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   <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>
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   <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>
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   <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>
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   <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>
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   <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>
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   <news:title>プレイヤーごとに利得が異なるマルチプレイヤーバンディットの実用アルゴリズム（A Practical Algorithm for Multiplayer Bandits when Arm Means Vary Among Players）</news:title>
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   <news:title>PIPPSが示した「混沌の呪い」の克服（Probabilistic Inference for Particle-based Policy Search）</news:title>
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   <news:genres>Blog</news:genres>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>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>
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   <news:genres>Blog</news:genres>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>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>
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   <news:genres>Blog</news:genres>
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    <news:language>ja</news:language>
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   <news:title>臨床用語の非教師あり翻訳の実用性（Unsupervised Clinical Language Translation）</news:title>
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   <news:genres>Blog</news:genres>
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    <news:language>ja</news:language>
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   <news:title>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>
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   <news:genres>Blog</news:genres>
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   <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>
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   <news:genres>Blog</news:genres>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>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>
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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>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>
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   <news:genres>Blog</news:genres>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>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>
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   <news:genres>Blog</news:genres>
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   <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>
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   <news:genres>Blog</news:genres>
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   <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>
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   <news:genres>Blog</news:genres>
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    <news:language>ja</news:language>
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   <news:title>行動の自然言語化（The Natural Language of Actions）</news:title>
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   <news:genres>Blog</news:genres>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>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>
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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>
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   <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>
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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>
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   <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>
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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>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>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>ス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>
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  <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>
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  <lastmod>2026-08-02T23:19:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <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>
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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>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>
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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>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>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <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>
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  <lastmod>2026-08-02T22:27:09Z</lastmod>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>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>
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  <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>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <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>
   <news:publication_date>2026-08-02T16:02:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718752</loc>
  <lastmod>2026-08-02T16:01:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <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>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718750</loc>
  <lastmod>2026-08-02T16:01:31Z</lastmod>
  <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>
   <news:publication_date>2026-08-02T16:01:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718748</loc>
  <lastmod>2026-08-02T16:01:14Z</lastmod>
  <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>
  </news:news>
 </url>
 <url>
  <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>
  </news:news>
 </url>
 <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>
  </news:news>
 </url>
 <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>
  </news:news>
 </url>
 <url>
  <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>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718738</loc>
  <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>
  </news:news>
 </url>
 <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>
  </news:news>
 </url>
 <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>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718732</loc>
  <lastmod>2026-08-02T14:16:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>線形力学系を学習するための半パラメトリック最小二乗法の改良（Learning Linear Dynamical Systems with Semi-Parametric Least Squares）</news:title>
   <news:publication_date>2026-08-02T14:16:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718730</loc>
  <lastmod>2026-08-02T14:16:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次元半教師あり学習による平均推定の最適化探索（HIGH-DIMENSIONAL SEMI-SUPERVISED LEARNING: IN SEARCH FOR OPTIMAL INFERENCE OF THE MEAN）</news:title>
   <news:publication_date>2026-08-02T14:16:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718728</loc>
  <lastmod>2026-08-02T14:15:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数ネットワーク埋め込みによるPOI推薦の統合モデル（RELINE: Point-of-Interest Recommendations using Multiple Network Embeddings）</news:title>
   <news:publication_date>2026-08-02T14:15:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718726</loc>
  <lastmod>2026-08-02T14:14:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>食料品認識のためのドメイン不変階層埋め込み（Domain invariant hierarchical embedding for grocery products recognition）</news:title>
   <news:publication_date>2026-08-02T14:14:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718724</loc>
  <lastmod>2026-08-02T14:14:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>生成パラメータを持つグラフニューラルネットワークによる関係抽出（Graph Neural Networks with Generated Parameters for Relation Extraction）</news:title>
   <news:publication_date>2026-08-02T14:14:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718722</loc>
  <lastmod>2026-08-02T14:13:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>少数パラメータで実現するNLPの転移学習（Parameter-Efficient Transfer Learning for NLP）</news:title>
   <news:publication_date>2026-08-02T14:13:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718720</loc>
  <lastmod>2026-08-02T14:13:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>RGB画像と疎な深度情報の融合による密な深度補完（DFuseNet: Deep Fusion of RGB and Sparse Depth Information for Image Guided Dense Depth Completion）</news:title>
   <news:publication_date>2026-08-02T14:13:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718718</loc>
  <lastmod>2026-08-02T13:21:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニュートリノ相互作用イベントの頂点再構成に対する深層学習（DEEP LEARNING FOR VERTEX RECONSTRUCTION OF NEUTRINO-NUCLEUS INTERACTION EVENTS WITH COMBINED ENERGY AND TIME DATA）</news:title>
   <news:publication_date>2026-08-02T13:21:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718716</loc>
  <lastmod>2026-08-02T13:14:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オンライン多人数追跡における二重マッチング注意機構（Online Multi-Object Tracking with Dual Matching Attention Networks）</news:title>
   <news:publication_date>2026-08-02T13:14:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718714</loc>
  <lastmod>2026-08-02T13:13:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非対称谷が示す新しい最適化観点（Asymmetric Valleys: Beyond Sharp and Flat Local Minima）</news:title>
   <news:publication_date>2026-08-02T13:13:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718712</loc>
  <lastmod>2026-08-02T13:13:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多腕バンディットにおける標本平均のバイアス、リスク、一貫性（On the bias, risk and consistency of sample means in multi-armed bandits）</news:title>
   <news:publication_date>2026-08-02T13:13:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718710</loc>
  <lastmod>2026-08-02T13:12:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>空間インターフェース評価による強化学習でのユーザー嗜好学習（Learning User Preferences via Reinforcement Learning with Spatial Interface Valuing）</news:title>
   <news:publication_date>2026-08-02T13:12:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718708</loc>
  <lastmod>2026-08-02T13:12:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>予測を用いるスケジューリングと誤予測の代償（Scheduling with Predictions and the Price of Misprediction）</news:title>
   <news:publication_date>2026-08-02T13:12:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718706</loc>
  <lastmod>2026-08-02T13:11:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>協調フィルタリングと強化学習の融合（When Collaborative Filtering Meets Reinforcement Learning）</news:title>
   <news:publication_date>2026-08-02T13:11:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718704</loc>
