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   <news:title>特徴次元に最適化されたパラメトリックQ学習（Sample-Optimal Parametric Q-Learning Using Linearly Additive Features）</news:title>
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   <news:title>拡張変分推論の収束性に関する考察（On the Convergence of Extended Variational Inference for Non-Gaussian Statistical Models）</news:title>
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   <news:title>半教師あり学習における効率的な交差検証の近似（Efficient Cross-Validation for Semi-Supervised Learning）</news:title>
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   <news:title>軽量なプライバシー保護型協調学習とIoT端末のための設計（On Lightweight Privacy-Preserving Collaborative Learning for Internet-of-Things Objects）</news:title>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>幅広いニューラルネットワークのスケーリング限界（Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation）</news:title>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>セッション内の連続的スキップ予測（Session-based Sequential Skip Prediction via Recurrent Neural Networks）</news:title>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>集合ベースの顔認識を変えた多プロトタイプ学習（Multi-Prototype Networks for Unconstrained Set-based Face Recognition）</news:title>
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  <lastmod>2026-08-06T15:03:52Z</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>一様収束（ユニフォームコンバージェンス）は深層学習の汎化を説明できないかもしれない（Uniform convergence may be unable to explain generalization in deep learning）</news:title>
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   <news:genres>Blog</news:genres>
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    <news:language>ja</news:language>
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   <news:title>候補者選別を学ぶ（Learning to Screen）</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>生物輸送モデルのための偏微分方程式学習（Learning partial differential equations for biological transport models from noisy spatiotemporal data）</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>安定インスタンスによる頑健なマルチインスタンス学習（Robust Multi-Instance Learning with Stable Instances）</news:title>
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   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/720189</loc>
  <lastmod>2026-08-06T14:11:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>フィード配信推薦における長期エンゲージメント最適化（Reinforcement Learning to Optimize Long-term User Engagement in Recommender Systems）</news:title>
   <news:publication_date>2026-08-06T14:11:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/720187</loc>
  <lastmod>2026-08-06T14:11:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>独立欠損を許容するイジングモデルの学習（Learning Ising Models with Independent Failures）</news:title>
   <news:publication_date>2026-08-06T14:11:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/720185</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>精密な3D細胞分割法（ACCURATE 3D CELL SEGMENTATION USING DEEP FEATURE AND CRF REFINEMENT）</news:title>
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   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/720183</loc>
  <lastmod>2026-08-06T14:10:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>マルチモーダル関心関連アイテム類似度モデル（Multimodal Interest-Related Item Similarity for Top-N Recommendation）</news:title>
   <news:publication_date>2026-08-06T14:10:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/720181</loc>
  <lastmod>2026-08-06T14:10:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>衛星画像で食料安全保障を予測するCNNと衛星タスク化（Predicting Food Security Outcomes Using CNNs for Satellite Tasking）</news:title>
   <news:publication_date>2026-08-06T14:10:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/720179</loc>
  <lastmod>2026-08-06T14:10:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>視神経乳頭と杯の自動分割（Automated Segmentation of the Optic Disk and Cup using Dual-Stage Fully Convolutional Networks）</news:title>
   <news:publication_date>2026-08-06T14:10:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/720177</loc>
  <lastmod>2026-08-06T14:10:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>視覚と特徴量に基づく制御方策を同時学習する手法（Simultaneously Learning Vision and Feature-based Control Policies for Real-world Ball-in-a-Cup）</news:title>
   <news:publication_date>2026-08-06T14:10:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/720175</loc>
  <lastmod>2026-08-06T13:18:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>自己適応型単一および複数照明推定フレームワーク（Self-adaptive Single and Multi-illuminant Estimation Framework based on Deep Learning）</news:title>
   <news:publication_date>2026-08-06T13:18:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/720173</loc>
  <lastmod>2026-08-06T13:18:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>神経回路網モデルと深層学習—生物学者のための入門 (Neural network models and deep learning – a primer for biologists)</news:title>
   <news:publication_date>2026-08-06T13:18:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-08-06T13:18:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>ハイパーパラメータ選択のための差分記述長（Differential Description Length for Hyperparameter Selection in Machine Learning）</news:title>
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   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/720169</loc>
  <lastmod>2026-08-06T13:17:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>記憶と一般化の境界：過剰パラメータ化下のアイデンティティ課題（Identity Crisis: Memorization and Generalization under Extreme Overparameterization）</news:title>
   <news:publication_date>2026-08-06T13:17:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/720167</loc>
  <lastmod>2026-08-06T13:17:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>生成モデルのモードカバレッジ再考（Rethinking Generative Mode Coverage: A Pointwise Guaranteed Approach）</news:title>
   <news:publication_date>2026-08-06T13:17:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/720165</loc>
  <lastmod>2026-08-06T13:16:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>概算モデルを用いたロボット学習（Using Approximate Models in Robot Learning）</news:title>
   <news:publication_date>2026-08-06T13:16:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/720163</loc>
  <lastmod>2026-08-06T13:16:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>線形回帰のプライバシーと有用性のトレードオフ（Privacy-Utility Trade-off of Linear Regression under Random Projections and Additive Noise）</news:title>
   <news:publication_date>2026-08-06T13:16:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/720161</loc>
  <lastmod>2026-08-06T12:23:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>勾配を小さくすることの複雑さ（The Complexity of Making the Gradient Small in Stochastic Convex Optimization）</news:title>
   <news:publication_date>2026-08-06T12:23:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/720159</loc>
  <lastmod>2026-08-06T12:23:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>中程度の過学習化で収束を保証する浅いニューラルネットワークの理論（Towards moderate overparameterization: global convergence guarantees for training shallow neural networks）</news:title>
   <news:publication_date>2026-08-06T12:23:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/720157</loc>
  <lastmod>2026-08-06T12:22:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>画像のフォレンジック類似性による改ざん検出の新潮流（Forensic Similarity for Digital Images）</news:title>
   <news:publication_date>2026-08-06T12:22:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
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 <url>
  <loc>https://aibr.jp/archives/720155</loc>
  <lastmod>2026-08-06T12:21:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>心臓・胸部CTにおける直接的な冠動脈石灰化スコア算出（Direct Automatic Coronary Calcium Scoring in Cardiac and Chest CT）</news:title>
   <news:publication_date>2026-08-06T12:21:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/720153</loc>
  <lastmod>2026-08-06T12:21:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>構造化信号の重ね合わせから生成モデルを学ぶ—GANを用いたデノイズとデミキシング（Learning Generative Models of Structured Signals from Their Superposition Using GANs with Application to Denoising and Demixing）</news:title>
   <news:publication_date>2026-08-06T12:21:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/720151</loc>
  <lastmod>2026-08-06T12:21:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>PLIT：植物トランスクリプトームで長鎖非コードRNAを同定するアラインメント不要ツール（PLIT: An alignment-free computational tool for identification of long non-coding RNAs in plant transcriptomic datasets）</news:title>
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   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/720149</loc>
  <lastmod>2026-08-06T12:21:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率単体上の外れ値耐性推定における信頼領域とミニマックス速度（Confidence regions and minimax rates in outlier-robust estimation on the probability simplex）</news:title>
   <news:publication_date>2026-08-06T12:21:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
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  <lastmod>2026-08-06T11:29:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>調整可能な損失関数 α-loss が示す実務上の示唆（A Tunable Loss Function for Binary Classification）</news:title>
   <news:publication_date>2026-08-06T11:29:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/720145</loc>
  <lastmod>2026-08-06T11:28:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>低メモリ環境での適応的前処理を可能にする「Extreme Tensoring」（Extreme Tensoring for Low-Memory Preconditioning）</news:title>
   <news:publication_date>2026-08-06T11:28:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720143</loc>
  <lastmod>2026-08-06T11:28:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>クラウドソースによるノイズ下のPAC学習の実務的意義（Crowdsourced PAC Learning under Classification Noise）</news:title>
   <news:publication_date>2026-08-06T11:28:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720141</loc>
  <lastmod>2026-08-06T11:28:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時系列データ学習における遅延埋め込みとPrecision Annealing（Machine Learning of Time Series Using Time-delay Embedding and Precision Annealing）</news:title>
   <news:publication_date>2026-08-06T11:28:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720139</loc>
  <lastmod>2026-08-06T11:28:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習理論とサポートベクターマシンの入門（LEARNING THEORY AND SUPPORT VECTOR MACHINES - A PRIMER）</news:title>
   <news:publication_date>2026-08-06T11:28:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720137</loc>
  <lastmod>2026-08-06T11:28:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>報酬と制約を動的に両立させる連続制御学習の実務的インパクト（Value constrained model-free continuous control）</news:title>
   <news:publication_date>2026-08-06T11:28:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720135</loc>
  <lastmod>2026-08-06T10:37:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>細粒度GPU共有のためのSalus（Salus: Fine-Grained GPU Sharing Primitives for Deep Learning Applications）</news:title>
   <news:publication_date>2026-08-06T10:37:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720133</loc>
  <lastmod>2026-08-06T10:36:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高解像度衛星画像の屋根セグメンテーションにおける漸進的生成対抗ネットワークの有効性（Progressively Growing GANs for High Resolution Semantic Segmentation of Satellite Images）</news:title>
   <news:publication_date>2026-08-06T10:36:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720131</loc>
  <lastmod>2026-08-06T10:36:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>対照的変分オートエンコーダによる顕在特徴強調（Contrastive Variational Autoencoder Enhances Salient Features）</news:title>
   <news:publication_date>2026-08-06T10:36:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720129</loc>
  <lastmod>2026-08-06T10:35:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>低表面輝度銀河の形成と進化（The formation and evolution of low-surface-brightness galaxies）</news:title>
   <news:publication_date>2026-08-06T10:35:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720127</loc>
  <lastmod>2026-08-06T10:35:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>集団のパラメータ学習の最尤推定（Maximum Likelihood Estimation for Learning Populations of Parameters）</news:title>
   <news:publication_date>2026-08-06T10:35:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720125</loc>
  <lastmod>2026-08-06T10:35:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>少数ショット学習のための無限混合プロトタイプ（Infinite Mixture Prototypes for Few-Shot Learning）</news:title>
   <news:publication_date>2026-08-06T10:35:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720123</loc>
  <lastmod>2026-08-06T10:34:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>フォーナックス矮小球状銀河における第六の星団の再発見（REDISCOVERY OF THE SIXTH STAR CLUSTER IN THE FORNAX DWARF SPHEROIDAL GALAXY）</news:title>
   <news:publication_date>2026-08-06T10:34:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720121</loc>
  <lastmod>2026-08-06T09:42:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ACTRCEによる経験強化――言語で目標を与えて希薄報酬問題を突破する手法（Augmenting Experience via Teacher’s Advice）</news:title>
   <news:publication_date>2026-08-06T09:42:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720119</loc>
  <lastmod>2026-08-06T09:42:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>超電導磁気トラップ中の冷分子間衝突の観測（Collisions between cold molecules in a superconducting magnetic trap）</news:title>
   <news:publication_date>2026-08-06T09:42:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720117</loc>
  <lastmod>2026-08-06T09:42:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ケプラー光度で観測された赤色巨星連星の潮汐と軌道円化（OBSERVATIONS OF TIDES AND CIRCULARIZATION IN RED-GIANT BINARIES FROM KEPLER PHOTOMETRY）</news:title>
   <news:publication_date>2026-08-06T09:42:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720115</loc>
  <lastmod>2026-08-06T09:41:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Binary Stochastic Filteringによる特徴選択とニューラルネットワーク縮小の実務的意義（Binary Stochastic Filtering: a Method for Neural Network Size Minimization and Supervised Feature Selection）</news:title>
   <news:publication_date>2026-08-06T09:41:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720113</loc>
  <lastmod>2026-08-06T09:41:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>獣医病理領域で示された領域同定の優位性（Deep learning algorithms out-perform veterinary pathologists in detecting the mitotically most active tumor region）</news:title>
   <news:publication_date>2026-08-06T09:41:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720111</loc>
  <lastmod>2026-08-06T09:40:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ベイズによる変化点のオンライン予測 (Bayesian Online Prediction of Change Points)</news:title>
   <news:publication_date>2026-08-06T09:40:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720109</loc>
  <lastmod>2026-08-06T08:48:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Fast-SCNNによる高速セマンティックセグメンテーション（Fast-SCNN: Fast Semantic Segmentation Network）</news:title>
   <news:publication_date>2026-08-06T08:48:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720107</loc>
  <lastmod>2026-08-06T08:47:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>外側銀河円盤のA・F星の全空間運動の研究（A study of full space motions of outer Galactic disk A and F stars in two deep pencil-beams）</news:title>
   <news:publication_date>2026-08-06T08:47:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720105</loc>
  <lastmod>2026-08-06T08:47:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>プライバシーの代償：差分プライバシー下での最適収束率（THE COST OF PRIVACY: OPTIMAL RATES OF CONVERGENCE FOR PARAMETER ESTIMATION WITH DIFFERENTIAL PRIVACY）</news:title>
   <news:publication_date>2026-08-06T08:47:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720103</loc>
  <lastmod>2026-08-06T08:46:24Z</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「容量配分」解析が示す設計原理（Capacity allocation analysis of neural networks: A tool for principled architecture design）</news:title>
   <news:publication_date>2026-08-06T08:46:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720101</loc>
