication_date>2026-05-08T07:46:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687783</loc>
  <lastmod>2026-05-08T07:46:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>制約付きグラフ変分オートエンコーダによる分子設計（Constrained Graph Variational Autoencoders for Molecule Design）</news:title>
   <news:publication_date>2026-05-08T07:46:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687781</loc>
  <lastmod>2026-05-08T06:55:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>表現バランスによるMDPモデルでオフポリシー評価を改善する（Representation Balancing MDPs for Off-Policy Policy Evaluation）</news:title>
   <news:publication_date>2026-05-08T06:55:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687779</loc>
  <lastmod>2026-05-08T06:45:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単純な探索がサンプル効率的である条件（When Simple Exploration is Sample Efficient: Identifying Sufficient Conditions for Random Exploration to Yield PAC RL Algorithms）</news:title>
   <news:publication_date>2026-05-08T06:45:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687777</loc>
  <lastmod>2026-05-08T06:45:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>海馬-内嗅皮質系における構造的知識の一般化 (Generalisation of structural knowledge in the hippocampal-entorhinal system)</news:title>
   <news:publication_date>2026-05-08T06:45:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687775</loc>
  <lastmod>2026-05-08T06:44:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Phocas: 分散学習における次元不変のByzantine耐性を実現する新しい集約法（Phocas: dimensional Byzantine-resilient stochastic gradient descent）</news:title>
   <news:publication_date>2026-05-08T06:44:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687773</loc>
  <lastmod>2026-05-08T06:44:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>顕著領域に注目したオーロラ画像検索の効率化（Saliency Deep Embedding for Aurora Image Search）</news:title>
   <news:publication_date>2026-05-08T06:44:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687771</loc>
  <lastmod>2026-05-08T06:44:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文脈ベクトルのアモタイゼーションによる注意機構の改良（Amortized Context Vector Inference for Sequence-to-Sequence Networks）</news:title>
   <news:publication_date>2026-05-08T06:44:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687769</loc>
  <lastmod>2026-05-08T06:43:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>偏極深部散乱における重フレーバー生成のNLO QCD修正（Next-to-Leading Order QCD Corrections to Inclusive Heavy-Flavor Production in Polarized Deep-Inelastic Scattering）</news:title>
   <news:publication_date>2026-05-08T06:43:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687767</loc>
  <lastmod>2026-05-08T05:52:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>コンテキスト対応アプリのためのオントロジー駆動推論フレームワーク（An Ontology-Based Reasoning Framework for Context-Aware Applications）</news:title>
   <news:publication_date>2026-05-08T05:52:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687765</loc>
  <lastmod>2026-05-08T05:52:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>属性駆動型アクティブラーニングによるアイテムのコールドスタート問題への対処（Addressing the Item Cold-start Problem by Attribute-driven Active Learning）</news:title>
   <news:publication_date>2026-05-08T05:52:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687763</loc>
  <lastmod>2026-05-08T05:52:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>イラスト画像分類のための転移学習（Transfer Learning for Illustration Classification）</news:title>
   <news:publication_date>2026-05-08T05:52:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687761</loc>
  <lastmod>2026-05-08T05:51:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動的占有格子地図上での深層物体追跡（Deep Object Tracking on Dynamic Occupancy Grid Maps Using RNNs）</news:title>
   <news:publication_date>2026-05-08T05:51:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687759</loc>
  <lastmod>2026-05-08T05:51:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>刺激と神経状態の一対一写像—記憶と分類（One-to-one mapping between stimulus and neural state: Memory and classification）</news:title>
   <news:publication_date>2026-05-08T05:51:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687757</loc>
  <lastmod>2026-05-08T05:51:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>音楽活動が生徒の技能に与える影響を推定する二重機械学習アプローチ（A Double Machine Learning Approach to Estimate the Effects of Musical Practice on Student’s Skills）</news:title>
   <news:publication_date>2026-05-08T05:51:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687755</loc>
  <lastmod>2026-05-08T05:50:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>対称性を保つ機械学習原子間ポテンシャルの実装（End-to-end Symmetry Preserving Inter-atomic Potential Energy Model for Finite and Extended Systems）</news:title>
   <news:publication_date>2026-05-08T05:50:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687753</loc>