  <lastmod>2026-08-02T12:20:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>RemNetによるカメラモデル識別の新展開（RemNet: Remnant Convolutional Neural Network for Camera Model Identification）</news:title>
   <news:publication_date>2026-08-02T12:20:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718702</loc>
  <lastmod>2026-08-02T12:20:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューロモルフィック回路で逆問題を解く「脳の系列」を設計できるか（Can One Design a Series of Brains for Neuromorphic Computing to solve complex inverse problems?）</news:title>
   <news:publication_date>2026-08-02T12:20:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718700</loc>
  <lastmod>2026-08-02T12:20:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Majorana星による一般三量子ビット状態のスリーテンブルの可視化（Three-Tangle of a General Three-Qubit State in the Representation of Majorana Stars）</news:title>
   <news:publication_date>2026-08-02T12:20:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718698</loc>
  <lastmod>2026-08-02T12:19:54Z</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（ミニマックス）確率変換による教師あり分類の再考（Supervised classification via minimax probabilistic transformations）</news:title>
   <news:publication_date>2026-08-02T12:19:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718696</loc>
  <lastmod>2026-08-02T12:19:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Thompson Samplingの一次ベイズ後悔解析（First-Order Bayesian Regret Analysis of Thompson Sampling）</news:title>
   <news:publication_date>2026-08-02T12:19:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718694</loc>
  <lastmod>2026-08-02T12:19:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>医療診断のためのSVM–CoDOAハイブリッド手法（Medical Diagnosis with a Novel SVM-CoDOA Based Hybrid Approach）</news:title>
   <news:publication_date>2026-08-02T12:19:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718692</loc>
  <lastmod>2026-08-02T12:19:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自然言語処理、感情分析と臨床アナリティクス（Natural Language Processing, Sentiment Analysis and Clinical Analytics）</news:title>
   <news:publication_date>2026-08-02T12:19:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718690</loc>
  <lastmod>2026-08-02T11:27:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>信念（belief）ダイナミクスの抽出──行動から内部状態を読み解く（Belief dynamics extraction）</news:title>
   <news:publication_date>2026-08-02T11:27:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718688</loc>
  <lastmod>2026-08-02T11:27:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多解像度単語埋め込みによる大規模非構造化知識ベースからの文書検索（A Multi-Resolution Word Embedding for Document Retrieval from Large Unstructured Knowledge Bases）</news:title>
   <news:publication_date>2026-08-02T11:27:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718686</loc>
  <lastmod>2026-08-02T11:27:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動的ワークロード向け学習済みインデックス（Learned Indexes for Dynamic Workloads）</news:title>
   <news:publication_date>2026-08-02T11:27:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718684</loc>
  <lastmod>2026-08-02T11:27:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>FurcaNetによる単一チャネル音声分離のエンドツーエンド手法（FURCANET: AN END-TO-END DEEP GATED CONVOLUTIONAL, LONG SHORT-TERM MEMORY, DEEP NEURAL NETWORKS FOR SINGLE CHANNEL SPEECH SEPARATION）</news:title>
   <news:publication_date>2026-08-02T11:27:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718682</loc>
  <lastmod>2026-08-02T11:27:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>効率的なクロスモーダルハッシュ学習を可能にする共通クラスタ単一損失（Joint Cluster Unary Loss for Efficient Cross-Modal Hashing）</news:title>
   <news:publication_date>2026-08-02T11:27:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718680</loc>
  <lastmod>2026-08-02T11:26:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>エンドユーザーがどのように教え、学ぶかをロボットが推定する方法（Enabling Robots to Infer how End-Users Teach and Learn through Human-Robot Interaction）</news:title>
   <news:publication_date>2026-08-02T11:26:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718678</loc>
  <lastmod>2026-08-02T11:26:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>無限幅モデルのパラメータ積分を高速に近似する手法（Fast Approximation and Estimation Bounds of Kernel Quadrature for Infinitely Wide Models）</news:title>
   <news:publication_date>2026-08-02T11:26:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718676</loc>
  <lastmod>2026-08-02T10:35:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ペアワイズ教師–生徒ネットワークによる半教師ありハッシング（Pairwise Teacher-Student Network for Semi-Supervised Hashing）</news:title>
   <news:publication_date>2026-08-02T10:35:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718674</loc>
  <lastmod>2026-08-02T10:35:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CodedPrivateML：分散学習でデータとモデルを同時に守る仕組み（CodedPrivateML: A Fast and Privacy-Preserving Framework for Distributed Machine Learning）</news:title>
   <news:publication_date>2026-08-02T10:35:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718672</loc>
  <lastmod>2026-08-02T10:35:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Particle Flow Bayes’ Rule（Particle Flow Bayes’ Rule）</news:title>
   <news:publication_date>2026-08-02T10:35:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718670</loc>
  <lastmod>2026-08-02T10:34:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時空間分解に基づく深層ニューラルネットワークによる時系列予測（A Spatial-Temporal Decomposition Based Deep Neural Network for Time Series Forecasting）</news:title>
   <news:publication_date>2026-08-02T10:34:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718668</loc>
  <lastmod>2026-08-02T10:34:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スライディングウィンドウでのAUC推定を効率化する方法（Efficient estimation of AUC in a sliding window）</news:title>
   <news:publication_date>2026-08-02T10:34:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718666</loc>
  <lastmod>2026-08-02T10:34:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチユーザ動画ストリーミングの物理層資源認識型深層強化学習アプローチ（Multiuser Video Streaming Rate Adaptation: A Physical Layer Resource-Aware Deep Reinforcement Learning Approach）</news:title>
   <news:publication_date>2026-08-02T10:34:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718664</loc>
  <lastmod>2026-08-02T10:33:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>定常時間での弱誤差解析によるSGDの拡張的理解（Uniform–in–Time Weak Error Analysis for Stochastic Gradient Descent Algorithms via Diffusion Approximation）</news:title>
   <news:publication_date>2026-08-02T10:33:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718662</loc>
  <lastmod>2026-08-02T09:43:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>バイアス付き確率近似の非漸近解析（Non-asymptotic Analysis of Biased Stochastic Approximation Scheme）</news:title>
   <news:publication_date>2026-08-02T09:43:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718660</loc>
  <lastmod>2026-08-02T09:42:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非パラメトリック曲線整列（Nonparametric Curve Alignment）</news:title>
   <news:publication_date>2026-08-02T09:42:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718658</loc>
  <lastmod>2026-08-02T09:42:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CQTを前処理に用いた単一音声分離の実証的検証（IS CQT MORE SUITABLE FOR MONAURAL SPEECH SEPARATION THAN STFT? AN EMPIRICAL STUDY）</news:title>
   <news:publication_date>2026-08-02T09:42:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718656</loc>
  <lastmod>2026-08-02T09:41:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ノイズ付き勾配法の一般化誤差境界（ON GENERALIZATION ERROR BOUNDS OF NOISY GRADIENT METHODS FOR NON-CONVEX LEARNING）</news:title>
   <news:publication_date>2026-08-02T09:41:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718654</loc>
  <lastmod>2026-08-02T09:41:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>クロスモーダル類似検索のための協調量子化（Collaborative Quantization for Cross-Modal Similarity Search）</news:title>
   <news:publication_date>2026-08-02T09:41:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718652</loc>
  <lastmod>2026-08-02T09:41:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>監督付き量子化による類似検索の精度向上（Supervised Quantization for Similarity Search）</news:title>
   <news:publication_date>2026-08-02T09:41:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718650</loc>
  <lastmod>2026-08-02T09:41:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非凸非凹ミニマックス最適化における局所最適性とは（What is Local Optimality in Nonconvex-Nonconcave Minimax Optimization?）</news:title>
   <news:publication_date>2026-08-02T09:41:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718648</loc>
  <lastmod>2026-08-02T08:49:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単語埋め込みの合成理解：テンソル分解による解析 (Understanding Composition of Word Embeddings via Tensor Decomposition)</news:title>
   <news:publication_date>2026-08-02T08:49:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718646</loc>
  <lastmod>2026-08-02T08:48:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>周回学習による自動レーシング車の軌道追従（Multiple-Lap Path Tracking for an Autonomous Race Vehicle via Iterative Learning Control）</news:title>
   <news:publication_date>2026-08-02T08:48:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718644</loc>
  <lastmod>2026-08-02T08:48:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>信頼度トリガ検出によるリアルタイム追跡の高速化（Confidence-Triggered Detection: Accelerating Real-time Tracking-by-detection Systems）</news:title>
   <news:publication_date>2026-08-02T08:48:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718642</loc>
  <lastmod>2026-08-02T08:47:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>摂動法の最適性（On the Optimality of Perturbations in Stochastic and Adversarial Multi-armed Bandit Problems）</news:title>
   <news:publication_date>2026-08-02T08:47:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718640</loc>
  <lastmod>2026-08-02T08:47:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自然な対面場面での視線（アイコンタクト）検出と児童評価への応用（Detecting Gaze Towards Eyes in Natural Social Interactions and Its Use in Child Assessment）</news:title>
   <news:publication_date>2026-08-02T08:47:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718638</loc>
  <lastmod>2026-08-02T08:47:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>離散確率モデルの効率的学習（Efficient Learning of Discrete Graphical Models）</news:title>
   <news:publication_date>2026-08-02T08:47:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718636</loc>
  <lastmod>2026-08-02T08:47:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ループネストの到達可能な性能に向けて (Towards an Achievable Performance for the Loop Nests)</news:title>
   <news:publication_date>2026-08-02T08:47:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718634</loc>
  <lastmod>2026-08-02T07:54:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所プライバシー下の推定に関する下限理論（Lower Bounds for Locally Private Estimation via Communication Complexity）</news:title>
   <news:publication_date>2026-08-02T07:54:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718632</loc>
  <lastmod>2026-08-02T07:54:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>原子間距離分布関数から空間群を推定する機械学習法（Using a machine learning approach to determine the space group of a structure from the atomic pair distribution function (PDF)）</news:title>