  <lastmod>2026-08-06T08:46:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>クエリごとのばらつきを埋めるランキング設計（A Domain Generalization Perspective on Listwise Context Modeling）</news:title>
   <news:publication_date>2026-08-06T08:46:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720099</loc>
  <lastmod>2026-08-06T08:45:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Extended 2Dコンセンサスによる海馬セグメンテーションの実践的意義（Extended 2D Consensus Hippocampus Segmentation）</news:title>
   <news:publication_date>2026-08-06T08:45:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720097</loc>
  <lastmod>2026-08-06T08:45:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>教師ラベルを汚染しないCNNへの新しいバックドア攻撃（A NEW BACKDOOR ATTACK IN CNNS BY TRAINING SET CORRUPTION WITHOUT LABEL POISONING）</news:title>
   <news:publication_date>2026-08-06T08:45:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720095</loc>
  <lastmod>2026-08-06T07:53:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>潜在連続時間確率力学系の解釈可能な学習（Interpretable continuous-time latent stochastic dynamical models）</news:title>
   <news:publication_date>2026-08-06T07:53:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720093</loc>
  <lastmod>2026-08-06T07:53:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>顧客対応チャットボットの回答再ランキングに機械読解を使う意義（Machine Reading Comprehension for Answer Re-Ranking in Customer Support Chatbots）</news:title>
   <news:publication_date>2026-08-06T07:53:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720091</loc>
  <lastmod>2026-08-06T07:53:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>結合学習をEnd-to-Endで訓練すべきか（To Ensemble or Not Ensemble: When does End-To-End Training Fail?）</news:title>
   <news:publication_date>2026-08-06T07:53:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720089</loc>
  <lastmod>2026-08-06T07:52:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフベースのIoTマルウェア検出に対する敵対的学習の検証（Examining Adversarial Learning against Graph-based IoT Malware Detection Systems）</news:title>
   <news:publication_date>2026-08-06T07:52:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720087</loc>
  <lastmod>2026-08-06T07:52:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>TensorSCONE：Intel SGXを用いた安全なTensorFlowフレームワーク（TensorSCONE: A Secure TensorFlow Framework using Intel SGX）</news:title>
   <news:publication_date>2026-08-06T07:52:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720085</loc>
  <lastmod>2026-08-06T07:52:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>検証コード認識における能動学習と深層学習の併用（Verification Code Recognition Based on Active and Deep Learning）</news:title>
   <news:publication_date>2026-08-06T07:52:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720083</loc>
  <lastmod>2026-08-06T07:51:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ピアノの多声音楽自動書き起こしにおけるマルチタスク学習の実践（Multitask Learning for Polyphonic Piano Transcription, a Case Study）</news:title>
   <news:publication_date>2026-08-06T07:51:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720081</loc>
  <lastmod>2026-08-06T07:00:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>資源配分のための適応的確率最適化アルゴリズム（An adaptive stochastic optimization algorithm for resource allocation）</news:title>
   <news:publication_date>2026-08-06T07:00:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720079</loc>
  <lastmod>2026-08-06T07:00:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>画像コントラストの深層ネットワークでの顕在化（Manifestation of Image Contrast in Deep Networks）</news:title>
   <news:publication_date>2026-08-06T07:00:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720077</loc>
  <lastmod>2026-08-06T06:59:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Apollo：あらゆるリードを使える普遍的アセンブリ研磨アルゴリズム（Apollo: A Universal Assembly Polishing Algorithm）</news:title>
   <news:publication_date>2026-08-06T06:59:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720075</loc>
  <lastmod>2026-08-06T06:59:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Gaussian Mean Fieldが示す学習情報の上限による正則化（Gaussian Mean Field Regularizes by Limiting Learned Information）</news:title>
   <news:publication_date>2026-08-06T06:59:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720073</loc>
  <lastmod>2026-08-06T06:59:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-06T06:59:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720071</loc>
  <lastmod>2026-08-06T06:58:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ビッグデータ賞受賞手法による電力グリッド流量の高速・高信頼予測（Winning the Big Data Technologies Horizon Prize: Fast and reliable forecasting of electricity grid traffic by identification of recurrent fluctuations）</news:title>
   <news:publication_date>2026-08-06T06:58:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720069</loc>
  <lastmod>2026-08-06T06:58:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-06T06:58:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720067</loc>
  <lastmod>2026-08-06T06:07:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>優先付けしないオートエンコーダによる画像生成（UNPRIORITIZED AUTOENCODER FOR IMAGE GENERATION）</news:title>
   <news:publication_date>2026-08-06T06:07:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720065</loc>
  <lastmod>2026-08-06T06:07:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>近傍矮小銀河の化学進化を示す惑星状星雲とH II領域の知見（What do planetary nebulae and H ii regions reveal about the chemical evolution of nearby dwarf galaxies?）</news:title>
   <news:publication_date>2026-08-06T06:07:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720063</loc>
  <lastmod>2026-08-06T06:06:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>顔表情認識の精度を3～5%向上させる単純で強力な工夫（Improving Facial Emotion Recognition Systems Using Gradient and Laplacian Images）</news:title>
   <news:publication_date>2026-08-06T06:06:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720061</loc>
  <lastmod>2026-08-06T06:06:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>サターンの深層大気流の解明（Saturn’s deep atmospheric flows revealed by the Cassini Grand Finale gravity measurements）</news:title>
   <news:publication_date>2026-08-06T06:06:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720059</loc>
  <lastmod>2026-08-06T06:06:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>FurcaNeXt: 時間領域での一歩進んだ単一マイク話者分離（FurcaNeXt: End-to-end monaural speech separation with dynamic gated dilated temporal convolutional networks）</news:title>
   <news:publication_date>2026-08-06T06:06:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720057</loc>
  <lastmod>2026-08-06T06:06:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>長短の需要を考慮した次アイテム推薦モデル（A Long-Short Demands-Aware Model for Next-Item Recommendation）</news:title>
   <news:publication_date>2026-08-06T06:06:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720055</loc>
  <lastmod>2026-08-06T06:05:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自己教師ありによる高次センサーフュージョンの実用性（Towards Self-Supervised High Level Sensor Fusion）</news:title>
   <news:publication_date>2026-08-06T06:05:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720053</loc>
  <lastmod>2026-08-06T05:15:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>データベースを直接読む自然言語質問応答（Table2answer: Read the database and answer without SQL）</news:title>
   <news:publication_date>2026-08-06T05:15:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720051</loc>
  <lastmod>2026-08-06T05:14:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ヘテロジニアス無線ネットワークにおける動的コンテンツ更新（Dynamic Content Updates in Heterogeneous Wireless Networks）</news:title>
   <news:publication_date>2026-08-06T05:14:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720049</loc>
  <lastmod>2026-08-06T05:14:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>人の評価で深層強化学習を鍛える（Deep Reinforcement Learning from Policy-Dependent Human Feedback）</news:title>
   <news:publication_date>2026-08-06T05:14:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720047</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>カーネル判別分析におけるスパース特徴選択（Sparse Feature Selection in Kernel Discriminant Analysis via Optimal Scoring）</news:title>
   <news:publication_date>2026-08-06T05:14:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720045</loc>
  <lastmod>2026-08-06T05:14:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>極端・野生モーションの姿勢推定を改善するポストデータ拡張（Post-Data Augmentation to Improve Deep Pose Estimation of Extreme and Wild Motions）</news:title>
   <news:publication_date>2026-08-06T05:14:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720043</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>選択的予測の理論 (A Theory of Selective Prediction)</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720041</loc>
  <lastmod>2026-08-06T05:13:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-06T05:13:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720039</loc>
  <lastmod>2026-08-06T04:22:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-06T04:22:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720037</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>PPGから呼吸波を抽出する深層学習の実用性（RespNet: A deep learning model for extraction of respiration from photoplethysmogram）</news:title>
   <news:publication_date>2026-08-06T04:22:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720035</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-06T04:21:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720033</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-06T04:21:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720031</loc>
  <lastmod>2026-08-06T04:21:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>シミュレーション誘導反復プルーニングによる効率的なネットワーク圧縮（Effective Network Compression Using Simulation-Guided Iterative Pruning）</news:title>
   <news:publication_date>2026-08-06T04:21:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720029</loc>
  <lastmod>2026-08-06T04:20:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>目的間の優先度を組み込んだ多目的ベイズ最適化 (Multi-objective Bayesian optimisation with preferences over objectives)</news:title>
   <news:publication_date>2026-08-06T04:20:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720027</loc>
  <lastmod>2026-08-06T04:20:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Meta Diagramに基づく能動的ソーシャルネットワーク整合（Meta Diagram based Active Social Networks Alignment）</news:title>
   <news:publication_date>2026-08-06T04:20:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720025</loc>
  <lastmod>2026-08-06T03:29:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>VCクラスの敵対的ロバスト学習は不適切学習でのみ可能（VC Classes are Adversarially Robustly Learnable, but Only Improperly）</news:title>
   <news:publication_date>2026-08-06T03:29:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720023</loc>
  <lastmod>2026-08-06T03:29:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マスキング畳み込み生成フロー（MaCow: Masked Convolutional Generative Flow）</news:title>
   <news:publication_date>2026-08-06T03:29:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720021</loc>
  <lastmod>2026-08-06T03:29:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習可能性を高めるための補助軸の追加（Improving learnability of neural networks: adding supplementary axes to disentangle data representation）</news:title>
   <news:publication_date>2026-08-06T03:29:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720019</loc>
  <lastmod>2026-08-06T03:28:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>世界の状態に潜む好み（PREFERENCES IMPLICIT IN THE STATE OF THE WORLD）</news:title>
   <news:publication_date>2026-08-06T03:28:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720017</loc>
  <lastmod>2026-08-06T03:28:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-06T03:28:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720015</loc>
  <lastmod>2026-08-06T03:27:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習による二行要素（TLE）推定の実務的意義（Two-Line Element Estimation Using Machine Learning）</news:title>
   <news:publication_date>2026-08-06T03:27:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720013</loc>
  <lastmod>2026-08-06T03:27:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ディリクレL関数のオイラー積漸近（EULER PRODUCT ASYMPTOTICS FOR DIRICHLET L-FUNCTIONS）</news:title>
   <news:publication_date>2026-08-06T03:27:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720011</loc>
  <lastmod>2026-08-06T02:36:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>リーマン幾何に基づく次元削減と辞書学習の統合理論（Riemannian joint dimensionality reduction and dictionary learning）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720009</loc>
  <lastmod>2026-08-06T02:36:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>二値データ向け近傍中央値シフトクラスタリング（Nearest Neighbor Median Shift Clustering for Binary Data）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720007</loc>
  <lastmod>2026-08-06T02:35:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-06T02:35:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720005</loc>
  <lastmod>2026-08-06T02:34:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720003</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-06T02:34:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720001</loc>
  <lastmod>2026-08-06T02:34:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ReStoCNet：メモリ効率の高いニューロモルフィック計算のための残差確率的バイナリ畳み込みスパイキングニューラルネットワーク（ReStoCNet: Residual Stochastic Binary Convolutional Spiking Neural Network for Memory-Efficient Neuromorphic Computing）</news:title>
   <news:publication_date>2026-08-06T02:34:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719999</loc>
  <lastmod>2026-08-06T02:34:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的強化学習の考え方と実運用インパクト（Stochastic Reinforcement Learning）</news:title>
   <news:publication_date>2026-08-06T02:34:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719997</loc>
  <lastmod>2026-08-06T01:42:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ノイズ下の2次元周期パターンの結晶学的対称性分類の比較（On classification approaches for crystallographic symmetries of noisy 2D periodic patterns）</news:title>
   <news:publication_date>2026-08-06T01:42:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719995</loc>
  <lastmod>2026-08-06T01:42:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>負荷モデルが電力流最適化に与える影響（Impact of Load Models on Power Flow Optimization）</news:title>
   <news:publication_date>2026-08-06T01:42:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719993</loc>
  <lastmod>2026-08-06T01:41:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的不確実性下のトポロジ最適化を確率勾配で効率化する手法（Topology Optimization under Uncertainty using a Stochastic Gradient-based Approach）</news:title>
   <news:publication_date>2026-08-06T01:41:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719991</loc>
  <lastmod>2026-08-06T01:41:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層マルチバイオメトリクスハッシュとニューラル復号による認証学習 (Learning to Authenticate with Deep Multibiometric Hashing and Neural Network Decoding)</news:title>
   <news:publication_date>2026-08-06T01:41:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719989</loc>
  <lastmod>2026-08-06T01:40:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ネットワーク埋め込みで未観測交絡を補正する手法（Using Embeddings to Correct for Unobserved Confounding in Networks）</news:title>
   <news:publication_date>2026-08-06T01:40:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719987</loc>
  <lastmod>2026-08-06T01:40:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自律走行のための安全な深層強化学習フレームワーク（WiseMove: A Framework for Safe Deep Reinforcement Learning for Autonomous Driving）</news:title>
   <news:publication_date>2026-08-06T01:40:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719985</loc>
  <lastmod>2026-08-06T01:40:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>属性誘導型顔画像検索における深層クロスモーダルハッシュと誤り訂正符号の併用（USING DEEP CROSS MODAL HASHING AND ERROR CORRECTING CODES FOR IMPROVING THE EFFICIENCY OF ATTRIBUTE GUIDED FACIAL IMAGE RETRIEVAL）</news:title>
