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   <news:title>SCADAシステムテストベッドによるサイバーセキュリティ研究（SCADA System Testbed for Cybersecurity Research Using Machine Learning Approach）</news:title>
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   <news:title>密度推定における最適近似係数の解明（The Optimal Approximation Factor in Density Estimation）</news:title>
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   <news:title>電子回折におけるパラダイムシフト（Paradigm shift in electron-based crystallography via machine learning）</news:title>
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   <news:title>ノイズ付きラベルから学ぶ：注釈者混乱行列の正則化推定（Learning From Noisy Labels By Regularized Estimation Of Annotator Confusion）</news:title>
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   <news:title>高次元空間における差分類似性の理論と応用（Differential Similarity in Higher Dimensional Spaces）</news:title>
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   <news:title>弱い重力レンズ観測における畳み込みニューラルネットワークの有用性（Weak lensing cosmology with convolutional neural networks on noisy data）</news:title>
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   <news:title>ギリシャ語コーパスにおけるメタファー検出のためのニューラル埋め込み（Neural embeddings for metaphor detection in a corpus of Greek texts）</news:title>
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   <news:title>個人の文体を捉える単語埋め込み（Word embeddings for idiolect identification）</news:title>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>強化学習エージェントの最適選択のためのバンディット枠組み（A Bandit Framework for Optimal Selection of Reinforcement Learning Agents）</news:title>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>究極の深宇宙光通信容量への接近（Approaching the ultimate capacity limit in deep-space optical communication）</news:title>
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   <news:genres>Blog</news:genres>
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   <news:title>低障壁磁石を用いた効率的なハードウェア型バイナリ確率ニューロン設計（Low Barrier Magnet Design for Efficient Hardware Binary Stochastic Neurons）</news:title>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>複数グラフィカルモデルの同時推定に対するベイズ的アプローチ (A Bayesian Approach to Joint Estimation of Multiple Graphical Models)</news:title>
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    <news:language>ja</news:language>
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   <news:title>コンテキストを考慮した視覚的互換性予測（Context-Aware Visual Compatibility Prediction）</news:title>
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   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/719821</loc>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>反復的最小トリム二乗法による混合線形回帰の頑健化（Iterative Least Trimmed Squares for Mixed Linear Regression）</news:title>
   <news:publication_date>2026-08-05T14:27:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/719815</loc>
  <lastmod>2026-08-05T13:35:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>主成分分析を用いた特徴選択によるマルウェア検出の機械学習（Machine Learning With Feature Selection Using Principal Component Analysis for Malware Detection: A Case Study）</news:title>
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   <news:genres>Blog</news:genres>
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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>inf‑projectionによるKurdyka‑Łojasiewicz指数の保存性（Kurdyka‑Lojasiewicz exponent via inf‑projection）</news:title>
   <news:publication_date>2026-08-05T13:33:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-08-05T13:24:49Z</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>単一隠れ層ニューラルネットワークによる連続関数の近似アルゴリズム（An Algorithm for Approximating Continuous Functions on Compact Subsets with a Neural Network with one Hidden Layer）</news:title>
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   <news:genres>Blog</news:genres>
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  <lastmod>2026-08-05T13:23:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>(q,p)-Wasserstein GANsにおける基底距離の比較 ((q,p)-Wasserstein GANs: Comparing Ground Metrics for Wasserstein GANs)</news:title>
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   <news:genres>Blog</news:genres>
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  <lastmod>2026-08-05T13:22:54Z</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>浅い三重ストリーム三次元CNNによる微表情認識（Shallow Triple Stream Three-dimensional CNN for Micro-expression Recognition）</news:title>
   <news:publication_date>2026-08-05T13:22:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/719805</loc>
  <lastmod>2026-08-05T13:22:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>NICA/MPD ECalの空間分解能改善（Improving the spatial resolution of NICA/MPD ECAL with new reconstruction methods）</news:title>
   <news:publication_date>2026-08-05T13:22:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/719803</loc>
  <lastmod>2026-08-05T13:22:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>共役方策による多様な探索手法（Diverse Exploration via Conjugate Policies for Policy Gradient Methods）</news:title>
   <news:publication_date>2026-08-05T13:22:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/719793</loc>
  <lastmod>2026-08-05T12:30:42Z</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>PAUカメラによる高精度フォトメトリック測光とその意義（The Physics of the Accelerating Universe Camera）</news:title>
   <news:publication_date>2026-08-05T12:30:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>トランジェント検出における深層学習 (Deep learning detection of transients)</news:title>
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   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/719789</loc>
  <lastmod>2026-08-05T12:30:15Z</lastmod>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>ソーシャルメディアにおけるフェイクニュース検出の幾何学的ディープラーニング（Fake News Detection on Social Media using Geometric Deep Learning）</news:title>
   <news:publication_date>2026-08-05T12:30:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/719787</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>上気道消化管領域における病変自動分類への示唆（Towards Automatic Lesion Classification in the Upper Aerodigestive Tract Using OCT and Deep Transfer Learning Methods）</news:title>
   <news:publication_date>2026-08-05T12:29:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/719785</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>ハイブリッドフォレスト：概念ドリフトに強いデータストリーム解析手法（Hybrid Forest: A Concept Drift Aware Data Stream Mining Algorithm）</news:title>
   <news:publication_date>2026-08-05T12:29:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>ELKI: 大規模オープンソースデータ解析ライブラリの現状と示唆（ELKI: A large open-source library for data analysis）</news:title>
   <news:publication_date>2026-08-05T12:28:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/719781</loc>
  <lastmod>2026-08-05T12:28:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>敵対的学習による分離型3D顔形状モデルの提案（A Decoupled 3D Facial Shape Model by Adversarial Training）</news:title>
   <news:publication_date>2026-08-05T12:28:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/719779</loc>
  <lastmod>2026-08-05T11:37:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>統一視覚認識モデルによる自動運転の多機能化（NeurAll: Towards a Unified Visual Perception Model for Automated Driving）</news:title>
   <news:publication_date>2026-08-05T11:37:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/719777</loc>
  <lastmod>2026-08-05T11:37:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>回帰ファジィモデルによるソフトウェア工数見積りの実践知（Software Development Effort Estimation Using Regression Fuzzy Models）</news:title>
   <news:publication_date>2026-08-05T11:37:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
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 <url>
  <loc>https://aibr.jp/archives/719775</loc>
  <lastmod>2026-08-05T11:37:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>脆弱な道路利用者検出の最前線と課題（Vulnerable road user detection: state-of-the-art and open challenges）</news:title>
   <news:publication_date>2026-08-05T11:37:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/719773</loc>
  <lastmod>2026-08-05T11:36:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大腸がんの予後予測を切り拓く組織画像解析（Colorectal Cancer Outcome Prediction from H&amp;amp;E Whole Slide Images using Machine Learning and Automatically Inferred Phenotype Profiles）</news:title>
   <news:publication_date>2026-08-05T11:36:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/719771</loc>
  <lastmod>2026-08-05T11:36:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>深層ニューラルネットワークによる到来角推定の性能優位性（PERFORMANCE ADVANTAGES OF DEEP NEURAL NETWORKS FOR ANGLE OF ARRIVAL ESTIMATION）</news:title>
   <news:publication_date>2026-08-05T11:36:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/719769</loc>
  <lastmod>2026-08-05T11:36:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>広告取引所でのエージェント最適応答学習（Learning Best Response Strategies for Agents in Ad Exchanges）</news:title>
   <news:publication_date>2026-08-05T11:36:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/719767</loc>
  <lastmod>2026-08-05T11:36:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>前眼部OCTに基づく隅角閉塞検出の多階層深層ネットワーク（Angle-Closure Detection in Anterior Segment OCT based on Multi-Level Deep Network）</news:title>
   <news:publication_date>2026-08-05T11:36:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/719765</loc>
  <lastmod>2026-08-05T10:44:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>TASK2VECによるタスク埋め込みとメタラーニング（TASK2VEC: Task Embedding for Meta-Learning）</news:title>
   <news:publication_date>2026-08-05T10:44:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719763</loc>
  <lastmod>2026-08-05T10:44:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Twitter共有データからのフェイクニュース検出（Identifying Fake News from Twitter Sharing Data: A Large-Scale Study）</news:title>
   <news:publication_date>2026-08-05T10:44:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719761</loc>
  <lastmod>2026-08-05T10:43:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>NIR-VIS異スペクトル顔補完の実用的意義（Cross-spectral Face Completion for NIR-VIS Heterogeneous Face Recognition）</news:title>
   <news:publication_date>2026-08-05T10:43:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719759</loc>
  <lastmod>2026-08-05T10:42:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>小児の活動量計データに基づくADHD分類（Classifying attention deficit hyperactivity disorder in children with non-linearities in actigraphy）</news:title>
   <news:publication_date>2026-08-05T10:42:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719757</loc>
  <lastmod>2026-08-05T10:42:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>一般化された空間ポアソン点過程の散乱統計 (Scattering Statistics of Generalized Spatial Poisson Point Processes)</news:title>
   <news:publication_date>2026-08-05T10:42:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719755</loc>
  <lastmod>2026-08-05T10:42:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多ラベル変数の特徴選択における依存性最大化（Feature Selection for Multi-Labeled Variables via Dependency Maximization）</news:title>
   <news:publication_date>2026-08-05T10:42:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719753</loc>
  <lastmod>2026-08-05T10:42:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>IoTマルウェアのエンドポイント解析が示す攻撃の構図（Analyzing Endpoints in the Internet of Things）</news:title>
   <news:publication_date>2026-08-05T10:42:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719751</loc>
  <lastmod>2026-08-05T09:50:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ガウス仮定を外したVAEの実装と意義（Biadversarial Variational Autoencoder）</news:title>
   <news:publication_date>2026-08-05T09:50:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719749</loc>
  <lastmod>2026-08-05T09:49:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スケーラブルな公平クラスタリング（Scalable Fair Clustering）</news:title>
   <news:publication_date>2026-08-05T09:49:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719747</loc>
  <lastmod>2026-08-05T09:49:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層学習と顔認識の現状（Deep learning and face recognition: the state of the art）</news:title>
   <news:publication_date>2026-08-05T09:49:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719745</loc>
  <lastmod>2026-08-05T09:48:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Besov空間下におけるGANの非パラメトリック密度推定と収束（Nonparametric Density Estimation and Convergence of GANs under Besov IPM Losses）</news:title>
   <news:publication_date>2026-08-05T09:48:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719743</loc>
  <lastmod>2026-08-05T09:48:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数ドメイン翻訳のための非結合オートエンコーダ学習（Multi-Domain Translation by Learning Uncoupled Autoencoders）</news:title>
   <news:publication_date>2026-08-05T09:48:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719741</loc>
  <lastmod>2026-08-05T09:48:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>顔のミクロ表情の検出と認識を時間差分特徴と記憶モジュールで強化する手法（FACIAL MICRO-EXPRESSION SPOTTING AND RECOGNITION USING TIME CONTRASTED FEATURE WITH VISUAL MEMORY）</news:title>
   <news:publication_date>2026-08-05T09:48:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719739</loc>
  <lastmod>2026-08-05T09:48:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>逆投影表現とカテゴリ貢献率による頑健な腫瘍分類（Inverse Projection Representation and Category Contribution Rate for Robust Tumor Recognition）</news:title>
   <news:publication_date>2026-08-05T09:48:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719737</loc>
  <lastmod>2026-08-05T08:55:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習モデルの局所的解釈可能性の評価（Assessing the Local Interpretability of Machine Learning Models）</news:title>
   <news:publication_date>2026-08-05T08:55:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719735</loc>
  <lastmod>2026-08-05T08:53:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多言語ニューラル機械翻訳における語彙表現の分離化（Multilingual Neural Machine Translation with Soft Decoupled Encoding）</news:title>
   <news:publication_date>2026-08-05T08:53:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719733</loc>
  <lastmod>2026-08-05T08:53:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ストリーミングモデルにおける線形予測の空間下限（Space lower bounds for linear prediction in the streaming model）</news:title>
   <news:publication_date>2026-08-05T08:53:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719731</loc>
  <lastmod>2026-08-05T08:52:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ルーメン境界検出における不確定性クラスタリングの実用性（Lumen boundary detection using neutrosophic c-means in IVOCT images）</news:title>
   <news:publication_date>2026-08-05T08:52:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719729</loc>
  <lastmod>2026-08-05T08:52:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>未分類スペクトルからM型星を掘り起こすハッシュ学習（Recognition of M-type stars in the unclassified spectra of LAMOST DR5 using a hash learning method）</news:title>
   <news:publication_date>2026-08-05T08:52:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719727</loc>
  <lastmod>2026-08-05T08:52:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>アルゴリズム展開による深層画像デブラーリング（AN ALGORITHM UNROLLING APPROACH TO DEEP IMAGE DEBLURRING）</news:title>
   <news:publication_date>2026-08-05T08:52:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719725</loc>
  <lastmod>2026-08-05T08:51:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層アルゴリズム・アンローリングによるブラインド画像復元（Deep Algorithm Unrolling for Blind Image Deblurring）</news:title>
   <news:publication_date>2026-08-05T08:51:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719723</loc>
  <lastmod>2026-08-05T08:00:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文脈で初期状態を学習するRNN（Contextual Recurrent Neural Networks）</news:title>
   <news:publication_date>2026-08-05T08:00:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719721</loc>
  <lastmod>2026-08-05T08:00:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>合成データ生成と差分プライバシーの接点（Synthetic Data Generators – Sequential and Private）</news:title>
   <news:publication_date>2026-08-05T08:00:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719719</loc>
  <lastmod>2026-08-05T07:53:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ケイ酸塩ガラスのための機械学習フォースフィールド（Machine Learning Forcefield for Silicate Glasses）</news:title>
   <news:publication_date>2026-08-05T07:53:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719717</loc>
  <lastmod>2026-08-05T07:52:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数回答を持つ純探索問題のサンプル複雑性（Pure Exploration with Multiple Correct Answers）</news:title>
   <news:publication_date>2026-08-05T07:52:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719715</loc>