  <lastmod>2026-05-08T04:59:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自己注意を用いたメッセージ関連応答生成（Self-Attention-Based Message-Relevant Response Generation for Neural Conversation Model）</news:title>
   <news:publication_date>2026-05-08T04:59:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687751</loc>
  <lastmod>2026-05-08T04:59:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数暗号通貨市場における戦略的マイニングのゲーム化と報酬設計（Game of Coins）</news:title>
   <news:publication_date>2026-05-08T04:59:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687749</loc>
  <lastmod>2026-05-08T04:59:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>RGBと熱画像の追跡ベンチマークと基準手法（RGB-T Object Tracking: Benchmark and Baseline）</news:title>
   <news:publication_date>2026-05-08T04:59:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687747</loc>
  <lastmod>2026-05-08T04:57:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深度順位で3次元姿勢を解く：DRPose3D（DRPose3D: Depth Ranking in 3D Human Pose Estimation）</news:title>
   <news:publication_date>2026-05-08T04:57:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687745</loc>
  <lastmod>2026-05-08T04:57:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>優れたImageNetモデルは他タスクでも強いのか（Do Better ImageNet Models Transfer Better?）</news:title>
   <news:publication_date>2026-05-08T04:57:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687743</loc>
  <lastmod>2026-05-08T04:57:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Particle Filter Networksによる視覚的自己位置推定の学習（Particle Filter Networks with Application to Visual Localization）</news:title>
   <news:publication_date>2026-05-08T04:57:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687741</loc>
  <lastmod>2026-05-08T04:57:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>思考する顕微鏡へ――光学顕微鏡と画像再構成におけるDeep Learningの到来（Toward a Thinking Microscope: Deep Learning in Optical Microscopy and Image Reconstruction）</news:title>
   <news:publication_date>2026-05-08T04:57:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687739</loc>
  <lastmod>2026-05-08T04:05:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>意味的ネットワーク解釈（Semantic Network Interpretation）</news:title>
   <news:publication_date>2026-05-08T04:05:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687737</loc>
  <lastmod>2026-05-08T04:04:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>強化学習における盲点の発見（Discovering Blind Spots in Reinforcement Learning）</news:title>
   <news:publication_date>2026-05-08T04:04:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687735</loc>
  <lastmod>2026-05-08T04:04:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動的ニューラルネットワークによる辞書学習（Dictionary Learning by Dynamical Neural Networks）</news:title>
   <news:publication_date>2026-05-08T04:04:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687733</loc>
  <lastmod>2026-05-08T04:03:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>乳房腫瘍の可視化と診断説明を目指すICADx（ICADx: Interpretable computer aided diagnosis of breast masses）</news:title>
   <news:publication_date>2026-05-08T04:03:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687731</loc>
  <lastmod>2026-05-08T04:03:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>加重確率的ブロックモデルにおけるハイパーグラフ・スペクトルクラスタリング（Hypergraph Spectral Clustering in the Weighted Stochastic Block Model）</news:title>
   <news:publication_date>2026-05-08T04:03:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687729</loc>
  <lastmod>2026-05-08T04:03:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GANを用いた半教師あり学習とマニフォールド正則化（SEMI-SUPERVISED LEARNING WITH GANS: REVISITING MANIFOLD REGULARIZATION）</news:title>
   <news:publication_date>2026-05-08T04:03:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687727</loc>
  <lastmod>2026-05-08T04:02:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ブランド単位のランキングシステムとAttention-GRUの応用（A Brand-level Ranking System with the Customized Attention-GRU Model）</news:title>
   <news:publication_date>2026-05-08T04:02:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687725</loc>
  <lastmod>2026-05-08T03:11:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>強相関フェルミ超流体における臨界渦放出の実験的解明（Critical Vortex Shedding in a Strongly Interacting Fermionic Superfluid）</news:title>
   <news:publication_date>2026-05-08T03:11:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687723</loc>
  <lastmod>2026-05-08T03:10:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Stack Overflow由来の自然言語とコードの整列ペア抽出法（Learning to Mine Aligned Code and Natural Language Pairs from Stack Overflow）</news:title>
   <news:publication_date>2026-05-08T03:10:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687721</loc>
  <lastmod>2026-05-08T03:10:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>同時強化学習におけるスケーラブルな協調探索（Scalable Coordinated Exploration in Concurrent Reinforcement Learning）</news:title>