   <news:publication_date>2026-08-06T01:40:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719983</loc>
  <lastmod>2026-08-06T00:49:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>暗号分野における機械学習の応用（Applications of Machine Learning in Cryptography: A Survey）</news:title>
   <news:publication_date>2026-08-06T00:49:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719981</loc>
  <lastmod>2026-08-06T00:49:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オンラインEMのダイバージェンス動機づけと隠れ変数モデルの結合（Divergence-Based Motivation for Online EM and Combining Hidden Variable Models）</news:title>
   <news:publication_date>2026-08-06T00:49:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719979</loc>
  <lastmod>2026-08-06T00:48:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>残差ネットワークは恒等写像からの摂動を学習する（On Residual Networks Learning a Perturbation from Identity）</news:title>
   <news:publication_date>2026-08-06T00:48:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719977</loc>
  <lastmod>2026-08-06T00:48:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>BERTに口ができた：BERTをMarkov Random Field言語モデルとして見る（BERT has a Mouth, and It Must Speak: BERT as a Markov Random Field Language Model）</news:title>
   <news:publication_date>2026-08-06T00:48:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719975</loc>
  <lastmod>2026-08-06T00:47:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Yelpの料理画像識別とそのビジネス的意義（Yelp Food Identification via Image Feature Extraction and Classification）</news:title>
   <news:publication_date>2026-08-06T00:47:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719973</loc>
  <lastmod>2026-08-06T00:47:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的生成深層学習による分子設計の新展開（Probabilistic Generative Deep Learning for Molecular Design）</news:title>
   <news:publication_date>2026-08-06T00:47:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719971</loc>
  <lastmod>2026-08-06T00:47:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Psi-Net：形状と境界を意識した共同マルチタスク深層ネットワークによる医用画像セグメンテーション（Psi-Net: Shape and boundary aware joint multi-task deep network for medical image segmentation）</news:title>
   <news:publication_date>2026-08-06T00:47:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719969</loc>
  <lastmod>2026-08-05T23:55:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>AKARI NEP-Deepデータに基づくfuzzy SVMによるAGN候補選別（AGN selection in the AKARI NEP deep field with the fuzzy SVM algorithm）</news:title>
   <news:publication_date>2026-08-05T23:55:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719967</loc>
  <lastmod>2026-08-05T23:55:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>乱雑相互作用系における競合秩序の多面的機械学習（Multi-faceted machine learning of competing orders in disordered interacting systems）</news:title>
   <news:publication_date>2026-08-05T23:55:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719965</loc>
  <lastmod>2026-08-05T23:55:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多体系量子の効率的変分シミュレーションを可能にする深層自己回帰モデル（Deep autoregressive models for the efficient variational simulation of many-body quantum systems）</news:title>
   <news:publication_date>2026-08-05T23:55:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719963</loc>
  <lastmod>2026-08-05T23:54:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ランキングの公平性のための方策学習（Policy Learning for Fairness in Ranking）</news:title>
   <news:publication_date>2026-08-05T23:54:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719961</loc>
  <lastmod>2026-08-05T23:54:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オンライン学習者行動のデータ駆動型非教師ありクラスタリング（DATA-DRIVEN UNSUPERVISED CLUSTERING OF ONLINE LEARNER BEHAVIOUR）</news:title>
   <news:publication_date>2026-08-05T23:54:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719959</loc>
  <lastmod>2026-08-05T23:54:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械は惑星衝突の結果を学べるか（Can a machine learn the outcome of planetary collisions?）</news:title>
   <news:publication_date>2026-08-05T23:54:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719957</loc>
  <lastmod>2026-08-05T23:03:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ゲージ等変畳み込みネットワークとイコサヘドロンCNN（Gauge Equivariant Convolutional Networks and the Icosahedral CNN）</news:title>
   <news:publication_date>2026-08-05T23:03:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719955</loc>
  <lastmod>2026-08-05T22:54:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>波が運ぶ拡散力学：表面波が粒子拡散を加速する仕組み（Surface waves enhance particle dispersion）</news:title>
   <news:publication_date>2026-08-05T22:54:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719953</loc>
  <lastmod>2026-08-05T22:54:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>StarCraftを用いた協調型マルチエージェント強化学習の標準化（The StarCraft Multi-Agent Challenge）</news:title>
   <news:publication_date>2026-08-05T22:54:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719951</loc>
  <lastmod>2026-08-05T22:54:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>イベント認証と画像再利用検出の深層学習手法（Deep Learning Methods for Event Verification and Image Repurposing Detection）</news:title>
   <news:publication_date>2026-08-05T22:54:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719949</loc>
  <lastmod>2026-08-05T22:53:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>KTBoost：カーネルと木を組み合わせるブースティング（KTBoost: Combined Kernel and Tree Boosting）</news:title>
   <news:publication_date>2026-08-05T22:53:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719947</loc>
  <lastmod>2026-08-05T22:53:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Real Time Recurrent Learningの最適近似とその意義（Optimal Kronecker-Sum Approximation of Real Time Recurrent Learning）</news:title>
   <news:publication_date>2026-08-05T22:53:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719945</loc>
  <lastmod>2026-08-05T22:53:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>全体チェーン推薦（Whole-Chain Recommendations）</news:title>
   <news:publication_date>2026-08-05T22:53:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719943</loc>
  <lastmod>2026-08-05T22:01:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>判別器の一般化と安定化を促す勾配ペナルティ（Improving Generalization and Stability of Generative Adversarial Networks）</news:title>
   <news:publication_date>2026-08-05T22:01:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719941</loc>
  <lastmod>2026-08-05T21:50:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>前向き・後向き確率微分方程式を用いた深層確率最適制御の学習（Learning Deep Stochastic Optimal Control Policies using Forward-Backward SDEs）</news:title>
   <news:publication_date>2026-08-05T21:50:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719939</loc>
  <lastmod>2026-08-05T21:50:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Interaction-Transformationを用いた進化的シンボリック回帰（Interaction-Transformation Evolutionary Algorithm for Symbolic Regression）</news:title>
   <news:publication_date>2026-08-05T21:50:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719937</loc>
  <lastmod>2026-08-05T21:49:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Deep Node Rankingによるネットワークノード埋め込みと分類（Deep Node Ranking for Neuro-symbolic Structural Node Embedding and Classification）</news:title>
   <news:publication_date>2026-08-05T21:49:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719935</loc>
  <lastmod>2026-08-05T21:49:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>小データ環境での物理認識確率的機械学習による粗視化（A physics-aware, probabilistic machine learning framework for coarse-graining high-dimensional systems in the Small Data regime）</news:title>
   <news:publication_date>2026-08-05T21:49:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719933</loc>
  <lastmod>2026-08-05T21:49:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>実世界の携帯データに対する頑健な予測モデル（A Machine Learning based Robust Prediction Model for Real-life Mobile Phone Data）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719931</loc>
  <lastmod>2026-08-05T21:48:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-05T21:48:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-05T20:56:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719927</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-05T20:55:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719925</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-05T20:55:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719923</loc>
  <lastmod>2026-08-05T20:54:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>特徴量ランキングで再構築する動的ネットワーク（Reconstructing dynamical networks via feature ranking）</news:title>
   <news:publication_date>2026-08-05T20:54:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719921</loc>
  <lastmod>2026-08-05T20:54:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時間周波数特徴の敵対的生成（Adversarial Generation of Time-Frequency Features）</news:title>
   <news:publication_date>2026-08-05T20:54:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719919</loc>
  <lastmod>2026-08-05T20:54:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-05T20:54:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719917</loc>
  <lastmod>2026-08-05T20:54:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-05T20:54:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719915</loc>
  <lastmod>2026-08-05T20:03:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Curvelet領域におけるロバスト統計に基づく参照なし画像品質評価（Robust statistics and no-reference image quality assessment in Curvelet domain）</news:title>
   <news:publication_date>2026-08-05T20:03:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719913</loc>
  <lastmod>2026-08-05T20:03:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所的相互作用による大域的協調（Global Collaboration through Local Interaction in Competitive Learning）</news:title>
   <news:publication_date>2026-08-05T20:03:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719911</loc>
  <lastmod>2026-08-05T20:02:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散近似近傍探索による大規模Mean Shiftクラスタリングの効率化（A Distributed and Approximated Nearest Neighbors Algorithm for an Efficient Large Scale Mean Shift Clustering）</news:title>
   <news:publication_date>2026-08-05T20:02:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719909</loc>
  <lastmod>2026-08-05T20:01:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-05T20:01:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719907</loc>
  <lastmod>2026-08-05T20:01:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-05T20:01:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719905</loc>
  <lastmod>2026-08-05T20:01:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-05T20:01:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719903</loc>
  <lastmod>2026-08-05T20:00:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-05T20:00:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719901</loc>
  <lastmod>2026-08-05T19:08:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-05T19:08:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719899</loc>
  <lastmod>2026-08-05T19:08:32Z</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・タスク駆動型データ拡張による医用画像セグメンテーションの進化（Semi-Supervised and Task-Driven Data Augmentation）</news:title>
   <news:publication_date>2026-08-05T19:08:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719897</loc>
  <lastmod>2026-08-05T19:07:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-05T19:07:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719895</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-05T19:06:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719893</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719891</loc>
  <lastmod>2026-08-05T19:05:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-05T19:05:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719889</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-05T18:14:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719887</loc>
  <lastmod>2026-08-05T18:14:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-05T18:14:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719885</loc>
  <lastmod>2026-08-05T18:14:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-05T18:14:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719883</loc>
  <lastmod>2026-08-05T18:13:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719881</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-05T18:13:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719879</loc>
  <lastmod>2026-08-05T18:13:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-05T18:13:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719877</loc>
  <lastmod>2026-08-05T18:12:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719875</loc>
  <lastmod>2026-08-05T17:21:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719873</loc>
  <lastmod>2026-08-05T17:21:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>低精度データを活用してベイズ最適化を高速化する手法（Harnessing Low-Fidelity Data to Accelerate Bayesian Optimization via Posterior Regularization）</news:title>
   <news:publication_date>2026-08-05T17:21:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719871</loc>
  <lastmod>2026-08-05T17:20:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>低推力推進転送の高速評価（Fast Evaluation of Low-Thrust Transfers via Deep Neural Networks）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719869</loc>
  <lastmod>2026-08-05T17:20:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-05T17:20:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフラプラシアン正則化推定器の誤差解析 (Error Analysis on Graph Laplacian Regularized Estimator)</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719865</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多様体最適化を用いたガウス変分近似 (Manifold optimization Assisted Gaussian Variational Approximation)</news:title>
   <news:publication_date>2026-08-05T16:28:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719859</loc>
  <lastmod>2026-08-05T16:28:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動くものを何でも分割する方向性（Towards Segmenting Anything That Moves）</news:title>
   <news:publication_date>2026-08-05T16:28:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719857</loc>
  <lastmod>2026-08-05T16:27:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>切断ガウス混合変分オートエンコーダ（Truncated Gaussian-Mixture Variational AutoEncoder）</news:title>
   <news:publication_date>2026-08-05T16:27:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719855</loc>
  <lastmod>2026-08-05T16:26:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>シンプレクティック離散化による加速――高解像度常微分方程式の役割（Acceleration via Symplectic Discretization of High-Resolution Differential Equations）</news:title>
   <news:publication_date>2026-08-05T16:26:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719853</loc>
  <lastmod>2026-08-05T16:26:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>エッジ置換文法でグラフを生成する手法の要点（Edge Replacement Grammars : A Formal Language Approach for Generating Graphs）</news:title>
   <news:publication_date>2026-08-05T16:26:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719851</loc>
  <lastmod>2026-08-05T16:26:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチクラウド上のNFVにおける障害・性能管理と浅層／深層予測構造（Fault and Performance Management in Multi-Cloud Based NFV using Shallow and Deep Predictive Structures）</news:title>
   <news:publication_date>2026-08-05T16:26:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719849</loc>
  <lastmod>2026-08-05T16:26:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>シミュレーションを活用した一般化学習（Generalization through Simulation: Integrating Simulated and Real Data into Deep Reinforcement Learning for Vision-Based Autonomous Flight）</news:title>
   <news:publication_date>2026-08-05T16:26:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719847</loc>