  <lastmod>2026-08-05T07:52:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Omniglotチャレンジ：3年の進捗報告（The Omniglot challenge: a 3-year progress report）</news:title>
   <news:publication_date>2026-08-05T07:52:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719713</loc>
  <lastmod>2026-08-05T07:50:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>超高速リアルタイム顔ランドマーク検出と形状フィッティング（SUPER-REALTIME FACIAL LANDMARK DETECTION AND SHAPE FITTING BY DEEP REGRESSION OF SHAPE MODEL PARAMETERS）</news:title>
   <news:publication_date>2026-08-05T07:50:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719711</loc>
  <lastmod>2026-08-05T07:50:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層型マルチタスク深層ニューラルネットワークによるエンドツーエンド運転（Hierarchical Multi-task Deep Neural Network Architecture for End-to-End Driving）</news:title>
   <news:publication_date>2026-08-05T07:50:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719709</loc>
  <lastmod>2026-08-05T06:58:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>野外画像からの3D手形状とポーズ（3D Hand Shape and Pose from Images in the Wild）</news:title>
   <news:publication_date>2026-08-05T06:58:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719707</loc>
  <lastmod>2026-08-05T06:49:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>構造的近傍に基づく距離尺度学習（Distance metric learning based on structural neighborhoods for dimensionality reduction and classification performance improvement）</news:title>
   <news:publication_date>2026-08-05T06:49:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719705</loc>
  <lastmod>2026-08-05T06:49:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Venn GANによる複数分布の共通点と差異の発見（Venn GAN: Discovering Commonalities and Particularities of Multiple Distributions）</news:title>
   <news:publication_date>2026-08-05T06:49:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719703</loc>
  <lastmod>2026-08-05T06:48:15Z</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GANを応用する可能性と現実（Yes, we GAN: Applying Adversarial Techniques for Autonomous Driving）</news:title>
   <news:publication_date>2026-08-05T06:48:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719701</loc>
  <lastmod>2026-08-05T06:47:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>低域通過フィルタをベイズ推論として捉える（Low‑Pass Filtering as Bayesian Inference）</news:title>
   <news:publication_date>2026-08-05T06:47:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719699</loc>
  <lastmod>2026-08-05T06:47:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>パラメータ化ネットワークによる力学系の外挿シミュレーション（Simulating extrapolated dynamics with parameterization networks）</news:title>
   <news:publication_date>2026-08-05T06:47:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719697</loc>
  <lastmod>2026-08-05T06:47:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>3次元化学空間での生理活性分子クラスタリング（Clustering Bioactive Molecules in 3D Chemical Space with Unsupervised Deep Learning）</news:title>
   <news:publication_date>2026-08-05T06:47:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719695</loc>
  <lastmod>2026-08-05T05:55:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>代理モデルと表現型距離で効率化するニューラル進化 (Improving NeuroEvolution Efficiency by Surrogate Model-based Optimization with Phenotypic Distance Kernels)</news:title>
   <news:publication_date>2026-08-05T05:55:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719693</loc>
  <lastmod>2026-08-05T05:54:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
  </news:news>
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 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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 <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:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>中間モデルを挟む知識蒸留の改善（Improved Knowledge Distillation via Teacher Assistant）</news:title>
   <news:publication_date>2026-08-05T05:53:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-05T05:01:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-05T05:01:42Z</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>領域ベースのアンサンブル学習ネットワークによる細粒度分類（Region based Ensemble Learning Network for Fine-grained Classification）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>医療データに学ぶ患者類似度の測定（Measuring Patient Similarities via a Deep Architecture with Medical Concept Embedding）</news:title>
   <news:publication_date>2026-08-05T05:00:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719673</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>最小記憶でSDPを解く近似相補性の手法（An Optimal-Storage Approach to Semidefinite Programming Using Approximate Complementarity）</news:title>
   <news:publication_date>2026-08-05T05:00:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719671</loc>
  <lastmod>2026-08-05T05:00:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>皮膚病変解析とメラノーマ検出に関する挑戦（Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC))</news:title>
   <news:publication_date>2026-08-05T05:00:16Z</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>
   <news:title>動的系の潜在表現――二つ持つほうが優れている理由（Latent Representations of Dynamical Systems: When Two is Better Than One）</news:title>
   <news:publication_date>2026-08-05T05:00:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719667</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>スパースビュー・マイクロCTのシノグラム補間と深層学習（Sinogram interpolation for sparse-view micro-CT with deep learning neural network）</news:title>
   <news:publication_date>2026-08-05T04:08:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719665</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>生成モデルによる画像分解と分類（Image Decomposition and Classification through a Generative Model）</news:title>
   <news:publication_date>2026-08-05T04:08:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719663</loc>
  <lastmod>2026-08-05T04:07:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的変分不等式に対する分散削減を備えた前進―後退―前進法（Forward-Backward-Forward Methods with Variance Reduction for Stochastic Variational Inequalities）</news:title>
   <news:publication_date>2026-08-05T04:07:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719661</loc>
  <lastmod>2026-08-05T04:07:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>メタカーブチャーで速く適応する学習法の本質（Meta-Curvature）</news:title>
   <news:publication_date>2026-08-05T04:07:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-05T04:07:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>WarpFlowによるペタバイト空間時間データの探索（WarpFlow: Exploring Petabytes of Space-Time Data）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>打ち切りデータに強い分位点推定を可能にする手法（Censored Quantile Regression Forests）</news:title>
   <news:publication_date>2026-08-05T03:16:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-05T03:15:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ミニバッチ学習による指数族有限混合モデルの最尤推定（Mini-batch learning of exponential family finite mixture models）</news:title>
   <news:publication_date>2026-08-05T03:15:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-05T03:15:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>状態非依存可逆VAMPnetsによる遅い分子モードの非線形発見（Nonlinear Discovery of Slow Molecular Modes using State-Free Reversible VAMPnets）</news:title>
   <news:publication_date>2026-08-05T03:15:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719647</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフ構造的スパース性を用いたベイズモデル選択（Bayesian Model Selection with Graph Structured Sparsity）</news:title>
   <news:publication_date>2026-08-05T03:14:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719645</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>2026-08-05T03:14:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-05T03:14:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>アーキテクチャ圧縮（Architecture Compression）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-05T03:14:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>アクティブエリアカバレッジと平衡状態からのデータ取得（Active Area Coverage from Equilibrium）</news:title>
   <news:publication_date>2026-08-05T03:14:12Z</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>スケーラブルなホリスティック線形回帰（Scalable Holistic Linear Regression）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-05T02:15:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-05T02:15:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習可能な活性化関数の単純で効率的な構造（A simple and efficient architecture for trainable activation functions）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-05T02:14:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719631</loc>
  <lastmod>2026-08-05T02:13:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-05T02:13:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719629</loc>
  <lastmod>2026-08-05T02:13:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>データ駆動型ネットワークアラインメント（Data-driven network alignment）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719627</loc>
  <lastmod>2026-08-05T02:12:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>知識表現と認識的学習によるELオントロジー学習（Learning Ontologies with Epistemic Reasoning: The EL Case）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-05T01:21:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>FSNetによる畳み込みニューラルネットワークの圧縮（FSNet: Compression of Deep Convolutional Neural Networks by Filter Summary）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-05T01:21:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news: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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   <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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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>肺がん検出と診断のための3D確率的深層学習システム（A 3D Probabilistic Deep Learning System for Detection and Diagnosis of Lung Cancer Using Low-Dose CT Scans）</news:title>
   <news:publication_date>2026-08-05T01:19:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-05T01:19:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ロバストなストリーミング主成分分析（Robust Streaming PCA）</news:title>
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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>
   </news:publication>
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   <news:publication_date>2026-08-05T00:16:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-05T00:16:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>プレート化された因子グラフのためのテンソル変数消去（Tensor Variable Elimination for Plated Factor Graphs）</news:title>
   <news:publication_date>2026-08-05T00:16:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719601</loc>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>2Dセルフアテンションによる話者ダイアリゼーション（SPEAKER DIARISATION USING 2D SELF-ATTENTIVE COMBINATION OF EMBEDDINGS）</news:title>
   <news:publication_date>2026-08-05T00:16:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719599</loc>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ソフトウェア定義FPGAアクセラレータ設計によるモバイル向け深層学習の高速化（Software-Defined FPGA Accelerator Design for Mobile Deep Learning Applications）</news:title>
   <news:publication_date>2026-08-05T00:16:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
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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>2026-08-04T23:24:08Z</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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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>行動列の編集距離に基づく新奇探索による深層強化学習ポリシー重みの探索（Novelty Search for Deep Reinforcement Learning Policy Network Weights by Action Sequence Edit Metric Distance）</news:title>
   <news:publication_date>2026-08-04T23:23:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>線形関数近似を伴う分布型強化学習（Distributional reinforcement learning with linear function approximation）</news:title>
   <news:publication_date>2026-08-04T23:22:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>時空間相関を持つ時間辞書の学習によるカルシウムイメージングの刷新（LEARNING SPATIALLY-CORRELATED TEMPORAL DICTIONARIES FOR CALCIUM IMAGING）</news:title>
   <news:publication_date>2026-08-04T22:31:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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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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  <lastmod>2026-08-04T22:30:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ガウス型グラフィカルモデルの学習（Learning Gaussian Graphical Models by symmetric parallel regression technique）</news:title>
   <news:publication_date>2026-08-04T22:30:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719571</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>背景知識を用いたアイテム集合のランキング（Using Background Knowledge to Rank Itemsets）</news:title>
   <news:publication_date>2026-08-04T22:29:33Z</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>点群分類における過学習対策：Atrous XCRF（Addressing Overfitting on Pointcloud Classification using Atrous XCRF）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719567</loc>
  <lastmod>2026-08-04T21:38:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>重い裾の分布に対するアフィン不変共分散推定（Affine Invariant Covariance Estimation for Heavy-Tailed Distributions）</news:title>
   <news:publication_date>2026-08-04T21:38:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719565</loc>
  <lastmod>2026-08-04T21:38:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スケルトンに基づくオンライン行動予測とスケール選択ネットワーク（Skeleton-Based Online Action Prediction Using Scale Selection Network）</news:title>
   <news:publication_date>2026-08-04T21:38:03Z</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>
   <news:title>階層的批評家から学ぶ強化学習（REINFORCEMENT LEARNING FROM HIERARCHICAL CRITICS）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719561</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>Knowledge Graphの事実予測を進化させるテンソル分解（Knowledge Graph Fact Prediction via Knowledge-Enriched Tensor Factorization）</news:title>
   <news:publication_date>2026-08-04T21:36:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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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>
   <news:title>サイズ非依存のニューラル転移学習によるRDDL計画（Size Independent Neural Transfer for RDDL Planning）</news:title>
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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>
  <loc>https://aibr.jp/archives/719549</loc>
  <lastmod>2026-08-04T20:42:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>X(3872)とそのパートナー探索の再検討（Heavy-quark spin and flavour symmetry partners of the X(3872) revisited）</news:title>
   <news:publication_date>2026-08-04T20:42:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719547</loc>
  <lastmod>2026-08-04T20:42:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自己共役性による正則化経験リスク最小化の高速収束（Beyond Least-Squares: Fast Rates for Regularized Empirical Risk Minimization through Self-Concordance）</news:title>
   <news:publication_date>2026-08-04T20:42:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719545</loc>
  <lastmod>2026-08-04T20:42:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>心拍だけで感情を推定する確率的枠組み（A Bayesian Deep Learning Framework for End-To-End Prediction of Emotion from Heartbeat）</news:title>
   <news:publication_date>2026-08-04T20:42:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719543</loc>
  <lastmod>2026-08-04T20:41:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>部分ラベル学習における自己誘導再学習（Partial Label Learning with Self-Guided Retraining）</news:title>
   <news:publication_date>2026-08-04T20:41:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719541</loc>
  <lastmod>2026-08-04T19:49:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>バンディット主成分分析が変える部分観測下の学習法（Bandit Principal Component Analysis）</news:title>
   <news:publication_date>2026-08-04T19:49:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719539</loc>
  <lastmod>2026-08-04T19:49:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>暗いガンマ線バーストGRB 140713Aの詳細な多波長解析（Detailed multi-wavelength modelling of the dark GRB 140713A and its host galaxy）</news:title>
   <news:publication_date>2026-08-04T19:49:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719537</loc>
  <lastmod>2026-08-04T19:48:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>構造和（structural sums）を用いたランダム複合材料の特徴ベクトル化と分類（Classifying and analysis of random composites using structural sums feature vector）</news:title>
   <news:publication_date>2026-08-04T19:48:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719535</loc>