   <news:publication_date>2026-05-08T03:10:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687719</loc>
  <lastmod>2026-05-08T03:09:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>AutoPruner: エンドツーエンドで学習できるフィルタプルーニング手法（AutoPruner: An End-to-End Trainable Filter Pruning Method for Efficient Deep Model Inference）</news:title>
   <news:publication_date>2026-05-08T03:09:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687717</loc>
  <lastmod>2026-05-08T03:09:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GPGPU上でのDNN学習高速化のための近似ランダムドロップアウト（Approximate Random Dropout for DNN training acceleration in GPGPU）</news:title>
   <news:publication_date>2026-05-08T03:09:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687715</loc>
  <lastmod>2026-05-08T03:09:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>産業用ビンピッキングの学習的手法（Learning Based Industrial Bin-picking Trained with Approximate Physics Simulator）</news:title>
   <news:publication_date>2026-05-08T03:09:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687713</loc>
  <lastmod>2026-05-08T03:09:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模建物抽出におけるCNNの実用化とその意義（Building Extraction at Scale using Convolutional Neural Network: Mapping of the United States）</news:title>
   <news:publication_date>2026-05-08T03:09:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687711</loc>
  <lastmod>2026-05-08T02:17:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模ニューロモルフィック・スパイキングアレイ処理器の探求（Large-Scale Neuromorphic Spiking Array Processors: A quest to mimic the brain）</news:title>
   <news:publication_date>2026-05-08T02:17:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687709</loc>
  <lastmod>2026-05-08T02:16:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフを知らなくても最適に近い意思決定ができる？（Analysis of Thompson Sampling for Graphical Bandits Without the Graphs）</news:title>
   <news:publication_date>2026-05-08T02:16:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687707</loc>
  <lastmod>2026-05-08T02:16:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>近似Newtonベースの確率的勾配のみを用いた統計的推定（Approximate Newton-based statistical inference using only stochastic gradients）</news:title>
   <news:publication_date>2026-05-08T02:16:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687705</loc>
  <lastmod>2026-05-08T02:15:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>状況認識を用いたミリ波ビーム予測（MmWave Beam Prediction with Situational Awareness: A Machine Learning Approach）</news:title>
   <news:publication_date>2026-05-08T02:15:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687703</loc>
  <lastmod>2026-05-08T02:15:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分布を意識したアクティブラーニング（Distribution Aware Active Learning）</news:title>
   <news:publication_date>2026-05-08T02:15:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687701</loc>
  <lastmod>2026-05-08T02:15:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文字特徴を動的に統合して中国語の意図分類を改善する（Enhancing Chinese Intent Classification by Dynamically Integrating Character Features into Word Embeddings with Ensemble Techniques）</news:title>
   <news:publication_date>2026-05-08T02:15:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687699</loc>
  <lastmod>2026-05-08T02:15:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>アモータイズド推論の正則化（Amortized Inference Regularization）</news:title>
   <news:publication_date>2026-05-08T02:15:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687697</loc>
  <lastmod>2026-05-08T01:23:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>AffinityNetによる少量ラベル学習の実用性と病型推定への応用（AffinityNet: Semi-supervised Few-shot Learning for Disease Type Prediction）</news:title>
   <news:publication_date>2026-05-08T01:23:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687695</loc>
  <lastmod>2026-05-08T01:23:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GPUメモリ使用量を削減するコンパイラEcho（Echo: Compiler-based GPU Memory Footprint Reduction for LSTM RNN Training）</news:title>
   <news:publication_date>2026-05-08T01:23:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687693</loc>
  <lastmod>2026-05-08T01:22:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>教室における教師の認知—視線と一人称動画で解き明かす授業観察（Teachers’ Perception in the Classroom）</news:title>
   <news:publication_date>2026-05-08T01:22:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687691</loc>
  <lastmod>2026-05-08T01:22:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチタスク最大因果エントロピー逆強化学習（Multi-task Maximum Causal Entropy Inverse Reinforcement Learning）</news:title>
   <news:publication_date>2026-05-08T01:22:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687689</loc>
  <lastmod>2026-05-08T01:22:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>新卒ソフトウェア人材は現場で通用するか（Are Computer Science and Engineering Graduates Ready for the Software Industry?）</news:title>
