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   <news:title>共同研究データをネットワーク層に変換して分析を強化する手法（Transforming Collaboration Data into Network Layers for Enhanced Analytics）</news:title>
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   <news:title>構文と意味を分離して協調学習する手法（Cooperative Learning of Disjoint Syntax and Semantics）</news:title>
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   <news:title>コンパクトファジーモデル構築のための分散ルール導出アルゴリズムCFM-BD（CFM-BD: a distributed rule induction algorithm for building Compact Fuzzy Models in Big Data classification problems）</news:title>
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    <news:language>ja</news:language>
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   <news:title>半教師付き・弱教師付き階層テキスト分類のための効率的パス予測 (Efficient Path Prediction for Semi-Supervised and Weakly Supervised Hierarchical Text Classification)</news:title>
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   <news:genres>Blog</news:genres>
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  <lastmod>2026-08-10T20:47:15Z</lastmod>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>履歴を重視する視覚対話学習（Making History Matter: History-Advantage Sequence Training for Visual Dialog）</news:title>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>重み付きパーソナライズ行列因子分解によるマルチラベルネットワーク分類（Multi-Label Network Classification via Weighted Personalized Factorizations）</news:title>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>Wasserstein-Wassersteinオートエンコーダ（Wasserstein-Wasserstein Auto-Encoders）</news:title>
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   <news:genres>Blog</news:genres>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>パーソナライズされた仮想教育アシスタント（A Virtual Teaching Assistant for Personalized Learning）</news:title>
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   <news:genres>Blog</news:genres>
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    <news:name>AI Benchmark Research</news:name>
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   <news:title>密な軌跡と欠損軌跡を同時に扱う都市全域交通量推定（Joint Modeling of Dense and Incomplete Trajectories for Citywide Traffic Volume Inference）</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>周波数依存ビームの影響下でEoR信号を分離する畳み込みデノイジングオートエンコーダ（Separating the EoR signal with a convolutional denoising autoencoder: a deep-learning-based method）</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>明示的文脈条件付けを用いた関係抽出（Relation Extraction using Explicit Context Conditioning）</news:title>
   <news:publication_date>2026-08-10T19:52:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-08-10T19:52:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>人の目に見えない敵対的攻撃の隠し方（Adversarial attacks hidden in plain sight）</news:title>
   <news:publication_date>2026-08-10T19:52:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-08-10T19:01:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>機械学習で明かす量子カオスの姿（Revealing quantum chaos with machine learning）</news:title>
   <news:publication_date>2026-08-10T19:01:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-08-10T18:52:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>MBPEP：高品質な不確かさ予測を実現する深層アンサンブル剪定アルゴリズム (The MBPEP: a deep ensemble pruning algorithm providing high quality uncertainty prediction)</news:title>
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   <news:genres>Blog</news:genres>
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  <lastmod>2026-08-10T18:52:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>少ない内部センサで高精度な触覚を実現する（Robust Affordable 3D Haptic Sensation via Learning Deformation Patterns）</news:title>
   <news:publication_date>2026-08-10T18:52:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-08-10T18:52:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>モジュール化によるニューラルネットワークの複雑性管理（Modularity as a Means for Complexity Management in Neural Networks Learning）</news:title>
   <news:publication_date>2026-08-10T18:52:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-08-10T18:51:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>高解像度表現学習による人物姿勢推定の刷新（Deep High-Resolution Representation Learning for Human Pose Estimation）</news:title>
   <news:publication_date>2026-08-10T18:51:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-08-10T18:51:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>最大尤度とGANの調和による多モーダル条件付き生成（HARMONIZING MAXIMUM LIKELIHOOD WITH GANS FOR MULTIMODAL CONDITIONAL GENERATION）</news:title>
   <news:publication_date>2026-08-10T18:51:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/721716</loc>
  <lastmod>2026-08-10T18:51:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Contrastive Learningの理論的枠組みが示した本質（A Theoretical Analysis of Contrastive Unsupervised Representation Learning）</news:title>
   <news:publication_date>2026-08-10T18:51:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/721714</loc>
  <lastmod>2026-08-10T17:58:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフ畳み込みネットワークに対するバッチ仮想敵対的訓練（Batch Virtual Adversarial Training for Graph Convolutional Networks）</news:title>
   <news:publication_date>2026-08-10T17:58:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/721712</loc>
  <lastmod>2026-08-10T17:58:07Z</lastmod>
  <news:news>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>実生活下における鏡像系の皮質活動（Cortical Mirror-System Activation During Real-Life Game Playing: An Intracranial Electroencephalography (EEG) Study）</news:title>
   <news:publication_date>2026-08-10T17:58:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/721710</loc>
  <lastmod>2026-08-10T17:57:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>応答の多様性を高める周波数対応交差エントロピー損失（Improving Neural Response Diversity with Frequency-Aware Cross-Entropy Loss）</news:title>
   <news:publication_date>2026-08-10T17:57:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
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  <loc>https://aibr.jp/archives/721708</loc>
  <lastmod>2026-08-10T17:57:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>宇宙の塵に覆われた星形成銀河の統計的性質—Herschelデータの多波長de-blend解析 (A multi-wavelength de-blended Herschel view of the statistical properties of dusty star-forming galaxies across cosmic time)</news:title>
   <news:publication_date>2026-08-10T17:57:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-08-10T17:56:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数領域を同時に学習する短答自動採点（Joint Multi-Domain Learning for Automatic Short Answer Grading）</news:title>
   <news:publication_date>2026-08-10T17:56:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
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  <loc>https://aibr.jp/archives/721704</loc>
  <lastmod>2026-08-10T17:56:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自動運転におけるコーナーケース検出の実装と評価（Towards Corner Case Detection for Autonomous Driving）</news:title>
   <news:publication_date>2026-08-10T17:56:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
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  <loc>https://aibr.jp/archives/721702</loc>
  <lastmod>2026-08-10T17:56:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GFCN：並列フローに基づく新しいグラフ畳み込みネットワーク（GFCN: A New Graph Convolutional Network Based on Parallel Flows）</news:title>
   <news:publication_date>2026-08-10T17:56:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
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  <loc>https://aibr.jp/archives/721700</loc>
  <lastmod>2026-08-10T17:05:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>医療画像解析におけるクラウドソーシングの概観（A Survey of Crowdsourcing in Medical Image Analysis）</news:title>
   <news:publication_date>2026-08-10T17:05:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721698</loc>
  <lastmod>2026-08-10T17:04:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オンラインクラスタリングバンディットの改良アルゴリズム（Improved Algorithm on Online Clustering of Bandits）</news:title>
   <news:publication_date>2026-08-10T17:04:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721696</loc>
  <lastmod>2026-08-10T17:04:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>不確かな穴への迅速なペグ挿入（Quickly Inserting Pegs into Uncertain Holes using Multi-view Images and Deep Network Trained on Synthetic Data）</news:title>
   <news:publication_date>2026-08-10T17:04:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721694</loc>
  <lastmod>2026-08-10T17:04:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>長読みに基づくウイルスゲノム進化の未来的方法（Futuristic methods in virus genome evolution using the Third-Generation DNA sequencing and artificial neural networks）</news:title>
   <news:publication_date>2026-08-10T17:04:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/721692</loc>
  <lastmod>2026-08-10T17:04:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチグリッド偏微分方程式（PDE）ソルバーの最適化を学習する（Learning to Optimize Multigrid PDE Solvers）</news:title>
   <news:publication_date>2026-08-10T17:04:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721690</loc>
  <lastmod>2026-08-10T17:03:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Deep Bayesian Multi-Target Learningによる推薦最適化の実務的理解（Deep Bayesian Multi-Target Learning for Recommender Systems）</news:title>
   <news:publication_date>2026-08-10T17:03:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/721688</loc>
  <lastmod>2026-08-10T17:03:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DDFlow: ラベルなしデータから学ぶ光学フローの蒸留学習（DDFlow: Learning Optical Flow with Unlabeled Data Distillation）</news:title>
   <news:publication_date>2026-08-10T17:03:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721686</loc>
  <lastmod>2026-08-10T16:12:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフの集合を“点”で扱う時代へ（Unsupervised Network Embedding for Graph Visualization, Clustering and Classification）</news:title>
   <news:publication_date>2026-08-10T16:12:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721684</loc>
  <lastmod>2026-08-10T16:12:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>注意機構を備えたグラフ畳み込みLSTMによるスケルトン動作認識（An Attention Enhanced Graph Convolutional LSTM Network for Skeleton-Based Action Recognition）</news:title>
   <news:publication_date>2026-08-10T16:12:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721682</loc>
  <lastmod>2026-08-10T16:11:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ストリップされたバイナリのニューラル逆解析（Neural Reverse Engineering of Stripped Binaries using Augmented Control Flow Graphs）</news:title>
   <news:publication_date>2026-08-10T16:11:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721680</loc>
  <lastmod>2026-08-10T16:10:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ピクセル誤差を超えて: 自己教師ありエゴモーション推定における幾何学的マッチングの導入 (Beyond Photometric Loss for Self-Supervised Ego-Motion Estimation)</news:title>
   <news:publication_date>2026-08-10T16:10:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721678</loc>
  <lastmod>2026-08-10T16:10:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>低ランク生成モデルによる動画表現と再構成（Generative Models for Low-Rank Video Representation and Reconstruction）</news:title>
   <news:publication_date>2026-08-10T16:10:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721676</loc>
  <lastmod>2026-08-10T16:10:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>可視化、判別性、解釈可能なSaak特徴の応用（Visualization, Discriminability and Applications of Interpretable Saak Features）</news:title>
   <news:publication_date>2026-08-10T16:10:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721674</loc>
  <lastmod>2026-08-10T16:09:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Marathon Environments：商用ゲームエンジン上での連続制御ベンチマーク（Marathon Environments: Multi-Agent Continuous Control Benchmarks in a Modern Video Game Engine）</news:title>
   <news:publication_date>2026-08-10T16:09:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721672</loc>
  <lastmod>2026-08-10T15:18:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>FPGA上のグラフ処理の分類と課題（Graph Processing on FPGAs: Taxonomy, Survey, Challenges）</news:title>
   <news:publication_date>2026-08-10T15:18:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721670</loc>
  <lastmod>2026-08-10T15:09:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>フィールド対応ニューラル因子分解機によるクリック率予測（Field-aware Neural Factorization Machine for Click-Through Rate Prediction）</news:title>
   <news:publication_date>2026-08-10T15:09:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721668</loc>
  <lastmod>2026-08-10T15:09:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DRAMの電源立ち上げ特性を機器認証に使う新手法（DRAMNet: Authentication based on Physical Unique Features of DRAM Using Deep Convolutional Neural Networks）</news:title>
   <news:publication_date>2026-08-10T15:09:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721666</loc>
  <lastmod>2026-08-10T15:08:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>系列データの転移学習：単語の共起を学んで移す（TRANSFER LEARNING FOR SEQUENCES VIA LEARNING TO COLLOCATE）</news:title>
   <news:publication_date>2026-08-10T15:08:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721664</loc>
  <lastmod>2026-08-10T15:08:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>極端温度下で動作する自己整流BFOメモリスタの学習・記憶機能（Synaptic Learning and Memory Functions Achieved in Self-rectifying BFO Memristor under Extreme Environmental Temperature）</news:title>
   <news:publication_date>2026-08-10T15:08:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721662</loc>
  <lastmod>2026-08-10T15:07:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>レンズレスな秘匿映像で行動認識を可能にする技術（Privacy-Preserving Action Recognition using Coded Aperture Videos）</news:title>
   <news:publication_date>2026-08-10T15:07:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721660</loc>
  <lastmod>2026-08-10T15:07:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>知識ベースを活用するLSTMによる機械読解の向上（Leveraging Knowledge Bases in LSTMs for Improving Machine Reading）</news:title>
   <news:publication_date>2026-08-10T15:07:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721658</loc>
  <lastmod>2026-08-10T14:15:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>歩行者検出における意味的自己注意による精度向上（SSA-CNN: Semantic Self-Attention CNN for Pedestrian Detection）</news:title>
   <news:publication_date>2026-08-10T14:15:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721656</loc>
  <lastmod>2026-08-10T14:15:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>チャネル敵対的学習によるクロスチャネル話者認識の改善（Channel Adversarial Training for Cross-Channel Text-Independent Speaker Recognition）</news:title>
   <news:publication_date>2026-08-10T14:15:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721654</loc>
  <lastmod>2026-08-10T14:15:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CFD駆動機械学習によるRANS乱流モデル開発（RANS Turbulence Model Development using CFD-Driven Machine Learning）</news:title>
   <news:publication_date>2026-08-10T14:15:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721652</loc>
  <lastmod>2026-08-10T14:14:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>アルゴンにおける39Arと37Arの宇宙生成（Cosmogenic production of 39Ar and 37Ar in argon）</news:title>
   <news:publication_date>2026-08-10T14:14:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721650</loc>
  <lastmod>2026-08-10T14:13:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Wasserstein GANがPCAを実現する可能性（Wasserstein GAN Can Perform PCA）</news:title>
   <news:publication_date>2026-08-10T14:13:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721648</loc>
  <lastmod>2026-08-10T14:13:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>運転意図予測の実用的アプローチ（A Driving Intention Prediction Method Based on Hidden Markov Model for Autonomous Driving）</news:title>
   <news:publication_date>2026-08-10T14:13:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721646</loc>
  <lastmod>2026-08-10T14:13:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>アフリカ森林ゾウの受動音響モニタリングにおける自動検出と圧縮（Automatic Detection and Compression for Passive Acoustic Monitoring of the African Forest Elephant）</news:title>
   <news:publication_date>2026-08-10T14:13:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721644</loc>
  <lastmod>2026-08-10T13:21:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>部分観測下における自律型コンピュータネットワーク防御のための敵対的強化学習（Adversarial Reinforcement Learning under Partial Observability in Autonomous Computer Network Defence）</news:title>
   <news:publication_date>2026-08-10T13:21:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721642</loc>
  <lastmod>2026-08-10T13:21:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ベクトル演算による高価なベイズ計算の高速化（Vector operations for accelerating expensive Bayesian computations – a tutorial guide）</news:title>
   <news:publication_date>2026-08-10T13:21:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721640</loc>
  <lastmod>2026-08-10T13:21:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>移動ロボットによる堅牢で適応的なドア操作（Robust and Adaptive Door Operation with a Mobile Robot）</news:title>
   <news:publication_date>2026-08-10T13:21:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721638</loc>
  <lastmod>2026-08-10T13:20:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>最急適応モーメント推定（Rapidly Adapting Moment Estimation）</news:title>
   <news:publication_date>2026-08-10T13:20:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721636</loc>
  <lastmod>2026-08-10T13:20:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単調単一インデックスモデルの非線形一般化（Nonlinear generalization of the monotone single index model）</news:title>
   <news:publication_date>2026-08-10T13:20:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721634</loc>
  <lastmod>2026-08-10T13:19:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単一前向きステップによる射影分割：ココーシビティの活用（Single-Forward-Step Projective Splitting: Exploiting Cocoercivity）</news:title>
   <news:publication_date>2026-08-10T13:19:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721632</loc>
  <lastmod>2026-08-10T13:19:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>適応的推定器は深層ニューラルネットの情報圧縮を示す（Adaptive Estimators Show Information Compression in Deep Neural Networks）</news:title>
   <news:publication_date>2026-08-10T13:19:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721630</loc>