  <lastmod>2026-08-04T19:48:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフ上の共分散・相関に基づく類似度測定（Covariance and Correlation Measures on a Graph in a Generalized Bag-of-Paths Formalism）</news:title>
   <news:publication_date>2026-08-04T19:48:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719533</loc>
  <lastmod>2026-08-04T19:47:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>初期重みのセキュリティ重要性（On the security relevance of weights in deep learning）</news:title>
   <news:publication_date>2026-08-04T19:47:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719531</loc>
  <lastmod>2026-08-04T19:47:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次モチーフ特徴に基づくリンク予測（Link Prediction via Higher-Order Motif Features）</news:title>
   <news:publication_date>2026-08-04T19:47:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719529</loc>
  <lastmod>2026-08-04T19:47:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>残差自己相関の分布と季節性ARMAモデルの診断（Distribution of residual autocorrelations for multiplicative seasonal ARMA models with uncorrelated but non-independent error terms）</news:title>
   <news:publication_date>2026-08-04T19:47:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719523</loc>
  <lastmod>2026-08-04T18:55:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ハイパーボリック空間上のラップド正規分布（A Wrapped Normal Distribution on Hyperbolic Space for Gradient-Based Learning）</news:title>
   <news:publication_date>2026-08-04T18:55:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719521</loc>
  <lastmod>2026-08-04T18:55:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>不完全な予測下での公正な意思決定（Fair Decisions Despite Imperfect Predictions）</news:title>
   <news:publication_date>2026-08-04T18:55:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719519</loc>
  <lastmod>2026-08-04T18:54:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>バイナライズド知識グラフ埋め込み（Binarized Knowledge Graph Embeddings）</news:title>
   <news:publication_date>2026-08-04T18:54:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719517</loc>
  <lastmod>2026-08-04T18:53:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>不完全なアルゴリズムに対処する人間中心ツール（Human-Centered Tools for Coping with Imperfect Algorithms During Medical Decision-Making）</news:title>
   <news:publication_date>2026-08-04T18:53:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719515</loc>
  <lastmod>2026-08-04T18:53:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフで学ぶ物理の差分表現──Differentiable Physics-informed Graph Networks（Differentiable Physics-informed Graph Networks）</news:title>
   <news:publication_date>2026-08-04T18:53:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719513</loc>
  <lastmod>2026-08-04T18:53:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>相関バンディットとオンラインでの平均二乗誤差最小化（Correlated bandits or: How to minimize mean-squared error online）</news:title>
   <news:publication_date>2026-08-04T18:53:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719511</loc>
  <lastmod>2026-08-04T18:53:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>遺伝的プログラミングでマニホールド学習はできるか（Can Genetic Programming Do Manifold Learning Too?）</news:title>
   <news:publication_date>2026-08-04T18:53:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719509</loc>
  <lastmod>2026-08-04T18:02:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>一画素攻撃の理解—伝播マップと局所性解析（Understanding the One-pixel Attack: Propagation Maps and Locality Analysis）</news:title>
   <news:publication_date>2026-08-04T18:02:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719507</loc>
  <lastmod>2026-08-04T17:53:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>EILearn：過去知識を利用した逐次学習手法（EILearn: Learning Incrementally Using Previous Knowledge Obtained From an Ensemble of Classifiers）</news:title>
   <news:publication_date>2026-08-04T17:53:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719505</loc>
  <lastmod>2026-08-04T17:52:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>磁化曲線とスピンギャップの推定に機械学習を使う（Machine learning as an improved estimator for magnetization curve and spin gap）</news:title>
   <news:publication_date>2026-08-04T17:52:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719503</loc>
  <lastmod>2026-08-04T17:51:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>極値損失による支持（サポート）の生成（Generating the support with extreme value losses）</news:title>
   <news:publication_date>2026-08-04T17:51:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719501</loc>
  <lastmod>2026-08-04T17:51:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>対象依存感情分類のためのマルチタスク学習（Multi-task Learning for Target-dependent Sentiment Classification）</news:title>
   <news:publication_date>2026-08-04T17:51:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719499</loc>
  <lastmod>2026-08-04T17:50:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>手書き数式認識の堅牢な符号化器–復号器学習枠組み（Robust Encoder-Decoder Learning Framework towards Offline Handwritten Mathematical Expression Recognition Based on Multi-Scale Deep Neural Network）</news:title>
   <news:publication_date>2026-08-04T17:50:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719497</loc>
  <lastmod>2026-08-04T17:50:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>最適輸送地図の不連続性とGANのモード崩壊の理論的接続（MODE COLLAPSE AND REGULARITY OF OPTIMAL TRANSPORTATION MAPS）</news:title>
   <news:publication_date>2026-08-04T17:50:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719495</loc>
  <lastmod>2026-08-04T16:59:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>特徴の統合と強化で速く正確に検出する単発物体検出器（A SINGLE-SHOT OBJECT DETECTOR WITH FEATURE AGGREGATION AND ENHANCEMENT）</news:title>
   <news:publication_date>2026-08-04T16:59:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719493</loc>
  <lastmod>2026-08-04T16:59:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>AdaScaleによるリアルタイム動画物体検出の高速化と精度向上（AdaScale: Towards Real-time Video Object Detection Using Adaptive Scaling）</news:title>
   <news:publication_date>2026-08-04T16:59:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719491</loc>
  <lastmod>2026-08-04T16:58:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ランダム化スムージングによる認証付き敵対的堅牢性（Certified Adversarial Robustness via Randomized Smoothing）</news:title>
   <news:publication_date>2026-08-04T16:58:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719489</loc>
  <lastmod>2026-08-04T16:57:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>モビリティ・オンデマンド導入時のモード切替行動の異質性を解く（Modeling Heterogeneity in Mode-Switching Behavior Under a Mobility-on-Demand Transit System: An Interpretable Machine Learning Approach）</news:title>
   <news:publication_date>2026-08-04T16:57:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719487</loc>
  <lastmod>2026-08-04T16:57:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>IoMTにおけるデータ有用性とプライバシーの両立（Achieving Data Utility-Privacy Tradeoff in Internet of Medical Things: A Machine Learning Approach）</news:title>
   <news:publication_date>2026-08-04T16:57:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719485</loc>
  <lastmod>2026-08-04T16:57:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Source TracesによるTemporal Difference学習の新視点（Source Traces for Temporal Difference Learning）</news:title>
   <news:publication_date>2026-08-04T16:57:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719483</loc>
  <lastmod>2026-08-04T16:57:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>割引率の再考：意思決定理論的アプローチ（Rethinking the Discount Factor in Reinforcement Learning: A Decision Theoretic Approach）</news:title>
   <news:publication_date>2026-08-04T16:57:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719481</loc>
  <lastmod>2026-08-04T16:05:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>通信制約下での分布学習に関する下限—フィッシャー情報を用いて（Lower Bounds for Learning Distributions under Communication Constraints via Fisher Information）</news:title>
   <news:publication_date>2026-08-04T16:05:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719479</loc>
  <lastmod>2026-08-04T16:04:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数近傍LBPを用いた土地利用分類（Land Use Classification Using Multi-neighborhood LBPs）</news:title>
   <news:publication_date>2026-08-04T16:04:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719477</loc>
  <lastmod>2026-08-04T16:03:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文脈を用いたオンライン偽発見率制御（Contextual Online False Discovery Rate Control）</news:title>
   <news:publication_date>2026-08-04T16:03:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719475</loc>
  <lastmod>2026-08-04T16:02:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習率減衰と重み減衰を複合的に扱う複雑度勾配降下法（Combining Learning Rate Decay and Weight Decay with Complexity Gradient Descent）</news:title>
   <news:publication_date>2026-08-04T16:02:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719473</loc>
  <lastmod>2026-08-04T16:02:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ロボット支援作業のための深層実行モニタ（Deep execution monitor for robot assistive tasks）</news:title>
   <news:publication_date>2026-08-04T16:02:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719471</loc>
  <lastmod>2026-08-04T16:02:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>HYDRA: ハイブリッド深層磁気共鳴フィンガープリンティング（HYDRA: Hybrid Deep Magnetic Resonance Fingerprinting）</news:title>
   <news:publication_date>2026-08-04T16:02:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719469</loc>
  <lastmod>2026-08-04T16:01:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多層ニューラルネットワークの学習ダイナミクスの平均場極限（Mean Field Limit of the Learning Dynamics of Multilayer Neural Networks）</news:title>
   <news:publication_date>2026-08-04T16:01:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719467</loc>
  <lastmod>2026-08-04T15:09:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動的学習によるダイナミクスの習得（Dynamical learning of dynamics）</news:title>
   <news:publication_date>2026-08-04T15:09:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719465</loc>
  <lastmod>2026-08-04T15:09:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ロボットの作業実行と監視のための視覚探索と認識 (Visual search and recognition for robot task execution and monitoring)</news:title>
   <news:publication_date>2026-08-04T15:09:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719463</loc>
  <lastmod>2026-08-04T15:09:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>作物収量予測における深層ニューラルネットワークの適用（Crop Yield Prediction Using Deep Neural Networks）</news:title>
   <news:publication_date>2026-08-04T15:09:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719461</loc>
  <lastmod>2026-08-04T15:07:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>相関するIoTセンサーの寿命を延ばすDeep Q-learningの応用（Using Deep Q-learning To Prolong the Lifetime of Correlated Internet of Things Devices）</news:title>
   <news:publication_date>2026-08-04T15:07:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719459</loc>
  <lastmod>2026-08-04T15:07:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ALMA開発ロードマップ（The ALMA Development Roadmap）</news:title>
   <news:publication_date>2026-08-04T15:07:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719457</loc>
  <lastmod>2026-08-04T15:07:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>FaceSpoof Buster: 顔認証のなりすまし検知を深める手法（FaceSpoof Buster: a Presentation Attack Detector Based on Intrinsic Image Properties and Deep Learning）</news:title>
   <news:publication_date>2026-08-04T15:07:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719455</loc>
  <lastmod>2026-08-04T15:07:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ディープなマルチビュー2D姿勢からの3D人体姿勢推定 (3D Human Pose Estimation from Deep Multi-View 2D Pose)</news:title>
   <news:publication_date>2026-08-04T15:07:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719453</loc>
  <lastmod>2026-08-04T14:14:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>低コストセンサーを深層学習で校正して高gショック信号を測る（LOW-COST MEASUREMENT OF INDUSTRIAL SHOCK SIGNALS VIA DEEP LEARNING CALIBRATION）</news:title>
   <news:publication_date>2026-08-04T14:14:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719451</loc>
  <lastmod>2026-08-04T14:13:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>互換性のある自然勾配による方策探索（Compatible Natural Gradient Policy Search）</news:title>
   <news:publication_date>2026-08-04T14:13:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719449</loc>
  <lastmod>2026-08-04T14:13:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>態度と信念のアンケート調査の運用ベストプラクティス（Best Practices for Administering Belief Surveys）</news:title>
   <news:publication_date>2026-08-04T14:13:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719447</loc>
  <lastmod>2026-08-04T14:12:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ML Health: 本番モデルの健診指標としてのSimilarity score（ML HEALTH: FITNESS TRACKING FOR PRODUCTION MODELS）</news:title>
   <news:publication_date>2026-08-04T14:12:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719445</loc>
  <lastmod>2026-08-04T14:12:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>AKARIとHyper Suprime-Camによる18バンド赤外光度関数と宇宙星形成史（Infrared luminosity functions based on 18 mid-infrared bands: revealing cosmic star formation history with AKARI and Hyper Suprime-Cam）</news:title>
   <news:publication_date>2026-08-04T14:12:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719443</loc>
  <lastmod>2026-08-04T14:12:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SiamVGG：深いSiameseネットワークを用いたビジュアルトラッキングの実装（SiamVGG: Visual Tracking using Deeper Siamese Networks）</news:title>
   <news:publication_date>2026-08-04T14:12:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719441</loc>
  <lastmod>2026-08-04T14:11:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高速初期化器と遅延ソルバーの協調学習（Cooperative Training of Fast Thinking Initializer and Slow Thinking Solver for Conditional Learning）</news:title>
   <news:publication_date>2026-08-04T14:11:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719439</loc>
  <lastmod>2026-08-04T13:20:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Sup-KLUCBによるCopeland Dueling Banditsへのアプローチ（KLUCB Approach to Copeland Bandits）</news:title>
   <news:publication_date>2026-08-04T13:20:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719437</loc>
  <lastmod>2026-08-04T13:11:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>淡い銀河のイオン化光子生産効率とHα等価幅の測定（The mean Hα EW and Lyman-continuum photon production efficiency for faint z ≈4−5 galaxies）</news:title>
   <news:publication_date>2026-08-04T13:11:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719435</loc>
  <lastmod>2026-08-04T13:11:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>サジタリウスIIの深堀り観測 — Pristine調査による衛星矮小銀河の再評価（The Pristine Dwarf-Galaxy survey – II. In-depth observational study of the faint Milky Way satellite Sagittarius II）</news:title>
   <news:publication_date>2026-08-04T13:11:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719433</loc>
  <lastmod>2026-08-04T13:09:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層可逆特徴を用いたハイブリッドモデル（Hybrid Models with Deep and Invertible Features）</news:title>
   <news:publication_date>2026-08-04T13:09:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719431</loc>
  <lastmod>2026-08-04T13:09:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>画像から製造指示を生成するニューラル逆編み学（Neural Inverse Knitting: From Images to Manufacturing Instructions）</news:title>
   <news:publication_date>2026-08-04T13:09:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719429</loc>
  <lastmod>2026-08-04T13:09:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>メモリ効率の良い可逆的画像変換モデル（Reversible GANs for Memory-efficient Image-to-Image Translation）</news:title>
   <news:publication_date>2026-08-04T13:09:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719427</loc>
  <lastmod>2026-08-04T13:07:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散システム上の深層強化学習におけるメタ最適化（RL Metaoptimization on a Distributed System for Deep Reinforcement Learning）</news:title>
   <news:publication_date>2026-08-04T13:07:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719425</loc>
  <lastmod>2026-08-04T12:16:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフ分類のための変分リカレントニューラルネットワーク（Variational Recurrent Neural Networks for Graph Classification）</news:title>
   <news:publication_date>2026-08-04T12:16:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719423</loc>