   <news:publication_date>2026-05-08T01:22:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687687</loc>
  <lastmod>2026-05-08T01:21:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Afﬁnity Network Fusionによるがん患者クラスタリングと半教師あり学習（Affinity Network Fusion and Semi-supervised Learning for Cancer Patient Clustering）</news:title>
   <news:publication_date>2026-05-08T01:21:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687685</loc>
  <lastmod>2026-05-08T01:21:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ステップサイズが深層学習にもたらす本質的影響（Step Size Matters in Deep Learning）</news:title>
   <news:publication_date>2026-05-08T01:21:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687683</loc>
  <lastmod>2026-05-08T00:30:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ARiA：Richardの曲線を活用して活性化関数の非単調性を制御する手法（ARiA: Utilizing Richard’s Curve for Controlling the Non-monotonicity of the Activation Function in Deep Neural Nets）</news:title>
   <news:publication_date>2026-05-08T00:30:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687681</loc>
  <lastmod>2026-05-08T00:30:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>対立的ラベリング学習による弱教師あり分類の堅牢化（Adversarial Label Learning）</news:title>
   <news:publication_date>2026-05-08T00:30:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687679</loc>
  <lastmod>2026-05-08T00:30:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ハイパーグラフ一致による教師なしドメイン適応（UNSUPERVISED DOMAIN ADAPTATION USING REGULARIZED HYPER-GRAPH MATCHING）</news:title>
   <news:publication_date>2026-05-08T00:30:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687677</loc>
  <lastmod>2026-05-08T00:29:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的勾配下でのアンダーダンパード・ランジュバン法の離散化と評価（Langevin Markov Chain Monte Carlo with stochastic gradients）</news:title>
   <news:publication_date>2026-05-08T00:29:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687675</loc>
  <lastmod>2026-05-08T00:29:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>反事実平均埋め込み（Counterfactual Mean Embeddings）</news:title>
   <news:publication_date>2026-05-08T00:29:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687673</loc>
  <lastmod>2026-05-08T00:29:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層生成モデルによる最適ノイズ除去（Rate-Optimal Denoising with Deep Neural Networks）</news:title>
   <news:publication_date>2026-05-08T00:29:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687671</loc>
  <lastmod>2026-05-08T00:28:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>医療画像翻訳における分布一致損失が特徴を</news:title>
   <news:publication_date>2026-05-08T00:28:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687669</loc>
  <lastmod>2026-05-07T23:37:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>量子スロー・フィーチャー解析によるMNIST分類（Classification of MNIST dataset with Quantum Slow Feature Analysis）</news:title>
   <news:publication_date>2026-05-07T23:37:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687667</loc>
  <lastmod>2026-05-07T23:37:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>クラスタリングの本質はモデル選択にあり（Clustering - What Both Theoreticians and Practitioners are Doing Wrong）</news:title>
   <news:publication_date>2026-05-07T23:37:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687665</loc>
  <lastmod>2026-05-07T23:36:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>敵対的損失による非パラメトリック密度推定（Nonparametric Density Estimation with Adversarial Losses）</news:title>
   <news:publication_date>2026-05-07T23:36:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687663</loc>
  <lastmod>2026-05-07T23:36:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>無限タスク学習の枠組みと応用（Infinite Task Learning in RKHSs）</news:title>
   <news:publication_date>2026-05-07T23:36:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687661</loc>
  <lastmod>2026-05-07T23:36:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ヒトが注目する場所を学ぶ注意機構の学習（LEARNING WHAT AND WHERE TO ATTEND）</news:title>
   <news:publication_date>2026-05-07T23:36:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687659</loc>
  <lastmod>2026-05-07T23:35:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層生成ネットワークによる地震波速度逆算の高速化（Rapid seismic domain transfer: Seismic velocity inversion and modeling using deep generative neural networks）</news:title>
   <news:publication_date>2026-05-07T23:35:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687657</loc>
  <lastmod>2026-05-07T23:34:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>惑星の居住可能性分類における機械学習の洞察（Habitability Classification of Exoplanets: A Machine Learning Insight）</news:title>
   <news:publication_date>2026-05-07T23:34:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687655</loc>