  <lastmod>2026-08-10T12:26:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>役割と充填子の結合学習（Learning to Perform Role-Filler Binding with Schematic Knowledge）</news:title>
   <news:publication_date>2026-08-10T12:26:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721628</loc>
  <lastmod>2026-08-10T12:24:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>エアリービームを用いた蛍光イメージングの深部透過（Deep penetration fluorescence imaging through dense yeast cells suspensions using Airy beams）</news:title>
   <news:publication_date>2026-08-10T12:24:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721626</loc>
  <lastmod>2026-08-10T12:24:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大きなマージンを持つ半空間の差分プライバシー学習アルゴリズム（Efficient Private Algorithms for Learning Large-Margin Halfspaces）</news:title>
   <news:publication_date>2026-08-10T12:24:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721624</loc>
  <lastmod>2026-08-10T12:23:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>無制約オンライン学習のための人工的制約とリプシッツヒント（Artificial Constraints and Lipschitz Hints for Unconstrained Online Learning）</news:title>
   <news:publication_date>2026-08-10T12:23:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721622</loc>
  <lastmod>2026-08-10T12:22:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次元制約付き連合モデル選択と分布シフト下の多目的ベイズ最適化 (High Dimensional Restrictive Federated Model Selection with multi-objective Bayesian Optimization over shifted distributions)</news:title>
   <news:publication_date>2026-08-10T12:22:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721620</loc>
  <lastmod>2026-08-10T12:22:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>AgentBuddy: 顧客対応支援のための文脈型バンディット（AgentBuddy: A Contextual Bandit based Decision Support System for Customer Support Agents）</news:title>
   <news:publication_date>2026-08-10T12:22:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721618</loc>
  <lastmod>2026-08-10T12:21:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数のオンライン学習アルゴリズムを安全に組み合わせる方法（Combining Online Learning Guarantees）</news:title>
   <news:publication_date>2026-08-10T12:21:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721616</loc>
  <lastmod>2026-08-10T11:29:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>U-NetPlusによる手術器具セグメンテーションの改良（U-NetPlus: A Modified Encoder-Decoder U-Net Architecture for Semantic and Instance Segmentation of Surgical Instrument）</news:title>
   <news:publication_date>2026-08-10T11:29:52Z</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>2026-08-10T11:28:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news: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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 </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>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 </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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 </url>
 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 </url>
 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 </url>
 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 </url>
 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news: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: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>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/721572</loc>
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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:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>医療マルチモーダル分類器の低データ環境における性能（Medical Multimodal Classifiers Under Low Data Situations）</news:title>
   <news:publication_date>2026-08-10T08:47:30Z</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>
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   <news:title>Lassoのバイアス補正と自由度補正の意義（De-Biasing The Lasso With Degrees-of-Freedom Adjustment）</news:title>
   <news:publication_date>2026-08-10T08:47:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721566</loc>
  <lastmod>2026-08-10T08:47:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>異種ソースを階層強化学習で統合するEコマース検索（Aggregating E-commerce Search Results from Heterogeneous Sources via Hierarchical Reinforcement Learning）</news:title>
   <news:publication_date>2026-08-10T08:47:03Z</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>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-10T07:54:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <lastmod>2026-08-10T07:46:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>天の川外縁衛星におけるセールシック指数と有効半径の単一関係（A MEGACAM SURVEY OF OUTER HALO SATELLITES. VII. A SINGLE SÉRSIC INDEX V/S EFFECTIVE RADIUS RELATION FOR MILKY WAY OUTER HALO SATELLITES）</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:title>潜在変数としての行動空間再考――対話エージェントにおける強化学習の新展開 (Rethinking Action Spaces for Reinforcement Learning in End-to-end Dialog Agents with Latent Variable Models)</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <lastmod>2026-08-10T07:44:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>対話応答評価モデルADEMの再検証（Re-evaluating ADEM: A Deeper Look at Scoring Dialogue Responses）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-10T07:44:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-10T07:44:24Z</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:title>転移学習を用いた非侵襲負荷監視の実務的インパクト（Transfer Learning for Non-Intrusive Load Monitoring）</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:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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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:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 <url>
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 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
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 <url>
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    <news:name>AI Benchmark Research</news:name>
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    <news:name>AI Benchmark Research</news:name>
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    <news:name>AI Benchmark Research</news:name>
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    <news:name>AI Benchmark Research</news:name>
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    <news:name>AI Benchmark Research</news:name>
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 <url>
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 <url>
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 <url>
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    <news:name>AI Benchmark Research</news:name>
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 <url>
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    <news:name>AI Benchmark Research</news:name>
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 </url>
 <url>
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  <news:news>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 </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: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>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 <url>
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    <news:name>AI Benchmark Research</news:name>
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 <url>
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    <news:name>AI Benchmark Research</news:name>
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 <url>
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 <url>
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    <news:name>AI Benchmark Research</news:name>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721470</loc>
  <lastmod>2026-08-10T02:14:29Z</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>2026-08-10T02:14:29Z</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>スペクトロ光度距離とGaia DR2パララックスゼロポイントの同時較正（Simultaneous calibration of spectro-photometric distances and the Gaia DR2 parallax zero-point offset with deep learning）</news:title>
   <news:publication_date>2026-08-10T02:14:10Z</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>2026-08-10T02:14:04Z</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>モデル性能予測器（MPP: Model Performance Predictor）</news:title>
   <news:publication_date>2026-08-10T02:13:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721462</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>ガス圧縮と消耗によるクエンチング（Quenching by gas compression and consumption）</news:title>
   <news:publication_date>2026-08-10T01:22:39Z</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>長距離相互作用ハミルトニアンにおける動的臨界性とドメインウォール結合（Dynamical criticality and domain-wall coupling in long-range Hamiltonians）</news:title>
   <news:publication_date>2026-08-10T01:22:15Z</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>測定不確実性下でのベイズ的異常検知と分類（Bayesian Anomaly Detection and Classification）</news:title>
   <news:publication_date>2026-08-10T01:22:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721456</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>長期依存を扱う混合型ニューラル推薦モデルの実務的理解（Towards Neural Mixture Recommender for Long Range Dependent User Sequences）</news:title>
   <news:publication_date>2026-08-10T01:20:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721454</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>ニューラルネットワーク層を通したキャパシティ配分（Capacity allocation through neural network layers）</news:title>
   <news:publication_date>2026-08-10T01:20:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721452</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>少数ショット学習ベンチマークは本当に難しいか（Are Few-Shot Learning Benchmarks too Simple ? Solving them without Test-Time Labels）</news:title>
   <news:publication_date>2026-08-10T01:20:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721450</loc>
  <lastmod>2026-08-10T01:20:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非定常報酬と遅延フィードバックに強いバンディット戦略の設計（Multi-Armed Bandit Strategies for Non-Stationary Reward Distributions and Delayed Feedback Processes）</news:title>
   <news:publication_date>2026-08-10T01:20:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721448</loc>
  <lastmod>2026-08-10T00:27:30Z</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>2026-08-10T00:27:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-10T00:27:14Z</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>2026-08-10T00:27:14Z</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>多言語文埋め込みの改良（Improving Multilingual Sentence Embedding using Bi-directional Dual Encoder with Additive Margin Softmax）</news:title>
   <news:publication_date>2026-08-10T00:26:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721442</loc>
  <lastmod>2026-08-10T00:26:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>既製の深層モデルから抽出した複合特徴による画像美学評価（Image Aesthetics Assessment Using Composite Features from Off-the-Shelf Deep Models）</news:title>
   <news:publication_date>2026-08-10T00:26:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721440</loc>
  <lastmod>2026-08-10T00:26:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>TESS惑星候補の迅速分類（Rapid Classification of TESS Planet Candidates with Convolutional Neural Networks）</news:title>
   <news:publication_date>2026-08-10T00:26:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721438</loc>
  <lastmod>2026-08-10T00:26:01Z</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>2026-08-10T00:26:01Z</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>
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   <news:publication_date>2026-08-09T23:35:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721432</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>プロトコル非依存のグラフベース異常検知によるボット検出（Anomaly- and Graph-Based Bot Detection）</news:title>
   <news:publication_date>2026-08-09T23:34:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721430</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>冷媒漏れのオンライントラブル診断を変えるスケーリング則（Fault Diagnosis Method Based on Scaling Law for On-line Refrigerant Leak Detection）</news:title>
   <news:publication_date>2026-08-09T23:34:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721428</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>複数運転モードを持つ産業システムの故障検知に向けたソフトセンサ半教師あり手法（Semi-supervised Approach to Soft Sensor Modeling for Fault Detection in Industrial Systems with Multiple Operation Modes）</news:title>
   <news:publication_date>2026-08-09T23:33:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721426</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>ネットワーク内集約による分散学習の高速化（Scaling Distributed Machine Learning with In-Network Aggregation）</news:title>
   <news:publication_date>2026-08-09T23:33:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721424</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>2026-08-09T23:33:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721422</loc>
  <lastmod>2026-08-09T23:32:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>AReSとMaRS—SDE推定の敵対的・MMD最小化回帰（AReS and MaRS - Adversarial and MMD-Minimizing Regression for SDEs）</news:title>
   <news:publication_date>2026-08-09T23:32:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721420</loc>
  <lastmod>2026-08-09T22:40:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高速多言語LSTMベースのオンライン手書き認識（Fast Multi-language LSTM-based Online Handwriting Recognition）</news:title>
   <news:publication_date>2026-08-09T22:40:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721418</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>高次元でのモデルベースクラスタリングと適応射影（Model-based clustering in very high dimensions via adaptive projections）</news:title>
   <news:publication_date>2026-08-09T22:40:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721416</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>データストリーム分類におけるアンサンブルの多様性（Diversity of Ensembles for Data Stream Classification）</news:title>
   <news:publication_date>2026-08-09T22:40:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721414</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>詳細な探索空間分類による集合問題の列挙困難変種の高速化 (Fine-grained Search Space Classification for Hard Enumeration Variants of Subset Problems)</news:title>
   <news:publication_date>2026-08-09T22:39:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721412</loc>
  <lastmod>2026-08-09T22:38:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非凸分布の効率的サンプリング手法の改善（Nonconvex sampling with the Metropolis-adjusted Langevin algorithm）</news:title>
   <news:publication_date>2026-08-09T22:38:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721410</loc>
  <lastmod>2026-08-09T22:38:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オンライン異常検知がHPC運用を変える（Online Anomaly Detection in HPC Systems）</news:title>
   <news:publication_date>2026-08-09T22:38:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721408</loc>
  <lastmod>2026-08-09T22:38:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>サイバーフィジカル生産システムにおける認知アーキテクチャの評価（Evaluation of Cognitive Architectures for Cyber-Physical Production Systems）</news:title>
   <news:publication_date>2026-08-09T22:38:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721406</loc>
  <lastmod>2026-08-09T21:47:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オンラインメタラーニングの教科書的解説（Online Meta-Learning）</news:title>
   <news:publication_date>2026-08-09T21:47:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721404</loc>
  <lastmod>2026-08-09T21:47:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-09T21:47:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721402</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>差分非対称を用いたトランスベシティ分布の抽出（Transversity distributions from difference asymmetries in semi-inclusive DIS）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>心臓病学における深層学習（Deep Learning in Cardiology）</news:title>
   <news:publication_date>2026-08-09T21:46:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721398</loc>
  <lastmod>2026-08-09T21:46:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>整列名義集合上の高速計算（Fast Computations on Ordered Nominal Sets）</news:title>
   <news:publication_date>2026-08-09T21:46:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721396</loc>
  <lastmod>2026-08-09T21:46:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>量子ゲート制御に深層強化学習を使う意義（Deep Reinforcement Learning for Quantum Gate Control）</news:title>
   <news:publication_date>2026-08-09T21:46:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721394</loc>
  <lastmod>2026-08-09T21:45:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチレーン・カプセルネットワークの実務的理解（The Multi-Lane Capsule Network）</news:title>
   <news:publication_date>2026-08-09T21:45:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721392</loc>
  <lastmod>2026-08-09T20:54:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>メタ学習を用いたグラフニューラルネットワークへの敵対的攻撃（ADVERSARIAL ATTACKS ON GRAPH NEURAL NETWORKS VIA META LEARNING）</news:title>
   <news:publication_date>2026-08-09T20:54:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721390</loc>
  <lastmod>2026-08-09T20:53:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模皮質モデルの活動状態と並列シミュレーション性能のスケーリング（Scaling of a Large-Scale Simulation of Synchronous Slow-Wave and Asynchronous Awake-Like Activity of a Cortical Model With Long-Range Interconnections）</news:title>
   <news:publication_date>2026-08-09T20:53:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721388</loc>
  <lastmod>2026-08-09T20:52:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スパイキングニューラルネットワークによる二値マルコフ確率場の確率的推論（Probabilistic Inference of Binary Markov Random Fields in Spiking Neural Networks through Mean-field Approximation）</news:title>
   <news:publication_date>2026-08-09T20:52:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721386</loc>
  <lastmod>2026-08-09T20:52:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>PhysNetによる分子エネルギーと力の高精度予測（PHYSNET: A NEURAL NETWORK FOR PREDICTING ENERGIES, FORCES, DIPOLE MOMENTS AND PARTIAL CHARGES）</news:title>
   <news:publication_date>2026-08-09T20:52:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721384</loc>