  <lastmod>2026-08-04T12:16:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スパース回帰と適応的特徴生成による力学系同定（Sparse Regression and Adaptive Feature Generation for the Discovery of Dynamical Systems）</news:title>
   <news:publication_date>2026-08-04T12:16:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719421</loc>
  <lastmod>2026-08-04T12:15:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>移動知能を活用した遅延最適化型の車載フォグネットワークにおける計算オフロード（Exploiting Moving Intelligence: Delay-Optimized Computation Offloading in Vehicular Fog Networks）</news:title>
   <news:publication_date>2026-08-04T12:15:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719419</loc>
  <lastmod>2026-08-04T12:15:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>結合条件クエリ結果のランク付き列挙手法（RANKED ENUMERATION OF CONJUNCTIVE QUERY RESULTS）</news:title>
   <news:publication_date>2026-08-04T12:15:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719417</loc>
  <lastmod>2026-08-04T12:14:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>アスペクト特化の意見表現抽出（Aspect Specific Opinion Expression Extraction using Attention based LSTM-CRF Network）</news:title>
   <news:publication_date>2026-08-04T12:14:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719415</loc>
  <lastmod>2026-08-04T12:14:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>D&amp;amp;C: IRベースのバグ局所化を分割して解く（D&amp;amp;C: A Divide-and-Conquer Approach to IR-based Bug Localization）</news:title>
   <news:publication_date>2026-08-04T12:14:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719413</loc>
  <lastmod>2026-08-04T12:14:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>チャット文の理解によるスタンプ推薦の仕組み（Understanding Chat Messages for Sticker Recommendation in Messaging Apps）</news:title>
   <news:publication_date>2026-08-04T12:14:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719411</loc>
  <lastmod>2026-08-04T11:22:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>STAMPNET による教師なし多クラス物体発見（STAMPNET: UNSUPERVISED MULTI-CLASS OBJECT DISCOVERY）</news:title>
   <news:publication_date>2026-08-04T11:22:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719409</loc>
  <lastmod>2026-08-04T11:13:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SDSS DR7の銀河ハロー質量予測（Prediction of Galaxy Halo Masses in SDSS DR7 via a Machine Learning Approach）</news:title>
   <news:publication_date>2026-08-04T11:13:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719407</loc>
  <lastmod>2026-08-04T11:13:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>β-Ga2O3の近辺光学特性に関する第一原理計算（First-Principles Calculations of the Near-Edge Optical Properties of β-Ga2O3）</news:title>
   <news:publication_date>2026-08-04T11:13:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719405</loc>
  <lastmod>2026-08-04T11:11:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>1NNプロトタイプ集合のVC次元に関する境界（Bounds for the VC Dimension of 1NN Prototype Sets）</news:title>
   <news:publication_date>2026-08-04T11:11:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719403</loc>
  <lastmod>2026-08-04T11:11:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深くまばらなサンプリングによるベイズ強化学習（Bayesian Reinforcement Learning via Deep, Sparse Sampling）</news:title>
   <news:publication_date>2026-08-04T11:11:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719401</loc>
  <lastmod>2026-08-04T11:11:09Z</lastmod>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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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>
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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>
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    <news:name>AI Benchmark Research</news:name>
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   <news:publication_date>2026-08-04T10:09:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>ラジアルと方向性の事後分布によるベイズニューラルネットワーク（Radial and Directional Posteriors for Bayesian Neural Networks）</news:title>
   <news:publication_date>2026-08-04T10:07:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>混合整数計画法におけるコンフリクト駆動ヒューリスティクス（Conflict-Driven Heuristics for Mixed Integer Programming）</news:title>
   <news:publication_date>2026-08-04T10:07:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
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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>エンドポイント上でのリアルタイムマルウェア検出と自動プロセス停止（Real-time malware process detection and automated process killing）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>視覚検索における教師なしデータ不確実性学習（Unsupervised Data Uncertainty Learning in Visual Retrieval Systems）</news:title>
   <news:publication_date>2026-08-04T09:15:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
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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>オープンソース脆弱性修正データセットの作成（A Manually-Curated Dataset of Fixes to Vulnerabilities of Open-Source Software）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所Rapid Learningを用いた整数計画問題の探索高速化（Local Rapid Learning for Integer Programs）</news:title>
   <news:publication_date>2026-08-04T09:15:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Actor-Advisor: オフポリシー助言を活用する方策勾配（The Actor-Advisor: Policy Gradient With Off-Policy Advice）</news:title>
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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>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>異種エッジ埋め込みによる友人推薦（Heterogeneous Edge Embeddings for Friend Recommendation）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ランダム行列による共分散推定の改良（Random Matrix Improved Covariance Estimation for a Large Class of Metrics）</news:title>
   <news:publication_date>2026-08-04T08:22:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ペナルティ付き重み付けGMMによるオンラインクラスタリング（Online Clustering by Penalized Weighted GMM）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719365</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>多体散乱におけるRmatReact法の拡張（RmatReact methodology for reactive scattering）</news:title>
   <news:publication_date>2026-08-04T08:21:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DoPAMINE: 乗算性ノイズ（スペックル）除去のための二面マスクCNN（Double-sided Masked CNN for Pixel Adaptive Multiplicative Noise Despeckling）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719357</loc>
  <lastmod>2026-08-04T08:20:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>全体予測・局所修正：ニューラルネットの時間並列最適制御（Predict Globally, Correct Locally: Parallel-in-Time Optimal Control of Neural Networks）</news:title>
   <news:publication_date>2026-08-04T08:20:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719355</loc>
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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>
  <loc>https://aibr.jp/archives/719345</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719341</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news: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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  <lastmod>2026-08-04T06:27:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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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:title>コスト効率の良いインセンティブ配分のための構造化反事実推論（Cost-Effective Incentive Allocation via Structured Counterfactual Inference）</news:title>
   <news:publication_date>2026-08-04T06:26:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719329</loc>
  <lastmod>2026-08-04T06:25:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>二重参照によるホログラフィック位相回復の革新（Dual-Reference Design for Holographic Coherent Diffraction Imaging）</news:title>
   <news:publication_date>2026-08-04T06:25:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719327</loc>
  <lastmod>2026-08-04T05:32:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スピーカーベリフィケーションのためのエンドツーエンド損失と全話者ハードネガティブマイニング（End-to-end losses based on speaker basis vectors and all-speaker hard negative mining for speaker verification）</news:title>
   <news:publication_date>2026-08-04T05:32:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719325</loc>
  <lastmod>2026-08-04T05:32:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>相関するノイズ対を用いたSURE拡張による深層デノイザの教師なし学習（Extending Stein’s unbiased risk estimator to train deep denoisers with correlated pairs of noisy images）</news:title>
   <news:publication_date>2026-08-04T05:32:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719323</loc>
  <lastmod>2026-08-04T05:31:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Ising-Dropoutによる賢いドロップアウトとモデル圧縮（ISING-DROPOUT: A REGULARIZATION METHOD FOR TRAINING AND COMPRESSION OF DEEP NEURAL NETWORKS）</news:title>
   <news:publication_date>2026-08-04T05:31:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719321</loc>
  <lastmod>2026-08-04T05:31:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>義肢のための人工知能 — チャレンジ解法（Artificial Intelligence for Prosthetics — challenge solutions）</news:title>
   <news:publication_date>2026-08-04T05:31:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719319</loc>
  <lastmod>2026-08-04T05:30:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>最適化アルゴリズムの収束を動的に加速するCNNの活用（Accelerating Optimization Algorithms With Dynamic Parameter Selections Using Convolutional Neural Networks For Inverse Problems In Image Processing）</news:title>
   <news:publication_date>2026-08-04T05:30:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719317</loc>
  <lastmod>2026-08-04T05:30:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>尺度不変のフラットネス指標が示す深層学習の新しい見方（A Scale Invariant Flatness Measure for Deep Network Minima）</news:title>
   <news:publication_date>2026-08-04T05:30:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719315</loc>
  <lastmod>2026-08-04T05:30:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>LSTMを用いたうっ血性心不全発症予測の有効性（Effectiveness of LSTMs in Predicting Congestive Heart Failure Onset）</news:title>
   <news:publication_date>2026-08-04T05:30:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719313</loc>
  <lastmod>2026-08-04T04:38:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>重み付きSimplex戦略の動的化が示す現場適用の道（Dynamic-Weighted Simplex Strategy for Learning Enabled Cyber Physical Systems）</news:title>
   <news:publication_date>2026-08-04T04:38:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719311</loc>
  <lastmod>2026-08-04T04:37:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>電波SETI信号の分類における機械視覚と深層学習（Machine Vision and Deep Learning for Classification of Radio SETI Signals）</news:title>
   <news:publication_date>2026-08-04T04:37:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719309</loc>
  <lastmod>2026-08-04T04:36:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>主成分モデル解析（Principal Model Analysis Based on Partial Least Squares）</news:title>
   <news:publication_date>2026-08-04T04:36:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719307</loc>
  <lastmod>2026-08-04T04:36:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Unbiased Online Recurrent Optimizationの分散解析（On the Variance of Unbiased Online Recurrent Optimization）</news:title>
   <news:publication_date>2026-08-04T04:36:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719305</loc>
  <lastmod>2026-08-04T04:36:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>小さなデータから高速にハイパーパラメータを見つける方法（Fast Hyperparameter Tuning using Bayesian Optimization with Directional Derivatives）</news:title>
   <news:publication_date>2026-08-04T04:36:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719303</loc>
  <lastmod>2026-08-04T04:36:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>最近傍補間法のL2一貫性について（On L2-consistency of nearest neighbor matching）</news:title>
   <news:publication_date>2026-08-04T04:36:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719301</loc>
  <lastmod>2026-08-04T04:35:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分類に基づく集計のバイアス補正（A Bayesian Approach for Accurate Classification-Based Aggregates）</news:title>
   <news:publication_date>2026-08-04T04:35:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719299</loc>
  <lastmod>2026-08-04T03:44:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>神経科学に学ぶ創造的デコーダ（TOWARD A NEURO-INSPIRED CREATIVE DECODER）</news:title>
   <news:publication_date>2026-08-04T03:44:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719297</loc>
  <lastmod>2026-08-04T03:44:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>根格子におけるCVPの折り畳みによる解法と深いReLUニューラルネットワーク（On the CVP for the root lattices via folding with deep ReLU neural networks）</news:title>
   <news:publication_date>2026-08-04T03:44:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719295</loc>
  <lastmod>2026-08-04T03:43:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ドメイン適応によるステンス検出の敵対的学習（Adversarial Domain Adaptation for Stance Detection）</news:title>
   <news:publication_date>2026-08-04T03:43:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719293</loc>
  <lastmod>2026-08-04T03:42:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>再帰型ニューラルネットワークの圧縮による実用的言語モデルの実装（Compression of Recurrent Neural Networks for Efficient Language Modeling）</news:title>
   <news:publication_date>2026-08-04T03:42:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719291</loc>
  <lastmod>2026-08-04T03:42:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単眼カメラで交通コーンの3D位置をリアルタイム推定する手法（Real-time 3D Traffic Cone Detection for Autonomous Driving）</news:title>
   <news:publication_date>2026-08-04T03:42:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719289</loc>
  <lastmod>2026-08-04T03:42:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DiffEqFlux.jl—Neural Differential Equationsを実現するJuliaライブラリ（DiffEqFlux.jl — A Julia Library for Neural Differential Equations）</news:title>
   <news:publication_date>2026-08-04T03:42:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719287</loc>
  <lastmod>2026-08-04T03:42:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>エンドツーエンド・アンカード音声認識の要点整理（END-TO-END ANCHORED SPEECH RECOGNITION）</news:title>
   <news:publication_date>2026-08-04T03:42:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719285</loc>
  <lastmod>2026-08-04T02:50:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>セントロイドに基づく深層距離学習による話者認識（CENTROID-BASED DEEP METRIC LEARNING FOR SPEAKER RECOGNITION）</news:title>
   <news:publication_date>2026-08-04T02:50:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719283</loc>
  <lastmod>2026-08-04T02:50:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>専門家アドバイスによる予測でのコム戦略の漸近的最適性（On the Asymptotic Optimality of the Comb Strategy for Prediction with Expert Advice）</news:title>
   <news:publication_date>2026-08-04T02:50:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719281</loc>
  <lastmod>2026-08-04T02:50:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Stack Exchangeのタグ付けネットワークのモデリングと解析 (Modeling and Analysis of Tagging Networks in Stack Exchange Communities)</news:title>
   <news:publication_date>2026-08-04T02:50:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719279</loc>
  <lastmod>2026-08-04T02:49:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>層の役割をガウス過程の視点で解く（The role of a layer in deep neural networks: a Gaussian Process perspective）</news:title>
   <news:publication_date>2026-08-04T02:49:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719277</loc>
  <lastmod>2026-08-04T02:49:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>µJy帯の電波観測が示す銀河進化の新たな視座（eMERGE Data Release 1）</news:title>
   <news:publication_date>2026-08-04T02:49:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719275</loc>
  <lastmod>2026-08-04T02:49:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層ニューラルネットワークにおけるヘッセ行列の負の固有値の意義（Negative eigenvalues of the Hessian in deep neural networks）</news:title>
   <news:publication_date>2026-08-04T02:49:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719273</loc>
  <lastmod>2026-08-04T02:48:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>孤立した矮小銀河対の発見とその意味（Discovery of an isolated dwarf–dwarf galaxy pair at z = 0.30）</news:title>
   <news:publication_date>2026-08-04T02:48:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719271</loc>
  <lastmod>2026-08-04T01:54:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ヒルベルト位相解析による大気変動の時間的規則性の発見（Uncovering temporal regularity in atmospheric dynamics through Hilbert phase analysis）</news:title>
   <news:publication_date>2026-08-04T01:54:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719269</loc>