   <news:publication_date>2026-08-02T07:54:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718630</loc>
  <lastmod>2026-08-02T07:53:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習ベクトル量子化モデルの敵対的攻撃に対する堅牢性（Robustness of Generalized Learning Vector Quantization Models against Adversarial Attacks）</news:title>
   <news:publication_date>2026-08-02T07:53:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718628</loc>
  <lastmod>2026-08-02T07:53:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>電力系統の時間内不均衡の予測（Forecasting Intra-Hour Imbalances in Electric Power Systems）</news:title>
   <news:publication_date>2026-08-02T07:53:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718626</loc>
  <lastmod>2026-08-02T07:53:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>空間認識型機械学習による不動産予測の新潮流（The Spatially-Conscious Machine Learning Model）</news:title>
   <news:publication_date>2026-08-02T07:53:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718624</loc>
  <lastmod>2026-08-02T07:53:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Local Outlier Factorの自動ハイパーパラメータ調整法（Automatic Hyperparameter Tuning Method for Local Outlier Factor, with Applications to Anomaly Detection）</news:title>
   <news:publication_date>2026-08-02T07:53:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718622</loc>
  <lastmod>2026-08-02T07:52:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Atariゲームにおける深層強化学習の視覚的根拠表示（Visual Rationalizations in Deep Reinforcement Learning for Atari Games）</news:title>
   <news:publication_date>2026-08-02T07:52:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718620</loc>
  <lastmod>2026-08-02T07:01:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>浅層（Shallow）EDSLとオブジェクト指向の接点（Shallow EDSLs and Object-Oriented Programming）</news:title>
   <news:publication_date>2026-08-02T07:01:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718618</loc>
  <lastmod>2026-08-02T07:01:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>予算制約下のハイパーパラメータ調整（Hyper-parameter Tuning under a Budget Constraint）</news:title>
   <news:publication_date>2026-08-02T07:01:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718616</loc>
  <lastmod>2026-08-02T07:01:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Shieldの有効性評価：異なる脅威モデル下での検証（The Efficacy of Shield under Different Threat Models）</news:title>
   <news:publication_date>2026-08-02T07:01:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718614</loc>
  <lastmod>2026-08-02T07:00:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチビュー学習によるソフトウェア理解の応用（Applications of Multi-view Learning Approaches for Software Comprehension）</news:title>
   <news:publication_date>2026-08-02T07:00:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718612</loc>
  <lastmod>2026-08-02T07:00:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>顧客レビューにおけるジェンダーバイアスの検証（Examining the Presence of Gender Bias in Customer Reviews Using Word Embedding）</news:title>
   <news:publication_date>2026-08-02T07:00:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718610</loc>
  <lastmod>2026-08-02T07:00:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>競合的経験再生が変える希少報酬下の探索（COMPETITIVE EXPERIENCE REPLAY）</news:title>
   <news:publication_date>2026-08-02T07:00:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718608</loc>
  <lastmod>2026-08-02T07:00:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>映像のための微分可能文法（Differentiable Grammars for Videos）</news:title>
   <news:publication_date>2026-08-02T07:00:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718606</loc>
  <lastmod>2026-08-02T06:09:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>情報理論に基づく部分監視下の最小最大後悔（An Information-Theoretic Approach to Minimax Regret in Partial Monitoring）</news:title>
   <news:publication_date>2026-08-02T06:09:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718604</loc>
  <lastmod>2026-08-02T06:00:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DANTE：交互最小化にもとづくニューラルネットワーク訓練法（DANTE: Deep AlterNations for Training nEural networks）</news:title>
   <news:publication_date>2026-08-02T06:00:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718602</loc>
  <lastmod>2026-08-02T05:59:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>近似論理合成に強化学習を用いる技術マッピング（Approximate Logic Synthesis: A Reinforcement Learning-Based Technology Mapping Approach）</news:title>
   <news:publication_date>2026-08-02T05:59:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718600</loc>
  <lastmod>2026-08-02T05:58:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多段階モンテカルロ変分推論（Multilevel Monte Carlo Variational Inference）</news:title>
   <news:publication_date>2026-08-02T05:58:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718598</loc>
  <lastmod>2026-08-02T05:58:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>超音波散乱体の再構築のためのScatGAN（SCATGAN FOR RECONSTRUCTION OF ULTRASOUND SCATTERERS USING GENERATIVE ADVERSARIAL NETWORKS）</news:title>
   <news:publication_date>2026-08-02T05:58:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718596</loc>
  <lastmod>2026-08-02T05:58:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>人口希薄地域の公共意思決定支援：時空間犯罪予測のための不均衡対応ハイパーアンサンブル（Public decision support for low population density areas: An imbalance-aware hyper-ensemble for spatio-temporal crime prediction）</news:title>
   <news:publication_date>2026-08-02T05:58:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718594</loc>
  <lastmod>2026-08-02T05:58:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>TF-Replicator：研究者向け分散機械学習の実装抽象化（TF-Replicator: Distributed machine learning for researchers）</news:title>
   <news:publication_date>2026-08-02T05:58:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718592</loc>
  <lastmod>2026-08-02T05:06:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時系列デコンファウンダー（Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders）</news:title>
   <news:publication_date>2026-08-02T05:06:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718590</loc>
  <lastmod>2026-08-02T05:06:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>空間・時間・テキストを同時に扱う犯罪連関検出（Spatial-Temporal-Textual Point Processes for Crime Linkage Detection）</news:title>
   <news:publication_date>2026-08-02T05:06:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718588</loc>
  <lastmod>2026-08-02T05:05:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>組合せベイズ最適化とグラフ直積の活用（Combinatorial Bayesian Optimization using the Graph Cartesian Product）</news:title>
   <news:publication_date>2026-08-02T05:05:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718586</loc>
  <lastmod>2026-08-02T05:05:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>社会学習と政府学習を組み込む政策の事前評価の重要性（The Importance of Social and Government Learning in Ex Ante Policy Evaluation）</news:title>
   <news:publication_date>2026-08-02T05:05:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718584</loc>
  <lastmod>2026-08-02T05:05:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>一般化スライスド・ワッサースタイン距離（Generalized Sliced Wasserstein Distances）</news:title>
   <news:publication_date>2026-08-02T05:05:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718582</loc>
  <lastmod>2026-08-02T05:05:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CIFARのテストに訓練データが混入していた問題の是正（Do we train on test data? Purging CIFAR of near-duplicates）</news:title>
   <news:publication_date>2026-08-02T05:05:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718580</loc>
  <lastmod>2026-08-02T05:04:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>語彙分類学を用いた短文分類の解釈可能な特徴生成（tax2vec: Constructing Interpretable Features from Taxonomies for Short Text Classification）</news:title>
   <news:publication_date>2026-08-02T05:04:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718578</loc>
  <lastmod>2026-08-02T04:13:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>混合分布における正規化Wasserstein距離（Normalized Wasserstein for Mixture Distributions with Applications in Adversarial Learning and Domain Adaptation）</news:title>
   <news:publication_date>2026-08-02T04:13:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718576</loc>
  <lastmod>2026-08-02T04:13:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次の損失近似と特徴が深層学習解釈に与える影響（Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation）</news:title>
   <news:publication_date>2026-08-02T04:13:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718574</loc>
  <lastmod>2026-08-02T04:12:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ReLUネットワークに対するロバストネス証明の考え方（Robustness Certificates Against Adversarial Examples for ReLU Networks）</news:title>
   <news:publication_date>2026-08-02T04:12:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718572</loc>
  <lastmod>2026-08-02T04:12:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>圧縮動的MRIのスケーラブル学習ベースサンプリング最適化（Scalable Learning-Based Sampling Optimization for Compressive Dynamic MRI）</news:title>
   <news:publication_date>2026-08-02T04:12:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718570</loc>
  <lastmod>2026-08-02T04:12:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スパース合成正則化と深層ニューラルネットワーク（Sparse synthesis regularization with deep neural networks）</news:title>
   <news:publication_date>2026-08-02T04:12:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718568</loc>
  <lastmod>2026-08-02T04:11:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動的公平性——自動意思決定における悪循環の断ち切り（Dynamic fairness – Breaking vicious cycles in automatic decision making）</news:title>
   <news:publication_date>2026-08-02T04:11:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718566</loc>
  <lastmod>2026-08-02T04:11:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習可能な埋め込み空間による効率的ニューラルアーキテクチャ圧縮（LEARNABLE EMBEDDING SPACE FOR EFFICIENT NEURAL ARCHITECTURE COMPRESSION）</news:title>
   <news:publication_date>2026-08-02T04:11:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718564</loc>
  <lastmod>2026-08-02T03:20:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>アイコン外観の類似性学習（Learning Icons Appearance Similarity）</news:title>
   <news:publication_date>2026-08-02T03:20:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718562</loc>
  <lastmod>2026-08-02T03:20:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>エントロピーに基づくセンシング行列の学習（ENTROPY-BASED LEARNING OF SENSING MATRICES）</news:title>
   <news:publication_date>2026-08-02T03:20:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718560</loc>
  <lastmod>2026-08-02T03:20:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>木構造で拡張するWasserstein距離の新展開（Tree-Sliced Variants of Wasserstein Distances）</news:title>
   <news:publication_date>2026-08-02T03:20:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718558</loc>
  <lastmod>2026-08-02T03:19:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散確率的最適化と圧縮通信を用いたゴシップアルゴリズム（Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication）</news:title>
   <news:publication_date>2026-08-02T03:19:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718556</loc>