  <lastmod>2026-08-09T20:52:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューラル・コンディショナーによる指数的条件分布学習（Learning about an exponential amount of conditional distributions）</news:title>
   <news:publication_date>2026-08-09T20:52:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721382</loc>
  <lastmod>2026-08-09T20:51:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>明示的テンソル表現とカプセルネットワークによるグラフ分類（Capsule Neural Networks for Graph Classification using Explicit Tensorial Graph Representations）</news:title>
   <news:publication_date>2026-08-09T20:51:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721380</loc>
  <lastmod>2026-08-09T20:51:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>autoAxによる近似回路ライブラリを用いた自動設計空間探索と回路構築（autoAx: An Automatic Design Space Exploration and Circuit Building Methodology utilizing Libraries of Approximate Components）</news:title>
   <news:publication_date>2026-08-09T20:51:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721378</loc>
  <lastmod>2026-08-09T20:00:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>LSTMによる極性符号のSCフリップ復号学習（Learning to Flip Successive Cancellation Decoding of Polar Codes with LSTM Networks）</news:title>
   <news:publication_date>2026-08-09T20:00:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721376</loc>
  <lastmod>2026-08-09T19:59:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>エンドツーエンド自己符号化器通信系に対する物理的敵対的攻撃（Physical Adversarial Attacks Against End-to-End Autoencoder Communication Systems）</news:title>
   <news:publication_date>2026-08-09T19:59:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721374</loc>
  <lastmod>2026-08-09T19:59:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>幾何的輸送問題の前処理（Preconditioning for the Geometric Transportation Problem）</news:title>
   <news:publication_date>2026-08-09T19:59:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721372</loc>
  <lastmod>2026-08-09T19:58:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ℓ1最小化における唯一の鋭い局所最小点（Unique Sharp Local Minimum in ℓ1-minimization Complete Dictionary Learning）</news:title>
   <news:publication_date>2026-08-09T19:58:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721370</loc>
  <lastmod>2026-08-09T19:58:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自然言語監督から学ぶセマンティックパーサの学習（Learning to Learn Semantic Parsers from Natural Language Supervision）</news:title>
   <news:publication_date>2026-08-09T19:58:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721368</loc>
  <lastmod>2026-08-09T19:57:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>超臨界流体における普遍性・スケーリング・崩壊（Universality, scaling and collapse in supercritical fluids）</news:title>
   <news:publication_date>2026-08-09T19:57:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721366</loc>
  <lastmod>2026-08-09T19:57:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>揮発性ディープユーテクトリック溶媒からの結晶化（Crystallisation From Volatile Deep Eutectic Solvents）</news:title>
   <news:publication_date>2026-08-09T19:57:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721364</loc>
  <lastmod>2026-08-09T19:06:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非自己回帰翻訳と補助正則化の実用的意義（Non-Autoregressive Machine Translation with Auxiliary Regularization）</news:title>
   <news:publication_date>2026-08-09T19:06:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721362</loc>
  <lastmod>2026-08-09T19:05:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模版・質問者の心の中にいる回答者による視覚対話質問生成（LARGE-SCALE ANSWERER IN QUESTIONER’S MIND FOR VISUAL DIALOG QUESTION GENERATION）</news:title>
   <news:publication_date>2026-08-09T19:05:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721360</loc>
  <lastmod>2026-08-09T19:05:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時系列データの因子分離を可能にするFAVAE（FAVAE: Sequence Disentanglement using Information Bottleneck Principle）</news:title>
   <news:publication_date>2026-08-09T19:05:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721358</loc>
  <lastmod>2026-08-09T19:04:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>入力データ分布に対する敵対的頑健性の感度（On the Sensitivity of Adversarial Robustness to Input Data Distributions）</news:title>
   <news:publication_date>2026-08-09T19:04:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721356</loc>
  <lastmod>2026-08-09T19:04:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動的ネットワークのリンク予測を変えるE-LSTM-D（E-LSTM-D: A Deep Learning Framework for Dynamic Network Link Prediction）</news:title>
   <news:publication_date>2026-08-09T19:04:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721354</loc>
  <lastmod>2026-08-09T19:04:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時空間データに対する深層階層モデルと深層ニューラルモデルの比較（Comparison of Deep Neural Networks and Deep Hierarchical Models for Spatio-Temporal Data）</news:title>
   <news:publication_date>2026-08-09T19:04:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721352</loc>
  <lastmod>2026-08-09T19:03:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械ネットワークにおける学習された多安定性（Learned multi-stability in mechanical networks）</news:title>
   <news:publication_date>2026-08-09T19:03:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721350</loc>
  <lastmod>2026-08-09T18:12:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>辞書に基づくRobust PCAの一般化とハイパースペクトル画像におけるターゲット局所化（A Dictionary-Based Generalization of Robust PCA with Applications to Target Localization in Hyperspectral Imaging）</news:title>
   <news:publication_date>2026-08-09T18:12:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721348</loc>
  <lastmod>2026-08-09T18:03:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>NIGENS 一般音イベントデータベースの意義（NIGENS general sound events database）</news:title>
   <news:publication_date>2026-08-09T18:03:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721346</loc>
  <lastmod>2026-08-09T18:03:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所的距離尺度学習の効率化と縮約（Reduced-Rank Local Distance Metric Learning for k-NN Classification）</news:title>
   <news:publication_date>2026-08-09T18:03:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721344</loc>
  <lastmod>2026-08-09T18:01:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>反復一階法による非凸ミンマックス問題の解法（Solving a Class of Non-Convex Min-Max Games Using Iterative First Order Methods）</news:title>
   <news:publication_date>2026-08-09T18:01:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721342</loc>
  <lastmod>2026-08-09T18:01:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>クエリ再最適化で性能問題を克服する方法（How I Learned to Stop Worrying and Love Re-optimization）</news:title>
   <news:publication_date>2026-08-09T18:01:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721340</loc>
  <lastmod>2026-08-09T18:01:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>一方非凸ミンマックス問題に対するハイブリッドブロック逐次近似（Hybrid Block Successive Approximation for One-Sided Non-Convex Min-Max Problems: Algorithms and Applications）</news:title>
   <news:publication_date>2026-08-09T18:01:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721338</loc>
  <lastmod>2026-08-09T18:00:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Lingvo: シーケンス・ツー・シーケンス研究のためのモジュラー・フレームワーク（Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling）</news:title>
   <news:publication_date>2026-08-09T18:00:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721336</loc>
  <lastmod>2026-08-09T17:09:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オンラインサンプルからオフライン母集団の規模を推定する方法（Using an online sample to estimate the size of an offline population）</news:title>
   <news:publication_date>2026-08-09T17:09:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721334</loc>
  <lastmod>2026-08-09T17:08:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>データベース修復による因果的公平性の実現（CAPUCHIN: Causal Database Repair for Algorithmic Fairness）</news:title>
   <news:publication_date>2026-08-09T17:08:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721332</loc>
  <lastmod>2026-08-09T17:08:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ベイズ最適早期停止ポリシーによるブラックボックス最適化の高速化（Bayes Optimal Early Stopping Policies for Black-Box Optimization）</news:title>
   <news:publication_date>2026-08-09T17:08:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721330</loc>
  <lastmod>2026-08-09T17:06:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>即時対応者配置のためのオンライン意思決定パイプライン（An Online Decision-Theoretic Pipeline for Responder Dispatch）</news:title>
   <news:publication_date>2026-08-09T17:06:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721328</loc>
  <lastmod>2026-08-09T17:06:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>超伝導宇宙ひもと初期宇宙の21cm信号による制約（Constraints on Superconducting Cosmic Strings from the Global 21-cm Signal before Reionization）</news:title>
   <news:publication_date>2026-08-09T17:06:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721326</loc>
  <lastmod>2026-08-09T17:06:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>An IDEA: Ingestionによるデータ強化フレームワーク（An IDEA: An Ingestion Framework for Data Enrichment in AsterixDB）</news:title>
   <news:publication_date>2026-08-09T17:06:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721324</loc>
  <lastmod>2026-08-09T17:06:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>クォークとグルーオンのエンドツーエンド分類（End-to-End Jet Classification of Quarks and Gluons with the CMS Open Data）</news:title>
   <news:publication_date>2026-08-09T17:06:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721322</loc>
  <lastmod>2026-08-09T16:14:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>歩行者検出における予測的不均衡（Predictive Inequity in Object Detection）</news:title>
   <news:publication_date>2026-08-09T16:14:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721320</loc>
  <lastmod>2026-08-09T16:14:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>畳み込み解析オペレータ学習と訓練データ依存性（Convolutional Analysis Operator Learning: Dependence on Training Data）</news:title>
   <news:publication_date>2026-08-09T16:14:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721318</loc>
  <lastmod>2026-08-09T16:14:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>敵対的例の知覚歪みの定量化（Quantifying Perceptual Distortion of Adversarial Examples）</news:title>
   <news:publication_date>2026-08-09T16:14:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721316</loc>
  <lastmod>2026-08-09T16:12:31Z</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RFI検出を最適化する深層学習（Optimizing Sparse RFI Prediction using Deep Learning）</news:title>
   <news:publication_date>2026-08-09T16:12:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721314</loc>
  <lastmod>2026-08-09T16:12:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>不確実性を備えた同時刻ヘルス指標（UQ-CHI: An Uncertainty Quantification-Based Contemporaneous Health Index for Degenerative Disease Monitoring）</news:title>
   <news:publication_date>2026-08-09T16:12:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721312</loc>
  <lastmod>2026-08-09T16:12:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>潜在表現を橋渡ししてモダリティを越える（Latent Translation: Crossing Modalities by Bridging Generative Models）</news:title>
   <news:publication_date>2026-08-09T16:12:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721310</loc>
  <lastmod>2026-08-09T16:12:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>胸部CT画像と臨床情報の共同学習による肺がん検出（Lung Cancer Detection using Co-learning from Chest CT Images and Clinical Demographics）</news:title>
   <news:publication_date>2026-08-09T16:12:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721308</loc>
  <lastmod>2026-08-09T15:19:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模バッチSGDに構造化共分散ノイズを加える手法（Large-Batch Stochastic Gradient Descent with Structured Covariance Noise）</news:title>
   <news:publication_date>2026-08-09T15:19:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721306</loc>
  <lastmod>2026-08-09T15:19:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動物個体再識別のための類似度学習ネットワーク（Similarity Learning Networks for Animal Individual Re-Identification – Beyond the Capabilities of a Human Observer）</news:title>
   <news:publication_date>2026-08-09T15:19:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721304</loc>
  <lastmod>2026-08-09T15:19:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチモデル忘却の克服（Overcoming Multi-model Forgetting）</news:title>
   <news:publication_date>2026-08-09T15:19:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721302</loc>
  <lastmod>2026-08-09T15:18:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オンライン対数凸分布からのサンプリング（Online Sampling from Log-Concave Distributions）</news:title>
   <news:publication_date>2026-08-09T15:18:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721300</loc>
  <lastmod>2026-08-09T15:18:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>視覚に基づくサブワード音声単位の発見に向けて（TOWARDS VISUALLY GROUNDED SUB-WORD SPEECH UNIT DISCOVERY）</news:title>
   <news:publication_date>2026-08-09T15:18:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721298</loc>
  <lastmod>2026-08-09T15:18:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>インスタンス認識型トラッカーと動的モデル更新によるオンライン多対象追跡（Online Multi-Object Tracking with Instance-Aware Tracker and Dynamic Model Refreshment）</news:title>
   <news:publication_date>2026-08-09T15:18:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721296</loc>
  <lastmod>2026-08-09T15:18:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ステルス攻撃の学習データ要件（Learning Requirements for Stealth Attacks）</news:title>
   <news:publication_date>2026-08-09T15:18:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721294</loc>
  <lastmod>2026-08-09T14:26:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ロボット運動計画における学習ベースの代理衝突検出の意義（Learning-Based Proxy Collision Detection for Robot Motion Planning Applications）</news:title>
   <news:publication_date>2026-08-09T14:26:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721292</loc>
  <lastmod>2026-08-09T14:26:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>辞書に基づくRobust PCAの一般化（A Dictionary Based Generalization of Robust PCA）</news:title>
   <news:publication_date>2026-08-09T14:26:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721290</loc>
  <lastmod>2026-08-09T14:25:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>畳み込みニューラルネットワークによる銀河形状測定の実務的示唆（Galaxy shape measurement with convolutional neural networks）</news:title>
   <news:publication_date>2026-08-09T14:25:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721288</loc>
  <lastmod>2026-08-09T14:25:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習可能なステップサイズ量子化（Learned Step Size Quantization）</news:title>
   <news:publication_date>2026-08-09T14:25:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721286</loc>
  <lastmod>2026-08-09T14:24:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Neural Architecture Searchの探索フェーズの評価（Evaluating the Search Phase of Neural Architecture Search）</news:title>
   <news:publication_date>2026-08-09T14:24:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721284</loc>
  <lastmod>2026-08-09T14:24:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習過程に現れる重みの位相（Topology of Learning in Artificial Neural Networks）</news:title>
   <news:publication_date>2026-08-09T14:24:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721282</loc>
  <lastmod>2026-08-09T14:24:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>EPIC 204376071 における深く長い非対称な食の解析（Deep Long Asymmetric Occultation in EPIC 204376071）</news:title>
   <news:publication_date>2026-08-09T14:24:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721280</loc>
  <lastmod>2026-08-09T13:33:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>計測された木造フレーム建物の地震被害評価（Seismic Damage Assessment of Instrumented Wood-frame Buildings: A Case-study of NEESWood Full-scale Shake Table Tests）</news:title>
   <news:publication_date>2026-08-09T13:33:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721278</loc>
  <lastmod>2026-08-09T13:32:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>細胞種特異的遺伝子制御ネットワーク推定の非パラメトリック多視点モデル（A Nonparametric Multi-view Model for Estimating Cell Type-Specific Gene Regulatory Networks）</news:title>
   <news:publication_date>2026-08-09T13:32:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721276</loc>
  <lastmod>2026-08-09T13:31:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>コミックのフキダシ検出とセグメンテーション（Deep CNN-based Speech Balloon Detection and Segmentation for Comic Books）</news:title>
   <news:publication_date>2026-08-09T13:31:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721274</loc>
  <lastmod>2026-08-09T13:30:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分布を分割してモード崩壊を解消する手法の要点（Domain Partitioning Network）</news:title>
   <news:publication_date>2026-08-09T13:30:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721272</loc>
  <lastmod>2026-08-09T13:30:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分布型強化学習における統計量とサンプルの役割（Statistics and Samples in Distributional Reinforcement Learning）</news:title>
   <news:publication_date>2026-08-09T13:30:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721270</loc>
  <lastmod>2026-08-09T13:30:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>境界重み付きドメイン適応ニューラルネットワークによる前立腺MR画像セグメンテーション（Boundary-weighted Domain Adaptive Neural Network for Prostate MR Image Segmentation）</news:title>
   <news:publication_date>2026-08-09T13:30:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721268</loc>
  <lastmod>2026-08-09T13:30:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>バッチ正規化の平均場理論と勾配爆発の本質（A Mean Field Theory of Batch Normalization）</news:title>
   <news:publication_date>2026-08-09T13:30:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721265</loc>
  <lastmod>2026-08-09T12:38:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DeepHoopsによるバスケットボール微小アクション評価（DeepHoops: Evaluating Micro-Actions in Basketball Using Deep Feature Representations of Spatio-Temporal Data）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721263</loc>