  <lastmod>2026-08-04T01:54:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>コライダー事象の距離空間化（The Metric Space of Collider Events）</news:title>
   <news:publication_date>2026-08-04T01:54:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719267</loc>
  <lastmod>2026-08-04T01:54:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>脳MRI登録を単純化する形態簡略化ネットワーク（Morphological Simplification Network）</news:title>
   <news:publication_date>2026-08-04T01:54:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719265</loc>
  <lastmod>2026-08-04T01:53:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>集中制御者の模倣による分散化（Decentralization by Imitation of a Centralized Controller）</news:title>
   <news:publication_date>2026-08-04T01:53:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719263</loc>
  <lastmod>2026-08-04T01:52:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ラベル勾配整合による半教師あり学習（Label Gradient Alignment）</news:title>
   <news:publication_date>2026-08-04T01:52:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719261</loc>
  <lastmod>2026-08-04T01:52:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>AmI（Attacks Meet Interpretability）は敵対的事例に対して堅牢か？（Is AmI (Attacks Meet Interpretability) Robust to Adversarial Examples?）</news:title>
   <news:publication_date>2026-08-04T01:52:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719259</loc>
  <lastmod>2026-08-04T01:52:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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   <news:genres>Blog</news:genres>
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 <url>
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   <news:genres>Blog</news:genres>
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   <news:publication>
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   <news:genres>Blog</news:genres>
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   <news:publication_date>2026-08-04T00:07:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-04T00:06:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-04T00:05:44Z</news:publication_date>
   <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: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>
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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>
 <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:title>ニューラルネットワークが導く式変換と証明の自動復元（Neural-Network Guided Expression Transformation）</news:title>
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   <news:genres>Blog</news:genres>
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 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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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:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>複素値カーネル活性化関数の広義線形カーネル（Widely Linear Kernels for Complex-Valued Kernel Activation Functions）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
  </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>固有表現を取り込む単語埋め込みの再設計（Word Embeddings for Entity-annotated Texts）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>テンソルの教師付き部分空間学習における共通モードパターン（COMMON MODE PATTERNS FOR SUPERVISED TENSOR SUBSPACE LEARNING）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>特徴間依存性を利用した正則化による生成モデルの改善（Knowledge-Based Regularization in Generative Modeling）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>深層学習におけるADMMの収束性と飽和回避（On ADMM in Deep Learning: Convergence and Saturation-Avoidance）</news:title>
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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>
   </news:publication>
   <news:title>到達しない状態を許すマルコフ連鎖の同一性検定（Testing Markov Chains Without Hitting）</news:title>
   <news:publication_date>2026-08-03T20:21:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
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 <url>
  <loc>https://aibr.jp/archives/719185</loc>
  <lastmod>2026-08-03T20:13:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>双方向推論ネットワーク（Bidirectional Inference Networks: A Class of Deep Bayesian Networks for Health Profiling）</news:title>
   <news:publication_date>2026-08-03T20:13:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719183</loc>
  <lastmod>2026-08-03T20:13:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューラルネットワーク解釈を騙す手法—説明の安定性を問う（Fooling Neural Network Interpretations via Adversarial Model Manipulation）</news:title>
   <news:publication_date>2026-08-03T20:13:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719181</loc>
  <lastmod>2026-08-03T20:13:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分布系統における頑健な行列補完による状態推定（Robust Matrix Completion State Estimation in Distribution Systems）</news:title>
   <news:publication_date>2026-08-03T20:13:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719179</loc>
  <lastmod>2026-08-03T20:12:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高速な平均推定とサブガウス誤差率（Fast Mean Estimation with Sub-Gaussian Rates）</news:title>
   <news:publication_date>2026-08-03T20:12:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719177</loc>
  <lastmod>2026-08-03T20:12:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>層は等しくないのか（Are All Layers Created Equal?）</news:title>
   <news:publication_date>2026-08-03T20:12:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719175</loc>
  <lastmod>2026-08-03T20:12:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多尺度データに対する自動スペクトラルクラスタリング（AN AUTOMATED SPECTRAL CLUSTERING FOR MULTI-SCALE DATA）</news:title>
   <news:publication_date>2026-08-03T20:12:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719173</loc>
  <lastmod>2026-08-03T19:21:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CodedReduceによる分散学習の高速化と堅牢化（CodedReduce: A Fast and Robust Framework for Gradient Aggregation in Distributed Learning）</news:title>
   <news:publication_date>2026-08-03T19:21:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719171</loc>
  <lastmod>2026-08-03T19:21:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>フィードフォワード設計畳み込みニューラルネットワークによる半教師あり学習（SEMI-SUPERVISED LEARNING VIA FEEDFORWARD-DESIGNED CONVOLUTIONAL NEURAL NETWORKS）</news:title>
   <news:publication_date>2026-08-03T19:21:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719169</loc>
  <lastmod>2026-08-03T19:20:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>交換可能な生成モデルとFlowScan（Exchangeable Generative Models with Flow Scans）</news:title>
   <news:publication_date>2026-08-03T19:20:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719167</loc>
  <lastmod>2026-08-03T19:19:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ポリフォニック音楽作曲におけるLSTMと強化学習（Polyphonic Music Composition with LSTM Neural Networks and Reinforcement Learning）</news:title>
   <news:publication_date>2026-08-03T19:19:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719165</loc>
  <lastmod>2026-08-03T19:19:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>MRIにおけるセマンティックセグメンテーションの技術的考察（Technical Considerations for Semantic Segmentation in MRI using Convolutional Neural Networks）</news:title>
   <news:publication_date>2026-08-03T19:19:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719163</loc>
  <lastmod>2026-08-03T19:19:28Z</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・ホウリハン：高スループット同定不能性問題への実践的解法（The Parameter Houlihan: a solution to high-throughput identifiability indeterminacy for brutally ill-posed problems）</news:title>
   <news:publication_date>2026-08-03T19:19:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719161</loc>
  <lastmod>2026-08-03T18:27:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数カーネル予測ネットワークによるバースト画像のノイズ除去（MULTI-KERNEL PREDICTION NETWORKS FOR DENOISING OF BURST IMAGES）</news:title>
   <news:publication_date>2026-08-03T18:27:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719159</loc>
  <lastmod>2026-08-03T18:27:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>滑らかな凸代替損失に基づく構造化予測の一般理論 (A General Theory for Structured Prediction with Smooth Convex Surrogates)</news:title>
   <news:publication_date>2026-08-03T18:27:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719157</loc>
  <lastmod>2026-08-03T18:26:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>VISCACHAサーベイの概観と初期成果（The VISCACHA survey - I. Overview and First Results）</news:title>
   <news:publication_date>2026-08-03T18:26:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719155</loc>
  <lastmod>2026-08-03T18:26:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>PS1データで星と銀河を自動判別する手法の実装とZTFパイプラインへの応用（A Morphological Classification Model to Identify Unresolved PanSTARRS1 Sources: Application in the ZTF Real-Time Pipeline）</news:title>
   <news:publication_date>2026-08-03T18:26:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719153</loc>
  <lastmod>2026-08-03T18:25:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Zwicky Transient Facilityにおける機械学習の実装と応用（Machine Learning for the Zwicky Transient Facility）</news:title>
   <news:publication_date>2026-08-03T18:25:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719151</loc>
  <lastmod>2026-08-03T18:25:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>サブルーチンの自然言語要約を生むニューラルモデル（A Neural Model for Generating Natural Language Summaries of Program Subroutines）</news:title>
   <news:publication_date>2026-08-03T18:25:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719149</loc>
  <lastmod>2026-08-03T18:25:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>メタアモータイズド変分推論と学習（Meta-Amortized Variational Inference and Learning）</news:title>
   <news:publication_date>2026-08-03T18:25:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719147</loc>
  <lastmod>2026-08-03T17:33:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>弱相互作用に関する一様収束と対称化（Uniform Concentration and Symmetrization for Weak Interactions）</news:title>
   <news:publication_date>2026-08-03T17:33:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719145</loc>
  <lastmod>2026-08-03T17:33:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次元二値特徴に対する能動学習（Active Learning for High-Dimensional Binary Features）</news:title>
   <news:publication_date>2026-08-03T17:33:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719143</loc>
  <lastmod>2026-08-03T17:33:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-03T17:33:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719141</loc>
  <lastmod>2026-08-03T17:32:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自動運転車の適応ストレステスト（Adaptive Stress Testing for Autonomous Vehicles）</news:title>
   <news:publication_date>2026-08-03T17:32:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719139</loc>
  <lastmod>2026-08-03T17:32:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>最適な分割負荷配分順序を学習する強化学習の応用（Reinforcement Learning for Optimal Load Distribution Sequencing in Resource-Sharing System）</news:title>
   <news:publication_date>2026-08-03T17:32:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719137</loc>
  <lastmod>2026-08-03T17:32:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ハイペルエントロピーでつなぐ勾配と乗法的更新（Exponentiated Gradient vs. Meets Gradient Descent）</news:title>
   <news:publication_date>2026-08-03T17:32:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719135</loc>
  <lastmod>2026-08-03T17:32:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>α‑Rényi近似事後分布の漸近的一貫性（Asymptotic Consistency of α‑R´enyi‑Approximate Posteriors）</news:title>
   <news:publication_date>2026-08-03T17:32:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719133</loc>
  <lastmod>2026-08-03T16:40:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Population Based Trainingを実務で使いこなすための要点解説（A Generalized Framework for Population Based Training）</news:title>
   <news:publication_date>2026-08-03T16:40:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719131</loc>
  <lastmod>2026-08-03T16:40:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>面心立方ニッケルにおける結晶粒界と転位の原子論的調査（Atomistic survey of grain boundary dislocation interactions in FCC Nickel）</news:title>
   <news:publication_date>2026-08-03T16:40:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719129</loc>
  <lastmod>2026-08-03T16:40:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>TzK：フローに基づく条件付き生成モデル（TzK: Flow-Based Conditional Generative Model）</news:title>
   <news:publication_date>2026-08-03T16:40:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719127</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>2026-08-03T16:39:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719125</loc>
  <lastmod>2026-08-03T16:39:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>量子スパースサポートベクターマシン（Quantum Sparse Support Vector Machines）</news:title>
   <news:publication_date>2026-08-03T16:39:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719123</loc>
  <lastmod>2026-08-03T16:38:57Z</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>2026-08-03T16:38:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719121</loc>
  <lastmod>2026-08-03T16:38:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時間スケールごとに価値関数を分離する手法（Separating value functions across time-scales）</news:title>
   <news:publication_date>2026-08-03T16:38:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719119</loc>
  <lastmod>2026-08-03T15:46:50Z</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>2026-08-03T15:46:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-03T15:46:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-03T15:45:57Z</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:publication_date>2026-08-03T15:44:52Z</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>2026-08-03T15:44:46Z</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>2026-08-03T15:44:23Z</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>2026-08-03T15:44:15Z</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>複雑なソフトウェア解析を“DODGE”する方法 (How to “DODGE” Complex Software Analytics)</news:title>
   <news:publication_date>2026-08-03T14:52:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/719103</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>ニューロンの出生・消滅ダイナミクスによる大域収束（Global convergence of neuron birth-death dynamics）</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:publication_date>2026-08-03T14:52:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719099</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>共感ロボットによるグループ学習の実証研究（Empathic Robot for Group Learning: A Field Study）</news:title>
   <news:publication_date>2026-08-03T14:51:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719097</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>インタラクティブ分子動力学とマルチパーソンVR（Interactive molecular dynamics in virtual reality from quantum chemistry to drug binding: An open-source multi-person framework）</news:title>
   <news:publication_date>2026-08-03T14:51:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719095</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>モジュール化されたブロック対角カーブチャ近似（Modular Block-diagonal Curvature Approximations for Feedforward Architectures）</news:title>
   <news:publication_date>2026-08-03T14:51:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719093</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-03T14:51:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719091</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>2026-08-03T13:59:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>脳波で捉える「心のさ迷い」自動検出技術（Deep Convolutional Neural Network for Automated Detection of Mind Wandering using EEG Signals）</news:title>
   <news:publication_date>2026-08-03T13:59:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news: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: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>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719077</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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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:publication_date>2026-08-03T13:04:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719071</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-03T13:04:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719069</loc>
  <lastmod>2026-08-03T13:03:55Z</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>2026-08-03T13:03:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719067</loc>