  <lastmod>2026-08-05T15:33:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SCADAシステムテストベッドによるサイバーセキュリティ研究（SCADA System Testbed for Cybersecurity Research Using Machine Learning Approach）</news:title>
   <news:publication_date>2026-08-05T15:33:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719845</loc>
  <lastmod>2026-08-05T15:23:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>密度推定における最適近似係数の解明（The Optimal Approximation Factor in Density Estimation）</news:title>
   <news:publication_date>2026-08-05T15:23:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719843</loc>
  <lastmod>2026-08-05T15:23:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>電子回折におけるパラダイムシフト（Paradigm shift in electron-based crystallography via machine learning）</news:title>
   <news:publication_date>2026-08-05T15:23:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719841</loc>
  <lastmod>2026-08-05T15:22:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ノイズ付きラベルから学ぶ：注釈者混乱行列の正則化推定（Learning From Noisy Labels By Regularized Estimation Of Annotator Confusion）</news:title>
   <news:publication_date>2026-08-05T15:22:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719839</loc>
  <lastmod>2026-08-05T15:21:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次元空間における差分類似性の理論と応用（Differential Similarity in Higher Dimensional Spaces）</news:title>
   <news:publication_date>2026-08-05T15:21:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719837</loc>
  <lastmod>2026-08-05T15:21:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>弱い重力レンズ観測における畳み込みニューラルネットワークの有用性（Weak lensing cosmology with convolutional neural networks on noisy data）</news:title>
   <news:publication_date>2026-08-05T15:21:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719835</loc>
  <lastmod>2026-08-05T15:21:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ギリシャ語コーパスにおけるメタファー検出のためのニューラル埋め込み（Neural embeddings for metaphor detection in a corpus of Greek texts）</news:title>
   <news:publication_date>2026-08-05T15:21:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719833</loc>
  <lastmod>2026-08-05T14:29:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>個人の文体を捉える単語埋め込み（Word embeddings for idiolect identification）</news:title>
   <news:publication_date>2026-08-05T14:29:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719831</loc>
  <lastmod>2026-08-05T14:29:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>強化学習エージェントの最適選択のためのバンディット枠組み（A Bandit Framework for Optimal Selection of Reinforcement Learning Agents）</news:title>
   <news:publication_date>2026-08-05T14:29:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719829</loc>
  <lastmod>2026-08-05T14:28:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>究極の深宇宙光通信容量への接近（Approaching the ultimate capacity limit in deep-space optical communication）</news:title>
   <news:publication_date>2026-08-05T14:28:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719827</loc>
  <lastmod>2026-08-05T14:28:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>低障壁磁石を用いた効率的なハードウェア型バイナリ確率ニューロン設計（Low Barrier Magnet Design for Efficient Hardware Binary Stochastic Neurons）</news:title>
   <news:publication_date>2026-08-05T14:28:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719825</loc>
  <lastmod>2026-08-05T14:27:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数グラフィカルモデルの同時推定に対するベイズ的アプローチ (A Bayesian Approach to Joint Estimation of Multiple Graphical Models)</news:title>
   <news:publication_date>2026-08-05T14:27:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719823</loc>
  <lastmod>2026-08-05T14:27:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>コンテキストを考慮した視覚的互換性予測（Context-Aware Visual Compatibility Prediction）</news:title>
   <news:publication_date>2026-08-05T14:27:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719821</loc>
  <lastmod>2026-08-05T14:27:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>反復的最小トリム二乗法による混合線形回帰の頑健化（Iterative Least Trimmed Squares for Mixed Linear Regression）</news:title>
   <news:publication_date>2026-08-05T14:27:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719815</loc>
  <lastmod>2026-08-05T13:35:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>主成分分析を用いた特徴選択によるマルウェア検出の機械学習（Machine Learning With Feature Selection Using Principal Component Analysis for Malware Detection: A Case Study）</news:title>
   <news:publication_date>2026-08-05T13:35:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719813</loc>
  <lastmod>2026-08-05T13:33:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>inf‑projectionによるKurdyka‑Łojasiewicz指数の保存性（Kurdyka‑Lojasiewicz exponent via inf‑projection）</news:title>
   <news:publication_date>2026-08-05T13:33:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719811</loc>
  <lastmod>2026-08-05T13:24:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単一隠れ層ニューラルネットワークによる連続関数の近似アルゴリズム（An Algorithm for Approximating Continuous Functions on Compact Subsets with a Neural Network with one Hidden Layer）</news:title>
   <news:publication_date>2026-08-05T13:24:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719809</loc>
  <lastmod>2026-08-05T13:23:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>(q,p)-Wasserstein GANsにおける基底距離の比較 ((q,p)-Wasserstein GANs: Comparing Ground Metrics for Wasserstein GANs)</news:title>
   <news:publication_date>2026-08-05T13:23:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719807</loc>
  <lastmod>2026-08-05T13:22:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>浅い三重ストリーム三次元CNNによる微表情認識（Shallow Triple Stream Three-dimensional CNN for Micro-expression Recognition）</news:title>
   <news:publication_date>2026-08-05T13:22:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719805</loc>
  <lastmod>2026-08-05T13:22:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>NICA/MPD ECalの空間分解能改善（Improving the spatial resolution of NICA/MPD ECAL with new reconstruction methods）</news:title>
   <news:publication_date>2026-08-05T13:22:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719803</loc>
  <lastmod>2026-08-05T13:22:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>共役方策による多様な探索手法（Diverse Exploration via Conjugate Policies for Policy Gradient Methods）</news:title>
   <news:publication_date>2026-08-05T13:22:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719793</loc>
  <lastmod>2026-08-05T12:30:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>PAUカメラによる高精度フォトメトリック測光とその意義（The Physics of the Accelerating Universe Camera）</news:title>
   <news:publication_date>2026-08-05T12:30:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719791</loc>
  <lastmod>2026-08-05T12:30:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>トランジェント検出における深層学習 (Deep learning detection of transients)</news:title>
   <news:publication_date>2026-08-05T12:30:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719789</loc>
  <lastmod>2026-08-05T12:30:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ソーシャルメディアにおけるフェイクニュース検出の幾何学的ディープラーニング（Fake News Detection on Social Media using Geometric Deep Learning）</news:title>
   <news:publication_date>2026-08-05T12:30:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719787</loc>
  <lastmod>2026-08-05T12:29:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>上気道消化管領域における病変自動分類への示唆（Towards Automatic Lesion Classification in the Upper Aerodigestive Tract Using OCT and Deep Transfer Learning Methods）</news:title>
   <news:publication_date>2026-08-05T12:29:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719785</loc>
  <lastmod>2026-08-05T12:29:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-05T12:29:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719783</loc>
  <lastmod>2026-08-05T12:28:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ELKI: 大規模オープンソースデータ解析ライブラリの現状と示唆（ELKI: A large open-source library for data analysis）</news:title>
   <news:publication_date>2026-08-05T12:28:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719781</loc>
  <lastmod>2026-08-05T12:28:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>敵対的学習による分離型3D顔形状モデルの提案（A Decoupled 3D Facial Shape Model by Adversarial Training）</news:title>
   <news:publication_date>2026-08-05T12:28:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719779</loc>
  <lastmod>2026-08-05T11:37:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-05T11:37:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719777</loc>
  <lastmod>2026-08-05T11:37:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>回帰ファジィモデルによるソフトウェア工数見積りの実践知（Software Development Effort Estimation Using Regression Fuzzy Models）</news:title>
   <news:publication_date>2026-08-05T11:37:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719775</loc>
  <lastmod>2026-08-05T11:37:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>脆弱な道路利用者検出の最前線と課題（Vulnerable road user detection: state-of-the-art and open challenges）</news:title>
   <news:publication_date>2026-08-05T11:37:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719773</loc>
  <lastmod>2026-08-05T11:36:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大腸がんの予後予測を切り拓く組織画像解析（Colorectal Cancer Outcome Prediction from H&amp;amp;E Whole Slide Images using Machine Learning and Automatically Inferred Phenotype Profiles）</news:title>
   <news:publication_date>2026-08-05T11:36:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719771</loc>
  <lastmod>2026-08-05T11:36:22Z</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よる到来角推定の性能優位性（PERFORMANCE ADVANTAGES OF DEEP NEURAL NETWORKS FOR ANGLE OF ARRIVAL ESTIMATION）</news:title>
   <news:publication_date>2026-08-05T11:36:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719769</loc>
  <lastmod>2026-08-05T11:36:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>広告取引所でのエージェント最適応答学習（Learning Best Response Strategies for Agents in Ad Exchanges）</news:title>
   <news:publication_date>2026-08-05T11:36:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719767</loc>
  <lastmod>2026-08-05T11:36:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>前眼部OCTに基づく隅角閉塞検出の多階層深層ネットワーク（Angle-Closure Detection in Anterior Segment OCT based on Multi-Level Deep Network）</news:title>
   <news:publication_date>2026-08-05T11:36:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719765</loc>
  <lastmod>2026-08-05T10:44:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>TASK2VECによるタスク埋め込みとメタラーニング（TASK2VEC: Task Embedding for Meta-Learning）</news:title>
   <news:publication_date>2026-08-05T10:44:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719763</loc>
  <lastmod>2026-08-05T10:44:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Twitter共有データからのフェイクニュース検出（Identifying Fake News from Twitter Sharing Data: A Large-Scale Study）</news:title>
   <news:publication_date>2026-08-05T10:44:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719761</loc>
  <lastmod>2026-08-05T10:43:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>NIR-VIS異スペクトル顔補完の実用的意義（Cross-spectral Face Completion for NIR-VIS Heterogeneous Face Recognition）</news:title>
   <news:publication_date>2026-08-05T10:43:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719759</loc>
  <lastmod>2026-08-05T10:42:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>小児の活動量計データに基づくADHD分類（Classifying attention deficit hyperactivity disorder in children with non-linearities in actigraphy）</news:title>
   <news:publication_date>2026-08-05T10:42:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719757</loc>
  <lastmod>2026-08-05T10:42:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>一般化された空間ポアソン点過程の散乱統計 (Scattering Statistics of Generalized Spatial Poisson Point Processes)</news:title>
   <news:publication_date>2026-08-05T10:42:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719755</loc>
  <lastmod>2026-08-05T10:42:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多ラベル変数の特徴選択における依存性最大化（Feature Selection for Multi-Labeled Variables via Dependency Maximization）</news:title>
   <news:publication_date>2026-08-05T10:42:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719753</loc>
  <lastmod>2026-08-05T10:42:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>IoTマルウェアのエンドポイント解析が示す攻撃の構図（Analyzing Endpoints in the Internet of Things）</news:title>
   <news:publication_date>2026-08-05T10:42:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719751</loc>
  <lastmod>2026-08-05T09:50:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ガウス仮定を外したVAEの実装と意義（Biadversarial Variational Autoencoder）</news:title>
   <news:publication_date>2026-08-05T09:50:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719749</loc>
  <lastmod>2026-08-05T09:49:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スケーラブルな公平クラスタリング（Scalable Fair Clustering）</news:title>
   <news:publication_date>2026-08-05T09:49:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719747</loc>
  <lastmod>2026-08-05T09:49:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層学習と顔認識の現状（Deep learning and face recognition: the state of the art）</news:title>
   <news:publication_date>2026-08-05T09:49:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719745</loc>
  <lastmod>2026-08-05T09:48:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Besov空間下におけるGANの非パラメトリック密度推定と収束（Nonparametric Density Estimation and Convergence of GANs under Besov IPM Losses）</news:title>
   <news:publication_date>2026-08-05T09:48:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719743</loc>
  <lastmod>2026-08-05T09:48:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数ドメイン翻訳のための非結合オートエンコーダ学習（Multi-Domain Translation by Learning Uncoupled Autoencoders）</news:title>
   <news:publication_date>2026-08-05T09:48:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719741</loc>
  <lastmod>2026-08-05T09:48:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>顔のミクロ表情の検出と認識を時間差分特徴と記憶モジュールで強化する手法（FACIAL MICRO-EXPRESSION SPOTTING AND RECOGNITION USING TIME CONTRASTED FEATURE WITH VISUAL MEMORY）</news:title>
   <news:publication_date>2026-08-05T09:48:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719739</loc>
  <lastmod>2026-08-05T09:48:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>逆投影表現とカテゴリ貢献率による頑健な腫瘍分類（Inverse Projection Representation and Category Contribution Rate for Robust Tumor Recognition）</news:title>
   <news:publication_date>2026-08-05T09:48:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719737</loc>
  <lastmod>2026-08-05T08:55:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習モデルの局所的解釈可能性の評価（Assessing the Local Interpretability of Machine Learning Models）</news:title>
   <news:publication_date>2026-08-05T08:55:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719735</loc>
  <lastmod>2026-08-05T08:53:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多言語ニューラル機械翻訳における語彙表現の分離化（Multilingual Neural Machine Translation with Soft Decoupled Encoding）</news:title>
   <news:publication_date>2026-08-05T08:53:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719733</loc>
  <lastmod>2026-08-05T08:53:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ストリーミングモデルにおける線形予測の空間下限（Space lower bounds for linear prediction in the streaming model）</news:title>
   <news:publication_date>2026-08-05T08:53:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719731</loc>
  <lastmod>2026-08-05T08:52:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ルーメン境界検出における不確定性クラスタリングの実用性（Lumen boundary detection using neutrosophic c-means in IVOCT images）</news:title>
   <news:publication_date>2026-08-05T08:52:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719729</loc>
  <lastmod>2026-08-05T08:52:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>未分類スペクトルからM型星を掘り起こすハッシュ学習（Recognition of M-type stars in the unclassified spectra of LAMOST DR5 using a hash learning method）</news:title>