  <lastmod>2026-08-09T12:37:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>画像から計画可能な一階述語論理表現を無教師で定着させる（Unsupervised Grounding of Plannable First-Order Logic Representation from Images）</news:title>
   <news:publication_date>2026-08-09T12:37:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721261</loc>
  <lastmod>2026-08-09T12:36:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習可能な単調点ごとの非線形性によるSoftmaxボトルネックの打破 (Breaking the Softmax Bottleneck via Learnable Monotonic Pointwise Non-linearities)</news:title>
   <news:publication_date>2026-08-09T12:36:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721259</loc>
  <lastmod>2026-08-09T12:36:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>画像分類パイプラインにおける誤差寄与と伝播の定量化 (Quantifying contribution and propagation of error from computational steps, algorithms and hyperparameter choices in image classification pipelines)</news:title>
   <news:publication_date>2026-08-09T12:36:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-09T12:36:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>会議録音の話者分離を速くする手法（INCREMENTAL TRANSFER LEARNING IN TWO-PASS INFORMATION BOTTLENECK BASED SPEAKER DIARIZATION SYSTEM FOR MEETINGS）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <lastmod>2026-08-09T12:36:11Z</lastmod>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>乳児の周産期脳卒中スクリーニングのための一般運動評価の自動化（Towards Reliable, Automated General Movement Assessment for Perinatal Stroke Screening in Infants Using Wearable Accelerometers）</news:title>
   <news:publication_date>2026-08-09T12:36:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721253</loc>
  <lastmod>2026-08-09T12:35:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>知識注入注意による短文分類（Deep Short Text Classification with Knowledge Powered Attention）</news:title>
   <news:publication_date>2026-08-09T12:35:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721251</loc>
  <lastmod>2026-08-09T11:44:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散Apriori類頻出アイテムセットの性能解析（Performance study of distributed Apriori-like frequent itemsets mining）</news:title>
   <news:publication_date>2026-08-09T11:44:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721249</loc>
  <lastmod>2026-08-09T11:43:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>能動的オンライン学習で二値パーセプトロンを解く（Active online learning in the binary perceptron problem）</news:title>
   <news:publication_date>2026-08-09T11:43:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721247</loc>
  <lastmod>2026-08-09T11:43:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多人数バンディットの敵対的事例 (Multi-Player Bandits: The Adversarial Case)</news:title>
   <news:publication_date>2026-08-09T11:43:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721245</loc>
  <lastmod>2026-08-09T11:42:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高度なデジタルフォレンジックのタイムライン分析のための形式化知識表現モデル（A complete formalized knowledge representation model for advanced digital forensics timeline analysis）</news:title>
   <news:publication_date>2026-08-09T11:42:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721243</loc>
  <lastmod>2026-08-09T11:42:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>等価関係構造の漸進学習（LIMIT LEARNING EQUIVALENCE STRUCTURES）</news:title>
   <news:publication_date>2026-08-09T11:42:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721241</loc>
  <lastmod>2026-08-09T11:42:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>全体としての装いを学ぶ：ノード別グラフニューラルネットワークによるアウトフィット互換性学習（Dressing as a Whole: Outfit Compatibility Learning Based on Node-wise Graph Neural Networks）</news:title>
   <news:publication_date>2026-08-09T11:42:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721239</loc>
  <lastmod>2026-08-09T11:41:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>探索・推論・予測の役割を整理する（Exploration, inference and prediction in neuroscience and biomedicine）</news:title>
   <news:publication_date>2026-08-09T11:41:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721237</loc>
  <lastmod>2026-08-09T10:50:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>心血管MRIの動きアーティファクト補正に敵対的生成ネットワークを使う（CMR Motion Artifact Correction using Generative Adversarial Nets）</news:title>
   <news:publication_date>2026-08-09T10:50:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721235</loc>
  <lastmod>2026-08-09T10:50:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>不整形検出器ジオメトリの表現学習（Learning representations of irregular particle-detector geometry with distance-weighted graph networks）</news:title>
   <news:publication_date>2026-08-09T10:50:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721233</loc>
  <lastmod>2026-08-09T10:49:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自動化された肝臓および腫瘍セグメンテーションの共同深層学習アプローチ（A Joint Deep Learning Approach for Automated Liver and Tumor Segmentation）</news:title>
   <news:publication_date>2026-08-09T10:49:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721231</loc>
  <lastmod>2026-08-09T10:49:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>補助変数を選ぶ情報量基準と欠測データ解析（An information criterion for auxiliary variable selection in incomplete data analysis）</news:title>
   <news:publication_date>2026-08-09T10:49:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721229</loc>
  <lastmod>2026-08-09T10:48:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所変位場を用いたスパース弾性率再構築とクラスタリング（Sparse Elasticity Reconstruction and Clustering using Local Displacement Fields）</news:title>
   <news:publication_date>2026-08-09T10:48:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721227</loc>
  <lastmod>2026-08-09T10:48:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次元データの深層学習型射影（Deep Learning Multidimensional Projections）</news:title>
   <news:publication_date>2026-08-09T10:48:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721225</loc>
  <lastmod>2026-08-09T10:48:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>注目マップを用いた深い識別表現学習（Deep Discriminative Representation Learning with Attention Map for Scene Classification）</news:title>
   <news:publication_date>2026-08-09T10:48:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721223</loc>
  <lastmod>2026-08-09T09:57:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単一粒子追跡データにおける拡散モード分類（Classification of diffusion modes in single-particle tracking data: Feature-based versus deep-learning approach）</news:title>
   <news:publication_date>2026-08-09T09:57:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721221</loc>
  <lastmod>2026-08-09T09:57:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Public Sphere 2.0: オンライン新聞におけるターゲット化コメント（Public Sphere 2.0: Targeted Commenting in Online News Media）</news:title>
   <news:publication_date>2026-08-09T09:57:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721219</loc>
  <lastmod>2026-08-09T09:55:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>事前学習言語モデルを用いたインドネシア語会話文の固有表現認識（Pretrained language model transfer on neural named entity recognition in Indonesian conversational texts）</news:title>
   <news:publication_date>2026-08-09T09:55:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721217</loc>
  <lastmod>2026-08-09T09:55:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>リング版Learning With Errorsの概観と経営視点での示唆（RING LEARNING WITH ERRORS: A CROSSROADS BETWEEN POSTQUANTUM CRYPTOGRAPHY, MACHINE LEARNING AND NUMBER THEORY）</news:title>
   <news:publication_date>2026-08-09T09:55:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721215</loc>
  <lastmod>2026-08-09T09:55:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>射影Sinkhorn反復によるWasserstein敵対的事例（Wasserstein Adversarial Examples via Projected Sinkhorn Iterations）</news:title>
   <news:publication_date>2026-08-09T09:55:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721213</loc>
  <lastmod>2026-08-09T09:55:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>不確実な入力下のベイズ最適化（Bayesian optimisation under uncertain inputs）</news:title>
   <news:publication_date>2026-08-09T09:55:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721211</loc>
  <lastmod>2026-08-09T09:54:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>無線ネットワークにおける決定的方策とターゲットを用いた電力制御学習（Learning Deterministic Policy with Target for Power Control in Wireless Networks）</news:title>
   <news:publication_date>2026-08-09T09:54:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721209</loc>
  <lastmod>2026-08-09T09:03:07Z</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>2026-08-09T09:03:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721207</loc>
  <lastmod>2026-08-09T09:02:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層ReLUニューラルネットワークによるSobolevノルム近似誤差境界（Error bounds for approximations with deep ReLU neural networks in W^{s,p} norms）</news:title>
   <news:publication_date>2026-08-09T09:02:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721205</loc>
  <lastmod>2026-08-09T09:02:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ブラウザ履歴に基づくリンク予測とカテゴリ別推薦（Web Links Prediction And Category-Wise Recommendation Based On Browser History）</news:title>
   <news:publication_date>2026-08-09T09:02:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721203</loc>
  <lastmod>2026-08-09T09:02:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層適応入力正規化（Deep Adaptive Input Normalization for Time Series Forecasting）</news:title>
   <news:publication_date>2026-08-09T09:02:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721201</loc>
  <lastmod>2026-08-09T09:01:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多モダリティ全心臓セグメンテーションの評価チャレンジ（Evaluation of Algorithms for Multi-Modality Whole Heart Segmentation: An Open-Access Grand Challenge）</news:title>
   <news:publication_date>2026-08-09T09:01:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721199</loc>
  <lastmod>2026-08-09T09:01:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>縦方向偏極核におけるSIDISの二ハドロン生成における単一スピン非対称性（Single-spin asymmetry in dihadron production in SIDIS off the longitudinally polarized nucleon target）</news:title>
   <news:publication_date>2026-08-09T09:01:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721197</loc>
  <lastmod>2026-08-09T09:01:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>会議音声の「誰が・何人・いつ」を同時に処理する全ニューラル手法（ALL-NEURAL ONLINE SOURCE SEPARATION, COUNTING, AND DIARIZATION FOR MEETING ANALYSIS）</news:title>
   <news:publication_date>2026-08-09T09:01:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721195</loc>
  <lastmod>2026-08-09T08:10:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時間周波数の視点でセンシング信号を学習する短時間フーリエニューラルネットワーク（STFNets: Learning Sensing Signals from the Time-Frequency Perspective with Short-Time Fourier Neural Networks）</news:title>
   <news:publication_date>2026-08-09T08:10:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721193</loc>
  <lastmod>2026-08-09T08:01:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>左心房瘢痕の自動定量化と多スケールCNNを用いたGraph-cutsフレームワークによる進展（Atrial Scar Quantification via Multi-scale CNN in the Graph-cuts Framework）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721191</loc>
  <lastmod>2026-08-09T08:01:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>会話文における感情検出モデルの実践──RCNNと事前学習表現の組合せ（ntuer at SemEval-2019 Task 3: Emotion Classification with Word and Sentence Representations in RCNN）</news:title>
   <news:publication_date>2026-08-09T08:01:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/721189</loc>
  <lastmod>2026-08-09T08:01:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的ニューラル記号モデルによる可解性の高い視覚質問応答（Probabilistic Neural-symbolic Models for Interpretable Visual Question Answering）</news:title>
   <news:publication_date>2026-08-09T08:01:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <lastmod>2026-08-09T08:00:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <lastmod>2026-08-09T08:00:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>連続学習に基づく堅牢な大規模推薦システム（Sequential Learning over Implicit Feedback for Robust Large-Scale Recommender Systems）</news:title>
   <news:publication_date>2026-08-09T08:00:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721183</loc>
  <lastmod>2026-08-09T08:00:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-09T08:00:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721181</loc>
  <lastmod>2026-08-09T07:09:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>段階的特徴集約による人体姿勢推定の改良（Cascade Feature Aggregation for Human Pose Estimation）</news:title>
   <news:publication_date>2026-08-09T07:09:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721179</loc>
  <lastmod>2026-08-09T07:09:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層マルチモーダル物体検出とセマンティックセグメンテーション（Deep Multi-modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges）</news:title>
   <news:publication_date>2026-08-09T07:09:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721177</loc>
  <lastmod>2026-08-09T07:08:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ConceptNetの経路品質予測（Predicting ConceptNet Path Quality Using Crowdsourced Assessments of Naturalness）</news:title>
   <news:publication_date>2026-08-09T07:08:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721175</loc>
  <lastmod>2026-08-09T07:07:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>二重空間─Winograd領域での同時スパース化による畳み込みニューラルネットワーク（JOINTLY SPARSE CONVOLUTIONAL NEURAL NETWORKS IN DUAL SPATIAL-WINOGRAD DOMAINS）</news:title>
   <news:publication_date>2026-08-09T07:07:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721173</loc>
  <lastmod>2026-08-09T07:07:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確からしさ等価が線形二次制御に効く理由（Certainty Equivalence is Efficient for Linear Quadratic Control）</news:title>
   <news:publication_date>2026-08-09T07:07:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721171</loc>
  <lastmod>2026-08-09T07:07:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>安定で公平な分類の設計（Stable and Fair Classification）</news:title>
   <news:publication_date>2026-08-09T07:07:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721169</loc>
  <lastmod>2026-08-09T07:07:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>対応分析をニューラルネットで拡張する（Correspondence Analysis Using Neural Networks）</news:title>
   <news:publication_date>2026-08-09T07:07:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721167</loc>
  <lastmod>2026-08-09T06:15:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多層プーリングによる深層スピーカー埋め込み学習（DEEP SPEAKER EMBEDDING LEARNING WITH MULTI-LEVEL POOLING FOR TEXT-INDEPENDENT SPEAKER VERIFICATION）</news:title>
   <news:publication_date>2026-08-09T06:15:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721165</loc>
  <lastmod>2026-08-09T06:14:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多様な機械翻訳を実現する混合モデルの工夫（Mixture Models for Diverse Machine Translation: Tricks of the Trade）</news:title>
   <news:publication_date>2026-08-09T06:14:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721163</loc>
  <lastmod>2026-08-09T06:14:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>音声文の音響と言語を同時にとらえる埋め込み（AUDIO-LINGUISTIC EMBEDDINGS FOR SPOKEN SENTENCES）</news:title>
   <news:publication_date>2026-08-09T06:14:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721161</loc>
  <lastmod>2026-08-09T06:13:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>半教師あり関係抽出のための二重検索モジュール学習（Learning Dual Retrieval Module for Semi-supervised Relation Extraction）</news:title>
   <news:publication_date>2026-08-09T06:13:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721159</loc>
  <lastmod>2026-08-09T06:13:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機会主義的文脈的バンディット学習の実践と評価（AdaLinUCB: Opportunistic Learning for Contextual Bandits）</news:title>
   <news:publication_date>2026-08-09T06:13:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721157</loc>
  <lastmod>2026-08-09T06:13:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>触覚を用いた力のシミュレーション（Simulating Forces: Learning Through Touch, Virtual Laboratories）</news:title>
   <news:publication_date>2026-08-09T06:13:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721155</loc>
  <lastmod>2026-08-09T06:12:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散型最適潮流と電圧制御のための回帰ベースのインバータ制御（Regression-based Inverter Control for Decentralized Optimal Power Flow and Voltage Regulation）</news:title>
   <news:publication_date>2026-08-09T06:12:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721153</loc>
  <lastmod>2026-08-09T05:20:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階級体論、ディオファントス解析と漸近的フェルマーの最終定理（Class Field Theory, Diophantine Analysis and The Asymptotic Fermat’s Last Theorem）</news:title>
   <news:publication_date>2026-08-09T05:20:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721151</loc>
  <lastmod>2026-08-09T05:20:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>整数値関数データ解析による麻疹予測（Integer-Valued Functional Data Analysis for Measles Forecasting）</news:title>
   <news:publication_date>2026-08-09T05:20:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721149</loc>
  <lastmod>2026-08-09T05:20:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>コーヒーショップで学ぶバイオフィジクスの教訓（Biophysics at the coffee shop: lessons learned working with George Oster）</news:title>
   <news:publication_date>2026-08-09T05:20:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721147</loc>
  <lastmod>2026-08-09T05:19:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>解析的グラフィックスタティクス（Analytical graphic statics）</news:title>
   <news:publication_date>2026-08-09T05:19:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721145</loc>
  <lastmod>2026-08-09T05:19:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スパース精度行列を持つ確率的局所相互作用モデルによる時空間補間（Stochastic Local Interaction Model with Sparse Precision Matrix for Space-Time Interpolation）</news:title>
   <news:publication_date>2026-08-09T05:19:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721143</loc>
  <lastmod>2026-08-09T05:18:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>知覚品質を保つブラックボックス攻撃（Perceptual quality-preserving black-box attack against deep learning image classifiers）</news:title>
   <news:publication_date>2026-08-09T05:18:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721141</loc>