  <lastmod>2026-08-02T03:19:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>顔画像における敏感情報を除去する表現学習：SensitiveNetsの実践（SensitiveNets: Learning Agnostic Representations with Application to Face Images）</news:title>
   <news:publication_date>2026-08-02T03:19:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718554</loc>
  <lastmod>2026-08-02T03:19:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層強化学習による結合エンティティリンク（Joint Entity Linking with Deep Reinforcement Learning）</news:title>
   <news:publication_date>2026-08-02T03:19:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718552</loc>
  <lastmod>2026-08-02T03:19:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>視覚的に重要な関係性に再焦点を当てる（VrR-VG: Refocusing Visually-Relevant Relationships）</news:title>
   <news:publication_date>2026-08-02T03:19:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718550</loc>
  <lastmod>2026-08-02T02:27:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>離散分布の近接性検定における局所ミニマックス率（Local minimax rates for closeness testing of discrete distributions）</news:title>
   <news:publication_date>2026-08-02T02:27:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718548</loc>
  <lastmod>2026-08-02T02:27:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Generative Smoke Removal（Generative Smoke Removal）</news:title>
   <news:publication_date>2026-08-02T02:27:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718546</loc>
  <lastmod>2026-08-02T02:27:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層ハイパースペクトル・プライオリ（Deep Hyperspectral Prior: Single-Image Denoising, Inpainting, Super-Resolution）</news:title>
   <news:publication_date>2026-08-02T02:27:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718544</loc>
  <lastmod>2026-08-02T02:26:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>微分可能な最小二乗法を組み込んだエンドツーエンド車線検出（End-to-end Lane Detection through Differentiable Least-Squares Fitting）</news:title>
   <news:publication_date>2026-08-02T02:26:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718542</loc>
  <lastmod>2026-08-02T02:26:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深部大気層のパルス加熱が引き起こす振動と上向きショック――フレア再結合の準周期的変調（Pulse-beam heating of deep atmospheric layers triggering their oscillations and upwards moving shocks that can modulate the reconnection in solar flares）</news:title>
   <news:publication_date>2026-08-02T02:26:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718540</loc>
  <lastmod>2026-08-02T02:25:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Wasserstein空間上の流れとしてのMCMCダイナミクス（Understanding MCMC Dynamics as Flows on the Wasserstein Space）</news:title>
   <news:publication_date>2026-08-02T02:25:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718538</loc>
  <lastmod>2026-08-02T02:25:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>因果シミュレーションによるアップリフトモデリングの検証手法（Causal Simulations for Uplift Modeling）</news:title>
   <news:publication_date>2026-08-02T02:25:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718536</loc>
  <lastmod>2026-08-02T01:34:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Flow++によるフロー型生成モデルの改良（Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design）</news:title>
   <news:publication_date>2026-08-02T01:34:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718534</loc>
  <lastmod>2026-08-02T01:34:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>TanDEM-Xデータを用いた森林分類における深層学習の適用（DEEP LEARNING SOLUTIONS FOR TANDEM-X-BASED FOREST CLASSIFICATION）</news:title>
   <news:publication_date>2026-08-02T01:34:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718532</loc>
  <lastmod>2026-08-02T01:34:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>HSTによる深宇宙観測で偶然見つかった矮小楕円銀河の発見（Serendipitous discovery of a dwarf Galaxy in background）</news:title>
   <news:publication_date>2026-08-02T01:34:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718530</loc>
  <lastmod>2026-08-02T01:32:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>継続的強化学習における方策統合（Policy Consolidation for Continual Reinforcement Learning）</news:title>
   <news:publication_date>2026-08-02T01:32:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718528</loc>
  <lastmod>2026-08-02T01:32:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>瓦礫塊天体モデルにおけるバイスタティック全波レーダートモグラフィーの検出能力（Bistatic full-wave radar tomography detects deep interior voids, cracks and boulders in a rubble-pile asteroid model）</news:title>
   <news:publication_date>2026-08-02T01:32:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718526</loc>
  <lastmod>2026-08-02T01:32:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層ニューラルネットワークによる最適軌道推定の実務的インパクト（Deep Networks as Approximators of Optimal Transfers）</news:title>
   <news:publication_date>2026-08-02T01:32:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718524</loc>
  <lastmod>2026-08-02T01:32:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>インスタンスセグメンテーションを画像セグメンテーション注釈として (Instance Segmentation as Image Segmentation Annotation)</news:title>
   <news:publication_date>2026-08-02T01:32:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718522</loc>
  <lastmod>2026-08-02T00:39:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ProteinNetによるタンパク質構造機械学習の標準化（ProteinNet: a standardized data set for machine learning of protein structure）</news:title>
   <news:publication_date>2026-08-02T00:39:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718520</loc>
  <lastmod>2026-08-02T00:39:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非凸最適化におけるSGDの鋭い解析と鞍点脱出の実務的示唆（Sharp Analysis for Nonconvex SGD Escaping from Saddle Points）</news:title>
   <news:publication_date>2026-08-02T00:39:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718518</loc>
  <lastmod>2026-08-02T00:38:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>組合せ推薦の逐次評価と生成フレームワーク（Sequential Evaluation and Generation Framework for Combinatorial Recommender System）</news:title>
   <news:publication_date>2026-08-02T00:38:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718516</loc>
  <lastmod>2026-08-02T00:38:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>画像変換に対する不変性を用いた自然誤分類と敵対的誤分類の検出（Natural and Adversarial Error Detection using Invariance to Image Transformations）</news:title>
   <news:publication_date>2026-08-02T00:38:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718514</loc>
  <lastmod>2026-08-02T00:37:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>合体に伴う衝撃波のX線証拠：ZwCl 0008.8+5215のChandra/Suzaku観測 (Evidence for a merger induced shock wave in ZwCl 0008.8+5215 with Chandra and Suzaku)</news:title>
   <news:publication_date>2026-08-02T00:37:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718512</loc>
  <lastmod>2026-08-02T00:37:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>事前定義された均等分布クラス中心に基づく分類監視オートエンコーダ（A Classification Supervised Auto-Encoder Based on Predefined Evenly-Distributed Class Centroids）</news:title>
   <news:publication_date>2026-08-02T00:37:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718510</loc>
  <lastmod>2026-08-02T00:37:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>既知経路から混合時間を推定する方法（Estimating the Mixing Time of Ergodic Markov Chains）</news:title>
   <news:publication_date>2026-08-02T00:37:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718508</loc>
  <lastmod>2026-08-01T23:45:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>因果に基づく増分型マルチタッチアトリビューションとRNN（Causally Driven Incremental Multi Touch Attribution Using a Recurrent Neural Network）</news:title>
   <news:publication_date>2026-08-01T23:45:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718506</loc>
  <lastmod>2026-08-01T23:45:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>環境を操作する自己回帰モデルへの最適攻撃（Optimal Attack against Autoregressive Models by Manipulating the Environment）</news:title>
   <news:publication_date>2026-08-01T23:45:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718504</loc>
  <lastmod>2026-08-01T23:44:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチアームドバンディット問題とバッチUCB規則（MULTI-ARMED BANDIT PROBLEM AND BATCH UCB RULE）</news:title>
   <news:publication_date>2026-08-01T23:44:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718502</loc>
  <lastmod>2026-08-01T23:44:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>弱識別モデルに対する期待値最大化法の詳細解析（Sharp Analysis of Expectation-Maximization for Weakly Identifiable Models）</news:title>
   <news:publication_date>2026-08-01T23:44:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718500</loc>
  <lastmod>2026-08-01T23:44:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>行動表現を学習する強化学習（Learning Action Representations for Reinforcement Learning）</news:title>
   <news:publication_date>2026-08-01T23:44:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718498</loc>
  <lastmod>2026-08-01T23:43:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模多言語転移による固有表現認識（Massively Multilingual Transfer for NER）</news:title>
   <news:publication_date>2026-08-01T23:43:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718496</loc>
  <lastmod>2026-08-01T23:43:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>異なるドメイン間での単語埋め込み学習のための単純な正則化アルゴリズム（A Simple Regularization-based Algorithm for Learning Cross-Domain Word Embeddings）</news:title>
   <news:publication_date>2026-08-01T23:43:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718494</loc>
  <lastmod>2026-08-01T22:52:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文脈をめくる――視覚認識における空間と時間の文脈推論（Lift-the-flap: what, where and when for context reasoning）</news:title>
   <news:publication_date>2026-08-01T22:52:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718492</loc>
  <lastmod>2026-08-01T22:44:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>連続近似によるバイナリニューラルネットワークの臨界初期化（CRITICAL INITIALISATION IN CONTINUOUS APPROXIMATIONS OF BINARY NEURAL NETWORKS）</news:title>
   <news:publication_date>2026-08-01T22:44:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718490</loc>
  <lastmod>2026-08-01T22:44:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>勾配最適化器の圧縮手法と実務への示唆（Compressing Gradient Optimizers via Count-Sketches）</news:title>
   <news:publication_date>2026-08-01T22:44:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718488</loc>
  <lastmod>2026-08-01T22:44:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文書の日付推定にGCNを使う試み（Dating Documents using Graph Convolution Networks）</news:title>
   <news:publication_date>2026-08-01T22:44:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718486</loc>
  <lastmod>2026-08-01T22:42:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オフポリシー評価におけるプライバシー保護（Privacy Preserving Off-Policy Evaluation）</news:title>
   <news:publication_date>2026-08-01T22:42:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718484</loc>
  <lastmod>2026-08-01T22:42:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DREAM: 対話型読解の課題とモデル（DREAM: A Challenge Dataset and Models for Dialogue-Based Reading Comprehension）</news:title>
   <news:publication_date>2026-08-01T22:42:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718482</loc>
  <lastmod>2026-08-01T22:42:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Open KBの正規化を自動化するCESI（CESI: Canonicalizing Open Knowledge Bases using Embeddings and Side Information）</news:title>