  <lastmod>2026-08-03T13:03:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-03T13:03:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719065</loc>
  <lastmod>2026-08-03T13:03:26Z</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>2026-08-03T13:03:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719063</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>全確率的勾配アルゴリズムと強化学習への応用（Total stochastic gradient algorithms and applications in reinforcement learning）</news:title>
   <news:publication_date>2026-08-03T12:11:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719061</loc>
  <lastmod>2026-08-03T12:11:27Z</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>2026-08-03T12:11:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719059</loc>
  <lastmod>2026-08-03T12:11:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>近似修復と部分標本化トラストリージョン法による有限和最小化（Inexact restoration with subsampled trust-region methods for finite-sum minimization）</news:title>
   <news:publication_date>2026-08-03T12:11:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719057</loc>
  <lastmod>2026-08-03T12:10:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-03T12:10:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719055</loc>
  <lastmod>2026-08-03T12:10:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非単調な逐次テキスト生成（Non-Monotonic Sequential Text Generation）</news:title>
   <news:publication_date>2026-08-03T12:10:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/719053</loc>
  <lastmod>2026-08-03T12:10:03Z</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>2026-08-03T12:10:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719051</loc>
  <lastmod>2026-08-03T12:09:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-03T12:09:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719049</loc>
  <lastmod>2026-08-03T11:18:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-03T11:17:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-03T11:17:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719045</loc>
  <lastmod>2026-08-03T11:17:28Z</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耐障害性強化（Enhancing Fault Tolerance of Neural Networks for Security-Critical Applications）</news:title>
   <news:publication_date>2026-08-03T11:17:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <lastmod>2026-08-03T11:16:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>高次元ファクトリアル隠れマルコフモデルにおける局所性の利用（Exploiting locality in high-dimensional Factorial hidden Markov models）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/719041</loc>
  <lastmod>2026-08-03T11:16:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>変動不等式に対する普遍アルゴリズム（A Universal Algorithm for Variational Inequalities Adaptive to Smoothness and Noise）</news:title>
   <news:publication_date>2026-08-03T11:16:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719039</loc>
  <lastmod>2026-08-03T11:15:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>トレースノルム球上での低ランク射影を用いた射影勾配法の収束性（On the Convergence of Projected-Gradient Methods with Low-Rank Projections for Smooth Convex Minimization over Trace-Norm Balls and Related Problems）</news:title>
   <news:publication_date>2026-08-03T11:15:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719037</loc>
  <lastmod>2026-08-03T11:15:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>一般化ステイフェル多様体における前処理付きリーマン最適化（Riemannian optimization with a preconditioning scheme on the generalized Stiefel manifold）</news:title>
   <news:publication_date>2026-08-03T11:15:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719035</loc>
  <lastmod>2026-08-03T10:23:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>低遅延の事前中断を用いたジョブスケジューリング（Low-latency job scheduling with preemption for the development of deep learning）</news:title>
   <news:publication_date>2026-08-03T10:23:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719033</loc>
  <lastmod>2026-08-03T10:22:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>最小記述長を目的関数とする非負値行列因子分解（Minimum description length as an objective function for non-negative matrix factorization）</news:title>
   <news:publication_date>2026-08-03T10:22:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719031</loc>
  <lastmod>2026-08-03T10:22:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>変分オートエンコーダによる音声強調：準教師付き分散モデルの実装と検証（A VARIANCE MODELING FRAMEWORK BASED ON VARIATIONAL AUTOENCODERS FOR SPEECH ENHANCEMENT）</news:title>
   <news:publication_date>2026-08-03T10:22:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719029</loc>
  <lastmod>2026-08-03T10:21:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>区分的定常バンディットに挑む効率的変化点検出（Efficient Change-Point Detection for Tackling Piecewise-Stationary Bandits）</news:title>
   <news:publication_date>2026-08-03T10:21:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719027</loc>
  <lastmod>2026-08-03T10:21:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>生存フォレストの検証：比例ハザード仮定がALSの予後・予測モデルに与える影響（Survival Forests under Test: Impact of the Proportional Hazards Assumption on Prognostic and Predictive Forests for ALS Survival）</news:title>
   <news:publication_date>2026-08-03T10:21:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719025</loc>
  <lastmod>2026-08-03T10:21:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>頑健な教師あり分類と特徴選択を同時に解くプライマル・デュアル法（Robust supervised classification and feature selection using a primal-dual method）</news:title>
   <news:publication_date>2026-08-03T10:21:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719023</loc>
  <lastmod>2026-08-03T10:20:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次元非線形偏微分方程式を解く深層バックワード法（Deep backward schemes for high-dimensional nonlinear PDEs）</news:title>
   <news:publication_date>2026-08-03T10:20:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719021</loc>
  <lastmod>2026-08-03T09:29:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Gradient Boostingを用いた油圧破砕の効率向上（Gradient Boosting to Boost the Efficiency of Hydraulic Fracturing）</news:title>
   <news:publication_date>2026-08-03T09:29:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719019</loc>
  <lastmod>2026-08-03T09:29:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>シミュレーションで学ぶ学習方法（Learning to Learn in Simulation）</news:title>
   <news:publication_date>2026-08-03T09:29:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719017</loc>
  <lastmod>2026-08-03T09:29:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Relevance Factor VAEによる意味的要因の自動識別（Relevance Factor VAE: Learning and Identifying Disentangled Factors）</news:title>
   <news:publication_date>2026-08-03T09:29:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719015</loc>
  <lastmod>2026-08-03T09:29:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層的相互作用学習の大規模化（Learning Hierarchical Interactions at Scale: A Convex Optimization Approach）</news:title>
   <news:publication_date>2026-08-03T09:29:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719013</loc>
  <lastmod>2026-08-03T09:28:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SASSEによるスケーラブルで適応可能な6自由度姿勢推定（SASSE: Scalable and Adaptable 6-DOF Pose Estimation）</news:title>
   <news:publication_date>2026-08-03T09:28:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719011</loc>
  <lastmod>2026-08-03T09:28:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>音声活動検出におけるSVMアンサンブルの実用性（An Ensemble SVM-based Approach for Voice Activity Detection）</news:title>
   <news:publication_date>2026-08-03T09:28:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719009</loc>
  <lastmod>2026-08-03T09:28:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチエージェント強化学習における通信スケジューリングの学習（Learning to Schedule Communication in Multi-Agent Reinforcement Learning）</news:title>
   <news:publication_date>2026-08-03T09:28:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719007</loc>
  <lastmod>2026-08-03T08:36:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>操作学習のための機能的オブジェクト指向ネットワーク（Functional Object-Oriented Network for Manipulation Learning）</news:title>
   <news:publication_date>2026-08-03T08:36:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719005</loc>
  <lastmod>2026-08-03T08:36:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>参照リーダー：照応解析のための再帰的実体ネットワーク（The Referential Reader: A Recurrent Entity Network for Anaphora Resolution）</news:title>
   <news:publication_date>2026-08-03T08:36:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719003</loc>
  <lastmod>2026-08-03T08:35:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Operational State ChangeによるNILM向けニューラルネットワークの要点解説（Neural Network for NILM Based on Operational State Change Classification）</news:title>
   <news:publication_date>2026-08-03T08:35:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719001</loc>
  <lastmod>2026-08-03T08:34:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>連続行動を持つ文脈付きバンディット：平滑化、ズーミング、適応（Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting）</news:title>
   <news:publication_date>2026-08-03T08:34:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718999</loc>
  <lastmod>2026-08-03T08:34:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>摂動的GAN（Perturbative GAN: GAN with Perturbation Layers）</news:title>
   <news:publication_date>2026-08-03T08:34:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718997</loc>
  <lastmod>2026-08-03T08:34:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>交差デザインから個別化治療方針を推定する（Estimating Individualized Treatment Regimes from Crossover Designs）</news:title>
   <news:publication_date>2026-08-03T08:34:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718995</loc>
  <lastmod>2026-08-03T08:33:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>効率的ラベリングのための能動的画像合成（Active Image Synthesis for Efficient Labeling）</news:title>
   <news:publication_date>2026-08-03T08:33:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718993</loc>
  <lastmod>2026-08-03T07:42:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非凸・非滑らか最適化のための遅延対応加速近接座標降下法（Asynchronous Delay-Aware Accelerated Proximal Coordinate Descent for Nonconvex Nonsmooth Problems）</news:title>
   <news:publication_date>2026-08-03T07:42:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718991</loc>
  <lastmod>2026-08-03T07:42:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>合成ノイズ学習により機械翻訳の誤字耐性を高める（Training on Synthetic Noise Improves Robustness to Natural Noise in Machine Translation）</news:title>
   <news:publication_date>2026-08-03T07:42:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718989</loc>
  <lastmod>2026-08-03T07:41:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>デジタル服薬データから処置を処方する学習（Learning to Prescribe Interventions for Tuberculosis Patients Using Digital Adherence Data）</news:title>
   <news:publication_date>2026-08-03T07:41:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718987</loc>
  <lastmod>2026-08-03T07:41:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>混同行列とラフ集合による分類評価の再考（Confusion matrices and rough set data analysis）</news:title>
   <news:publication_date>2026-08-03T07:41:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718985</loc>
  <lastmod>2026-08-03T07:41:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>バイナリデータの有効次元とは（What is the dimension of your binary data?）</news:title>
   <news:publication_date>2026-08-03T07:41:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718983</loc>
  <lastmod>2026-08-03T07:40:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ブートストラップ座標探索を用いた多次元尺度構成（Bootstrapped Coordinate Search for Multidimensional Scaling）</news:title>
   <news:publication_date>2026-08-03T07:40:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718981</loc>
  <lastmod>2026-08-03T07:40:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>パラメータフリーなオンライン凸最適化とサブ指数ノイズへの対応（Parameter-Free Online Convex Optimization with Sub-Exponential Noise）</news:title>
   <news:publication_date>2026-08-03T07:40:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718979</loc>
  <lastmod>2026-08-03T06:49:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動画中の物体検出と追跡を同時に行うネットワーク（TrackNet: Simultaneous Object Detection and Tracking and Its Application in Traffic Video Analysis）</news:title>
   <news:publication_date>2026-08-03T06:49:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718977</loc>
  <lastmod>2026-08-03T06:49:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>空間的事前情報と3D畳み込みで実現する頑健な脳MRI構造分割（Accurate and robust segmentation of neuroanatomy in T1-weighted MRI by combining spatial priors with deep convolutional neural networks）</news:title>
   <news:publication_date>2026-08-03T06:49:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718975</loc>
  <lastmod>2026-08-03T06:48:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ソーシャルメディアにおける暗号通貨操作の検出と分析（Identifying and Analyzing Cryptocurrency Manipulations in Social Media）</news:title>
   <news:publication_date>2026-08-03T06:48:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718973</loc>
  <lastmod>2026-08-03T06:47:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ROMANetによるオフチップメモリアクセス管理の革新（ROMANet: Fine-Grained Reuse-Driven Off-Chip Memory Access Management and Data Organization for Deep Neural Network Accelerators）</news:title>
   <news:publication_date>2026-08-03T06:47:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718971</loc>
  <lastmod>2026-08-03T06:47:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>依存データからの因果効果の同定と推定（Identification and Estimation Of Causal Effects from Dependent Data）</news:title>
   <news:publication_date>2026-08-03T06:47:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718969</loc>
  <lastmod>2026-08-03T06:47:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>PVNetによる時空間PV発電予測（PVNet: A LRCN Architecture for Spatio-Temporal Photovoltaic Power Forecasting from Numerical Weather Prediction）</news:title>
   <news:publication_date>2026-08-03T06:47:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718967</loc>
  <lastmod>2026-08-03T06:47:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オートエンコーダの汎化境界（Generalization Bounds For Unsupervised and Semi-Supervised Learning With Autoencoders）</news:title>
   <news:publication_date>2026-08-03T06:47:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718965</loc>
  <lastmod>2026-08-03T05:54:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>360度映像の長期視野予測（Very Long Term Field of View Prediction for 360-degree Video Streaming）</news:title>
   <news:publication_date>2026-08-03T05:54:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718963</loc>
  <lastmod>2026-08-03T05:53:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Blazeによるメモリ上MapReduceの高速化（Blaze: Simplified High Performance Cluster Computing）</news:title>
   <news:publication_date>2026-08-03T05:53:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718961</loc>
  <lastmod>2026-08-03T05:53:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>クラスタ解析におけるパラメータ選択の可視化ツール（Visualization tools for parameter selection in cluster analysis）</news:title>
   <news:publication_date>2026-08-03T05:53:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718959</loc>
  <lastmod>2026-08-03T05:52:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>森の中の生成：近傍を用いた生成手法（A Forest from the Trees: Generation through Neighborhoods）</news:title>
   <news:publication_date>2026-08-03T05:52:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718957</loc>
  <lastmod>2026-08-03T05:52:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ハッブル望遠鏡によるNGC 2419中心域の多重星団解析（Hubble Space Telescope photometry of multiple stellar populations in the inner parts of NGC 2419）</news:title>
   <news:publication_date>2026-08-03T05:52:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718955</loc>
  <lastmod>2026-08-03T05:51:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所学習則から導かれたスパイキングニューラルネットワーク（A SPIKING NEURAL NETWORK WITH LOCAL LEARNING RULES DERIVED FROM NONNEGATIVE SIMILARITY MATCHING）</news:title>
   <news:publication_date>2026-08-03T05:51:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718953</loc>
  <lastmod>2026-08-03T05:51:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>風力タービン駆動系軸受の監視に向けた辞書学習アプローチ (Dictionary learning approach to monitoring of wind turbine drivetrain bearings)</news:title>
   <news:publication_date>2026-08-03T05:51:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718951</loc>