  <lastmod>2026-08-09T05:18:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単眼内視鏡における密な深度推定（Dense Depth Estimation in Monocular Endoscopy with Self-supervised Learning Methods）</news:title>
   <news:publication_date>2026-08-09T05:18:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721139</loc>
  <lastmod>2026-08-09T04:25:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>N量子ビット系における実験的な対ペアもつれ推定（Experimental pairwise entanglement estimation for an N-qubit system）</news:title>
   <news:publication_date>2026-08-09T04:25:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721137</loc>
  <lastmod>2026-08-09T04:25:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Cryptϵによる暗号支援差分プライバシー（Cryptϵ: Crypto-Assisted Differential Privacy on Untrusted Servers）</news:title>
   <news:publication_date>2026-08-09T04:25:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721135</loc>
  <lastmod>2026-08-09T04:25:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マンモグラフィ画像分類のための敵対的データ拡張（Adversarial Augmentation for Enhancing Classification of Mammography Images）</news:title>
   <news:publication_date>2026-08-09T04:25:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721133</loc>
  <lastmod>2026-08-09T04:23:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>全文検索エンジン上でのハミング空間近傍探索の高速化（Fast and Exact Nearest Neighbor Search in Hamming Space on Full-Text Search Engines）</news:title>
   <news:publication_date>2026-08-09T04:23:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721131</loc>
  <lastmod>2026-08-09T04:23:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Fermi検出のBCU光学分類を機械学習で評価する（Evaluating the optical classification of Fermi BCUs using machine learning）</news:title>
   <news:publication_date>2026-08-09T04:23:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721129</loc>
  <lastmod>2026-08-09T04:23:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>デュアルエンド読み出しを用いた有機シンチレーターバーの位置・時間・エネルギー分解能（Interaction position, time, and energy resolution in organic scintillator bars with dual-ended readout）</news:title>
   <news:publication_date>2026-08-09T04:23:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721127</loc>
  <lastmod>2026-08-09T04:23:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>言語から目的を学ぶ：視覚ベースの指示遂行における逆強化学習（From Language to Goals: Inverse Reinforcement Learning for Vision-Based Instruction Following）</news:title>
   <news:publication_date>2026-08-09T04:23:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721125</loc>
  <lastmod>2026-08-09T03:31:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>雑音下の行列補完と凸緩和の統計的保証（Noisy Matrix Completion: Understanding Statistical Guarantees for Convex Relaxation via Nonconvex Optimization）</news:title>
   <news:publication_date>2026-08-09T03:31:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721123</loc>
  <lastmod>2026-08-09T03:31:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非平滑・非凸正則化問題に対する確率的手法の非漸近解析（Non-asymptotic Analysis of Stochastic Methods for Non-Smooth Non-Convex Regularized Problems）</news:title>
   <news:publication_date>2026-08-09T03:31:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721121</loc>
  <lastmod>2026-08-09T03:31:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>知識に基づくCT画像からの死亡率予測（Knowledge-based Analysis for Mortality Prediction from CT Images）</news:title>
   <news:publication_date>2026-08-09T03:31:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721119</loc>
  <lastmod>2026-08-09T03:30:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習率を自動で見つけるlossgrad（lossgrad: automatic learning rate in gradient descent）</news:title>
   <news:publication_date>2026-08-09T03:30:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721117</loc>
  <lastmod>2026-08-09T03:30:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>連続Q関数の学習と一般化ベンダーズカット（Learning continuous Q-functions using generalized Benders cuts）</news:title>
   <news:publication_date>2026-08-09T03:30:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721115</loc>
  <lastmod>2026-08-09T03:29:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>順序回帰における特徴関連性境界の提示（Feature Relevance Bounds for Ordinal Regression）</news:title>
   <news:publication_date>2026-08-09T03:29:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721113</loc>
  <lastmod>2026-08-09T03:29:50Z</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>2026-08-09T03:29:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721111</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>2026-08-09T02:38:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721109</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>初期視覚系の輪郭統合を捉えるSparse Deep Predictive Coding（Sparse Deep Predictive Coding captures contour integration capabilities of the early visual system）</news:title>
   <news:publication_date>2026-08-09T02:37:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721107</loc>
  <lastmod>2026-08-09T02:36:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>点ターゲットのオンライン学習によるフィルタリング（Filtering Point Targets via Online Learning of Motion Models）</news:title>
   <news:publication_date>2026-08-09T02:36:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721105</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>調査における能動的行列因子分解（Active Matrix Factorization for Surveys）</news:title>
   <news:publication_date>2026-08-09T02:36:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721103</loc>
  <lastmod>2026-08-09T02:36:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>advertorch: PyTorchベースの敵対的堅牢性ツールボックス（advertorch v0.1: An Adversarial Robustness Toolbox based on PyTorch）</news:title>
   <news:publication_date>2026-08-09T02:36:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721101</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>A最適サブサンプリングによる適応反復ヘッシアン・スケッチ（Adaptive Iterative Hessian Sketch via A-Optimal Subsampling）</news:title>
   <news:publication_date>2026-08-09T02:36:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721099</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>2026-08-09T01:44:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721097</loc>
  <lastmod>2026-08-09T01:43:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-09T01:43:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721095</loc>
  <lastmod>2026-08-09T01:43:25Z</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>2026-08-09T01:43:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721093</loc>
  <lastmod>2026-08-09T01:42:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>人間の発達段階を模倣するベイズニューラルネットワーク（Emulating Human Developmental Stages with Bayesian Neural Networks）</news:title>
   <news:publication_date>2026-08-09T01:42:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721091</loc>
  <lastmod>2026-08-09T01:42:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>光ネットワークにおけるジャミング攻撃の検出と防御を機械学習で強化する方法（On Detecting and Preventing Jamming Attacks with Machine Learning in Optical Networks）</news:title>
   <news:publication_date>2026-08-09T01:42:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721089</loc>
  <lastmod>2026-08-09T01:42:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>行列に対する能動確率的推論による確率的最適化の前処理（Active Probabilistic Inference on Matrices for Pre-Conditioning in Stochastic Optimization）</news:title>
   <news:publication_date>2026-08-09T01:42:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721087</loc>
  <lastmod>2026-08-09T01:41:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ヒューリスティクはどこから来るのか（Where Do Heuristics Come From?）</news:title>
   <news:publication_date>2026-08-09T01:41:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721085</loc>
  <lastmod>2026-08-09T00:50:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散データのデータ協調解析（Data collaboration analysis for distributed datasets）</news:title>
   <news:publication_date>2026-08-09T00:50:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721083</loc>
  <lastmod>2026-08-09T00:50:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スケール不変の適応オンライン学習法（Adaptive scale-invariant online algorithms for learning linear models）</news:title>
   <news:publication_date>2026-08-09T00:50:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721081</loc>
  <lastmod>2026-08-09T00:50:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>経験のレア度で学習を優先する好奇心駆動型優先付け（Curiosity-Driven Experience Prioritization via Density Estimation）</news:title>
   <news:publication_date>2026-08-09T00:50:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721079</loc>
  <lastmod>2026-08-09T00:49:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>効率的なハプティック形状探索の学習（LEARNING EFFICIENT HAPTIC SHAPE EXPLORATION WITH A RIGID TACTILE SENSOR ARRAY）</news:title>
   <news:publication_date>2026-08-09T00:49:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721077</loc>
  <lastmod>2026-08-09T00:49:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ノイズのあるマルチラベル半教師付き次元削減（Noisy multi-label semi-supervised dimensionality reduction）</news:title>
   <news:publication_date>2026-08-09T00:49:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721075</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>パッチベース出力空間敵対学習による視神経乳頭と杯の同時セグメンテーション（Patch-based Output Space Adversarial Learning for Joint Optic Disc and Cup Segmentation）</news:title>
   <news:publication_date>2026-08-09T00:49:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721073</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>C2UCBの後悔境界の検証（A Note on Bounding Regret of the C2UCB Contextual Combinatorial Bandit）</news:title>
   <news:publication_date>2026-08-09T00:48:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721071</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>協調型マルチエージェント強化学習における行動価値ネットワークの因子分解の解析（Analysing Factorizations of Action-Value Networks for Cooperative Multi-Agent Reinforcement Learning）</news:title>
   <news:publication_date>2026-08-08T23:55:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721069</loc>
  <lastmod>2026-08-08T23:55:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>解釈可能なニューラル注意型推薦システム（NAIRS: A Neural Attentive Interpretable Recommendation System）</news:title>
   <news:publication_date>2026-08-08T23:55:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721067</loc>
  <lastmod>2026-08-08T23:54:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Petaﬂops級スーパーコンピュータ「Zhores」の設計と初期評価（“Zhores” —Petaﬂops supercomputer for data-driven modeling, machine learning and artificial intelligence）</news:title>
   <news:publication_date>2026-08-08T23:54:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721065</loc>
  <lastmod>2026-08-08T23:54:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>空間適応型フィルタユニットによるコンパクトで効率的な深層ニューラルネットワーク（Spatially-Adaptive Filter Units for Compact and Efficient Deep Neural Networks）</news:title>
   <news:publication_date>2026-08-08T23:54:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721063</loc>
  <lastmod>2026-08-08T23:53:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>音声映像イベント局所化のための二重モダリティSeq2Seqネットワーク（DUAL-MODALITY SEQ2SEQ NETWORK FOR AUDIO-VISUAL EVENT LOCALIZATION）</news:title>
   <news:publication_date>2026-08-08T23:53:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721061</loc>
  <lastmod>2026-08-08T23:53:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Windows向けTLS実装の状態機械学習による脆弱性検出（Identification of Bugs and Vulnerabilities in TLS Implementation for Windows Operating System Using State Machine Learning）</news:title>
   <news:publication_date>2026-08-08T23:53:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721059</loc>
  <lastmod>2026-08-08T23:53:26Z</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>2026-08-08T23:53:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721057</loc>
  <lastmod>2026-08-08T23:02:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>FPGAベースCNNアクセラレータのためのエンドツーエンドコンパイラDNNVM（DNNVM: End-to-End Compiler Leveraging Heterogeneous Optimizations on FPGA-Based CNN Accelerators）</news:title>
   <news:publication_date>2026-08-08T23:02:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721055</loc>
  <lastmod>2026-08-08T23:01:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>線形解析による超新星コア内の高速対生成ニュートリノ振動の検討（Linear Analysis of Fast-Pairwise Collective Neutrino Oscillations in Core-Collapse Supernovae based on the Results of Boltzmann Simulations）</news:title>
   <news:publication_date>2026-08-08T23:01:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721053</loc>
  <lastmod>2026-08-08T23:01:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>クラウド負荷予測における簡便で実用的な時系列手法（Easily implementable time series forecasting techniques for resource provisioning in cloud computing）</news:title>
   <news:publication_date>2026-08-08T23:01:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721051</loc>
  <lastmod>2026-08-08T23:00:32Z</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>2026-08-08T23:00:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-08T23:00:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>不完全で誤った教師付き情報下での学習（Learning with Inadequate and Incorrect Supervision）</news:title>
   <news:publication_date>2026-08-08T23:00:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-08T23:00:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オンザフライ適応による非線形二重スケールシミュレーション（On-the-fly adaptivity for nonlinear twoscale simulations using artificial neural networks and reduced order modeling）</news:title>
   <news:publication_date>2026-08-08T23:00:16Z</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>2026-08-08T23:00:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>残差記号有限オートマトンのクエリ学習アルゴリズム (Query Learning Algorithm for Residual Symbolic Finite Automata)</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721041</loc>
  <lastmod>2026-08-08T22:08:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>メキシカンハットウェーブレットカーネルELMによる多クラス分類（Mexican Hat Wavelet Kernel ELM for Multiclass Classification）</news:title>
   <news:publication_date>2026-08-08T22:08:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721039</loc>
  <lastmod>2026-08-08T22:08:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ℓ0とTℓ1によるスパースニューラルネット学習（Learning Sparse Neural Networks via ℓ0 and Tℓ1）</news:title>
   <news:publication_date>2026-08-08T22:08:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721037</loc>
  <lastmod>2026-08-08T22:06:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフにおける敵対的訓練（Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure）</news:title>
   <news:publication_date>2026-08-08T22:06:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721035</loc>
  <lastmod>2026-08-08T22:06:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散TD(0)の有限時間解析（Finite-Time Analysis of Distributed TD(0) with Linear Function Approximation for Multi-Agent Reinforcement Learning）</news:title>
   <news:publication_date>2026-08-08T22:06:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721033</loc>
  <lastmod>2026-08-08T22:06:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>LipschitzLR: 理論に基づく適応学習率で学習を速くする（LipschitzLR: Using theoretically computed adaptive learning rates for fast convergence）</news:title>
   <news:publication_date>2026-08-08T22:06:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721031</loc>
  <lastmod>2026-08-08T22:06:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>因果フォレストによる処置効果推定（Estimating Treatment Effects with Causal Forests）</news:title>
   <news:publication_date>2026-08-08T22:06:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721029</loc>
  <lastmod>2026-08-08T21:15:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>保存-散逸形式とDoiの変分法の同値性の意義（Conservation-Dissipation Formalism for Soft Matter Physics: I. Equivalence with Doi’s Variational Approach）</news:title>
   <news:publication_date>2026-08-08T21:15:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721027</loc>
  <lastmod>2026-08-08T21:15:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オートエンコーダを用いた学習型画像圧縮の実装と評価（An Autoencoder-based Learned Image Compressor: Description of Challenge Proposal by NCTU）</news:title>
   <news:publication_date>2026-08-08T21:15:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721025</loc>
  <lastmod>2026-08-08T21:14:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>平均ケース最適還元によるスパースPCAの計算困難性の確立（Optimal Average-Case Reductions to Sparse PCA: From Weak Assumptions to Strong Hardness）</news:title>
   <news:publication_date>2026-08-08T21:14:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721023</loc>
  <lastmod>2026-08-08T21:13:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>発話単位のエンドツーエンド言語識別（UTTERANCE-LEVEL END-TO-END LANGUAGE IDENTIFICATION USING ATTENTION-BASED CNN-BLSTM）</news:title>
   <news:publication_date>2026-08-08T21:13:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721021</loc>
  <lastmod>2026-08-08T21:13:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>サンプル重み付けを自動で学ぶMeta-Weight-Net（Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting）</news:title>
   <news:publication_date>2026-08-08T21:13:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721019</loc>
  <lastmod>2026-08-08T21:13:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Gaussian Processを用いた動的ペア比較モデル（Gaussian Process Priors for Dynamic Paired Comparison Modelling）</news:title>
   <news:publication_date>2026-08-08T21:13:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721017</loc>
  <lastmod>2026-08-08T21:13:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>サブモジュラ負荷クラスタリングとロバスト主成分分析（Submodular Load Clustering with Robust Principal Component Analysis）</news:title>
   <news:publication_date>2026-08-08T21:13:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721015</loc>
  <lastmod>2026-08-08T20:21:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>成果重視と志向重視を組み合わせた割当機構（A Constrained Priority Mechanism Combining Outcome-Based and Preference-Based Matching）</news:title>
   <news:publication_date>2026-08-08T20:21:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721013</loc>