   <news:publication_date>2026-08-05T08:52:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719727</loc>
  <lastmod>2026-08-05T08:52:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>アルゴリズム展開による深層画像デブラーリング（AN ALGORITHM UNROLLING APPROACH TO DEEP IMAGE DEBLURRING）</news:title>
   <news:publication_date>2026-08-05T08:52:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719725</loc>
  <lastmod>2026-08-05T08:51:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層アルゴリズム・アンローリングによるブラインド画像復元（Deep Algorithm Unrolling for Blind Image Deblurring）</news:title>
   <news:publication_date>2026-08-05T08:51:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719723</loc>
  <lastmod>2026-08-05T08:00:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文脈で初期状態を学習するRNN（Contextual Recurrent Neural Networks）</news:title>
   <news:publication_date>2026-08-05T08:00:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719721</loc>
  <lastmod>2026-08-05T08:00:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>合成データ生成と差分プライバシーの接点（Synthetic Data Generators – Sequential and Private）</news:title>
   <news:publication_date>2026-08-05T08:00:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719719</loc>
  <lastmod>2026-08-05T07:53:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ケイ酸塩ガラスのための機械学習フォースフィールド（Machine Learning Forcefield for Silicate Glasses）</news:title>
   <news:publication_date>2026-08-05T07:53:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719717</loc>
  <lastmod>2026-08-05T07:52:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数回答を持つ純探索問題のサンプル複雑性（Pure Exploration with Multiple Correct Answers）</news:title>
   <news:publication_date>2026-08-05T07:52:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719715</loc>
  <lastmod>2026-08-05T07:52:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Omniglotチャレンジ：3年の進捗報告（The Omniglot challenge: a 3-year progress report）</news:title>
   <news:publication_date>2026-08-05T07:52:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719713</loc>
  <lastmod>2026-08-05T07:50:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>超高速リアルタイム顔ランドマーク検出と形状フィッティング（SUPER-REALTIME FACIAL LANDMARK DETECTION AND SHAPE FITTING BY DEEP REGRESSION OF SHAPE MODEL PARAMETERS）</news:title>
   <news:publication_date>2026-08-05T07:50:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719711</loc>
  <lastmod>2026-08-05T07:50:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層型マルチタスク深層ニューラルネットワークによるエンドツーエンド運転（Hierarchical Multi-task Deep Neural Network Architecture for End-to-End Driving）</news:title>
   <news:publication_date>2026-08-05T07:50:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719709</loc>
  <lastmod>2026-08-05T06:58:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>野外画像からの3D手形状とポーズ（3D Hand Shape and Pose from Images in the Wild）</news:title>
   <news:publication_date>2026-08-05T06:58:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719707</loc>
  <lastmod>2026-08-05T06:49:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>構造的近傍に基づく距離尺度学習（Distance metric learning based on structural neighborhoods for dimensionality reduction and classification performance improvement）</news:title>
   <news:publication_date>2026-08-05T06:49:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719705</loc>
  <lastmod>2026-08-05T06:49:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
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 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>データ駆動型車両軌道予測の実務的意義（Data-Driven Vehicle Trajectory Forecasting）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-05T05:53:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-05T05:53:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-05T05:53:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-05T05:01:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>医療データに学ぶ患者類似度の測定（Measuring Patient Similarities via a Deep Architecture with Medical Concept Embedding）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>最小記憶でSDPを解く近似相補性の手法（An Optimal-Storage Approach to Semidefinite Programming Using Approximate Complementarity）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スパースビュー・マイクロCTのシノグラム補間と深層学習（Sinogram interpolation for sparse-view micro-CT with deep learning neural network）</news:title>
   <news:publication_date>2026-08-05T04:08:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-05T04:08:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>確率的変分不等式に対する分散削減を備えた前進―後退―前進法（Forward-Backward-Forward Methods with Variance Reduction for Stochastic Variational Inequalities）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>メタカーブチャーで速く適応する学習法の本質（Meta-Curvature）</news:title>
   <news:publication_date>2026-08-05T04:07:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-05T04:07:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>WarpFlowによるペタバイト空間時間データの探索（WarpFlow: Exploring Petabytes of Space-Time Data）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ミニバッチ学習による指数族有限混合モデルの最尤推定（Mini-batch learning of exponential family finite mixture models）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>アーキテクチャ圧縮（Architecture Compression）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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    <news:name>AI Benchmark Research</news:name>
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   <news:title>コード進化で読み解くブラジル音楽のジャンル予測（MACHINE LEARNING AND CHORD BASED FEATURE ENGINEERING FOR GENRE PREDICTION IN POPULAR BRAZILIAN MUSIC）</news:title>
   <news:publication_date>2026-08-05T02:14:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/719631</loc>
  <lastmod>2026-08-05T02:13:46Z</lastmod>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-05T02:13:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719629</loc>
  <lastmod>2026-08-05T02:13:41Z</lastmod>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>データ駆動型ネットワークアラインメント（Data-driven network alignment）</news:title>
   <news:publication_date>2026-08-05T02:13:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719627</loc>
  <lastmod>2026-08-05T02:12:55Z</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>知識表現と認識的学習によるELオントロジー学習（Learning Ontologies with Epistemic Reasoning: The EL Case）</news:title>
   <news:publication_date>2026-08-05T02:12:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719625</loc>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>FSNetによる畳み込みニューラルネットワークの圧縮（FSNet: Compression of Deep Convolutional Neural Networks by Filter Summary）</news:title>
   <news:publication_date>2026-08-05T01:21:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719623</loc>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-05T01:21:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/719621</loc>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>構造化予測モデルのより滑らかな学習法（A Smoother Way to Train Structured Prediction Models）</news:title>
   <news:publication_date>2026-08-05T01:20:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719619</loc>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>不変・等変表現学習によるクラス表現の分離（Invariant-Equivariant Representation Learning for Multi-Class Data）</news:title>
   <news:publication_date>2026-08-05T01:20:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719617</loc>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Insertion Transformerによる挿入操作を用いた柔軟な系列生成（Insertion Transformer: Flexible Sequence Generation via Insertion Operations）</news:title>
   <news:publication_date>2026-08-05T01:20:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719615</loc>
  <lastmod>2026-08-05T01:19:53Z</lastmod>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>肺がん検出と診断のための3D確率的深層学習システム（A 3D Probabilistic Deep Learning System for Detection and Diagnosis of Lung Cancer Using Low-Dose CT Scans）</news:title>
   <news:publication_date>2026-08-05T01:19:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719613</loc>
  <lastmod>2026-08-05T01:19:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>高次元で安全かつ適応的に最適化する手法の本質（Adaptive and Safe Bayesian Optimization in High Dimensions via One-Dimensional Subspaces）</news:title>
   <news:publication_date>2026-08-05T01:19:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719611</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ロバストなストリーミング主成分分析（Robust Streaming PCA）</news:title>
   <news:publication_date>2026-08-05T00:27:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719607</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>プレート化された因子グラフのためのテンソル変数消去（Tensor Variable Elimination for Plated Factor Graphs）</news:title>
   <news:publication_date>2026-08-05T00:16:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719601</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-05T00:16:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-05T00:16:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ソフトウェア定義FPGAアクセラレータ設計によるモバイル向け深層学習の高速化（Software-Defined FPGA Accelerator Design for Mobile Deep Learning Applications）</news:title>
   <news:publication_date>2026-08-05T00:16:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-04T23:24:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-04T23:23:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719593</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>パートナー選択による協力の出現（Partner Selection for the Emergence of Cooperation in Multi-Agent Systems Using Reinforcement Learning）</news:title>
   <news:publication_date>2026-08-04T23:23:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719591</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>行動列の編集距離に基づく新奇探索による深層強化学習ポリシー重みの探索（Novelty Search for Deep Reinforcement Learning Policy Network Weights by Action Sequence Edit Metric Distance）</news:title>
   <news:publication_date>2026-08-04T23:23:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719589</loc>
  <lastmod>2026-08-04T23:23:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-04T23:23:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719587</loc>
  <lastmod>2026-08-04T23:23:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>離散化による敵対的攻撃耐性の強化（Discretization based Solutions for Secure Machine Learning against Adversarial Attacks）</news:title>
   <news:publication_date>2026-08-04T23:23:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719585</loc>
  <lastmod>2026-08-04T23:22:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>線形関数近似を伴う分布型強化学習（Distributional reinforcement learning with linear function approximation）</news:title>
   <news:publication_date>2026-08-04T23:22:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719583</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-04T22:31:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719581</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>時空間相関を持つ時間辞書の学習によるカルシウムイメージングの刷新（LEARNING SPATIALLY-CORRELATED TEMPORAL DICTIONARIES FOR CALCIUM IMAGING）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-04T22:31:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <lastmod>2026-08-04T22:30:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-04T22:30:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-04T22:30:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ガウス型グラフィカルモデルの学習（Learning Gaussian Graphical Models by symmetric parallel regression technique）</news:title>
   <news:publication_date>2026-08-04T22:30:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-04T22:30:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>背景知識を用いたアイテム集合のランキング（Using Background Knowledge to Rank Itemsets）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>スケルトンに基づくオンライン行動予測とスケール選択ネットワーク（Skeleton-Based Online Action Prediction Using Scale Selection Network）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Knowledge Graphの事実予測を進化させるテンソル分解（Knowledge Graph Fact Prediction via Knowledge-Enriched Tensor Factorization）</news:title>
   <news:publication_date>2026-08-04T21:36:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719559</loc>
  <lastmod>2026-08-04T21:36:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スペクトル・空間拡散幾何に基づくハイパースペクトル画像クラスタリング（Spectral-Spatial Diffusion Geometry for Hyperspectral Image Clustering）</news:title>
   <news:publication_date>2026-08-04T21:36:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719557</loc>
  <lastmod>2026-08-04T21:36:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>サイズ非依存のニューラル転移学習によるRDDL計画（Size Independent Neural Transfer for RDDL Planning）</news:title>
   <news:publication_date>2026-08-04T21:36:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719555</loc>
  <lastmod>2026-08-04T20:44:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オーソグラフィックネット：オープンエンド領域における3D物体認識の深層転移学習アプローチ (OrthographicNet: A Deep Transfer Learning Approach for 3D Object Recognition in Open-Ended Domains)</news:title>
   <news:publication_date>2026-08-04T20:44:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719553</loc>
  <lastmod>2026-08-04T20:44:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>HERAにおける接触相互作用とレプトクォークの制限 (Limits on contact interactions and leptoquarks at HERA)</news:title>
   <news:publication_date>2026-08-04T20:44:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719551</loc>
  <lastmod>2026-08-04T20:43:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>協調に基づくマルチラベル学習の要点（Collaboration based Multi-Label Learning）</news:title>
   <news:publication_date>2026-08-04T20:43:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719549</loc>
  <lastmod>2026-08-04T20:42:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>X(3872)とそのパートナー探索の再検討（Heavy-quark spin and flavour symmetry partners of the X(3872) revisited）</news:title>
   <news:publication_date>2026-08-04T20:42:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719547</loc>
  <lastmod>2026-08-04T20:42:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自己共役性による正則化経験リスク最小化の高速収束（Beyond Least-Squares: Fast Rates for Regularized Empirical Risk Minimization through Self-Concordance）</news:title>
   <news:publication_date>2026-08-04T20:42:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719545</loc>
  <lastmod>2026-08-04T20:42:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>心拍だけで感情を推定する確率的枠組み（A Bayesian Deep Learning Framework for End-To-End Prediction of Emotion from Heartbeat）</news:title>
   <news:publication_date>2026-08-04T20:42:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719543</loc>
  <lastmod>2026-08-04T20:41:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>部分ラベル学習における自己誘導再学習（Partial Label Learning with Self-Guided Retraining）</news:title>
   <news:publication_date>2026-08-04T20:41:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719541</loc>
  <lastmod>2026-08-04T19:49:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>バンディット主成分分析が変える部分観測下の学習法（Bandit Principal Component Analysis）</news:title>
   <news:publication_date>2026-08-04T19:49:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719539</loc>
  <lastmod>2026-08-04T19:49:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>暗いガンマ線バーストGRB 140713Aの詳細な多波長解析（Detailed multi-wavelength modelling of the dark GRB 140713A and its host galaxy）</news:title>
   <news:publication_date>2026-08-04T19:49:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719537</loc>
  <lastmod>2026-08-04T19:48:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>構造和（structural sums）を用いたランダム複合材料の特徴ベクトル化と分類（Classifying and analysis of random composites using structural sums feature vector）</news:title>
   <news:publication_date>2026-08-04T19:48:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719535</loc>
  <lastmod>2026-08-04T19:48:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフ上の共分散・相関に基づく類似度測定（Covariance and Correlation Measures on a Graph in a Generalized Bag-of-Paths Formalism）</news:title>