   <news:publication_date>2026-08-01T22:42:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718480</loc>
  <lastmod>2026-08-01T21:50:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>普遍的太陽光発電予測器の提案（A Novel Universal Photovoltaic Energy Predictor）</news:title>
   <news:publication_date>2026-08-01T21:50:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718478</loc>
  <lastmod>2026-08-01T21:50:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>長文を一貫して生成するための多層潜在変数モデル（Towards Generating Long and Coherent Text with Multi-Level Latent Variable Models）</news:title>
   <news:publication_date>2026-08-01T21:50:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718476</loc>
  <lastmod>2026-08-01T21:50:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GANの圧縮と知識蒸留による実装可能性の提示（Compressing GANs using Knowledge Distillation）</news:title>
   <news:publication_date>2026-08-01T21:50:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718474</loc>
  <lastmod>2026-08-01T21:49:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>バイフィデリティデータ支援ニューラルネットワークによる非侵襲型還元モデル（BIFIDELITY DATA-ASSISTED NEURAL NETWORKS IN NONINTRUSIVE REDUCED-ORDER MODELING）</news:title>
   <news:publication_date>2026-08-01T21:49:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718472</loc>
  <lastmod>2026-08-01T21:49:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>コラボレーティブ・インテリジェンスに適した深層学習アーキテクチャ（Towards Collaborative Intelligence Friendly Architectures for Deep Learning）</news:title>
   <news:publication_date>2026-08-01T21:49:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718470</loc>
  <lastmod>2026-08-01T21:48:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層トリプレット量子化（Deep Triplet Quantization）</news:title>
   <news:publication_date>2026-08-01T21:48:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718468</loc>
  <lastmod>2026-08-01T21:48:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>排他ラッソモデルの高速解法：二重ニュートン型前処理付き近接点法（A dual Newton based preconditioned proximal point algorithm for exclusive lasso models）</news:title>
   <news:publication_date>2026-08-01T21:48:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718466</loc>
  <lastmod>2026-08-01T20:55:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>アグノスティック・フェデレーテッド・ラーニングの本質（Agnostic Federated Learning）</news:title>
   <news:publication_date>2026-08-01T20:55:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718464</loc>
  <lastmod>2026-08-01T20:54:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフ抵抗と対比較学習（Graph Resistance and Learning from Pairwise Comparisons）</news:title>
   <news:publication_date>2026-08-01T20:54:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718462</loc>
  <lastmod>2026-08-01T20:54:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>敵対的攻撃に対して理論的耐性を持つ新しいニューラルネットワーク族（A New Family of Neural Networks Provably Resistant to Adversarial Attacks）</news:title>
   <news:publication_date>2026-08-01T20:54:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718460</loc>
  <lastmod>2026-08-01T20:53:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>正常細胞とがん細胞の識別におけるオートエンコーダのノード重要度（Distinguishing between Normal and Cancer Cells Using Autoencoder Node Saliency）</news:title>
   <news:publication_date>2026-08-01T20:53:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718458</loc>
  <lastmod>2026-08-01T20:53:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>メモリ効率の高い適応最適化が変える訓練スピードとモデル規模（Memory-Efficient Adaptive Optimization）</news:title>
   <news:publication_date>2026-08-01T20:53:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718456</loc>
  <lastmod>2026-08-01T20:52:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習が分子モデリングとシミュレーションを変える（Advances of Machine Learning in Molecular Modeling and Simulation）</news:title>
   <news:publication_date>2026-08-01T20:52:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718454</loc>
  <lastmod>2026-08-01T20:52:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>降下法と局所最小解の実用的示唆（Passed &amp;amp; Spurious: Descent Algorithms and Local Minima in Spiked Matrix-Tensor Models）</news:title>
   <news:publication_date>2026-08-01T20:52:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718452</loc>
  <lastmod>2026-08-01T20:01:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Natural Analystsによる適応的データ解析の再定式化（Natural Analysts in Adaptive Data Analysis）</news:title>
   <news:publication_date>2026-08-01T20:01:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718450</loc>
  <lastmod>2026-08-01T20:01:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>どのFactorization Machineが優れているか：最適保証を伴う理論的答え（Which Factorization Machine Modeling is Better: A Theoretical Answer with Optimal Guarantee）</news:title>
   <news:publication_date>2026-08-01T20:01:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718448</loc>
  <lastmod>2026-08-01T20:00:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Top-k分類の一貫性に関する解析（On the Consistency of Top-k Surrogate Losses）</news:title>
   <news:publication_date>2026-08-01T20:00:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718446</loc>
  <lastmod>2026-08-01T19:59:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>生成的正規化フローのための新しい畳み込み（Emerging Convolutions for Generative Normalizing Flows）</news:title>
   <news:publication_date>2026-08-01T19:59:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718444</loc>
  <lastmod>2026-08-01T19:59:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>経験的性能をモデル化するためのサロゲートはどれが有効か（Which Surrogate Works for Empirical Performance Modelling? A Case Study with Differential Evolution）</news:title>
   <news:publication_date>2026-08-01T19:59:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718442</loc>
  <lastmod>2026-08-01T19:59:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ディープな顔特徴量で「美しさ」を定量化する視点（Understanding Beauty via Deep Facial Features）</news:title>
   <news:publication_date>2026-08-01T19:59:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718440</loc>
  <lastmod>2026-08-01T19:59:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>進化したTransformer（The Evolved Transformer）</news:title>
   <news:publication_date>2026-08-01T19:59:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718438</loc>
  <lastmod>2026-08-01T19:07:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>病理画像の類似検索ツール SMILY（Similar Image Search for Histopathology: SMILY）</news:title>
   <news:publication_date>2026-08-01T19:07:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718436</loc>
  <lastmod>2026-08-01T19:07:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>計算流体力学におけるデータ復元と深層イメージプライア（Data recovery in computational fluid dynamics through deep image priors）</news:title>
   <news:publication_date>2026-08-01T19:07:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718434</loc>
  <lastmod>2026-08-01T19:07:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自己内省型機械学習アーキテクチャにおける量子力学の出現（Emergent Quantum Mechanics in an Introspective Machine Learning Architecture）</news:title>
   <news:publication_date>2026-08-01T19:07:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718432</loc>
  <lastmod>2026-08-01T19:06:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>派生商品のポートフォリオ評価におけるガウス過程回帰の適用（Gaussian Process Regression for Derivative Portfolio Modeling and Application to CVA Computations）</news:title>
   <news:publication_date>2026-08-01T19:06:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718430</loc>
  <lastmod>2026-08-01T19:06:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>期待値ベース強化学習と分布ベース強化学習の比較分析（A Comparative Analysis of Expected and Distributional Reinforcement Learning）</news:title>
   <news:publication_date>2026-08-01T19:06:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718428</loc>
  <lastmod>2026-08-01T19:06:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>走査型電子顕微鏡における深層学習を用いた解像度向上（Resolution enhancement in scanning electron microscopy using deep learning）</news:title>
   <news:publication_date>2026-08-01T19:06:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718426</loc>
  <lastmod>2026-08-01T19:05:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DDSLによる幾何学信号の微分可能ラスタライズ（Deep Differentiable Simplex Layer for Learning Geometric Signals）</news:title>
   <news:publication_date>2026-08-01T19:05:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718424</loc>
  <lastmod>2026-08-01T18:13:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>STM/STSデータにおけるネマティック秩序の検出（Detecting nematic order in STM/STS data with artificial intelligence）</news:title>
   <news:publication_date>2026-08-01T18:13:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718422</loc>
  <lastmod>2026-08-01T18:06:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>コラーゲンVI関連筋ジストロフィーの自動診断のための畳み込みニューラルネットワーク（A Convolutional Neural Network for the Automatic Diagnosis of Collagen VI related Muscular Dystrophies）</news:title>
   <news:publication_date>2026-08-01T18:06:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718420</loc>
  <lastmod>2026-08-01T18:05:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>リアルタイム時間分解オゾン予測における深層畳み込みニューラルネットワークの適用（A real-time hourly ozone prediction system using deep convolutional neural network）</news:title>
   <news:publication_date>2026-08-01T18:05:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718418</loc>
  <lastmod>2026-08-01T18:05:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>道路工事現場の注視点の実世界マッピング（REAL-WORLD MAPPING OF GAZE FIXATIONS USING INSTANCE SEGMENTATION FOR ROAD CONSTRUCTION SAFETY APPLICATIONS）</news:title>
   <news:publication_date>2026-08-01T18:05:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718416</loc>
  <lastmod>2026-08-01T18:04:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Metric Gaussian Variational Inferenceの要点と経営への示唆（Metric Gaussian Variational Inference）</news:title>
   <news:publication_date>2026-08-01T18:04:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718414</loc>
  <lastmod>2026-08-01T18:04:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Wilderness Areaデータセットによるクラスタリング評価の再設計（The Wilderness Area Data Set: Adapting the Covertype data set for unsupervised learning）</news:title>
   <news:publication_date>2026-08-01T18:04:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718412</loc>
  <lastmod>2026-08-01T18:04:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>HyperGANによる多様で高性能なニューラルネットワーク生成（HyperGAN: A Generative Model for Diverse, Performant Neural Networks）</news:title>
   <news:publication_date>2026-08-01T18:04:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718410</loc>
  <lastmod>2026-08-01T17:12:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>探索困難問題に対する新たなアプローチ Go-Explore（Go-Explore: a New Approach for Hard-Exploration Problems）</news:title>
   <news:publication_date>2026-08-01T17:12:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718408</loc>
  <lastmod>2026-08-01T17:12:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スライス逆回帰による星の基本パラメータ推定（Sliced Inverse Regression: application to fundamental stellar parameters）</news:title>