  <lastmod>2026-08-08T20:13:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所構造表現と時間的依存性の学習による人体運動予測（Human Motion Prediction via Learning Local Structure Representations and Temporal Dependencies）</news:title>
   <news:publication_date>2026-08-08T20:13:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721011</loc>
  <lastmod>2026-08-08T20:13:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>未トリミング動画における行動認識のための転移可能な自己注意表現学習（Learning Transferable Self-attentive Representations for Action Recognition in Untrimmed Videos with Weak Supervision）</news:title>
   <news:publication_date>2026-08-08T20:13:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721009</loc>
  <lastmod>2026-08-08T20:13:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>限られたセンサからの流体場復元を簡潔に実現する浅層ニューラルネットワーク（Shallow Neural Networks for Fluid Flow Reconstruction with Limited Sensors）</news:title>
   <news:publication_date>2026-08-08T20:13:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721007</loc>
  <lastmod>2026-08-08T20:12:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ハイパーボリック群における方程式解集合の形式言語化（Solutions sets to systems of equations in hyperbolic groups are EDT0L in PSPACE）</news:title>
   <news:publication_date>2026-08-08T20:12:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721005</loc>
  <lastmod>2026-08-08T20:12:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>XONNによる秘匿推論の効率化（XONN: XNOR-based Oblivious Deep Neural Network Inference）</news:title>
   <news:publication_date>2026-08-08T20:12:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721003</loc>
  <lastmod>2026-08-08T20:11:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>少数ショットで物体を分割する適応マスク型プロキシ（Adaptive Masked Proxies for Few-Shot Segmentation）</news:title>
   <news:publication_date>2026-08-08T20:11:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721001</loc>
  <lastmod>2026-08-08T19:20:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動画顔認識における成分別特徴集約ネットワーク（Video Face Recognition: Component-wise Feature Aggregation Network (C-FAN))</news:title>
   <news:publication_date>2026-08-08T19:20:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720999</loc>
  <lastmod>2026-08-08T19:19:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模マンモグラフィCADにおける変形畳み込みネットワークの活用（Large-scale mammography CAD with Deformable Conv-Nets）</news:title>
   <news:publication_date>2026-08-08T19:19:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720997</loc>
  <lastmod>2026-08-08T19:19:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>個別化オンライン適応による運動学的シナジーのパーソナライズ（Personalized On-line Adaptation of Kinematic Synergies for Human-Prosthesis Interfaces）</news:title>
   <news:publication_date>2026-08-08T19:19:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720995</loc>
  <lastmod>2026-08-08T19:18:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>木星のアンモニア分布とVLAマップによる解析（Jupiter’s Ammonia Distribution Derived from VLA Maps at 3–37 GHz）</news:title>
   <news:publication_date>2026-08-08T19:18:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720993</loc>
  <lastmod>2026-08-08T19:18:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>扁桃体の高精度自動分割と不確実性推定を可能にするベイズ型FCNN（Accurate Automatic Segmentation of Amygdala Subnuclei and Modeling of Uncertainty via Bayesian Fully Convolutional Neural Network）</news:title>
   <news:publication_date>2026-08-08T19:18:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720991</loc>
  <lastmod>2026-08-08T19:18:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>少ないデータで歌声をコピーする技術（DATA EFFICIENT VOICE CLONING FOR NEURAL SINGING SYNTHESIS）</news:title>
   <news:publication_date>2026-08-08T19:18:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720989</loc>
  <lastmod>2026-08-08T19:17:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DeepBall: ボール検出のための深層ニューラルネットワーク（DeepBall: Deep Neural-Network Ball Detector）</news:title>
   <news:publication_date>2026-08-08T19:17:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720987</loc>
  <lastmod>2026-08-08T18:26:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>連続変動下のオンライン学習：動的敗北（ダイナミックリグレット）と削減（Online Learning with Continuous Variations: Dynamic Regret and Reductions）</news:title>
   <news:publication_date>2026-08-08T18:26:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720985</loc>
  <lastmod>2026-08-08T18:26:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>一部特徴の敵対的破壊に強いサブスペース法（Subspace Methods That Are Resistant to a Limited Number of Features Corrupted by an Adversary）</news:title>
   <news:publication_date>2026-08-08T18:26:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720983</loc>
  <lastmod>2026-08-08T18:25:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>EPICによる敗血症予測モデルの実装と有効性の検証（Accuracy of the Epic Sepsis Prediction Model in a Regional Health System）</news:title>
   <news:publication_date>2026-08-08T18:25:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720981</loc>
  <lastmod>2026-08-08T18:25:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>シャドウプライスによる高速ニューラルネットワーク検証（Fast Neural Network Verification via Shadow Prices）</news:title>
   <news:publication_date>2026-08-08T18:25:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720979</loc>
  <lastmod>2026-08-08T18:24:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>RNNにおける記憶の理解と制御（Understanding and Controlling Memory in Recurrent Neural Networks）</news:title>
   <news:publication_date>2026-08-08T18:24:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720977</loc>
  <lastmod>2026-08-08T18:24:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DOM-Q-NET：構造化言語でのグラウンド強化学習 (DOM-Q-NET: Grounded RL on Structured Language)</news:title>
   <news:publication_date>2026-08-08T18:24:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720975</loc>
  <lastmod>2026-08-08T18:24:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>パラメータ最適化のための遺伝的アルゴリズムを用いた深層強化学習（Deep Reinforcement Learning using Genetic Algorithm for Parameter Optimization）</news:title>
   <news:publication_date>2026-08-08T18:24:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720973</loc>
  <lastmod>2026-08-08T17:32:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ソーシャル推薦のためのグラフニューラルネットワーク（Graph Neural Networks for Social Recommendation）</news:title>
   <news:publication_date>2026-08-08T17:32:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720971</loc>
  <lastmod>2026-08-08T17:31:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>最適な線形正則化の学習（Learning Optimal Linear Regularizers）</news:title>
   <news:publication_date>2026-08-08T17:31:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720969</loc>
  <lastmod>2026-08-08T17:31:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>最適輸送によるスケーラブルなトンプソンサンプリング（Scalable Thompson Sampling via Optimal Transport）</news:title>
   <news:publication_date>2026-08-08T17:31:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720967</loc>
  <lastmod>2026-08-08T17:31:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>銀河合体率の測定：z≈2クラスターにおけるHST観測解析（Galaxy Merger Fractions in Two Clusters at z ∼2 Using the Hubble Space Telescope）</news:title>
   <news:publication_date>2026-08-08T17:31:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720965</loc>
  <lastmod>2026-08-08T17:31:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習を用いた指向性タンパク質進化（Machine Learning-Assisted Directed Protein Evolution with Combinatorial Libraries）</news:title>
   <news:publication_date>2026-08-08T17:31:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720963</loc>
  <lastmod>2026-08-08T17:30:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スペクトル特徴選択による高精度パラメータ推定（Feature Selection for Better Spectral Characterization or: How I Learned to Start Worrying and Love Ensembles）</news:title>
   <news:publication_date>2026-08-08T17:30:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720961</loc>
  <lastmod>2026-08-08T17:30:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>銀河中心に存在する高齢星の“カスプ”の分光学的検出（Spectroscopic Detection of a Cusp of Late-type Stars around the Central Black Hole in the Milky Way）</news:title>
   <news:publication_date>2026-08-08T17:30:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720959</loc>
  <lastmod>2026-08-08T16:39:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>相互作用するAubry‑Andreモデルにおけるバタフライ効果（Butterfly effect in interacting Aubry‑Andre model: thermalization, slow scrambling, and many‑body localization）</news:title>
   <news:publication_date>2026-08-08T16:39:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720957</loc>
  <lastmod>2026-08-08T16:39:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>制約付き凸ポテンシャルによる2-ワッサースタイン近似（2-Wasserstein Approximation via Restricted Convex Potentials with Application to Improved Training for GANs）</news:title>
   <news:publication_date>2026-08-08T16:39:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720955</loc>
  <lastmod>2026-08-08T16:38:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>希薄かつ不十分な報酬からの一般化学習（Learning to Generalize from Sparse and Underspecified Rewards）</news:title>
   <news:publication_date>2026-08-08T16:38:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720953</loc>
  <lastmod>2026-08-08T16:37:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>PDS 70の遷移円盤は単一惑星で形成されうる（PDS 70: A TRANSITION DISK SCULPTED BY A SINGLE PLANET）</news:title>
   <news:publication_date>2026-08-08T16:37:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720951</loc>
  <lastmod>2026-08-08T16:37:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>表現学習における合成性の測定（MEASURING COMPOSITIONALITY IN REPRESENTATION LEARNING）</news:title>
   <news:publication_date>2026-08-08T16:37:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720949</loc>
  <lastmod>2026-08-08T16:37:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>生成的RNNによる非線形ダイナミクス同定とfMRI応用（Identifying nonlinear dynamical systems via generative recurrent neural networks with applications to fMRI）</news:title>
   <news:publication_date>2026-08-08T16:37:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720947</loc>
  <lastmod>2026-08-08T16:37:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>持続化図に対する写像の近似（Approximating Maps on Persistence Diagrams）</news:title>
   <news:publication_date>2026-08-08T16:37:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720945</loc>
  <lastmod>2026-08-08T15:46:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習によるジェット観測量の自動構築（Automating the Construction of Jet Observables with Machine Learning）</news:title>
   <news:publication_date>2026-08-08T15:46:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720943</loc>
  <lastmod>2026-08-08T15:45:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフ畳み込みネットワークの簡素化（Simplifying Graph Convolutional Networks）</news:title>
   <news:publication_date>2026-08-08T15:45:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720941</loc>
  <lastmod>2026-08-08T15:45:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>音声認識におけるスペリング補正モデルの提案（A SPELLING CORRECTION MODEL FOR END-TO-END SPEECH RECOGNITION）</news:title>
   <news:publication_date>2026-08-08T15:45:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720939</loc>
  <lastmod>2026-08-08T15:44:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>和集合ノルムクラスタリングによるガウス混合モデルの回復（Recovery of a mixture of Gaussians by sum-of-norms clustering）</news:title>
   <news:publication_date>2026-08-08T15:44:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720937</loc>
  <lastmod>2026-08-08T15:43:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>競争を通じて協調が生まれる仕組み（EMERGENT COORDINATION THROUGH COMPETITION）</news:title>
   <news:publication_date>2026-08-08T15:43:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720935</loc>
  <lastmod>2026-08-08T15:43:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>異種エージェントによるベイズ的探索と推奨政策（Bayesian Exploration with Heterogeneous Agents）</news:title>
   <news:publication_date>2026-08-08T15:43:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720933</loc>
  <lastmod>2026-08-08T15:43:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>医療データに強いエントロピック特徴選択法の実務的意義（An entropic feature selection method in perspective of Turing’s formula）</news:title>
   <news:publication_date>2026-08-08T15:43:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720931</loc>
  <lastmod>2026-08-08T14:51:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>適応勾配法による過剰パラメータ化ニューラルネットワークの全体収束（Global Convergence of Adaptive Gradient Methods for An Over-parameterized Neural Network）</news:title>
   <news:publication_date>2026-08-08T14:51:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720929</loc>
  <lastmod>2026-08-08T14:51:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>見出し生成における反復抑制と敵対報酬の新手法（A NOVEL REPETITION NORMALIZED ADVERSARIAL REWARD FOR HEADLINE GENERATION）</news:title>
   <news:publication_date>2026-08-08T14:51:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720927</loc>
  <lastmod>2026-08-08T14:50:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>適応的クロスモーダル少数ショット学習（Adaptive Cross-Modal Few-shot Learning）</news:title>
   <news:publication_date>2026-08-08T14:50:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720925</loc>
  <lastmod>2026-08-08T14:49:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>低遅延ディープクラスタリングによる話者分離の実用化可能性検討（LOW-LATENCY DEEP CLUSTERING FOR SPEECH SEPARATION）</news:title>
   <news:publication_date>2026-08-08T14:49:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720923</loc>
  <lastmod>2026-08-08T14:49:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>医療データを用いたコストセンシティブ診断と学習（Cost-Sensitive Diagnosis and Learning Leveraging Public Health Data）</news:title>
   <news:publication_date>2026-08-08T14:49:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720921</loc>
  <lastmod>2026-08-08T14:49:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大気プラズマの電磁波伝播に関する重要パラメータとEMC応用（Air plasma key parameters for electromagnetic wave propagation at and out of thermal equilibrium: applications to electromagnetic compatibility）</news:title>
   <news:publication_date>2026-08-08T14:49:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720919</loc>
  <lastmod>2026-08-08T14:48:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>知識グラフ埋め込みの変分量子回路モデル（Variational Quantum Circuit Model for Knowledge Graphs Embedding）</news:title>
   <news:publication_date>2026-08-08T14:48:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720917</loc>
  <lastmod>2026-08-08T13:56:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マシンラーニングによる非マルコフ量子動力学（Machine learning non-Markovian quantum dynamics）</news:title>
   <news:publication_date>2026-08-08T13:56:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720915</loc>
  <lastmod>2026-08-08T13:46:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>連続値深層強化学習における汎化の検証（Investigating Generalisation in Continuous Deep Reinforcement Learning）</news:title>
   <news:publication_date>2026-08-08T13:46:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720913</loc>
  <lastmod>2026-08-08T13:46:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Detector-in-Detector: マルチレベル人体パーツ検出の要点（Detector-in-Detector: Multi-Level Analysis for Human-Parts）</news:title>
   <news:publication_date>2026-08-08T13:46:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720911</loc>
  <lastmod>2026-08-08T13:45:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的条件付き勾配++（Stochastic Conditional Gradient++）</news:title>
   <news:publication_date>2026-08-08T13:45:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720909</loc>
  <lastmod>2026-08-08T13:45:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分類におけるモデル較正の評価（Evaluating model calibration in classification）</news:title>
   <news:publication_date>2026-08-08T13:45:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720907</loc>
  <lastmod>2026-08-08T13:45:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>金星の弓状波のメソスケールモデリング（Mesoscale modeling of Venus’ bow-shape waves）</news:title>
   <news:publication_date>2026-08-08T13:45:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720905</loc>
  <lastmod>2026-08-08T13:44:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>二腕協調組立におけるマスター・スレーブのコンプライアンス改善（Improving dual-arm assembly by master-slave compliance）</news:title>
   <news:publication_date>2026-08-08T13:44:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720903</loc>
  <lastmod>2026-08-08T12:53:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層生成モデルの幾何と特徴分離（Geometry of Deep Generative Models for Disentangled Representations）</news:title>
   <news:publication_date>2026-08-08T12:53:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720901</loc>
  <lastmod>2026-08-08T12:53:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DEDPULによるPositive-Unlabeled学習の革新（DEDPUL: Difference-of-Estimated-Densities-based Positive-Unlabeled Learning）</news:title>
   <news:publication_date>2026-08-08T12:53:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720899</loc>
  <lastmod>2026-08-08T12:53:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自動顔匿名化（AIM）の有効性評価（Evaluating the Effectiveness of Automated Identity Masking (AIM) Methods with Human Perception and a Deep Convolutional Neural Network (CNN))</news:title>
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   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/720897</loc>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>効率的な線形収束を示す正則化近接点法による融合型複数グラフィカルラッソ問題の解法（An Efficient Linearly Convergent Regularized Proximal Point Algorithm for Fused Multiple Graphical Lasso Problems）</news:title>
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   <news:genres>Blog</news:genres>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news: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:title>ハードウェア実装に向けたニューラルネットワークベースの通信アルゴリズム（Towards Hardware Implementation of Neural Network-based Communication Algorithms）</news:title>
   <news:publication_date>2026-08-08T12:00:00Z</news:publication_date>