  <lastmod>2026-05-07T22:43:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>変形部品ネットワークによる姿勢不変特徴学習（Deformable Part Networks）</news:title>
   <news:publication_date>2026-05-07T22:43:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687653</loc>
  <lastmod>2026-05-07T22:43:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>脳画像の複数ビューを統合するGraphニューラル法でPD判別精度が飛躍的に向上（Multi-View Graph Convolutional Network and Its Applications on Neuroimage Analysis for Parkinson’s Disease）</news:title>
   <news:publication_date>2026-05-07T22:43:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687651</loc>
  <lastmod>2026-05-07T22:43:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>資源に敏感なマルチ解像度人物再識別（Resource Aware Person Re-identification across Multiple Resolutions）</news:title>
   <news:publication_date>2026-05-07T22:43:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687649</loc>
  <lastmod>2026-05-07T22:42:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>通信量を劇的に削る分散学習の工夫（Sparse Binary Compression: Towards Distributed Deep Learning with minimal Communication）</news:title>
   <news:publication_date>2026-05-07T22:42:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687647</loc>
  <lastmod>2026-05-07T22:41:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>畳み込み特徴マップに基づく交通検出・分類向け深層ネットワーク（A Convolutional Feature Map based Deep Network targeted towards Traffic Detection and Classification）</news:title>
   <news:publication_date>2026-05-07T22:41:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687645</loc>
  <lastmod>2026-05-07T22:41:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>地球内に蓄積する相互作用型ダークマターの分布と制約（Dark Matter that Interacts with Baryons: Density Distribution within the Earth and New Constraints on the Interaction Cross-section）</news:title>
   <news:publication_date>2026-05-07T22:41:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687643</loc>
  <lastmod>2026-05-07T22:41:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>平均場理論が示す活性化関数の本質（Mean Field Theory of Activation Functions in Deep Neural Networks）</news:title>
   <news:publication_date>2026-05-07T22:41:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687641</loc>
  <lastmod>2026-05-07T21:49:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>半教師あり学習の利得と限界（On semi-supervised learning）</news:title>
   <news:publication_date>2026-05-07T21:49:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687639</loc>
  <lastmod>2026-05-07T21:49:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>偽ニュース検出のための深層拡散ニューラルネットワーク（FAKEDETECTOR: Effective Fake News Detection with Deep Diffusive Neural Network）</news:title>
   <news:publication_date>2026-05-07T21:49:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687637</loc>
  <lastmod>2026-05-07T21:48:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>HST/COS近傍の銀河赤方偏移サーベイの意義（A Galaxy Redshift Survey Near HST/COS AGN Sight Lines）</news:title>
   <news:publication_date>2026-05-07T21:48:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687635</loc>
  <lastmod>2026-05-07T21:47:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>EgoCoderによるインテリジェントなプログラム合成（EgoCoder: Intelligent Program Synthesis with Hierarchical Sequential Neural Network Model）</news:title>
   <news:publication_date>2026-05-07T21:47:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687633</loc>
  <lastmod>2026-05-07T21:47:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューラルネットの線形領域を数える熱帯幾何学的アプローチ（A Tropical Approach to Neural Networks with Piecewise Linear Activations）</news:title>
   <news:publication_date>2026-05-07T21:47:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687631</loc>
  <lastmod>2026-05-07T21:47:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CascadeCNN: 量子化の性能限界を押し上げる手法（CascadeCNN: Pushing the performance limits of quantisation）</news:title>
   <news:publication_date>2026-05-07T21:47:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687629</loc>
  <lastmod>2026-05-07T21:46:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>構造化勾配正則化による敵対的耐性の強化（Adversarially Robust Training through Structured Gradient Regularization）</news:title>
   <news:publication_date>2026-05-07T21:46:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687627</loc>
  <lastmod>2026-05-07T20:55:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>バスケット補完のためのWord2Vecの敵対的訓練（Adversarial Training of Word2Vec for Basket Completion）</news:title>
   <news:publication_date>2026-05-07T20:55:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687625</loc>
  <lastmod>2026-05-07T20:53:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分布ロバスト最適化を用いた効率的確率的勾配降下法（Efficient Stochastic Gradient Descent for Learning with Distributionally Robust Optimization）</news:title>
   <news:publication_date>2026-05-07T20:53:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687623</loc>