   <news:publication_date>2026-08-04T19:48:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719533</loc>
  <lastmod>2026-08-04T19:47:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>初期重みのセキュリティ重要性（On the security relevance of weights in deep learning）</news:title>
   <news:publication_date>2026-08-04T19:47:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719531</loc>
  <lastmod>2026-08-04T19:47:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次モチーフ特徴に基づくリンク予測（Link Prediction via Higher-Order Motif Features）</news:title>
   <news:publication_date>2026-08-04T19:47:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719529</loc>
  <lastmod>2026-08-04T19:47:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>残差自己相関の分布と季節性ARMAモデルの診断（Distribution of residual autocorrelations for multiplicative seasonal ARMA models with uncorrelated but non-independent error terms）</news:title>
   <news:publication_date>2026-08-04T19:47:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719523</loc>
  <lastmod>2026-08-04T18:55:06Z</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上のラップド正規分布（A Wrapped Normal Distribution on Hyperbolic Space for Gradient-Based Learning）</news:title>
   <news:publication_date>2026-08-04T18:55:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719521</loc>
  <lastmod>2026-08-04T18:55:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>不完全な予測下での公正な意思決定（Fair Decisions Despite Imperfect Predictions）</news:title>
   <news:publication_date>2026-08-04T18:55:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719519</loc>
  <lastmod>2026-08-04T18:54:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>バイナライズド知識グラフ埋め込み（Binarized Knowledge Graph Embeddings）</news:title>
   <news:publication_date>2026-08-04T18:54:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719517</loc>
  <lastmod>2026-08-04T18:53:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>不完全なアルゴリズムに対処する人間中心ツール（Human-Centered Tools for Coping with Imperfect Algorithms During Medical Decision-Making）</news:title>
   <news:publication_date>2026-08-04T18:53:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719515</loc>
  <lastmod>2026-08-04T18:53:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフで学ぶ物理の差分表現──Differentiable Physics-informed Graph Networks（Differentiable Physics-informed Graph Networks）</news:title>
   <news:publication_date>2026-08-04T18:53:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719513</loc>
  <lastmod>2026-08-04T18:53:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>相関バンディットとオンラインでの平均二乗誤差最小化（Correlated bandits or: How to minimize mean-squared error online）</news:title>
   <news:publication_date>2026-08-04T18:53:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719511</loc>
  <lastmod>2026-08-04T18:53:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>遺伝的プログラミングでマニホールド学習はできるか（Can Genetic Programming Do Manifold Learning Too?）</news:title>
   <news:publication_date>2026-08-04T18:53:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719509</loc>
  <lastmod>2026-08-04T18:02:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>一画素攻撃の理解—伝播マップと局所性解析（Understanding the One-pixel Attack: Propagation Maps and Locality Analysis）</news:title>
   <news:publication_date>2026-08-04T18:02:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719507</loc>
  <lastmod>2026-08-04T17:53:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>EILearn：過去知識を利用した逐次学習手法（EILearn: Learning Incrementally Using Previous Knowledge Obtained From an Ensemble of Classifiers）</news:title>
   <news:publication_date>2026-08-04T17:53:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719505</loc>
  <lastmod>2026-08-04T17:52:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>磁化曲線とスピンギャップの推定に機械学習を使う（Machine learning as an improved estimator for magnetization curve and spin gap）</news:title>
   <news:publication_date>2026-08-04T17:52:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719503</loc>
  <lastmod>2026-08-04T17:51:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>極値損失による支持（サポート）の生成（Generating the support with extreme value losses）</news:title>
   <news:publication_date>2026-08-04T17:51:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719501</loc>
  <lastmod>2026-08-04T17:51:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>対象依存感情分類のためのマルチタスク学習（Multi-task Learning for Target-dependent Sentiment Classification）</news:title>
   <news:publication_date>2026-08-04T17:51:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719499</loc>
  <lastmod>2026-08-04T17:50:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>手書き数式認識の堅牢な符号化器–復号器学習枠組み（Robust Encoder-Decoder Learning Framework towards Offline Handwritten Mathematical Expression Recognition Based on Multi-Scale Deep Neural Network）</news:title>
   <news:publication_date>2026-08-04T17:50:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719497</loc>
  <lastmod>2026-08-04T17:50:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>最適輸送地図の不連続性とGANのモード崩壊の理論的接続（MODE COLLAPSE AND REGULARITY OF OPTIMAL TRANSPORTATION MAPS）</news:title>
   <news:publication_date>2026-08-04T17:50:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719495</loc>
  <lastmod>2026-08-04T16:59:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>特徴の統合と強化で速く正確に検出する単発物体検出器（A SINGLE-SHOT OBJECT DETECTOR WITH FEATURE AGGREGATION AND ENHANCEMENT）</news:title>
   <news:publication_date>2026-08-04T16:59:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719493</loc>
  <lastmod>2026-08-04T16:59:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>AdaScaleによるリアルタイム動画物体検出の高速化と精度向上（AdaScale: Towards Real-time Video Object Detection Using Adaptive Scaling）</news:title>
   <news:publication_date>2026-08-04T16:59:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719491</loc>
  <lastmod>2026-08-04T16:58:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ランダム化スムージングによる認証付き敵対的堅牢性（Certified Adversarial Robustness via Randomized Smoothing）</news:title>
   <news:publication_date>2026-08-04T16:58:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719489</loc>
  <lastmod>2026-08-04T16:57:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>モビリティ・オンデマンド導入時のモード切替行動の異質性を解く（Modeling Heterogeneity in Mode-Switching Behavior Under a Mobility-on-Demand Transit System: An Interpretable Machine Learning Approach）</news:title>
   <news:publication_date>2026-08-04T16:57:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719487</loc>
  <lastmod>2026-08-04T16:57:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>IoMTにおけるデータ有用性とプライバシーの両立（Achieving Data Utility-Privacy Tradeoff in Internet of Medical Things: A Machine Learning Approach）</news:title>
   <news:publication_date>2026-08-04T16:57:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719485</loc>
  <lastmod>2026-08-04T16:57:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Source TracesによるTemporal Difference学習の新視点（Source Traces for Temporal Difference Learning）</news:title>
   <news:publication_date>2026-08-04T16:57:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719483</loc>
  <lastmod>2026-08-04T16:57:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>割引率の再考：意思決定理論的アプローチ（Rethinking the Discount Factor in Reinforcement Learning: A Decision Theoretic Approach）</news:title>
   <news:publication_date>2026-08-04T16:57:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719481</loc>
  <lastmod>2026-08-04T16:05:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>通信制約下での分布学習に関する下限—フィッシャー情報を用いて（Lower Bounds for Learning Distributions under Communication Constraints via Fisher Information）</news:title>
   <news:publication_date>2026-08-04T16:05:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719479</loc>
  <lastmod>2026-08-04T16:04:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数近傍LBPを用いた土地利用分類（Land Use Classification Using Multi-neighborhood LBPs）</news:title>
   <news:publication_date>2026-08-04T16:04:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719477</loc>
  <lastmod>2026-08-04T16:03:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文脈を用いたオンライン偽発見率制御（Contextual Online False Discovery Rate Control）</news:title>
   <news:publication_date>2026-08-04T16:03:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719475</loc>
  <lastmod>2026-08-04T16:02:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習率減衰と重み減衰を複合的に扱う複雑度勾配降下法（Combining Learning Rate Decay and Weight Decay with Complexity Gradient Descent）</news:title>
   <news:publication_date>2026-08-04T16:02:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719473</loc>
  <lastmod>2026-08-04T16:02:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ロボット支援作業のための深層実行モニタ（Deep execution monitor for robot assistive tasks）</news:title>
   <news:publication_date>2026-08-04T16:02:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719471</loc>
  <lastmod>2026-08-04T16:02:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>HYDRA: ハイブリッド深層磁気共鳴フィンガープリンティング（HYDRA: Hybrid Deep Magnetic Resonance Fingerprinting）</news:title>
   <news:publication_date>2026-08-04T16:02:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719469</loc>
  <lastmod>2026-08-04T16:01:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多層ニューラルネットワークの学習ダイナミクスの平均場極限（Mean Field Limit of the Learning Dynamics of Multilayer Neural Networks）</news:title>
   <news:publication_date>2026-08-04T16:01:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719467</loc>
  <lastmod>2026-08-04T15:09:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動的学習によるダイナミクスの習得（Dynamical learning of dynamics）</news:title>
   <news:publication_date>2026-08-04T15:09:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719465</loc>
  <lastmod>2026-08-04T15:09:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ロボットの作業実行と監視のための視覚探索と認識 (Visual search and recognition for robot task execution and monitoring)</news:title>
   <news:publication_date>2026-08-04T15:09:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719463</loc>
  <lastmod>2026-08-04T15:09:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>作物収量予測における深層ニューラルネットワークの適用（Crop Yield Prediction Using Deep Neural Networks）</news:title>
   <news:publication_date>2026-08-04T15:09:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719461</loc>
  <lastmod>2026-08-04T15:07:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>相関するIoTセンサーの寿命を延ばすDeep Q-learningの応用（Using Deep Q-learning To Prolong the Lifetime of Correlated Internet of Things Devices）</news:title>
   <news:publication_date>2026-08-04T15:07:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719459</loc>
  <lastmod>2026-08-04T15:07:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ALMA開発ロードマップ（The ALMA Development Roadmap）</news:title>
   <news:publication_date>2026-08-04T15:07:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719457</loc>
  <lastmod>2026-08-04T15:07:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>FaceSpoof Buster: 顔認証のなりすまし検知を深める手法（FaceSpoof Buster: a Presentation Attack Detector Based on Intrinsic Image Properties and Deep Learning）</news:title>
   <news:publication_date>2026-08-04T15:07:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719455</loc>
  <lastmod>2026-08-04T15:07:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ディープなマルチビュー2D姿勢からの3D人体姿勢推定 (3D Human Pose Estimation from Deep Multi-View 2D Pose)</news:title>
   <news:publication_date>2026-08-04T15:07:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719453</loc>
  <lastmod>2026-08-04T14:14:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>低コストセンサーを深層学習で校正して高gショック信号を測る（LOW-COST MEASUREMENT OF INDUSTRIAL SHOCK SIGNALS VIA DEEP LEARNING CALIBRATION）</news:title>
   <news:publication_date>2026-08-04T14:14:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719451</loc>
  <lastmod>2026-08-04T14:13:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>互換性のある自然勾配による方策探索（Compatible Natural Gradient Policy Search）</news:title>
   <news:publication_date>2026-08-04T14:13:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719449</loc>
  <lastmod>2026-08-04T14:13:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>態度と信念のアンケート調査の運用ベストプラクティス（Best Practices for Administering Belief Surveys）</news:title>
   <news:publication_date>2026-08-04T14:13:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719447</loc>
  <lastmod>2026-08-04T14:12:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ML Health: 本番モデルの健診指標としてのSimilarity score（ML HEALTH: FITNESS TRACKING FOR PRODUCTION MODELS）</news:title>
   <news:publication_date>2026-08-04T14:12:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719445</loc>
  <lastmod>2026-08-04T14:12:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>AKARIとHyper Suprime-Camによる18バンド赤外光度関数と宇宙星形成史（Infrared luminosity functions based on 18 mid-infrared bands: revealing cosmic star formation history with AKARI and Hyper Suprime-Cam）</news:title>
   <news:publication_date>2026-08-04T14:12:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719443</loc>
  <lastmod>2026-08-04T14:12:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SiamVGG：深いSiameseネットワークを用いたビジュアルトラッキングの実装（SiamVGG: Visual Tracking using Deeper Siamese Networks）</news:title>
   <news:publication_date>2026-08-04T14:12:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719441</loc>
  <lastmod>2026-08-04T14:11:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高速初期化器と遅延ソルバーの協調学習（Cooperative Training of Fast Thinking Initializer and Slow Thinking Solver for Conditional Learning）</news:title>
   <news:publication_date>2026-08-04T14:11:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719439</loc>
  <lastmod>2026-08-04T13:20:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Sup-KLUCBによるCopeland Dueling Banditsへのアプローチ（KLUCB Approach to Copeland Bandits）</news:title>
   <news:publication_date>2026-08-04T13:20:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719437</loc>
  <lastmod>2026-08-04T13:11:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>淡い銀河のイオン化光子生産効率とHα等価幅の測定（The mean Hα EW and Lyman-continuum photon production efficiency for faint z ≈4−5 galaxies）</news:title>
   <news:publication_date>2026-08-04T13:11:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719435</loc>
  <lastmod>2026-08-04T13:11:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>サジタリウスIIの深堀り観測 — Pristine調査による衛星矮小銀河の再評価（The Pristine Dwarf-Galaxy survey – II. In-depth observational study of the faint Milky Way satellite Sagittarius II）</news:title>
   <news:publication_date>2026-08-04T13:11:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719433</loc>
  <lastmod>2026-08-04T13:09:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層可逆特徴を用いたハイブリッドモデル（Hybrid Models with Deep and Invertible Features）</news:title>
   <news:publication_date>2026-08-04T13:09:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719431</loc>
  <lastmod>2026-08-04T13:09:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>画像から製造指示を生成するニューラル逆編み学（Neural Inverse Knitting: From Images to Manufacturing Instructions）</news:title>
   <news:publication_date>2026-08-04T13:09:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719429</loc>
  <lastmod>2026-08-04T13:09:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>メモリ効率の良い可逆的画像変換モデル（Reversible GANs for Memory-efficient Image-to-Image Translation）</news:title>
   <news:publication_date>2026-08-04T13:09:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719427</loc>
  <lastmod>2026-08-04T13:07:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散システム上の深層強化学習におけるメタ最適化（RL Metaoptimization on a Distributed System for Deep Reinforcement Learning）</news:title>
   <news:publication_date>2026-08-04T13:07:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719425</loc>