   <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>
 <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>K-Meansを組み込んだラベル非依存GAN（Label-Removed Generative Adversarial Networks Incorporating with K-Means）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <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>欠損値を扱う教師あり学習の一貫性（On the consistency of supervised learning with missing values）</news:title>
   <news:publication_date>2026-08-08T11:58:54Z</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>
   </news:publication>
   <news:title>多忠度ベイズ最適化による二項出力の扱い（Multifidelity Bayesian Optimization for Binomial Output）</news:title>
   <news:publication_date>2026-08-08T11:58:38Z</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>上空画像から地上視点画像を生成する条件付きGANの応用（USING CONDITIONAL GENERATIVE ADVERSARIAL NETWORKS TO GENERATE GROUND-LEVEL VIEWS FROM OVERHEAD IMAGERY）</news:title>
   <news:publication_date>2026-08-08T11:58:32Z</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>生成モデルを用いた高速かつ安定した圧縮センシング復元（FAST COMPRESSIVE SENSING RECOVERY USING GENERATIVE MODELS WITH STRUCTURED LATENT VARIABLES）</news:title>
   <news:publication_date>2026-08-08T11:07:06Z</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>サブマトリクス検出における計算下限の普遍性（Universality of Computational Lower Bounds for Submatrix Detection）</news:title>
   <news:publication_date>2026-08-08T10:58:46Z</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>ブラックボックスを説明するための変分情報ボトルネック法（Explaining A Black-box By Using A Deep Variational Information Bottleneck Approach）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </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>
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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>
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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:publication_date>2026-08-08T10:05:47Z</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>2026-08-08T10:04:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GPUクラスタにおける分散DNN学習のネットワーク最適化（Optimizing Network Performance for Distributed DNN Training on GPU Clusters: ImageNet/AlexNet Training in 1.5 Minutes）</news:title>
   <news:publication_date>2026-08-08T10:04:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-08T10:04:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>十分に正確なモデル学習（Sufficiently Accurate Model Learning）</news:title>
   <news:publication_date>2026-08-08T10:04:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720849</loc>
  <lastmod>2026-08-08T10:04:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>視覚情報から都市の安全感を予測する（Predicting city safety perception based on visual image content）</news:title>
   <news:publication_date>2026-08-08T10:04:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-08T09:12:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>干渉環境に適応する自己符号化器（An Adaptive Deep Learning Algorithm Based Autoencoder for Interference Channels）</news:title>
   <news:publication_date>2026-08-08T09:12:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720845</loc>
  <lastmod>2026-08-08T09:12:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>活性化関数が深層ニューラルネットワークの学習に与える影響（On the Impact of the Activation Function on Deep Neural Networks Training）</news:title>
   <news:publication_date>2026-08-08T09:12:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720843</loc>
  <lastmod>2026-08-08T09:11:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>原子スケールの動力学を自動で学ぶ――Graph Dynamical Networks（Graph Dynamical Networks for Unsupervised Learning of Atomic Scale Dynamics in Materials）</news:title>
   <news:publication_date>2026-08-08T09:11:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720841</loc>
  <lastmod>2026-08-08T09:11:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>音声から直接学ぶ単語ベクトル──文脈付き音響単語埋め込みの実装と有効性（LEARNED IN SPEECH RECOGNITION: CONTEXTUAL ACOUSTIC WORD EMBEDDINGS）</news:title>
   <news:publication_date>2026-08-08T09:11:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720839</loc>
  <lastmod>2026-08-08T09:10:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>イベントカメラ向け運動等変性ネットワークと時間正規化変換（Motion Equivariant Networks for Event Cameras with the Temporal Normalization Transform）</news:title>
   <news:publication_date>2026-08-08T09:10:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720837</loc>
  <lastmod>2026-08-08T09:10:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自律的航空収益管理：シート在庫制御とオーバーブッキングに対する深層強化学習アプローチ（A Deep Reinforcement Learning Approach to Seat Inventory Control and Overbooking）</news:title>
   <news:publication_date>2026-08-08T09:10:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720835</loc>
  <lastmod>2026-08-08T09:10:22Z</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低ビット量子化による効率的推論（Low-bit Quantization of Neural Networks for Efficient Inference）</news:title>
   <news:publication_date>2026-08-08T09:10:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-08T08:19:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>使える機械学習の民主化（Democratisation of Usable Machine Learning in Computer Vision）</news:title>
   <news:publication_date>2026-08-08T08:19:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-08T08:19:27Z</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>2026-08-08T08:19:27Z</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>2026-08-08T08:19:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/720827</loc>
  <lastmod>2026-08-08T08:18:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>AS0295 銀河団のChandra観測が示す合体の姿（CHANDRA OBSERVATIONS OF THE ABELL S0295 CLUSTER）</news:title>
   <news:publication_date>2026-08-08T08:18:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720825</loc>
  <lastmod>2026-08-08T08:18:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ブラックボックスモデルの解釈性を高める正則化（Regularizing Black-box Models for Improved Interpretability）</news:title>
   <news:publication_date>2026-08-08T08:18:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720823</loc>
  <lastmod>2026-08-08T08:18:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習研究における七つの誤解（Seven Myths in Machine Learning Research）</news:title>
   <news:publication_date>2026-08-08T08:18:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720821</loc>
  <lastmod>2026-08-08T08:18:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多声音楽のためのエンドツーエンド歌詞アライメント（END-TO-END LYRICS ALIGNMENT FOR POLYPHONIC MUSIC USING AN AUDIO-TO-CHARACTER RECOGNITION MODEL）</news:title>
   <news:publication_date>2026-08-08T08:18:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720819</loc>
  <lastmod>2026-08-08T07:26:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>音声クローン攻撃から音声インターフェースを守る方法（Securing Voice-driven Interfaces against Fake (Cloned) Audio Attacks）</news:title>
   <news:publication_date>2026-08-08T07:26:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720817</loc>
  <lastmod>2026-08-08T07:26:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>移動ロボットのための3D点群のマルチビュー逐次セグメンテーション（Multi-view Incremental Segmentation of 3D Point Clouds for Mobile Robots）</news:title>
   <news:publication_date>2026-08-08T07:26:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720815</loc>
  <lastmod>2026-08-08T07:25:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>メタ問題と意識理論間の知識移転：ソフトウェア技術者の視点（The meta-problem and the transfer of knowledge between theories of consciousness: a software engineer’s take）</news:title>
   <news:publication_date>2026-08-08T07:25:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720813</loc>
  <lastmod>2026-08-08T07:25:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習を使った認知モデリングの導き方（Using Machine Learning to Guide Cognitive Modeling）</news:title>
   <news:publication_date>2026-08-08T07:25:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720811</loc>
  <lastmod>2026-08-08T07:24:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>初期宇宙の10キロパーセク[CII]ハローの発見（FIRST IDENTIFICATION OF 10-kpc [C II] 158µm HALOS AROUND STAR-FORMING GALAXIES AT z = 5–7）</news:title>
   <news:publication_date>2026-08-08T07:24:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720809</loc>
  <lastmod>2026-08-08T07:24:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DIViSによるドメイン不変視覚サーボで衝突回避しつつ目標到達を実現する（Domain Invariant Visual Servoing for Collision-Free Goal Reaching）</news:title>
   <news:publication_date>2026-08-08T07:24:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720807</loc>
  <lastmod>2026-08-08T07:24:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>幅の大きなニューラルネットワークは線形モデルとして振る舞う（Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent）</news:title>
   <news:publication_date>2026-08-08T07:24:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720805</loc>
  <lastmod>2026-08-08T06:33:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Fortranで並列化されたニューラルネット実装の実用性（A parallel Fortran framework for neural networks and deep learning）</news:title>
   <news:publication_date>2026-08-08T06:33:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720803</loc>
  <lastmod>2026-08-08T06:33:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>増分クラスタ妥当性指標の拡張と比較研究（Incremental Cluster Validity Indices for Hard Partitions: Extensions and Comparative Study）</news:title>
   <news:publication_date>2026-08-08T06:33:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720801</loc>
  <lastmod>2026-08-08T06:32:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>敵対的頑健性の評価基準を明確にする（On Evaluating Adversarial Robustness）</news:title>
   <news:publication_date>2026-08-08T06:32:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720799</loc>
  <lastmod>2026-08-08T06:31:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>因果適合性への組合せ的解法 (A Combinatorial Solution to Causal Compatibility)</news:title>
   <news:publication_date>2026-08-08T06:31:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720797</loc>
  <lastmod>2026-08-08T06:31:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ハイパースペクトル画像分類における3D-2D融合CNNの提案（HybridSN: Exploring 3D-2D CNN Feature Hierarchy for Hyperspectral Image Classification）</news:title>
   <news:publication_date>2026-08-08T06:31:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720795</loc>
  <lastmod>2026-08-08T06:31:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ストリーミングデータ上の近傍探索を小容量で可能にするスケッチ（Sub-linear Memory Sketches for Near Neighbor Search on Streaming Data）</news:title>
   <news:publication_date>2026-08-08T06:31:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720793</loc>
  <lastmod>2026-08-08T06:31:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>網膜OCT画像を高精度に合成する生成敵対ネットワーク（Generative Adversarial Networks Synthesize Realistic OCT Images of the Retina）</news:title>
   <news:publication_date>2026-08-08T06:31:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720791</loc>
  <lastmod>2026-08-08T05:40:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単一のベイジアンネットワークで十分か（Is a single unique Bayesian network enough to accurately represent your data?）</news:title>
   <news:publication_date>2026-08-08T05:40:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720789</loc>
  <lastmod>2026-08-08T05:40:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>トルコ語の非定型短文に対する分割方法がニューラル感情分析に与える影響（Investigating the Effect of Segmentation Methods on Neural Model based Sentiment Analysis on Informal Short Texts in Turkish）</news:title>
   <news:publication_date>2026-08-08T05:40:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720787</loc>
  <lastmod>2026-08-08T05:39:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>限定的検討（Limited Consideration）下におけるリスク下の離散選択（Discrete Choice under Risk with Limited Consideration）</news:title>
   <news:publication_date>2026-08-08T05:39:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720785</loc>
  <lastmod>2026-08-08T05:38:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>短文テキストに潜むトピックを深掘りする手法（Deep Mixtures of Unigrams for uncovering Topics in Textual Data）</news:title>
   <news:publication_date>2026-08-08T05:38:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720783</loc>
  <lastmod>2026-08-08T05:38:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>MockingbirdによるWebsite Fingerprinting防御（Mockingbird: Defending Against Deep-Learning-Based Website Fingerprinting Attacks with Adversarial Traces）</news:title>
   <news:publication_date>2026-08-08T05:38:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720781</loc>
  <lastmod>2026-08-08T05:38:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>テキスト分類の浅層・深層・アンサンブル手法（Classifying textual data: shallow, deep and ensemble methods）</news:title>
   <news:publication_date>2026-08-08T05:38:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720779</loc>
  <lastmod>2026-08-08T05:38:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>UAVを用いたセルラーネットワークにおける軌道最適化とダブルQ学習（Optimized Trajectory Design in UAV Based Cellular Networks for 3D Users: A Double Q-Learning Approach）</news:title>
   <news:publication_date>2026-08-08T05:38:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720777</loc>
  <lastmod>2026-08-08T04:46:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多様な条件下での物体認識の信頼性と性能推定（Object Recognition Under Multifarious Conditions: A Reliability Analysis and a Feature Similarity-Based Performance Estimation）</news:title>
   <news:publication_date>2026-08-08T04:46:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720775</loc>
  <lastmod>2026-08-08T04:46:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高速で効率的なハイパーパラメータ調整（Fast Efficient Hyperparameter Tuning for Policy Gradient Methods）</news:title>
   <news:publication_date>2026-08-08T04:46:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720773</loc>
  <lastmod>2026-08-08T04:45:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>コンフォーマルキャリブレーター（Conformal Calibrators）</news:title>
   <news:publication_date>2026-08-08T04:45:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720771</loc>
  <lastmod>2026-08-08T04:45:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多目的ベイジアン最適化におけるカライ・スモロドンスキー解（The Kalai-Smorodinsky solution for many-objective Bayesian optimization）</news:title>
   <news:publication_date>2026-08-08T04:45:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720769</loc>
  <lastmod>2026-08-08T04:44:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>生の単一チャネル脳波から睡眠段階を判定するIITNet（Intra- and Inter-epoch Temporal Context Network）</news:title>
   <news:publication_date>2026-08-08T04:44:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720767</loc>
  <lastmod>2026-08-08T04:44:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>量子アニーリングで拡がる自動メタマテリアル探索（Expanding the horizon of automated metamaterials discovery via quantum annealing）</news:title>
   <news:publication_date>2026-08-08T04:44:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720765</loc>
  <lastmod>2026-08-08T04:43:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>STCNによる時系列表現の階層化と並列化（STCN: STOCHASTIC TEMPORAL CONVOLUTIONAL NETWORKS）</news:title>
   <news:publication_date>2026-08-08T04:43:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720763</loc>
  <lastmod>2026-08-08T03:52:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューラル集団におけるグリッド地図の計算モデル（A computational model for grid maps in neural populations）</news:title>
   <news:publication_date>2026-08-08T03:52:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720761</loc>
  <lastmod>2026-08-08T03:52:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>可視化できるニューラル地図とそれが拓く画像ベース位置推定（A Generative Map for Image-based Camera Localization）</news:title>
   <news:publication_date>2026-08-08T03:52:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720759</loc>
  <lastmod>2026-08-08T03:52:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>LocalNormによる画像分類の堅牢化（LocalNorm: Robust Image Classification through Dynamically Regularized Normalization）</news:title>
   <news:publication_date>2026-08-08T03:52:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720757</loc>
  <lastmod>2026-08-08T03:51:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>STRIPによるトロイ攻撃の検出（STRIP: A Defence Against Trojan Attacks on Deep Neural Networks）</news:title>
   <news:publication_date>2026-08-08T03:51:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720755</loc>
  <lastmod>2026-08-08T03:51:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>メッセージ・ドロップアウト：マルチエージェント深層強化学習の効率的学習法（Message-Dropout: An Efficient Training Method for Multi-Agent Deep Reinforcement Learning）</news:title>
   <news:publication_date>2026-08-08T03:51:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720753</loc>
  <lastmod>2026-08-08T03:51:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的勾配降下法（SGD）をテキスト分類で最適化する手法（Optimizing Stochastic Gradient Descent in Text Classification）</news:title>
   <news:publication_date>2026-08-08T03:51:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720751</loc>
  <lastmod>2026-08-08T03:51:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>透過機械学習による多孔質岩の有効度予測（PREDICTION OF POROSITY AND PERMEABILITY ALTERATION BASED ON MACHINE LEARNING ALGORITHMS）</news:title>
   <news:publication_date>2026-08-08T03:51:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720749</loc>
  <lastmod>2026-08-08T02:59:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自己学習型光学信号処理器と光学ニューラルネットワークチップ（Self-learning photonic signal processor with an optical neural network chip）</news:title>
   <news:publication_date>2026-08-08T02:59:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720747</loc>
  <lastmod>2026-08-08T02:59:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>L1-L1最小化アルゴリズムを展開して設計する再帰型ニューラルネットワーク（Designing Recurrent Neural Networks by Unfolding an L1-L1 Minimization Algorithm）</news:title>
   <news:publication_date>2026-08-08T02:59:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720745</loc>
  <lastmod>2026-08-08T02:59:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グリッド対グラフ：タクシー需要供給予測のための空間分割（Grids versus Graphs: Partitioning Space for Improved Taxi Demand-Supply Forecasts）</news:title>