  <lastmod>2026-05-07T20:53:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>データから幅と深さを自動で決める省力的ベイズ深層ネットワーク（Parsimonious Bayesian deep networks）</news:title>
   <news:publication_date>2026-05-07T20:53:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687621</loc>
  <lastmod>2026-05-07T20:51:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ランキングにおける露出格差を減らす学習法（Reducing Disparate Exposure in Ranking: A Learning To Rank Approach）</news:title>
   <news:publication_date>2026-05-07T20:51:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687619</loc>
  <lastmod>2026-05-07T20:51:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>食連星におけるセファイドから何が分かるか（Cepheids in Eclipsing Binaries. What and How We Can Learn About Them）</news:title>
   <news:publication_date>2026-05-07T20:51:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687617</loc>
  <lastmod>2026-05-07T20:51:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自己教師ありマルチビュー人物同定と応用（Self-supervised Multi-view Person Association and Its Applications）</news:title>
   <news:publication_date>2026-05-07T20:51:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687615</loc>
  <lastmod>2026-05-07T20:50:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>海水中40K崩壊を用いたANTARES光学モジュール効率の長期モニタリング（Long-term monitoring of the ANTARES optical module efficiencies using 40K decays in sea water）</news:title>
   <news:publication_date>2026-05-07T20:50:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687613</loc>
  <lastmod>2026-05-07T19:59:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オートエンコーディング変分ベイズにおける情報制約（Information Constraints on Auto-Encoding Variational Bayes）</news:title>
   <news:publication_date>2026-05-07T19:59:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687611</loc>
  <lastmod>2026-05-07T19:59:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>たった1個のニューロン追加で消える悪い局所最適解（Adding One Neuron Can Eliminate All Bad Local Minima）</news:title>
   <news:publication_date>2026-05-07T19:59:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687609</loc>
  <lastmod>2026-05-07T19:58:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>空間相関を明示的に扱う構造化ベイズGP-LVM（Structured Bayesian Gaussian process latent variable model）</news:title>
   <news:publication_date>2026-05-07T19:58:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687607</loc>
  <lastmod>2026-05-07T19:57:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単語レベル整合を伴う階層型注意戦略によるマルチモーダル感情解析（Multimodal Affective Analysis Using Hierarchical Attention Strategy with Word-Level Alignment）</news:title>
   <news:publication_date>2026-05-07T19:57:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687605</loc>
  <lastmod>2026-05-07T19:57:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所化された複数カーネル学習を高速に解くLMKL-Net（LMKL-Net: A Fast Localized Multiple Kernel Learning Solver via Deep Neural Networks）</news:title>
   <news:publication_date>2026-05-07T19:57:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687603</loc>
  <lastmod>2026-05-07T19:56:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>堅牢な条件付き生成敵対ネットワーク（ROBUST CONDITIONAL GENERATIVE ADVERSARIAL NETWORKS）</news:title>
   <news:publication_date>2026-05-07T19:56:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687601</loc>
  <lastmod>2026-05-07T19:56:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>COCO-CNによる多言語画像注釈と検索の基盤（COCO-CN for Cross-Lingual Image Tagging, Captioning and Retrieval）</news:title>
   <news:publication_date>2026-05-07T19:56:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687599</loc>
  <lastmod>2026-05-07T19:04:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>普遍的識別量子ニューラルネットワーク（Universal discriminative quantum neural networks）</news:title>
   <news:publication_date>2026-05-07T19:04:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687597</loc>
  <lastmod>2026-05-07T19:04:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数統計量を動的に選ぶ近似ベイズ計算の実務的意義（Multi-Statistic Approximate Bayesian Computation with Multi-Armed Bandits）</news:title>
   <news:publication_date>2026-05-07T19:04:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687595</loc>
  <lastmod>2026-05-07T19:03:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>補助変数を用いた非線形独立成分分析と一般化コントラスト学習（Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learning）</news:title>
   <news:publication_date>2026-05-07T19:03:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687593</loc>
  <lastmod>2026-05-07T19:01:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>タンパク質配列の確率文脈自由文法推定と接触マップ制約（Estimating probabilistic context-free grammars for proteins using contact map constraints）</news:title>
   <news:publication_date>2026-05-07T19:01:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687591</loc>