  <lastmod>2026-08-03T04:59:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>細粒度時間関係抽出の枠組みと実用性（Fine-Grained Temporal Relation Extraction）</news:title>
   <news:publication_date>2026-08-03T04:59:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718949</loc>
  <lastmod>2026-08-03T04:59:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>潜在変数の真価を問い直す──時系列モデルにおけるlatent変数の役割の再検討（Re-examination of the Role of Latent Variables in Sequence Modeling）</news:title>
   <news:publication_date>2026-08-03T04:59:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718947</loc>
  <lastmod>2026-08-03T04:58:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>具現化されたマルチモーダル・マルチタスク学習（Embodied Multimodal Multitask Learning）</news:title>
   <news:publication_date>2026-08-03T04:58:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718945</loc>
  <lastmod>2026-08-03T04:58:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単一画像からの霧除去を学習で実現する手法（End-to-End Single Image Fog Removal using Enhanced Cycle Consistent Adversarial Networks）</news:title>
   <news:publication_date>2026-08-03T04:58:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718943</loc>
  <lastmod>2026-08-03T04:58:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>FLORES評価データセット：ネパール語–英語とシンハラ語–英語（The FLORES Evaluation Datasets for Low-Resource Machine Translation: Nepali–English and Sinhala–English）</news:title>
   <news:publication_date>2026-08-03T04:58:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718941</loc>
  <lastmod>2026-08-03T04:58:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>過大パラメータ化された深層ReLUネットワークに対する勾配降下法の一般化誤差境界（Generalization Error Bounds of Gradient Descent for Learning Over-parameterized Deep ReLU Networks）</news:title>
   <news:publication_date>2026-08-03T04:58:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718939</loc>
  <lastmod>2026-08-03T04:57:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ベイズ推論のためのランジュバン拡散の確率的ゼロ階離散化（Stochastic Zeroth-order Discretizations of Langevin Diffusions for Bayesian Inference）</news:title>
   <news:publication_date>2026-08-03T04:57:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718937</loc>
  <lastmod>2026-08-03T04:06:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模なセキュリティ制約付きユニットコミット問題を学習で高速化する（Learning to Solve Large-Scale Security-Constrained Unit Commitment Problems）</news:title>
   <news:publication_date>2026-08-03T04:06:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718935</loc>
  <lastmod>2026-08-03T04:06:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>挿入ベースのデコーディングと自動推定生成順序（Insertion-based Decoding with Inferred Generation Order）</news:title>
   <news:publication_date>2026-08-03T04:06:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718933</loc>
  <lastmod>2026-08-03T04:06:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>隠れノード下の分布系トポロジ同定の堅牢化（Robust Hidden Topology Identification in Distribution Systems）</news:title>
   <news:publication_date>2026-08-03T04:06:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718931</loc>
  <lastmod>2026-08-03T04:05:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>最適化の視点からみる人工知能の学際的影響評価 (Evaluation of Multidisciplinary Effects of Artificial Intelligence with Optimization Perspective)</news:title>
   <news:publication_date>2026-08-03T04:05:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718929</loc>
  <lastmod>2026-08-03T04:05:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>欺瞞による防御フレームワーク（Deception-As-Defense Framework for Cyber-Physical Systems）</news:title>
   <news:publication_date>2026-08-03T04:05:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718927</loc>
  <lastmod>2026-08-03T04:04:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>構文に依存しないプロト役割ラベリングのためのマーカー・モデル（An Argument-Marker Model for Syntax-Agnostic Proto-Role Labeling）</news:title>
   <news:publication_date>2026-08-03T04:04:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718925</loc>
  <lastmod>2026-08-03T04:04:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ブラックボックス情報漏洩の高速推定（F-BLEAU: Fast Black-box Leakage Estimation）</news:title>
   <news:publication_date>2026-08-03T04:04:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718923</loc>
  <lastmod>2026-08-03T03:13:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>教師なし機械翻訳を実用領域へ近づけた手法（An Effective Approach to Unsupervised Machine Translation）</news:title>
   <news:publication_date>2026-08-03T03:13:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718921</loc>
  <lastmod>2026-08-03T02:55:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>推薦システムの共通表記の提案（Proposed Common Notation for Teaching and Research）</news:title>
   <news:publication_date>2026-08-03T02:55:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718919</loc>
  <lastmod>2026-08-03T02:55:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習で拡張するバンプハント（Extending the Bump Hunt with Machine Learning）</news:title>
   <news:publication_date>2026-08-03T02:55:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718917</loc>
  <lastmod>2026-08-03T02:54:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>近位大腿骨骨折の自動分類とその臨床応用可能性（Precise Proximal Femur Fracture Classification for Interactive Training and Surgical Planning）</news:title>
   <news:publication_date>2026-08-03T02:54:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718915</loc>
  <lastmod>2026-08-03T02:54:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>宣言型データ分析の概説（Declarative Data Analytics: a Survey）</news:title>
   <news:publication_date>2026-08-03T02:54:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718913</loc>
  <lastmod>2026-08-03T02:53:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>注意機構が変えた自然言語処理の地殻変動（A Survey on Neural Attention Models）</news:title>
   <news:publication_date>2026-08-03T02:53:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718911</loc>
  <lastmod>2026-08-03T02:01:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>熱力学データベースの効率的開発と不確かさ定量化を実現するESPEI（ESPEI for efficient thermodynamic database development, modification, and uncertainty quantification: application to Cu-Mg）</news:title>
   <news:publication_date>2026-08-03T02:01:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718909</loc>
  <lastmod>2026-08-03T02:01:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的な導関数不要最適化と重要度サンプリングの応用（A Stochastic Derivative-Free Optimization Method with Importance Sampling: Theory and Learning to Control）</news:title>
   <news:publication_date>2026-08-03T02:01:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718907</loc>
  <lastmod>2026-08-03T02:01:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Augmented Autoencodersによる6D物体検出の新境地（Augmented Autoencoders: Implicit 3D Orientation Learning for 6D Object Detection）</news:title>
   <news:publication_date>2026-08-03T02:01:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718905</loc>
  <lastmod>2026-08-03T02:00:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>2次元全変動によるノイズ除去の新しいリスク境界 (New Risk Bounds for 2D Total Variation Denoising)</news:title>
   <news:publication_date>2026-08-03T02:00:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718903</loc>
  <lastmod>2026-08-03T02:00:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>クラス内分割によるディープなワン・クラス分類（Deep One-Class Classification Using Intra-Class Splitting）</news:title>
   <news:publication_date>2026-08-03T02:00:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718901</loc>
  <lastmod>2026-08-03T01:59:58Z</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>2026-08-03T01:59:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718899</loc>
  <lastmod>2026-08-03T01:59:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>PIPPSが示した「混沌の呪い」の克服（Probabilistic Inference for Particle-based Policy Search）</news:title>
   <news:publication_date>2026-08-03T01:59:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718897</loc>
  <lastmod>2026-08-03T01:07:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>行列版多層パーセプトロンの構築とVAEへの応用 (Constructing the Matrix Multilayer Perceptron and its Application to the VAE)</news:title>
   <news:publication_date>2026-08-03T01:07:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718895</loc>
  <lastmod>2026-08-03T01:07:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>脳の接続性をグラフ上の測地線で比べる（COMPARISON OF BRAIN CONNECTOMES USING GEODESIC DISTANCE ON MANIFOLD: A TWINS STUDY）</news:title>
   <news:publication_date>2026-08-03T01:07:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718893</loc>
  <lastmod>2026-08-03T01:07:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>臨床用語の非教師あり翻訳の実用性（Unsupervised Clinical Language Translation）</news:title>
   <news:publication_date>2026-08-03T01:07:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718891</loc>
  <lastmod>2026-08-03T01:05:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>配列空間の統計力学が示す共進化解析の効率化（Statistical mechanical properties of sequence space determine the efficiency of the various algorithms to predict interaction energies and native contacts from protein coevolution）</news:title>
   <news:publication_date>2026-08-03T01:05:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718889</loc>
  <lastmod>2026-08-03T01:05:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CapStoreによるカプセルネット推論の省エネオンチップメモリ設計（CapStore: Energy-Efficient Design and Management of the On-Chip Memory for CapsuleNet Inference Accelerators）</news:title>
   <news:publication_date>2026-08-03T01:05:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718887</loc>
  <lastmod>2026-08-03T01:05:20Z</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は安全か（Is Spiking Secure? A Comparative Study on the Security Vulnerabilities of Spiking and Deep Neural Networks）</news:title>
   <news:publication_date>2026-08-03T01:05:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718885</loc>
  <lastmod>2026-08-03T01:05:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ランダム化による理論的な敵対的頑健性の裏付け（Theoretical evidence for adversarial robustness through randomization）</news:title>
   <news:publication_date>2026-08-03T01:05:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718882</loc>
  <lastmod>2026-08-03T00:13:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Adaptive Distinguishing Sequencesを用いた能動オートマトン学習の拡張（Active Automata Learning with Adaptive Distinguishing Sequences）</news:title>
   <news:publication_date>2026-08-03T00:13:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718880</loc>
  <lastmod>2026-08-03T00:13:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>行列多様体上のリーマン適応確率的勾配法（Riemannian adaptive stochastic gradient algorithms on matrix manifolds）</news:title>
   <news:publication_date>2026-08-03T00:13:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718878</loc>
  <lastmod>2026-08-03T00:13:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マーケティング予算配分の統一フレームワーク (A Unified Framework for Marketing Budget Allocation)</news:title>
   <news:publication_date>2026-08-03T00:13:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718876</loc>
  <lastmod>2026-08-03T00:12:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>行動の自然言語化（The Natural Language of Actions）</news:title>
   <news:publication_date>2026-08-03T00:12:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718874</loc>
  <lastmod>2026-08-03T00:12:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>領域句注意を用いた高現実画像生成（Realistic Image Generation using Region-phrase Attention）</news:title>
   <news:publication_date>2026-08-03T00:12:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718872</loc>
  <lastmod>2026-08-03T00:12:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模高次元データの2次元埋め込みを高速・低メモリで実現する方法（2-D Embedding of Large and High-dimensional Data with Minimal Memory and Computational Time Requirements）</news:title>
   <news:publication_date>2026-08-03T00:12:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718870</loc>
  <lastmod>2026-08-03T00:11:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>FMCWレーダーを用いた物体検出と3次元推定（OBJECT DETECTION AND 3D ESTIMATION VIA AN FMCW RADAR USING A FULLY CONVOLUTIONAL NETWORK）</news:title>
   <news:publication_date>2026-08-03T00:11:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718868</loc>
  <lastmod>2026-08-02T23:21:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>説明可能な行動変化検出（VEDAR: Accountable Behavioural Change Detection）</news:title>
   <news:publication_date>2026-08-02T23:21:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718866</loc>
  <lastmod>2026-08-02T23:20:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スパイクベースの環境音認識の効率化（Robust Environmental Sound Recognition with Sparse Key-point Encoding and Efficient Multi-spike Learning）</news:title>
   <news:publication_date>2026-08-02T23:20:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718864</loc>
  <lastmod>2026-08-02T23:20:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>服の互換性と多様性を考慮したファッション画像インペインティング（Compatible and Diverse Fashion Image Inpainting）</news:title>
   <news:publication_date>2026-08-02T23:20:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718862</loc>
  <lastmod>2026-08-02T23:19:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複合密度ネットワークによる予測不確実性の定量化（Predictive Uncertainty Quantification with Compound Density Networks）</news:title>
   <news:publication_date>2026-08-02T23:19:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718860</loc>
  <lastmod>2026-08-02T23:19:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>WideDTAによる薬物–標的結合親和性予測の革新（WIDEDTA: PREDICTION OF DRUG-TARGET BINDING AFFINITY）</news:title>
   <news:publication_date>2026-08-02T23:19:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718858</loc>
  <lastmod>2026-08-02T23:18:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Paracosmによる自動運転シミュレーション試験フレームワーク（Paracosm: A Test Framework for Autonomous Driving Simulations）</news:title>
   <news:publication_date>2026-08-02T23:18:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718856</loc>
  <lastmod>2026-08-02T23:18:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>計算的制約と堅牢分類の限界（Computational Limitations in Robust Classification and Win-Win Results）</news:title>
   <news:publication_date>2026-08-02T23:18:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718854</loc>
  <lastmod>2026-08-02T22:27:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数の不完全なデータ源からの因果効果同定（Causal Effect Identification from Multiple Incomplete Data Sources: A General Search-based Approach）</news:title>
   <news:publication_date>2026-08-02T22:27:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718852</loc>
  <lastmod>2026-08-02T22:26:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Saliency Tubesによる時空間畳み込みの可視化（SALIENCY TUBES: VISUAL EXPLANATIONS FOR SPATIO-TEMPORAL CONVOLUTIONS）</news:title>
   <news:publication_date>2026-08-02T22:26:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718850</loc>
  <lastmod>2026-08-02T22:26:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>混合環境に強い分散学習プロトコル Hop（Hop: Heterogeneity-aware Decentralized Training）</news:title>
   <news:publication_date>2026-08-02T22:26:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718848</loc>
  <lastmod>2026-08-02T22:25:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>RetinaNetを用いた歩行者検出の実用性と限界（Towards Pedestrian Detection Using RetinaNet）</news:title>
   <news:publication_date>2026-08-02T22:25:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718846</loc>
  <lastmod>2026-08-02T22:25:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単眼網膜画像から深度推定と視神経乳頭領域の分割を行う全畳み込みネットワーク（Fully Convolutional Networks for Monocular Retinal Depth Estimation and Optic Disc-Cup Segmentation）</news:title>
   <news:publication_date>2026-08-02T22:25:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718844</loc>
  <lastmod>2026-08-02T22:25:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>フェデレーテッドラーニングの大規模運用設計（Towards Federated Learning at Scale: System Design）</news:title>
   <news:publication_date>2026-08-02T22:25:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718842</loc>
  <lastmod>2026-08-02T22:24:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>部分フィードバック下での予測マージンに基づくオンライン多クラス分類（Online Multiclass Classification Based on Prediction Margin for Partial Feedback）</news:title>
   <news:publication_date>2026-08-02T22:24:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718840</loc>