  <lastmod>2026-08-04T12:16:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフ分類のための変分リカレントニューラルネットワーク（Variational Recurrent Neural Networks for Graph Classification）</news:title>
   <news:publication_date>2026-08-04T12:16:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719423</loc>
  <lastmod>2026-08-04T12:16:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スパース回帰と適応的特徴生成による力学系同定（Sparse Regression and Adaptive Feature Generation for the Discovery of Dynamical Systems）</news:title>
   <news:publication_date>2026-08-04T12:16:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719421</loc>
  <lastmod>2026-08-04T12:15:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>移動知能を活用した遅延最適化型の車載フォグネットワークにおける計算オフロード（Exploiting Moving Intelligence: Delay-Optimized Computation Offloading in Vehicular Fog Networks）</news:title>
   <news:publication_date>2026-08-04T12:15:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719419</loc>
  <lastmod>2026-08-04T12:15:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>結合条件クエリ結果のランク付き列挙手法（RANKED ENUMERATION OF CONJUNCTIVE QUERY RESULTS）</news:title>
   <news:publication_date>2026-08-04T12:15:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719417</loc>
  <lastmod>2026-08-04T12:14:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>アスペクト特化の意見表現抽出（Aspect Specific Opinion Expression Extraction using Attention based LSTM-CRF Network）</news:title>
   <news:publication_date>2026-08-04T12:14:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719415</loc>
  <lastmod>2026-08-04T12:14:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>D&amp;amp;C: IRベースのバグ局所化を分割して解く（D&amp;amp;C: A Divide-and-Conquer Approach to IR-based Bug Localization）</news:title>
   <news:publication_date>2026-08-04T12:14:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719413</loc>
  <lastmod>2026-08-04T12:14:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>チャット文の理解によるスタンプ推薦の仕組み（Understanding Chat Messages for Sticker Recommendation in Messaging Apps）</news:title>
   <news:publication_date>2026-08-04T12:14:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/719411</loc>
  <lastmod>2026-08-04T11:22:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>STAMPNET による教師なし多クラス物体発見（STAMPNET: UNSUPERVISED MULTI-CLASS OBJECT DISCOVERY）</news:title>
   <news:publication_date>2026-08-04T11:22:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719409</loc>
  <lastmod>2026-08-04T11:13:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SDSS DR7の銀河ハロー質量予測（Prediction of Galaxy Halo Masses in SDSS DR7 via a Machine Learning Approach）</news:title>
   <news:publication_date>2026-08-04T11:13:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719407</loc>
  <lastmod>2026-08-04T11:13:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>β-Ga2O3の近辺光学特性に関する第一原理計算（First-Principles Calculations of the Near-Edge Optical Properties of β-Ga2O3）</news:title>
   <news:publication_date>2026-08-04T11:13:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719405</loc>
  <lastmod>2026-08-04T11:11:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>1NNプロトタイプ集合のVC次元に関する境界（Bounds for the VC Dimension of 1NN Prototype Sets）</news:title>
   <news:publication_date>2026-08-04T11:11:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719403</loc>
  <lastmod>2026-08-04T11:11:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深くまばらなサンプリングによるベイズ強化学習（Bayesian Reinforcement Learning via Deep, Sparse Sampling）</news:title>
   <news:publication_date>2026-08-04T11:11:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719401</loc>
  <lastmod>2026-08-04T11:11:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>BERTとPALsによる多用途適応の効率化（BERT and PALs: Projected Attention Layers for Efficient Adaptation in Multi-Task Learning）</news:title>
   <news:publication_date>2026-08-04T11:11:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719399</loc>
  <lastmod>2026-08-04T11:10:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ストリートシーン理解のための単一ネットワークによるパンオプティックセグメンテーション（Single Network Panoptic Segmentation for Street Scene Understanding）</news:title>
   <news:publication_date>2026-08-04T11:10:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719397</loc>
  <lastmod>2026-08-04T10:19:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>音声ステガノグラフィに向けた深層ニューラルネットワーク（Hide and Speak: Towards Deep Neural Networks for Speech Steganography）</news:title>
   <news:publication_date>2026-08-04T10:19:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719395</loc>
  <lastmod>2026-08-04T10:17:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>行列共因子分解による表現学習と教師付き分類の統合（Matrix Cofactorization for Joint Representation Learning and Supervised Classification）</news:title>
   <news:publication_date>2026-08-04T10:17:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719393</loc>
  <lastmod>2026-08-04T10:09:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ポインティング認識を実用化する──簡潔で高速なRGB-Dベースの指示検出（Commodifying Pointing in HRI: Simple and Fast Pointing Gesture Detection from RGB-D Images）</news:title>
   <news:publication_date>2026-08-04T10:09:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719391</loc>
  <lastmod>2026-08-04T10:09:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>生物学的量子ネットワークはNP困難問題を解けるか（Can biological quantum networks solve NP-hard problems?）</news:title>
   <news:publication_date>2026-08-04T10:09:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719389</loc>
  <lastmod>2026-08-04T10:07:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ラジアルと方向性の事後分布によるベイズニューラルネットワーク（Radial and Directional Posteriors for Bayesian Neural Networks）</news:title>
   <news:publication_date>2026-08-04T10:07:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719387</loc>
  <lastmod>2026-08-04T10:07:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>混合整数計画法におけるコンフリクト駆動ヒューリスティクス（Conflict-Driven Heuristics for Mixed Integer Programming）</news:title>
   <news:publication_date>2026-08-04T10:07:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719385</loc>
  <lastmod>2026-08-04T10:07:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>エンドポイント上でのリアルタイムマルウェア検出と自動プロセス停止（Real-time malware process detection and automated process killing）</news:title>
   <news:publication_date>2026-08-04T10:07:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719383</loc>
  <lastmod>2026-08-04T09:15:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>視覚検索における教師なしデータ不確実性学習（Unsupervised Data Uncertainty Learning in Visual Retrieval Systems）</news:title>
   <news:publication_date>2026-08-04T09:15:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719381</loc>
  <lastmod>2026-08-04T09:15:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オープンソース脆弱性修正データセットの作成（A Manually-Curated Dataset of Fixes to Vulnerabilities of Open-Source Software）</news:title>
   <news:publication_date>2026-08-04T09:15:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719379</loc>
  <lastmod>2026-08-04T09:15:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所Rapid Learningを用いた整数計画問題の探索高速化（Local Rapid Learning for Integer Programs）</news:title>
   <news:publication_date>2026-08-04T09:15:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719377</loc>
  <lastmod>2026-08-04T09:14:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Actor-Advisor: オフポリシー助言を活用する方策勾配（The Actor-Advisor: Policy Gradient With Off-Policy Advice）</news:title>
   <news:publication_date>2026-08-04T09:14:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719375</loc>
  <lastmod>2026-08-04T09:14:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-04T09:14:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719373</loc>
  <lastmod>2026-08-04T09:14:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>少数派がより目立つ現象：人気順ランキングが生む“Few-get-richer”効果（The Few-get-richer: A Surprising Consequence of Popularity-based Rankings）</news:title>
   <news:publication_date>2026-08-04T09:14:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719371</loc>
  <lastmod>2026-08-04T09:14:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>異種エッジ埋め込みによる友人推薦（Heterogeneous Edge Embeddings for Friend Recommendation）</news:title>
   <news:publication_date>2026-08-04T09:14:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719369</loc>
  <lastmod>2026-08-04T08:22:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ランダム行列による共分散推定の改良（Random Matrix Improved Covariance Estimation for a Large Class of Metrics）</news:title>
   <news:publication_date>2026-08-04T08:22:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719367</loc>
  <lastmod>2026-08-04T08:21:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ペナルティ付き重み付けGMMによるオンラインクラスタリング（Online Clustering by Penalized Weighted GMM）</news:title>
   <news:publication_date>2026-08-04T08:21:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719365</loc>
  <lastmod>2026-08-04T08:21:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多体散乱におけるRmatReact法の拡張（RmatReact methodology for reactive scattering）</news:title>
   <news:publication_date>2026-08-04T08:21:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719363</loc>
  <lastmod>2026-08-04T08:21:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>競合リスクを伴う生存時間予測のアンサンブル手法（Ensemble Prediction of Time to Event Outcomes with Competing Risks）</news:title>
   <news:publication_date>2026-08-04T08:21:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719361</loc>
  <lastmod>2026-08-04T08:21:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>シード入力最適化によるファジング効率改善（Optimizing seed inputs in fuzzing with machine learning）</news:title>
   <news:publication_date>2026-08-04T08:21:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719359</loc>
  <lastmod>2026-08-04T08:20:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DoPAMINE: 乗算性ノイズ（スペックル）除去のための二面マスクCNN（Double-sided Masked CNN for Pixel Adaptive Multiplicative Noise Despeckling）</news:title>
   <news:publication_date>2026-08-04T08:20:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719357</loc>
  <lastmod>2026-08-04T08:20:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>全体予測・局所修正：ニューラルネットの時間並列最適制御（Predict Globally, Correct Locally: Parallel-in-Time Optimal Control of Neural Networks）</news:title>
   <news:publication_date>2026-08-04T08:20:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719355</loc>
  <lastmod>2026-08-04T07:29:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高性能な株価指数取引における深層LSTMの有効活用（High-performance stock index trading: making effective use of a deep long short-term memory network）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719353</loc>
  <lastmod>2026-08-04T07:28:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>差分型RRAMによるインメモリBNN実装が拓く省電力AI（In-Memory and Error-Immune Differential RRAM Implementation of Binarized Deep Neural Networks）</news:title>
   <news:publication_date>2026-08-04T07:28:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719351</loc>
  <lastmod>2026-08-04T07:28:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ADAPTIVE POSTERIOR LEARNING：サプライズベースのメモリで少数ショット学習を効率化 (ADAPTIVE POSTERIOR LEARNING: FEW-SHOT LEARNING WITH A SURPRISE-BASED MEMORY MODULE)</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719349</loc>
  <lastmod>2026-08-04T07:27:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-04T07:27:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719347</loc>
  <lastmod>2026-08-04T07:27:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-04T07:27:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719345</loc>
  <lastmod>2026-08-04T07:27:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>シミュレータベース統計モデルのモデル選択（Model Selection for Simulator-based Statistical Models: A Kernel Approach）</news:title>
   <news:publication_date>2026-08-04T07:27:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719343</loc>
  <lastmod>2026-08-04T07:26:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-04T07:26:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719341</loc>
  <lastmod>2026-08-04T06:35:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SWA-Gaussianによるベイズ不確実性の簡易ベースライン（A Simple Baseline for Bayesian Uncertainty in Deep Learning）</news:title>
   <news:publication_date>2026-08-04T06:35:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719339</loc>
  <lastmod>2026-08-04T06:29:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>鳥類種識別のための音声ハッシング（CONV-CODES: AUDIO HASHING FOR BIRD SPECIES CLASSIFICATION）</news:title>
   <news:publication_date>2026-08-04T06:29:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719337</loc>
  <lastmod>2026-08-04T06:28:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>知覚的グルーピングのための学習可能な深い事前分布を持つ空間混合モデル（Spatial Mixture Models with Learnable Deep Priors for Perceptual Grouping）</news:title>
   <news:publication_date>2026-08-04T06:28:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719335</loc>
  <lastmod>2026-08-04T06:27:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CHIP: チャンネル毎に特徴を分離して解釈する手法（CHIP: Channel-wise Disentangled Interpretation of Deep Convolutional Neural Networks）</news:title>
   <news:publication_date>2026-08-04T06:27:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719333</loc>
  <lastmod>2026-08-04T06:27:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>概念ベースの自動説明手法が拓くモデル解釈の地平（Towards Automatic Concept-based Explanations）</news:title>
   <news:publication_date>2026-08-04T06:27:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719331</loc>
  <lastmod>2026-08-04T06:26:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>コスト効率の良いインセンティブ配分のための構造化反事実推論（Cost-Effective Incentive Allocation via Structured Counterfactual Inference）</news:title>
   <news:publication_date>2026-08-04T06:26:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719329</loc>
  <lastmod>2026-08-04T06:25:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>二重参照によるホログラフィック位相回復の革新（Dual-Reference Design for Holographic Coherent Diffraction Imaging）</news:title>
   <news:publication_date>2026-08-04T06:25:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719327</loc>
  <lastmod>2026-08-04T05:32:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スピーカーベリフィケーションのためのエンドツーエンド損失と全話者ハードネガティブマイニング（End-to-end losses based on speaker basis vectors and all-speaker hard negative mining for speaker verification）</news:title>
   <news:publication_date>2026-08-04T05:32:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719325</loc>
  <lastmod>2026-08-04T05:32:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>相関するノイズ対を用いたSURE拡張による深層デノイザの教師なし学習（Extending Stein’s unbiased risk estimator to train deep denoisers with correlated pairs of noisy images）</news:title>
   <news:publication_date>2026-08-04T05:32:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719323</loc>
  <lastmod>2026-08-04T05:31:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Ising-Dropoutによる賢いドロップアウトとモデル圧縮（ISING-DROPOUT: A REGULARIZATION METHOD FOR TRAINING AND COMPRESSION OF DEEP NEURAL NETWORKS）</news:title>
   <news:publication_date>2026-08-04T05:31:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719321</loc>
  <lastmod>2026-08-04T05:31:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>義肢のための人工知能 — チャレンジ解法（Artificial Intelligence for Prosthetics — challenge solutions）</news:title>
   <news:publication_date>2026-08-04T05:31:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719319</loc>
  <lastmod>2026-08-04T05:30:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>最適化アルゴリズムの収束を動的に加速するCNNの活用（Accelerating Optimization Algorithms With Dynamic Parameter Selections Using Convolutional Neural Networks For Inverse Problems In Image Processing）</news:title>
   <news:publication_date>2026-08-04T05:30:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719317</loc>