  <lastmod>2026-05-07T19:01:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>リプレイ攻撃に対するCNNベースのエンドツーエンド検出の検証（A Study On Convolutional Neural Network Based End-To-End Replay Anti-Spoofing）</news:title>
   <news:publication_date>2026-05-07T19:01:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687589</loc>
  <lastmod>2026-05-07T19:00:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>組み込み機器における深層学習推論：固定小数点とポジット（Deep Learning Inference on Embedded Devices: Fixed-Point vs Posit）</news:title>
   <news:publication_date>2026-05-07T19:00:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687587</loc>
  <lastmod>2026-05-07T19:00:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>コスト意識のあるカスケード型バンディット（Cost-aware Cascading Bandits）</news:title>
   <news:publication_date>2026-05-07T19:00:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687585</loc>
  <lastmod>2026-05-07T18:08:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所最適とベイズ最適の最適な切り替え――BLOSSOMが示した効率的探索と停止基準 (Optimization, fast and slow: optimally switching between local and Bayesian optimization)</news:title>
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   <news:genres>Blog</news:genres>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ヒート拡散による深層特徴集約と画像再ランキング（Deep Feature Aggregation and Image Re-ranking with Heat Diffusion for Image Retrieval）</news:title>
   <news:publication_date>2026-05-07T18:08:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/687581</loc>
  <lastmod>2026-05-07T18:07:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>選択バイアスに強い方策改善（Confounding-Robust Policy Improvement）</news:title>
   <news:publication_date>2026-05-07T18:07:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
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  <loc>https://aibr.jp/archives/687579</loc>
  <lastmod>2026-05-07T18:07:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ロボット操作のためのフレームワーク（A Framework for Robot Manipulation: Skill Formalism, Meta Learning and Adaptive Control）</news:title>
   <news:publication_date>2026-05-07T18:07:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/687577</loc>
  <lastmod>2026-05-07T18:06:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ロジスティック回帰のコアセットに関する研究（On Coresets for Logistic Regression）</news:title>
   <news:publication_date>2026-05-07T18:06:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/687575</loc>
  <lastmod>2026-05-07T18:06:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>活性化関数のボトルネックを破る適応的パラメータ化（Breaking the Activation Function Bottleneck through Adaptive Parameterization）</news:title>
   <news:publication_date>2026-05-07T18:06:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/687573</loc>
  <lastmod>2026-05-07T18:06:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>インタラクティブ学習に信頼をもたらす説明の枠組み（Why Should I Trust Interactive Learners? Explaining Interactive Queries of Classifiers to Users）</news:title>
   <news:publication_date>2026-05-07T18:06:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
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 <url>
  <loc>https://aibr.jp/archives/687571</loc>
  <lastmod>2026-05-07T17:14:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチルーム環境における予測可能性と遅い特徴解析を用いた全域ナビゲーション（Global Navigation Using Predictable and Slow Feature Analysis in Multiroom Environments, Path Planning and Other Control Tasks）</news:title>
   <news:publication_date>2026-05-07T17:14:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/687569</loc>
  <lastmod>2026-05-07T17:13:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>2Dレーザーレンジファインダーを用いたパレット検出データセット（A 2D laser rangefinder scans dataset of standard EUR pallets）</news:title>
   <news:publication_date>2026-05-07T17:13:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/687567</loc>
  <lastmod>2026-05-07T17:12:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多様な世界で最良を選ぶ：オンライン教師あり学習のロバストなモデル選択（Best of many worlds: Robust model selection for online supervised learning）</news:title>
   <news:publication_date>2026-05-07T17:12:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
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 <url>
  <loc>https://aibr.jp/archives/687565</loc>
  <lastmod>2026-05-07T17:12:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ハッシュトリックの完全理解（Fully Understanding the Hashing Trick）</news:title>
   <news:publication_date>2026-05-07T17:12:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687563</loc>
  <lastmod>2026-05-07T17:12:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチタスクグラフ上の学習 — Part I: 安定性解析 (Learning over Multitask Graphs – Part I: Stability Analysis)</news:title>
   <news:publication_date>2026-05-07T17:12:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687561</loc>