   <news:publication_date>2026-08-01T17:12:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718406</loc>
  <lastmod>2026-08-01T17:11:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>限定された仮説集合からのロボット生態学的知覚のブートストラップ（Bootstrapping Robotic Ecological Perception from a Limited Set of Hypotheses Through Interactive Perception）</news:title>
   <news:publication_date>2026-08-01T17:11:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718404</loc>
  <lastmod>2026-08-01T17:11:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>QGPを可視化する電弱プローブの実験的総覧（Shining a Light on the QGP - Electroweak Probes Experimental Summary）</news:title>
   <news:publication_date>2026-08-01T17:11:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718402</loc>
  <lastmod>2026-08-01T17:11:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Successor Features と Generalised Policy Improvement による強化学習の転移（Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement）</news:title>
   <news:publication_date>2026-08-01T17:11:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718400</loc>
  <lastmod>2026-08-01T17:10:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>原理に基づく原子スケール特性の機械学習（Machine-learning of atomic-scale properties based on physical principles）</news:title>
   <news:publication_date>2026-08-01T17:10:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718398</loc>
  <lastmod>2026-08-01T16:19:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自己利益的な群衆を調整する仕組み（Coordinating the Crowd: Inducing Desirable Equilibria in Non-Cooperative Systems）</news:title>
   <news:publication_date>2026-08-01T16:19:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718396</loc>
  <lastmod>2026-08-01T16:12:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所学習のためのスパース結合を用いた直接フィードバックアライメント（Direct Feedback Alignment with Sparse Connections for Local Learning）</news:title>
   <news:publication_date>2026-08-01T16:12:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718394</loc>
  <lastmod>2026-08-01T16:12:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>長期時系列の欠損値を埋める非自己回帰型マルチ解像度補完（NAOMI: Non-Autoregressive Multiresolution Sequence Imputation）</news:title>
   <news:publication_date>2026-08-01T16:12:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718392</loc>
  <lastmod>2026-08-01T16:11:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>内部者脅威検知の分類器群 (Classifier Suites for Insider Threat Detection)</news:title>
   <news:publication_date>2026-08-01T16:11:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718390</loc>
  <lastmod>2026-08-01T16:10:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>フォグ・トゥ・シングス環境を保護するためのアンサンブル学習ベースの侵入検知システム（Securing Fog-to-Things Environment Using Intrusion Detection System Based On Ensemble Learning）</news:title>
   <news:publication_date>2026-08-01T16:10:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718388</loc>
  <lastmod>2026-08-01T16:10:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複雑な3D環境における古典的ナビゲーションと学習型ナビゲーションのベンチマーク（Benchmarking Classic and Learned Navigation in Complex 3D Environments）</news:title>
   <news:publication_date>2026-08-01T16:10:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718386</loc>
  <lastmod>2026-08-01T16:10:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>因果メカニズムの分離を学ぶためのメタ転移目的（A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms）</news:title>
   <news:publication_date>2026-08-01T16:10:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718384</loc>
  <lastmod>2026-08-01T15:18:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>情報ボトルネックによる転移と探索（INFOBOT: Transfer and Exploration via the Information Bottleneck）</news:title>
   <news:publication_date>2026-08-01T15:18:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718382</loc>
  <lastmod>2026-08-01T15:18:11Z</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が抽出する特徴の相関について（On Correlation of Features Extracted by Deep Neural Networks）</news:title>
   <news:publication_date>2026-08-01T15:18:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718380</loc>
  <lastmod>2026-08-01T15:18:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>準クローンネッカー積グラフィカルモデルの学習（Learning Quasi-Kronecker Product Graphical Models）</news:title>
   <news:publication_date>2026-08-01T15:18:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718378</loc>
  <lastmod>2026-08-01T15:16:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>二値重みパーセプトロンの計算的困難さと入力スパース性の利点（Understanding the computational difficulty of a binary-weight perceptron and the advantage of input sparseness）</news:title>
   <news:publication_date>2026-08-01T15:16:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718376</loc>
  <lastmod>2026-08-01T15:16:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>小さなハミング距離で生じる敵対的事例の単純な説明（A Simple Explanation for the Existence of Adversarial Examples with Small Hamming Distance）</news:title>
   <news:publication_date>2026-08-01T15:16:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718374</loc>
  <lastmod>2026-08-01T15:15:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多目的二値線形計画における射影学習（Learning to Project in Multi-Objective Binary Linear Programming）</news:title>
   <news:publication_date>2026-08-01T15:15:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718372</loc>
  <lastmod>2026-08-01T15:15:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>指数機構の長所と落とし穴：Hilbert空間と関数型PCAへの応用（Benefits and Pitfalls of the Exponential Mechanism with Applications to Hilbert Spaces and Functional PCA）</news:title>
   <news:publication_date>2026-08-01T15:15:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718370</loc>
  <lastmod>2026-08-01T14:23:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>視点不変の3次元人体姿勢推定（View Invariant 3D Human Pose Estimation）</news:title>
   <news:publication_date>2026-08-01T14:23:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718368</loc>
  <lastmod>2026-08-01T14:23:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ノイズに強い公平な分類（Noise-tolerant fair classification）</news:title>
   <news:publication_date>2026-08-01T14:23:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718366</loc>
  <lastmod>2026-08-01T14:22:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ディープニューラルネットワークが高次元偏微分方程式の次元の呪いを克服する証明（A proof that rectified deep neural networks overcome the curse of dimensionality in the numerical approximation of semilinear heat equations）</news:title>
   <news:publication_date>2026-08-01T14:22:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718364</loc>
  <lastmod>2026-08-01T14:21:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>UAVの深層強化学習による航行制御とMassive MIMO統合（Deep Reinforcement Learning for UAV Navigation Through Massive MIMO Technique）</news:title>
   <news:publication_date>2026-08-01T14:21:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718362</loc>
  <lastmod>2026-08-01T14:21:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的生成モデルによるメタマテリアルの逆設計（Probabilistic representation and inverse design of metamaterials based on a deep generative model with semi-supervised learning strategy）</news:title>
   <news:publication_date>2026-08-01T14:21:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718360</loc>
  <lastmod>2026-08-01T14:21:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>特徴の多様性でGANを安定化する手法（Diversity Regularized Adversarial Learning）</news:title>
   <news:publication_date>2026-08-01T14:21:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718358</loc>
  <lastmod>2026-08-01T14:20:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>回転不変畳み込み記述子による機械学習交換相関汎関数の設計と解析 (Design and Analysis of Machine Learning Exchange-Correlation Functionals via Rotationally Invariant Convolutional Descriptors)</news:title>
   <news:publication_date>2026-08-01T14:20:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718356</loc>
  <lastmod>2026-08-01T13:28:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>一般化テンソルモデルによる再帰型ニューラルネットワークの理論的拡張（Generalized Tensor Models for Recurrent Neural Networks）</news:title>
   <news:publication_date>2026-08-01T13:28:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718354</loc>
  <lastmod>2026-08-01T13:20:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>身体を持つロボットが語彙学習に与える効果（The effect of a physical robot on vocabulary learning）</news:title>
   <news:publication_date>2026-08-01T13:20:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718352</loc>
  <lastmod>2026-08-01T13:19:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>皮膚病変の自動判定を目指す深層学習の実践報告（Automated Skin Lesion Classification Using Ensemble of Deep Neural Networks）</news:title>
   <news:publication_date>2026-08-01T13:19:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718350</loc>
  <lastmod>2026-08-01T13:18:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>テンソライズド埋め込み層が変える語彙表現の圧縮（Tensorized Embedding Layers）</news:title>
   <news:publication_date>2026-08-01T13:18:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718348</loc>
  <lastmod>2026-08-01T13:18:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層アーキタイプ解析の概観（Deep Archetypal Analysis）</news:title>
   <news:publication_date>2026-08-01T13:18:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718346</loc>
  <lastmod>2026-08-01T13:18:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>P300信号検出における再帰型ニューラルネットワークの有用性（Recurrent Neural Networks for P300-based BCI）</news:title>
   <news:publication_date>2026-08-01T13:18:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718344</loc>
  <lastmod>2026-08-01T13:17:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>最小マージン最大化とブースティングの最適化（Optimal Minimal Margin Maximization with Boosting）</news:title>
   <news:publication_date>2026-08-01T13:17:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718342</loc>
  <lastmod>2026-08-01T12:25:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>不完全なフォワードシミュレータを伴うデータ同化のためのアンサンブルカーネル学習（Ensemble-based kernel learning for a class of data assimilation problems with imperfect forward simulators）</news:title>
   <news:publication_date>2026-08-01T12:25:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718340</loc>
  <lastmod>2026-08-01T12:25:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>識別的最小最大類似度/非類似度割当てに基づく非線形変換の共同学習によるクラスタリング（Clustering with Jointly Learned Nonlinear Transforms Over Discriminating Min-Max Similarity/Dissimilarity Assignment）</news:title>
   <news:publication_date>2026-08-01T12:25:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718338</loc>
  <lastmod>2026-08-01T12:25:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分布的に頑健な多目的非負値行列因子分解（Distributionally Robust and Multi-Objective Nonnegative Matrix Factorization）</news:title>
   <news:publication_date>2026-08-01T12:25:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718336</loc>
  <lastmod>2026-08-01T12:24:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多変量時系列のスケーラブルな表現学習（Unsupervised Scalable Representation Learning for Multivariate Time Series）</news:title>
   <news:publication_date>2026-08-01T12:24:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718334</loc>