  <lastmod>2026-08-04T05:30:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>尺度不変のフラットネス指標が示す深層学習の新しい見方（A Scale Invariant Flatness Measure for Deep Network Minima）</news:title>
   <news:publication_date>2026-08-04T05:30:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719315</loc>
  <lastmod>2026-08-04T05:30:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>LSTMを用いたうっ血性心不全発症予測の有効性（Effectiveness of LSTMs in Predicting Congestive Heart Failure Onset）</news:title>
   <news:publication_date>2026-08-04T05:30:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719313</loc>
  <lastmod>2026-08-04T04:38:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>重み付きSimplex戦略の動的化が示す現場適用の道（Dynamic-Weighted Simplex Strategy for Learning Enabled Cyber Physical Systems）</news:title>
   <news:publication_date>2026-08-04T04:38:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719311</loc>
  <lastmod>2026-08-04T04:37:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>電波SETI信号の分類における機械視覚と深層学習（Machine Vision and Deep Learning for Classification of Radio SETI Signals）</news:title>
   <news:publication_date>2026-08-04T04:37:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719309</loc>
  <lastmod>2026-08-04T04:36:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>主成分モデル解析（Principal Model Analysis Based on Partial Least Squares）</news:title>
   <news:publication_date>2026-08-04T04:36:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719307</loc>
  <lastmod>2026-08-04T04:36:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Unbiased Online Recurrent Optimizationの分散解析（On the Variance of Unbiased Online Recurrent Optimization）</news:title>
   <news:publication_date>2026-08-04T04:36:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719305</loc>
  <lastmod>2026-08-04T04:36:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>小さなデータから高速にハイパーパラメータを見つける方法（Fast Hyperparameter Tuning using Bayesian Optimization with Directional Derivatives）</news:title>
   <news:publication_date>2026-08-04T04:36:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719303</loc>
  <lastmod>2026-08-04T04:36:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>最近傍補間法のL2一貫性について（On L2-consistency of nearest neighbor matching）</news:title>
   <news:publication_date>2026-08-04T04:36:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719301</loc>
  <lastmod>2026-08-04T04:35:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分類に基づく集計のバイアス補正（A Bayesian Approach for Accurate Classification-Based Aggregates）</news:title>
   <news:publication_date>2026-08-04T04:35:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719299</loc>
  <lastmod>2026-08-04T03:44:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>神経科学に学ぶ創造的デコーダ（TOWARD A NEURO-INSPIRED CREATIVE DECODER）</news:title>
   <news:publication_date>2026-08-04T03:44:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719297</loc>
  <lastmod>2026-08-04T03:44:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>根格子におけるCVPの折り畳みによる解法と深いReLUニューラルネットワーク（On the CVP for the root lattices via folding with deep ReLU neural networks）</news:title>
   <news:publication_date>2026-08-04T03:44:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719295</loc>
  <lastmod>2026-08-04T03:43:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ドメイン適応によるステンス検出の敵対的学習（Adversarial Domain Adaptation for Stance Detection）</news:title>
   <news:publication_date>2026-08-04T03:43:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719293</loc>
  <lastmod>2026-08-04T03:42:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>再帰型ニューラルネットワークの圧縮による実用的言語モデルの実装（Compression of Recurrent Neural Networks for Efficient Language Modeling）</news:title>
   <news:publication_date>2026-08-04T03:42:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719291</loc>
  <lastmod>2026-08-04T03:42:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単眼カメラで交通コーンの3D位置をリアルタイム推定する手法（Real-time 3D Traffic Cone Detection for Autonomous Driving）</news:title>
   <news:publication_date>2026-08-04T03:42:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719289</loc>
  <lastmod>2026-08-04T03:42:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DiffEqFlux.jl—Neural Differential Equationsを実現するJuliaライブラリ（DiffEqFlux.jl — A Julia Library for Neural Differential Equations）</news:title>
   <news:publication_date>2026-08-04T03:42:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719287</loc>
  <lastmod>2026-08-04T03:42:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>エンドツーエンド・アンカード音声認識の要点整理（END-TO-END ANCHORED SPEECH RECOGNITION）</news:title>
   <news:publication_date>2026-08-04T03:42:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719285</loc>
  <lastmod>2026-08-04T02:50:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>セントロイドに基づく深層距離学習による話者認識（CENTROID-BASED DEEP METRIC LEARNING FOR SPEAKER RECOGNITION）</news:title>
   <news:publication_date>2026-08-04T02:50:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719283</loc>
  <lastmod>2026-08-04T02:50:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>専門家アドバイスによる予測でのコム戦略の漸近的最適性（On the Asymptotic Optimality of the Comb Strategy for Prediction with Expert Advice）</news:title>
   <news:publication_date>2026-08-04T02:50:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719281</loc>
  <lastmod>2026-08-04T02:50:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Stack Exchangeのタグ付けネットワークのモデリングと解析 (Modeling and Analysis of Tagging Networks in Stack Exchange Communities)</news:title>
   <news:publication_date>2026-08-04T02:50:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719279</loc>
  <lastmod>2026-08-04T02:49:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>層の役割をガウス過程の視点で解く（The role of a layer in deep neural networks: a Gaussian Process perspective）</news:title>
   <news:publication_date>2026-08-04T02:49:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719277</loc>
  <lastmod>2026-08-04T02:49:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>µJy帯の電波観測が示す銀河進化の新たな視座（eMERGE Data Release 1）</news:title>
   <news:publication_date>2026-08-04T02:49:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719275</loc>
  <lastmod>2026-08-04T02:49:06Z</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ヘッセ行列の負の固有値の意義（Negative eigenvalues of the Hessian in deep neural networks）</news:title>
   <news:publication_date>2026-08-04T02:49:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719273</loc>
  <lastmod>2026-08-04T02:48:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>孤立した矮小銀河対の発見とその意味（Discovery of an isolated dwarf–dwarf galaxy pair at z = 0.30）</news:title>
   <news:publication_date>2026-08-04T02:48:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719271</loc>
  <lastmod>2026-08-04T01:54:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
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 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-04T01:53:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>ニューラルネットワーク帰属の因果的視点（Neural Network Attributions: A Causal Perspective）</news:title>
   <news:publication_date>2026-08-04T01:00:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-04T01:00:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>WiFi CSIを用いた深層学習による歩容生体認証（Deep CSI Learning for Gait Biometric Sensing and Recognition）</news:title>
   <news:publication_date>2026-08-04T00:59:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-04T00:59:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>多言語音声合成を非教師で実現する手法（UNSUPERVISED POLYGLOT TEXT-TO-SPEECH）</news:title>
   <news:publication_date>2026-08-04T00:59:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>骨病変病理のための生成的画像翻訳によるデータ拡張（Generative Image Translation for Data Augmentation of Bone Lesion Pathology）</news:title>
   <news:publication_date>2026-08-04T00:58:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-04T00:58:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-04T00:07:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-04T00:06:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-04T00:05:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-04T00:03:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューラルネットワークが導く式変換と証明の自動復元（Neural-Network Guided Expression Transformation）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>マスクベースの多面表現による非構造化マルチビュー深度推定（Unstructured Multi-View Depth Estimation Using Mask-Based Multiplane Representation）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>音表現からの転移学習による発話の怒り検出（TRANSFER LEARNING FROM SOUND REPRESENTATIONS FOR ANGER DETECTION IN SPEECH）</news:title>
   <news:publication_date>2026-08-03T23:10:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SAPSAM—2値ラベルのみで肺CTを学習する新手法（SAPSAM - SPARSELY ANNOTATED PATHOLOGICAL SIGN ACTIVATION MAPS）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>強化学習が量子制御で輝く条件（When does reinforcement learning stand out in quantum control? A comparative study on state preparation）</news:title>
   <news:publication_date>2026-08-03T23:09:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Mol-CycleGANによる分子最適化（Mol-CycleGAN - a generative model for molecular optimization）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>複素値カーネル活性化関数の広義線形カーネル（Widely Linear Kernels for Complex-Valued Kernel Activation Functions）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>固有表現を取り込む単語埋め込みの再設計（Word Embeddings for Entity-annotated Texts）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-03T21:13:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>混合データのベイズ的共クラスタリングモデル（Un modèle Bayésien de co-clustering de données mixtes）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
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 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>実用的な虹彩認識の変革点（DeepIrisNet2: Learning Deep-IrisCodes from Scratch for Segmentation-Robust Visible Wavelength and Near Infrared Iris Recognition）</news:title>
   <news:publication_date>2026-08-03T21:13:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>都市の細粒度フロー推定（UrbanFM: Inferring Fine-Grained Urban Flows）</news:title>
   <news:publication_date>2026-08-03T21:12:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-03T21:12:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層学習におけるADMMの収束性と飽和回避（On ADMM in Deep Learning: Convergence and Saturation-Avoidance）</news:title>
   <news:publication_date>2026-08-03T21:12:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719187</loc>
  <lastmod>2026-08-03T20:21:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>到達しない状態を許すマルコフ連鎖の同一性検定（Testing Markov Chains Without Hitting）</news:title>
   <news:publication_date>2026-08-03T20:21:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719185</loc>
  <lastmod>2026-08-03T20:13:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>双方向推論ネットワーク（Bidirectional Inference Networks: A Class of Deep Bayesian Networks for Health Profiling）</news:title>
   <news:publication_date>2026-08-03T20:13:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/719183</loc>
  <lastmod>2026-08-03T20:13:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-03T20:13:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719181</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分布系統における頑健な行列補完による状態推定（Robust Matrix Completion State Estimation in Distribution Systems）</news:title>
   <news:publication_date>2026-08-03T20:13:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高速な平均推定とサブガウス誤差率（Fast Mean Estimation with Sub-Gaussian Rates）</news:title>
   <news:publication_date>2026-08-03T20:12:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-03T20:12:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>多尺度データに対する自動スペクトラルクラスタリング（AN AUTOMATED SPECTRAL CLUSTERING FOR MULTI-SCALE DATA）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CodedReduceによる分散学習の高速化と堅牢化（CodedReduce: A Fast and Robust Framework for Gradient Aggregation in Distributed Learning）</news:title>
   <news:publication_date>2026-08-03T19:21:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-03T19:21:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>フィードフォワード設計畳み込みニューラルネットワークによる半教師あり学習（SEMI-SUPERVISED LEARNING VIA FEEDFORWARD-DESIGNED CONVOLUTIONAL NEURAL NETWORKS）</news:title>
   <news:publication_date>2026-08-03T19:21:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <lastmod>2026-08-03T19:20:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>交換可能な生成モデルとFlowScan（Exchangeable Generative Models with Flow Scans）</news:title>
   <news:publication_date>2026-08-03T19:20:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-03T19:19:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>ポリフォニック音楽作曲におけるLSTMと強化学習（Polyphonic Music Composition with LSTM Neural Networks and Reinforcement Learning）</news:title>
   <news:publication_date>2026-08-03T19:19:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-03T19:19:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-03T18:27:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-03T18:27:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719157</loc>
  <lastmod>2026-08-03T18:26:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-03T18:26:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719155</loc>
  <lastmod>2026-08-03T18:26:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-03T18:26:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719153</loc>
  <lastmod>2026-08-03T18:25:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Zwicky Transient Facilityにおける機械学習の実装と応用（Machine Learning for the Zwicky Transient Facility）</news:title>
   <news:publication_date>2026-08-03T18:25:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719151</loc>
  <lastmod>2026-08-03T18:25:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-03T18:25:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719149</loc>
  <lastmod>2026-08-03T18:25:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>メタアモータイズド変分推論と学習（Meta-Amortized Variational Inference and Learning）</news:title>
   <news:publication_date>2026-08-03T18:25:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719147</loc>
  <lastmod>2026-08-03T17:33:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-03T17:33:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719145</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-03T17:33:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719143</loc>
  <lastmod>2026-08-03T17:33:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-03T17:33:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719141</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-03T17:32:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719139</loc>
  <lastmod>2026-08-03T17:32:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-03T17:32:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ハイペルエントロピーでつなぐ勾配と乗法的更新（Exponentiated Gradient vs. Meets Gradient Descent）</news:title>
   <news:publication_date>2026-08-03T17:32:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719135</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <lastmod>2026-08-03T16:40:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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    <news:name>AI Benchmark Research</news:name>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-03T15:46:50Z</news:publication_date>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-03T15:46:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-03T15:44:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-03T15:44:46Z</news:publication_date>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-03T15:44:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-08-03T15:44:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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