  <lastmod>2026-08-02T21:33:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>RNNがSGDで効率的に学べることの証明（Can SGD Learn Recurrent Neural Networks with Provable Generalization?）</news:title>
   <news:publication_date>2026-08-02T21:33:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718838</loc>
  <lastmod>2026-08-02T21:32:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>顔表情認識における注意付き畳み込みネットワーク（Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network）</news:title>
   <news:publication_date>2026-08-02T21:32:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718836</loc>
  <lastmod>2026-08-02T21:31:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>フィリピンの企業協賛IT卒業設計におけるスクラム導入の有効性（A Study of an Agile methodology with scrum approach to the Filipino company-sponsored I.T. capstone program）</news:title>
   <news:publication_date>2026-08-02T21:31:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718834</loc>
  <lastmod>2026-08-02T21:31:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>応答面の全域推定を多重等高線で行う手法（Global Fitting of the Response Surface via Estimating Multiple Contours of a Simulator）</news:title>
   <news:publication_date>2026-08-02T21:31:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718832</loc>
  <lastmod>2026-08-02T21:31:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>発作タイプ分類におけるEEGと機械学習のベンチマーク設定（Seizure Type Classification using EEG signals and Machine Learning: Setting a benchmark）</news:title>
   <news:publication_date>2026-08-02T21:31:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718830</loc>
  <lastmod>2026-08-02T21:31:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>シミュレーションで銀河とハローを探す手法（Hunting for Galaxies and Halos in simulations with VELOCIraptor）</news:title>
   <news:publication_date>2026-08-02T21:31:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718828</loc>
  <lastmod>2026-08-02T20:39:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>インパルシブ雑音に強い分散RLSの設計と応用（Study of Robust Distributed Diffusion RLS Algorithms with Side Information for Adaptive Networks）</news:title>
   <news:publication_date>2026-08-02T20:39:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718826</loc>
  <lastmod>2026-08-02T20:39:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>構文的ヒューリスティクスが招く誤判断の診断（Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference）</news:title>
   <news:publication_date>2026-08-02T20:39:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718824</loc>
  <lastmod>2026-08-02T20:39:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>BottleNet によるモバイル・クラウド協調の効率化（BottleNet: A Deep Learning Architecture for Intelligent Mobile Cloud Computing Services）</news:title>
   <news:publication_date>2026-08-02T20:39:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718822</loc>
  <lastmod>2026-08-02T20:38:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>MCMCにおける加速法の類似物は存在するか（Is There an Analog of Nesterov Acceleration for MCMC?）</news:title>
   <news:publication_date>2026-08-02T20:38:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718820</loc>
  <lastmod>2026-08-02T20:38:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>最小最大実験計画法――最小二乗回帰における統計的手法と最悪ケース手法の橋渡し（Minimax experimental design: Bridging the gap between statistical and worst-case approaches to least squares regression）</news:title>
   <news:publication_date>2026-08-02T20:38:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718818</loc>
  <lastmod>2026-08-02T20:38:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>個別用量反応曲線の反事実表現学習（Learning Counterfactual Representations for Estimating Individual Dose-Response Curves）</news:title>
   <news:publication_date>2026-08-02T20:38:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718816</loc>
  <lastmod>2026-08-02T20:38:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>敵対的ネットワークとオートエンコーダのプリマル・デュアル関係と一般化境界（Adversarial Networks and Autoencoders: The Primal-Dual Relationship and Generalization Bounds）</news:title>
   <news:publication_date>2026-08-02T20:38:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718814</loc>
  <lastmod>2026-08-02T19:47:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ベイズ的情報引き出し（Bayesian Elicitation）</news:title>
   <news:publication_date>2026-08-02T19:47:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718812</loc>
  <lastmod>2026-08-02T19:46:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非定常文脈バンディットの新アルゴリズム（A New Algorithm for Non-stationary Contextual Bandits）</news:title>
   <news:publication_date>2026-08-02T19:46:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718810</loc>
  <lastmod>2026-08-02T19:46:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ユニバーサル・レマタイザー：系列対系列モデルによる普遍的依存木バンクのレマタイジング（UNIVERSAL LEMMATIZER: A SEQUENCE TO SEQUENCE MODEL FOR LEMMATIZING UNIVERSAL DEPENDENCIES TREEBANKS）</news:title>
   <news:publication_date>2026-08-02T19:46:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718808</loc>
  <lastmod>2026-08-02T19:45:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的1次法とポテンシャル関数による非漸近解析（Stochastic first-order methods: non-asymptotic and computer-aided analyses via potential functions）</news:title>
   <news:publication_date>2026-08-02T19:45:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718806</loc>
  <lastmod>2026-08-02T19:45:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>伴奏から学ぶ自動ピッチ補正のデータ駆動手法（DEEP AUTOTUNER: A DATA-DRIVEN APPROACH TO NATURAL-SOUNDING PITCH CORRECTION FOR SINGING VOICE IN KARAOKE PERFORMANCES）</news:title>
   <news:publication_date>2026-08-02T19:45:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718804</loc>
  <lastmod>2026-08-02T19:45:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ツイートからの高解像度居住地推定（High-resolution home location prediction from tweets using deep learning with dynamic structure）</news:title>
   <news:publication_date>2026-08-02T19:45:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718802</loc>
  <lastmod>2026-08-02T19:44:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>KAUにおけるLMS利用評価と“FORCE”普及戦略の提案（Assessing the Usages of LMS at KAU and Proposing “FORCE” Strategy for the Diffusion）</news:title>
   <news:publication_date>2026-08-02T19:44:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718800</loc>
  <lastmod>2026-08-02T18:52:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>有限時間誤差境界とTD学習（Finite-Time Error Bounds For Linear Stochastic Approximation and TD Learning）</news:title>
   <news:publication_date>2026-08-02T18:52:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718798</loc>
  <lastmod>2026-08-02T18:44:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非線形パラメータ推定における最適実験設計と精密信頼領域（Optimal Experiment Design in Nonlinear Parameter Estimation with Exact Confidence Regions）</news:title>
   <news:publication_date>2026-08-02T18:44:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718796</loc>
  <lastmod>2026-08-02T18:44:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>MICADO-MCAOによる総合的なアストロメトリ誤差予算の構築（Towards an overall astrometric error budget with MICADO-MCAO）</news:title>
   <news:publication_date>2026-08-02T18:44:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718794</loc>
  <lastmod>2026-08-02T18:44:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>アブイニシオ核物理におけるベイズ最適化（Bayesian optimization in ab initio nuclear physics）</news:title>
   <news:publication_date>2026-08-02T18:44:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718792</loc>
  <lastmod>2026-08-02T18:43:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>APOGEE分光器の設計と性能（The APOGEE Spectrographs）</news:title>
   <news:publication_date>2026-08-02T18:43:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718790</loc>
  <lastmod>2026-08-02T18:42:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Depthwise Convolutionによるマルチドメイン学習の要点（Depthwise Convolution is All You Need for Learning Multiple Visual Domains）</news:title>
   <news:publication_date>2026-08-02T18:42:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718788</loc>
  <lastmod>2026-08-02T18:42:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分子特性予測における不確かさ推定と能動学習の統合（Bayesian semi-supervised learning for uncertainty-calibrated prediction of molecular properties and active learning）</news:title>
   <news:publication_date>2026-08-02T18:42:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718786</loc>
  <lastmod>2026-08-02T17:50:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>テキストコーパスから概念階層を推定する手法（Inferring Concept Hierarchies from Text Corpora via Hyperbolic Embeddings）</news:title>
   <news:publication_date>2026-08-02T17:50:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718784</loc>
  <lastmod>2026-08-02T17:50:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>層間の固有類似性を掘り起こす深層モデル圧縮（MIning Cross-Layer Inherent similarity Knowledge (MICIK)）</news:title>
   <news:publication_date>2026-08-02T17:50:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718782</loc>
  <lastmod>2026-08-02T17:49:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>有界勾配仮定を外した非凸学習における確率的勾配法の理論整理（Stochastic Gradient Descent for Nonconvex Learning without Bounded Gradient Assumptions）</news:title>
   <news:publication_date>2026-08-02T17:49:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718780</loc>
  <lastmod>2026-08-02T17:49:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Szegö最小化問題に関する考察（Notes on the Szegö minimum problem. I. Measures with deep zeroes）</news:title>
   <news:publication_date>2026-08-02T17:49:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718778</loc>
  <lastmod>2026-08-02T17:49:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>リレーショナルTucker分解によるマルチ関係リンク予測（A Relational Tucker Decomposition for Multi-Relational Link Prediction）</news:title>
   <news:publication_date>2026-08-02T17:49:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718776</loc>
  <lastmod>2026-08-02T17:48:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>部分ROC下面積を最大化する話者認証の精度向上手法（Speaker Verification By Partial AUC Optimization With Mahalanobis Distance Metric Learning）</news:title>
   <news:publication_date>2026-08-02T17:48:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718774</loc>
  <lastmod>2026-08-02T17:48:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>強く相互作用する系を通したQCDカラーの伝播（Propagation of QCD Color through Strongly Interacting Systems）</news:title>
   <news:publication_date>2026-08-02T17:48:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718772</loc>
  <lastmod>2026-08-02T16:56:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>量子版AdaBoostによる分類器学習の高速化（Quantum Speedup in Adaptive Boosting of Binary Classification）</news:title>
   <news:publication_date>2026-08-02T16:56:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718770</loc>
  <lastmod>2026-08-02T16:56:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層ニューラルネットワークの正則化としての局所ラデマッハ複雑度の実証研究（An Empirical Study on Regularization of Deep Neural Networks by Local Rademacher Complexity）</news:title>
   <news:publication_date>2026-08-02T16:56:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718768</loc>
  <lastmod>2026-08-02T16:56:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ワンビットADCを用いたMU-MIMO向け半教師あり検出器（Semi-Supervised Learning Detector for MU-MIMO Systems with One-bit ADCs）</news:title>
   <news:publication_date>2026-08-02T16:56:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718766</loc>
  <lastmod>2026-08-02T16:55:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>生涯強化学習のためのメタMDPアプローチ（A Meta-MDP Approach to Exploration for Lifelong Reinforcement Learning）</news:title>
   <news:publication_date>2026-08-02T16:55:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718764</loc>
  <lastmod>2026-08-02T16:55:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューラルによる抽出的要約と統語的圧縮（Neural Extractive Text Summarization with Syntactic Compression）</news:title>
   <news:publication_date>2026-08-02T16:55:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718762</loc>
  <lastmod>2026-08-02T16:55:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>価値認識型レコメンデーションによる利益最大化（Value-aware Recommendation based on Reinforced Profit Maximization in E-commerce Systems）</news:title>
   <news:publication_date>2026-08-02T16:55:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718760</loc>
  <lastmod>2026-08-02T16:55:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>夜間撮影画像のグロー（光輝）を除去する深層学習アーキテクチャ（DeGlow‑DeHaze for Nighttime Image Enhancement）</news:title>
   <news:publication_date>2026-08-02T16:55:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718758</loc>
  <lastmod>2026-08-02T16:03:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>屋内ロボットの実時間フリースペース分割（Real-Time Freespace Segmentation on Autonomous Robots for Detection of Obstacles and Drop-Offs）</news:title>
   <news:publication_date>2026-08-02T16:03:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718756</loc>
  <lastmod>2026-08-02T16:02:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>離散確率過程の定量的弱収束（Quantitative Weak Convergence for Discrete Stochastic Processes）</news:title>
   <news:publication_date>2026-08-02T16:02:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718754</loc>
  <lastmod>2026-08-02T16:02:30Z</lastmod>
  <news:news>
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   <news:title>インクリメンタル学習における最大エントロピー正則化とDropOut Sampling（Incremental Learning with Maximum Entropy Regularization: Rethinking Forgetting and Intransigence）</news:title>
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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>DeepPBM: 動画から背景を確率的に推定する手法（DEEPPBM: DEEP PROBABILISTIC BACKGROUND MODEL ESTIMATION FROM VIDEO SEQUENCES）</news:title>
   <news:publication_date>2026-08-02T16:01:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>遅延報酬を考慮した文脈付き多腕バンディットの非パラメトリック無作為化配分（Randomized Allocation with Nonparametric Estimation for Contextual Multi-Armed Bandits with Delayed Rewards）</news:title>
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   <news:genres>Blog</news:genres>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>記憶を持つランダムウォークのアンダーソン様局所化転移（Anderson-like localization transition of random walks with resetting）</news:title>
   <news:publication_date>2026-08-02T16:01:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/718746</loc>
  <lastmod>2026-08-02T16:00:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>量的分布性質の検査における量子優位の可能性（Distributional property testing in a quantum world）</news:title>
   <news:publication_date>2026-08-02T16:00:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/718744</loc>
  <lastmod>2026-08-02T15:09:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>生成モデルにおける判別器と協調するサンプリング（Collaborative Sampling in Generative Adversarial Networks）</news:title>
   <news:publication_date>2026-08-02T15:09:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/718742</loc>
  <lastmod>2026-08-02T15:09:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>皮膚病変の境界を高精度に切り出すアンサンブル深層学習（Skin Lesion Segmentation in Dermoscopic Images with Ensemble Deep Learning Methods）</news:title>
   <news:publication_date>2026-08-02T15:09:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/718740</loc>
  <lastmod>2026-08-02T15:08:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層ネットの複雑さと統計的リスクを結ぶ道筋（Complexity, Statistical Risk, and Metric Entropy of Deep Nets Using Total Path Variation）</news:title>
   <news:publication_date>2026-08-02T15:08:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-08-02T15:08:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>クラウドにおける学習ベースの動的キャッシュ管理（Learning-based Dynamic Cache Management in a Cloud）</news:title>
   <news:publication_date>2026-08-02T15:08:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/718736</loc>
  <lastmod>2026-08-02T15:08:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>連続的効用に対する変分ベイズ意思決定（Variational Bayesian Decision-making for Continuous Utilities）</news:title>
   <news:publication_date>2026-08-02T15:08:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/718734</loc>
  <lastmod>2026-08-02T15:07:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>地理教育におけるGoogle Classroomを用いたブレンデッドラーニングの実践と評価（Google Classroom as a Tool of Support of Blended Learning for Geography Students）</news:title>
   <news:publication_date>2026-08-02T15:07:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
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