  <lastmod>2026-08-01T12:23:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GeNetによるメタゲノム解析の再設計（GeNet: Deep Representations for Metagenomics）</news:title>
   <news:publication_date>2026-08-01T12:23:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718332</loc>
  <lastmod>2026-08-01T12:23:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>視覚ベースのステアリング角度推定（Autonomous Cars: Vision based Steering Wheel Angle Estimation）</news:title>
   <news:publication_date>2026-08-01T12:23:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718330</loc>
  <lastmod>2026-08-01T12:23:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>弱いアノテーションから学ぶ高速マッチングモデル（Learning Fast Matching Models from Weak Annotations）</news:title>
   <news:publication_date>2026-08-01T12:23:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718328</loc>
  <lastmod>2026-08-01T11:31:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局面評価関数を学習して探索精度を高める手法（Learning Position Evaluation Functions Used in Monte Carlo Softmax Search）</news:title>
   <news:publication_date>2026-08-01T11:31:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718326</loc>
  <lastmod>2026-08-01T11:31:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>格子型X線干渉計法における画像ドメインのモアレ除去法（Automatic image-domain Moiré artifact reduction method in grating-based x-ray interferometry imaging）</news:title>
   <news:publication_date>2026-08-01T11:31:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718324</loc>
  <lastmod>2026-08-01T11:30:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>PIRセンサーを用いたリアルタイム追跡の新手法（A new PIR-based method for real-time tracking）</news:title>
   <news:publication_date>2026-08-01T11:30:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718322</loc>
  <lastmod>2026-08-01T11:30:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オンラインのパンドラの箱とバンディット問題（Online Pandora’s Boxes and Bandits）</news:title>
   <news:publication_date>2026-08-01T11:30:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718320</loc>
  <lastmod>2026-08-01T11:29:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時系列の早期終端学習による作物種別のインシーズンマッピング（End-to-End Learned Early Classification of Time Series for In-Season Crop Type Mapping）</news:title>
   <news:publication_date>2026-08-01T11:29:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718318</loc>
  <lastmod>2026-08-01T11:29:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>外挿を伴う（確率的）勾配降下法の収束に関する考察（On the Convergence of (Stochastic) Gradient Descent with Extrapolation for Non-Convex Optimization）</news:title>
   <news:publication_date>2026-08-01T11:29:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718316</loc>
  <lastmod>2026-08-01T11:29:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率関数降下法が示す統一フレームワーク（Probability Functional Descent）</news:title>
   <news:publication_date>2026-08-01T11:29:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718314</loc>
  <lastmod>2026-08-01T10:37:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>二重スパースソフトマックス（Doubly Sparse: Sparse Mixture of Sparse Experts for Efficient Softmax Inference）</news:title>
   <news:publication_date>2026-08-01T10:37:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718312</loc>
  <lastmod>2026-08-01T10:37:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>一般化可能なアフォーダンス理解のための不変特徴写像（Invariant Feature Mappings for Generalizing Affordance Understanding Using Regularized Metric Learning）</news:title>
   <news:publication_date>2026-08-01T10:37:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718310</loc>
  <lastmod>2026-08-01T10:36:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>微細構造から応力-ひずみを予測する機械学習アプローチ（Predicting the mechanical response of oligocrystals with deep learning）</news:title>
   <news:publication_date>2026-08-01T10:36:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718308</loc>
  <lastmod>2026-08-01T10:36:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複雑モデル向けのドメイン差異指標の提案（Domain Discrepancy Measure for Complex Models in Unsupervised Domain Adaptation）</news:title>
   <news:publication_date>2026-08-01T10:36:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718306</loc>
  <lastmod>2026-08-01T10:36:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多クラス分類における拒否（リジェクション）の較正について（On the Calibration of Multiclass Classification with Rejection）</news:title>
   <news:publication_date>2026-08-01T10:36:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718304</loc>
  <lastmod>2026-08-01T10:36:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率分布学習におけるBregmanダイバージェンスの評価（Evaluating Bregman Divergences for Probability Learning from Crowd）</news:title>
   <news:publication_date>2026-08-01T10:36:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718302</loc>
  <lastmod>2026-08-01T10:35:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>特徴連結を用いたマルチビュー部分空間クラスタリング（Feature Concatenation Multi-view Subspace Clustering）</news:title>
   <news:publication_date>2026-08-01T10:35:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718300</loc>
  <lastmod>2026-08-01T09:44:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-01T09:44:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718298</loc>
  <lastmod>2026-08-01T09:43:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>位相回復モデルにおける支持集合復元の情報理論的限界（Support Recovery in the Phase Retrieval Model: Information-Theoretic Fundamental Limits）</news:title>
   <news:publication_date>2026-08-01T09:43:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718296</loc>
  <lastmod>2026-08-01T09:43:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>エッジでの計算オフロードに対するアクタークリティック強化学習（An Actor-Critic Reinforcement Learning Method for Computation Offloading with Delay Constraints in Mobile Edge Computing）</news:title>
   <news:publication_date>2026-08-01T09:43:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718294</loc>
  <lastmod>2026-08-01T09:42:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層的合成CNNによるスパースビューX線位相トモグラフィ再構成（Robust X-ray Sparse-view Phase Tomography via Hierarchical Synthesis Convolutional Neural Networks）</news:title>
   <news:publication_date>2026-08-01T09:42:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718292</loc>
  <lastmod>2026-08-01T09:42:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフを見て量子優位を予測する（Predicting quantum advantage by quantum walk with convolutional neural networks）</news:title>
   <news:publication_date>2026-08-01T09:42:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718290</loc>
  <lastmod>2026-08-01T09:41:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自動診断向けエンドツーエンド知識ルーティング関係対話システム (End-to-End Knowledge-Routed Relational Dialogue System for Automatic Diagnosis)</news:title>
   <news:publication_date>2026-08-01T09:41:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718288</loc>
  <lastmod>2026-08-01T09:41:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>連続空間における関数ノイズを用いた差分プライバシー対応Q学習（Privacy-preserving Q-Learning with Functional Noise in Continuous Spaces）</news:title>
   <news:publication_date>2026-08-01T09:41:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718286</loc>
  <lastmod>2026-08-01T08:50:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高齢者の対話システム利用実態と設計示唆（Evaluating Older Users’ Experiences with Commercial Dialogue Systems）</news:title>
   <news:publication_date>2026-08-01T08:50:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718284</loc>
  <lastmod>2026-08-01T08:50:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>信頼できるスマート道路標識の設計（Reliable Smart Road Signs）</news:title>
   <news:publication_date>2026-08-01T08:50:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718282</loc>
  <lastmod>2026-08-01T08:50:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ダイアディック変換による変分推論の強化（Enhanced Variational Inference with Dyadic Transformation）</news:title>
   <news:publication_date>2026-08-01T08:50:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718280</loc>
  <lastmod>2026-08-01T08:49:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>LiDAR 3D物体検出器の効率的学習のための能動学習（Deep Active Learning for Efficient Training of a LiDAR 3D Object Detector）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <lastmod>2026-08-01T08:49:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>バンディットにおける改善されたパス長後悔境界（Improved Path-length Regret Bounds for Bandits）</news:title>
   <news:publication_date>2026-08-01T08:49:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/718276</loc>
  <lastmod>2026-08-01T08:49:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>堅牢な深層マルチモーダルセンサ融合（Deep Multi-Modal Sensor Fusion using Fusion Weight Regularization and Target Learning）</news:title>
   <news:publication_date>2026-08-01T08:49:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/718274</loc>
  <lastmod>2026-08-01T08:49:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Twitter上の就業に関する発話コーパスの構築（Twitter Job/Employment Corpus: A Dataset of Job-Related Discourse Built with Humans in the Loop）</news:title>
   <news:publication_date>2026-08-01T08:49:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
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  <loc>https://aibr.jp/archives/718272</loc>
  <lastmod>2026-08-01T07:57:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>2Dでマルウェアファミリを可視化する手法（Throttling Malware Families in 2D）</news:title>
   <news:publication_date>2026-08-01T07:57:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718270</loc>
  <lastmod>2026-08-01T07:47:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層線形オートエンコーダの臨界点数値復元（Numerically Recovering the Critical Points of a Deep Linear Autoencoder）</news:title>
   <news:publication_date>2026-08-01T07:47:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/718268</loc>
  <lastmod>2026-08-01T07:47:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散オンライン学習：他者のデータから恩恵を得つつ自社データを共有しない方法（Decentralized Online Learning: Take Benefits from Others’ Data without Sharing Your Own to Track Global Trend）</news:title>
   <news:publication_date>2026-08-01T07:47:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
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 <url>
  <loc>https://aibr.jp/archives/718266</loc>
  <lastmod>2026-08-01T07:46:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>外れ値と欠損に頑健な時系列モデル（A Robust Time Series Model with Outliers and Missing Entries）</news:title>
   <news:publication_date>2026-08-01T07:46:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718264</loc>
  <lastmod>2026-08-01T07:46:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CN-Stream: 非線形海洋波を扱うオープンソースライブラリ（CN-Stream: Open-source library for nonlinear regular waves using stream function theory）</news:title>
   <news:publication_date>2026-08-01T07:46:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718262</loc>
  <lastmod>2026-08-01T07:45:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>組込み機器における深層推論の精度とエネルギーのトレードオフ（Trading-off Accuracy and Energy of Deep Inference on Embedded Systems: A Co-Design Approach）</news:title>