  <lastmod>2026-05-07T17:11:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>音楽源分離におけるスタックド・アワーグラスネットワーク（Music Source Separation Using Stacked Hourglass Networks）</news:title>
   <news:publication_date>2026-05-07T17:11:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687559</loc>
  <lastmod>2026-05-07T17:10:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチタスクグラフ上の学習 — 性能解析（Learning over Multitask Graphs – Part II: Performance Analysis）</news:title>
   <news:publication_date>2026-05-07T17:10:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687557</loc>
  <lastmod>2026-05-07T16:19:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>パラメータ—関数マップの単純さバイアスが深層学習の一般化を説明する（DEEP LEARNING GENERALIZES BECAUSE THE PARAMETER-FUNCTION MAP IS BIASED TOWARDS SIMPLE FUNCTIONS）</news:title>
   <news:publication_date>2026-05-07T16:19:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687555</loc>
  <lastmod>2026-05-07T16:17:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>次元が与えられたネットワークにおける加速的ゴシップ法（ACCELERATED GOSSIP IN NETWORKS OF GIVEN DIMENSION USING JACOBI POLYNOMIAL ITERATIONS）</news:title>
   <news:publication_date>2026-05-07T16:17:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687553</loc>
  <lastmod>2026-05-07T16:16:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>部分集合最小化のための安全要素スクリーニング（Safe Element Screening for Submodular Function Minimization）</news:title>
   <news:publication_date>2026-05-07T16:16:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687551</loc>
  <lastmod>2026-05-07T16:14:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>組み込み型分析データベース MonetDBLite の実装と意義（MonetDBLite: An Embedded Analytical Database）</news:title>
   <news:publication_date>2026-05-07T16:14:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687549</loc>
  <lastmod>2026-05-07T16:13:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>生成的音色空間：知覚メトリクスで正則化した変分オートエンコーダ（GENERATIVE TIMBRE SPACES: REGULARIZING VARIATIONAL AUTO-ENCODERS WITH PERCEPTUAL METRICS）</news:title>
   <news:publication_date>2026-05-07T16:13:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687547</loc>
  <lastmod>2026-05-07T16:13:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>画質をピクセル単位で「見る」技術が変える評価の現場（Blind Predicting Similar Quality Map for Image Quality Assessment）</news:title>
   <news:publication_date>2026-05-07T16:13:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687545</loc>
  <lastmod>2026-05-07T16:12:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>暗黙的再パラメータ化勾配（Implicit Reparameterization Gradients）</news:title>
   <news:publication_date>2026-05-07T16:12:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687543</loc>
  <lastmod>2026-05-07T15:20:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフを使ったプログラム生成モデル（GENERATIVE CODE MODELING WITH GRAPHS）</news:title>
   <news:publication_date>2026-05-07T15:20:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687541</loc>
  <lastmod>2026-05-07T15:20:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>知識ベースの全畳み込みネットワークと肺CT画像セグメンテーションへの応用（Knowledge-based Fully Convolutional Network and Its Application in Segmentation of Lung CT Images）</news:title>
   <news:publication_date>2026-05-07T15:20:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687539</loc>
  <lastmod>2026-05-07T15:19:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散非同期勾配降下法におけるエネルギー整合（Gradient Energy Matching for Distributed Asynchronous Gradient Descent）</news:title>
   <news:publication_date>2026-05-07T15:19:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687537</loc>
  <lastmod>2026-05-07T15:18:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>再編成による潜在凸テンソル分解の完全回復（Beyond Unfolding: Exact Recovery of Latent Convex Tensor Decomposition under Reshuffling）</news:title>
   <news:publication_date>2026-05-07T15:18:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687535</loc>
  <lastmod>2026-05-07T15:18:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>テンソルリングにおけるランク最小化：スケーラブルなテンソル分解と補完の新パラダイム (Rank Minimization on Tensor Ring: A New Paradigm in Scalable Tensor Decomposition and Completion)</news:title>
   <news:publication_date>2026-05-07T15:18:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687533</loc>
  <lastmod>2026-05-07T15:17:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ヘッセ行列を用いたメタ学習による深層ニューラルネット訓練（Meta-Learning with Hessian-Free Approach in Deep Neural Nets Training）</news:title>
   <news:publication_date>2026-05-07T15:17:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687531</loc>
  <lastmod>2026-05-07T15:16:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>集約出力に対する変分学習とガウス過程（Variational Learning on Aggregate Outputs with Gaussian Processes）</news:title>
   <news:publication_date>2026-05-07T15:16:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
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