   <news:publication_date>2026-08-08T02:59:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720743</loc>
  <lastmod>2026-08-08T02:58:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>相互作用系の構成的表現の学習（Learning Compositional Representations of Interacting Systems with Restricted Boltzmann Machines: Comparative Study of Lattice Proteins）</news:title>
   <news:publication_date>2026-08-08T02:58:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720741</loc>
  <lastmod>2026-08-08T02:58:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>連続学習の統一的ベイジアン視点（A Unifying Bayesian View of Continual Learning）</news:title>
   <news:publication_date>2026-08-08T02:58:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720739</loc>
  <lastmod>2026-08-08T02:58:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>メモリの壁を越える設計：深層学習向けメモリ中心HPCシステムの提案 (Beyond the Memory Wall: A Case for Memory-centric HPC System for Deep Learning)</news:title>
   <news:publication_date>2026-08-08T02:58:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720737</loc>
  <lastmod>2026-08-08T02:58:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>道路速度予測の構造的再帰ニューラルネットワーク（STRUCTURAL RECURRENT NEURAL NETWORK FOR TRAFFIC SPEED PREDICTION）</news:title>
   <news:publication_date>2026-08-08T02:58:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720735</loc>
  <lastmod>2026-08-08T02:06:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CBOWだけでは足りない：CBOWと行列合成モデルの組合せ（CBOW IS NOT ALL YOU NEED: COMBINING CBOW WITH THE COMPOSITIONAL MATRIX SPACE MODEL）</news:title>
   <news:publication_date>2026-08-08T02:06:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720733</loc>
  <lastmod>2026-08-08T01:58:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>構造強化型敵対的生成ネットワークによるCS-MRI再構成の要点（SEGAN: Structure-Enhanced Generative Adversarial Network for Compressed Sensing MRI Reconstruction）</news:title>
   <news:publication_date>2026-08-08T01:58:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720731</loc>
  <lastmod>2026-08-08T01:58:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>最適化されたデータ探索による化学プロセスシミュレーションの改善（Optimized data exploration applied to the simulation of a chemical process）</news:title>
   <news:publication_date>2026-08-08T01:58:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720729</loc>
  <lastmod>2026-08-08T01:58:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>海洋内部でのMeddy下における係留高解像度温度センサーによる乱流推定（Open-ocean interior moored sensor turbulence estimates, below a Meddy）</news:title>
   <news:publication_date>2026-08-08T01:58:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720727</loc>
  <lastmod>2026-08-08T01:57:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スパース残差木とフォレスト（Sparse residual tree and forest）</news:title>
   <news:publication_date>2026-08-08T01:57:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720725</loc>
  <lastmod>2026-08-08T01:56:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DeepMIMOによる無線チャネルの学習基盤の標準化（DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications）</news:title>
   <news:publication_date>2026-08-08T01:56:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720723</loc>
  <lastmod>2026-08-08T01:56:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ロボット手術器具のセグメンテーションチャレンジ（2017 Robotic Instrument Segmentation Challenge）</news:title>
   <news:publication_date>2026-08-08T01:56:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720721</loc>
  <lastmod>2026-08-08T01:05:23Z</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不確実性で建物初期故障を見つける（Detecting and Diagnosing Incipient Building Faults Using Uncertainty Information from Deep Neural Networks）</news:title>
   <news:publication_date>2026-08-08T01:05:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720719</loc>
  <lastmod>2026-08-08T00:57:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>AuxBlocksによる敵対的攻撃防御（AuxBlocks: Defense Adversarial Example via Auxiliary Blocks）</news:title>
   <news:publication_date>2026-08-08T00:57:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720717</loc>
  <lastmod>2026-08-08T00:57:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>パケットタイミングが明かす匿名性の穴（Tik-Tok: The Utility of Packet Timing in Website Fingerprinting Attacks）</news:title>
   <news:publication_date>2026-08-08T00:57:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720715</loc>
  <lastmod>2026-08-08T00:57:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CNNのユニットは言葉の「部品」を見つける（DISCOVERY OF NATURAL LANGUAGE CONCEPTS IN INDIVIDUAL UNITS OF CNNS）</news:title>
   <news:publication_date>2026-08-08T00:57:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720713</loc>
  <lastmod>2026-08-08T00:55:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>チャネル剪定の実務を速くする粗いランク付け（SPEEDING UP CONVOLUTIONAL NETWORKS PRUNING WITH COARSE RANKING）</news:title>
   <news:publication_date>2026-08-08T00:55:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720711</loc>
  <lastmod>2026-08-08T00:55:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>OC-SVMを用いた時系列変化点検出のキャリブレーション手法（A One-Class Support Vector Machine Calibration Method for Time Series Change Point Detection）</news:title>
   <news:publication_date>2026-08-08T00:55:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720709</loc>
  <lastmod>2026-08-08T00:55:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>周辺眼領域（Periocular）認識とOC-LBCPを用いたデュアルストリームCNNの応用（Periocular Recognition in the Wild with Orthogonal Combination of Local Binary Coded Pattern in Dual-stream Convolutional Neural Network）</news:title>
   <news:publication_date>2026-08-08T00:55:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720707</loc>
  <lastmod>2026-08-08T00:03:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散学習による共有スペクトラム上のチャネル割当（Distributed Learning for Channel Allocation Over a Shared Spectrum）</news:title>
   <news:publication_date>2026-08-08T00:03:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720705</loc>
  <lastmod>2026-08-08T00:03:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高速過渡現象の深層学習分類器FETCH（FETCH: A deep-learning based classifier for fast transient classification）</news:title>
   <news:publication_date>2026-08-08T00:03:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720703</loc>
  <lastmod>2026-08-08T00:03:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>プログラムスケッチ推論の学習 (Learning to Infer Program Sketches)</news:title>
   <news:publication_date>2026-08-08T00:03:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720701</loc>
  <lastmod>2026-08-08T00:02:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>通信量を大幅に削減する投影不要の最適化手法（Quantized Frank-Wolfe: Faster Optimization, Lower Communication, and Projection Free）</news:title>
   <news:publication_date>2026-08-08T00:02:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720699</loc>
  <lastmod>2026-08-08T00:02:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>タンパク質折りたたみを模擬するネイティブショートカットネットワークの形成 (Forming native shortcut networks to simulate protein folding)</news:title>
   <news:publication_date>2026-08-08T00:02:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720697</loc>
  <lastmod>2026-08-08T00:02:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>含有電子散乱における平均場と二体核効果のスーパースケーリング解析（Mean field and two-body nuclear effects in inclusive electron scattering on argon, carbon and titanium: the superscaling approach）</news:title>
   <news:publication_date>2026-08-08T00:02:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720695</loc>
  <lastmod>2026-08-08T00:01:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>意味的に解釈可能で制御可能なフィルタセット（Semantically Interpretable and Controllable Filter Sets）</news:title>
   <news:publication_date>2026-08-08T00:01:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720693</loc>
  <lastmod>2026-08-07T23:10:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ドメイン適応を“クロスグラフト”で橋渡しする手法（Unsupervised Domain Adaptation using Deep Networks with Cross-Grafted Stacks）</news:title>
   <news:publication_date>2026-08-07T23:10:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720691</loc>
  <lastmod>2026-08-07T23:00:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層学習のテスト手法の改善に向けて (Towards Improved Testing For Deep Learning)</news:title>
   <news:publication_date>2026-08-07T23:00:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720689</loc>
  <lastmod>2026-08-07T22:59:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散機械学習のための私的内積取得（Private Inner Product Retrieval）</news:title>
   <news:publication_date>2026-08-07T22:59:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720687</loc>
  <lastmod>2026-08-07T22:59:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>内生的切断バイアスの半パラメトリック補正（Semiparametric Correction for Endogenous Truncation Bias with Vox Populi Based Participation Decision）</news:title>
   <news:publication_date>2026-08-07T22:59:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720685</loc>
  <lastmod>2026-08-07T22:59:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非揮発性メモリのためのニューラルネットワーク基盤動的閾値検出（Neural Network-Based Dynamic Threshold Detection for Non-Volatile Memories）</news:title>
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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>
  </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>
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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:publication_date>2026-08-07T22:07:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <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>2026-08-07T22:05:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-07T22:05:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-07T22:05:16Z</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>2026-08-07T21:13:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-07T21:12:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-07T21:12:46Z</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>
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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>
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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>
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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>
  </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>
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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>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <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:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/720612</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>非凸スパース正則化を持つラッソに対するスクリーニングルール（Screening Rules for Lasso with Non-Convex Sparse Regularizers）</news:title>
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   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/720610</loc>
  <lastmod>2026-08-07T18:18:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>凸損失関数を外れ値に強くするe指数変換（Making Convex Loss Functions Robust to Outliers using e-Exponentiated Transformation）</news:title>
   <news:publication_date>2026-08-07T18:18:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/720608</loc>
  <lastmod>2026-08-07T17:27:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所差分プライバシーを用いた分散最適化（Local Differential Privacy in Decentralized Optimization）</news:title>
   <news:publication_date>2026-08-07T17:27:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/720606</loc>
  <lastmod>2026-08-07T17:27:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>可微分リザバーコンピューティングの理論的進展（Differentiable Reservoir Computing）</news:title>
   <news:publication_date>2026-08-07T17:27:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/720604</loc>
  <lastmod>2026-08-07T17:27:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>交互拡散過程によるグラフ上の半教師あり学習 (Semi-supervised Learning on Graph with an Alternating Diffusion Process)</news:title>
   <news:publication_date>2026-08-07T17:27:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/720602</loc>
  <lastmod>2026-08-07T17:25:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>画像超解像の深層学習サーベイ（Deep Learning for Image Super-resolution: A Survey）</news:title>
   <news:publication_date>2026-08-07T17:25:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
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  <loc>https://aibr.jp/archives/720600</loc>
  <lastmod>2026-08-07T17:25:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>再決定化情報集合MCTSによるHanabi探索改善（Re-determinizing Information Set Monte Carlo Tree Search in Hanabi）</news:title>
   <news:publication_date>2026-08-07T17:25:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
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 <url>
  <loc>https://aibr.jp/archives/720598</loc>
  <lastmod>2026-08-07T17:25:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DC-AL GANによる偽増悪と真の腫瘍増悪の識別（DC-AL GAN: Pseudoprogression and True Tumor Progression of Glioblastoma Multiform Image Classification Based on DCGAN and AlexNet）</news:title>
   <news:publication_date>2026-08-07T17:25:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/720596</loc>
  <lastmod>2026-08-07T17:25:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Res-SE-NetによるResNet改良（RES-SE-NET: BOOSTING PERFORMANCE OF RESNETS BY ENHANCING BRIDGE-CONNECTIONS）</news:title>
   <news:publication_date>2026-08-07T17:25:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720594</loc>
  <lastmod>2026-08-07T16:33:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スマートシティにおける短距離通勤者のモード選択（Short-distance commuters in the smart city）</news:title>
   <news:publication_date>2026-08-07T16:33:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
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  <loc>https://aibr.jp/archives/720592</loc>
  <lastmod>2026-08-07T16:32:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>メラノーマ検出の自動化に向けたデータ洗浄と増強（Towards Automated Melanoma Detection with Deep Learning: Data Purification and Augmentation）</news:title>
   <news:publication_date>2026-08-07T16:32:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/720590</loc>
  <lastmod>2026-08-07T16:32:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>弱監督物体検出のための最小エントロピー潜在モデル（Min-Entropy Latent Model for Weakly Supervised Object Detection）</news:title>
   <news:publication_date>2026-08-07T16:32:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
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  <loc>https://aibr.jp/archives/720588</loc>
  <lastmod>2026-08-07T16:31:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>アットメートル解像度と90 dB動的レンジ、THz帯域を同時に実現する光学ベクトル解析（Optical vector analysis with attometer resolution, 90‐dB dynamic range and THz bandwidth）</news:title>
   <news:publication_date>2026-08-07T16:31:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720586</loc>
  <lastmod>2026-08-07T16:31:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>PatchNetによる深層パッチ分類の実務的意義（PatchNet: A Tool for Deep Patch Classification）</news:title>
   <news:publication_date>2026-08-07T16:31:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720584</loc>
  <lastmod>2026-08-07T16:31:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>RF信号向けディープ分類器の敵対的事例軽減（Mitigation of Adversarial Examples in RF Deep Classiﬁers Utilizing AutoEncoder Pre-training）</news:title>
   <news:publication_date>2026-08-07T16:31:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720582</loc>
  <lastmod>2026-08-07T16:30:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ドメイン知識と深層学習を組み合わせた短文・非公式メッセージの感情分析（Combination of Domain Knowledge and Deep Learning for Sentiment Analysis of Short and Informal Messages on Social Media）</news:title>
   <news:publication_date>2026-08-07T16:30:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720580</loc>
  <lastmod>2026-08-07T15:39:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>イエメンのコレラ流行を機械学習で予測する（Forecasting the 2017-2018 Yemen Cholera Outbreak with Machine Learning）</news:title>
   <news:publication_date>2026-08-07T15:39:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720578</loc>
  <lastmod>2026-08-07T15:39:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>無線(RF)深層学習における敵対的事例：攻撃検知と物理的ロバストネス（Adversarial Examples in RF Deep Learning: Detection of the Attack and its Physical Robustness）</news:title>
   <news:publication_date>2026-08-07T15:39:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720576</loc>
  <lastmod>2026-08-07T15:38:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>トピックと数式を同時に扱う新手法の実務的意義（TopicEq: A Joint Topic and Mathematical Equation Model for Scientific Texts）</news:title>
   <news:publication_date>2026-08-07T15:38:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720574</loc>
  <lastmod>2026-08-07T15:38:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>幸福表現の特徴量に基づく解析手法の提案（CruzAffect: A feature-rich approach to characterize happiness）</news:title>
   <news:publication_date>2026-08-07T15:38:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
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  <loc>https://aibr.jp/archives/720572</loc>
  <lastmod>2026-08-07T15:37:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>空間パズル解法に効くヒューリスティック統合法（Heuristics, Answer Set Programming and Markov Decision Process for Solving a Set of Spatial Puzzles）</news:title>
   <news:publication_date>2026-08-07T15:37:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/720570</loc>
  <lastmod>2026-08-07T15:37:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューラルネットワークにおける変数の有意性検定（Significance Tests for Neural Networks）</news:title>
   <news:publication_date>2026-08-07T15:37:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720568</loc>
  <lastmod>2026-08-07T15:37:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>完全微分可能なビームサーチデコーダ（A FULLY DIFFERENTIABLE BEAM SEARCH DECODER）</news:title>
   <news:publication_date>2026-08-07T15:37:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
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