   <news:publication_date>2026-08-01T07:45:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718260</loc>
  <lastmod>2026-08-01T07:45:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>希少カテゴリを幾何学で見抜く――次元駆動統計による識別手法（Rare geometries: revealing rare categories via dimension-driven statistics）</news:title>
   <news:publication_date>2026-08-01T07:45:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718258</loc>
  <lastmod>2026-08-01T06:53:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非線形状態空間モデルのための確率勾配MCMC（Stochastic Gradient MCMC for Nonlinear State Space Models）</news:title>
   <news:publication_date>2026-08-01T06:53:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718256</loc>
  <lastmod>2026-08-01T06:53:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>反パターン検出のための機械学習ベースアンサンブル法（A Machine-learning Based Ensemble Method For Anti-patterns Detection）</news:title>
   <news:publication_date>2026-08-01T06:53:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718254</loc>
  <lastmod>2026-08-01T06:52:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>因果的異質性を用いた個別化施策選択（Personalized Treatment Selection using Causal Heterogeneity）</news:title>
   <news:publication_date>2026-08-01T06:52:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718252</loc>
  <lastmod>2026-08-01T06:51:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DNA/RNA配列結合特異性予測のための深層学習アーキテクチャ包括評価（Comprehensive Evaluation of Deep Learning Architectures for Prediction of DNA/RNA Sequence Binding Specificities）</news:title>
   <news:publication_date>2026-08-01T06:51:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718250</loc>
  <lastmod>2026-08-01T06:51:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>連続潜在空間で離散列を生成する新方向性（Latent Normalizing Flows for Discrete Sequences）</news:title>
   <news:publication_date>2026-08-01T06:51:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718248</loc>
  <lastmod>2026-08-01T06:51:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>数値解から厳密解へ：実数領域における線配置の新たな橋渡し（Exact Line Packings from Numerical Solutions）</news:title>
   <news:publication_date>2026-08-01T06:51:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718246</loc>
  <lastmod>2026-08-01T06:51:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スペクトルグラフフィルタの伝達可能性（On the Transferability of Spectral Graph Filters）</news:title>
   <news:publication_date>2026-08-01T06:51:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718244</loc>
  <lastmod>2026-08-01T05:59:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>部分集合サンプリングの再パラメータ化と連続緩和（Reparameterizable Subset Sampling via Continuous Relaxations）</news:title>
   <news:publication_date>2026-08-01T05:59:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718242</loc>
  <lastmod>2026-08-01T05:58:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>超球面プロトタイプネットワーク（Hyperspherical Prototype Networks）</news:title>
   <news:publication_date>2026-08-01T05:58:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718240</loc>
  <lastmod>2026-08-01T05:58:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時間的ネットワークにおけるスペクトル多重スケールコミュニティ検出（Spectral Multi-scale Community Detection in Temporal Networks with an Application）</news:title>
   <news:publication_date>2026-08-01T05:58:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718238</loc>
  <lastmod>2026-08-01T05:58:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時空間トレードオフ：衛星マルチスペクトル時系列における作物分類の最適化（TIME-SPACE TRADEOFF IN DEEP LEARNING MODELS FOR CROP CLASSIFICATION ON SATELLITE MULTI-SPECTRAL IMAGE TIME SERIES）</news:title>
   <news:publication_date>2026-08-01T05:58:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718236</loc>
  <lastmod>2026-08-01T05:58:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>車両追従における安全・効率・快適な速度制御（Safe, Efficient, and Comfortable Velocity Control based on Reinforcement Learning for Autonomous Driving）</news:title>
   <news:publication_date>2026-08-01T05:58:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718234</loc>
  <lastmod>2026-08-01T05:58:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>再学習なしでモデルを修復する（Repairing without Retraining: Avoiding Disparate Impact with Counterfactual Distributions）</news:title>
   <news:publication_date>2026-08-01T05:58:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718232</loc>
  <lastmod>2026-08-01T05:57:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ノイズ下の試験誤差が招く敵対的事例（Adversarial Examples Are a Natural Consequence of Test Error in Noise）</news:title>
   <news:publication_date>2026-08-01T05:57:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718230</loc>
  <lastmod>2026-08-01T05:06:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>連続行動空間の離散化がオンポリシー最適化を変える（Discretizing Continuous Action Space for On-Policy Optimization）</news:title>
   <news:publication_date>2026-08-01T05:06:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718228</loc>
  <lastmod>2026-08-01T05:05:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>訓練データ量がニューラル回答選択モデルに与える影響（Impact of Training Dataset Size on Neural Answer Selection Models）</news:title>
   <news:publication_date>2026-08-01T05:05:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718226</loc>
  <lastmod>2026-08-01T05:05:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>初の低光度クエーサーの発見とその意義（Discovery of the first low-luminosity quasar at z &amp;gt; 7）</news:title>
   <news:publication_date>2026-08-01T05:05:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718224</loc>
  <lastmod>2026-08-01T05:04:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オンライン広告オークションにおける差別制御への取り組み（Toward Controlling Discrimination in Online Ad Auctions）</news:title>
   <news:publication_date>2026-08-01T05:04:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718222</loc>
  <lastmod>2026-08-01T05:04:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SMUVSにおける受動・星形成銀河の星形成効率の違い（The SHMRs of passive and star-forming galaxies in SMUVS）</news:title>
   <news:publication_date>2026-08-01T05:04:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718220</loc>
  <lastmod>2026-08-01T05:04:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>公正な分類のための改良された敵対的学習（Improved Adversarial Learning for Fair Classification）</news:title>
   <news:publication_date>2026-08-01T05:04:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718218</loc>
  <lastmod>2026-08-01T05:03:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非同期バッチベイズ最適化と局所ペナルティの改良（Asynchronous Batch Bayesian Optimisation with Improved Local Penalisation）</news:title>
   <news:publication_date>2026-08-01T05:03:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718216</loc>
  <lastmod>2026-08-01T04:12:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>画像分類データセットに潜む冗長性の発見（Semantic Redundancies in Image-Classification Datasets: The 10% You Don’t Need）</news:title>
   <news:publication_date>2026-08-01T04:12:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718214</loc>
  <lastmod>2026-08-01T04:12:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>リハビリ運動の品質を自動評価する深層学習フレームワーク（A Deep Learning Framework for Assessing Physical Rehabilitation Exercises）</news:title>
   <news:publication_date>2026-08-01T04:12:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718212</loc>
  <lastmod>2026-08-01T04:12:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Deep学習時における虹彩セグメンテーションの影響（Influence of Segmentation on Deep Iris Recognition Performance）</news:title>
   <news:publication_date>2026-08-01T04:12:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718210</loc>
  <lastmod>2026-08-01T04:11:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>変分マッピング粒子フィルタにおけるカーネル埋め込み観測写像（Kernel embedded nonlinear observational mappings in the variational mapping particle filter）</news:title>
   <news:publication_date>2026-08-01T04:11:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718208</loc>
  <lastmod>2026-08-01T04:11:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>判別器の困難度を段階的に上げてGANを安定化する手法（Progressive Augmentation of GANs）</news:title>
   <news:publication_date>2026-08-01T04:11:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718206</loc>
  <lastmod>2026-08-01T04:11:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>幾何学的行列補完と深層条件付き確率場による構造化予測（Geometric Matrix Completion with Deep Conditional Random Fields）</news:title>
   <news:publication_date>2026-08-01T04:11:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/718204</loc>
  <lastmod>2026-08-01T04:10:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スライス生成モデルの実務的理解（Sliced generative models）</news:title>
   <news:publication_date>2026-08-01T04:10:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718202</loc>
  <lastmod>2026-08-01T03:19:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>MgNetが示した CNN と多重格子法の統一枠組み（MgNet: A Unified Framework of Multigrid and Convolutional Neural Network）</news:title>
   <news:publication_date>2026-08-01T03:19:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
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 <url>
  <loc>https://aibr.jp/archives/718200</loc>
  <lastmod>2026-08-01T03:19:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>重みの低ランク化が敵対的頑健性に与える影響（On the Effect of Low-Rank Weights on Adversarial Robustness of Neural Networks）</news:title>
   <news:publication_date>2026-08-01T03:19:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
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 <url>
  <loc>https://aibr.jp/archives/718198</loc>
  <lastmod>2026-08-01T03:18:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>線形時間で最適割当を求める手法とその応用（Computing Optimal Assignments in Linear Time for Approximate Graph Matching）</news:title>
   <news:publication_date>2026-08-01T03:18:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718196</loc>
  <lastmod>2026-08-01T03:18:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>短い発話に対する話者検証の品質測定（Quality Measures for Speaker Verification with Short Utterances）</news:title>
   <news:publication_date>2026-08-01T03:18:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718194</loc>
  <lastmod>2026-08-01T03:18:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>メモリ内演算を実現する超省電力アクセラレータの全体像（PUMA: A Programmable Ultra-efficient Memristor-based Accelerator for Machine Learning Inference）</news:title>
   <news:publication_date>2026-08-01T03:18:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718192</loc>
  <lastmod>2026-08-01T03:17:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的Frank‑Wolfe法による複合凸最適化の実用化（Stochastic Frank‑Wolfe for Composite Convex Minimization）</news:title>
   <news:publication_date>2026-08-01T03:17:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718190</loc>
  <lastmod>2026-08-01T03:17:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>予知保全による太陽光発電所の異常予測（PREDICTIVE MAINTENANCE IN PHOTOVOLTAIC PLANTS WITH A BIG DATA APPROACH）</news:title>
   <news:publication_date>2026-08-01T03:17:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718188</loc>
  <lastmod>2026-08-01T02:25:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ジェット内部の観察：ジェットサブストラクチャーとブースト対象の現象学入門 (Looking inside jets: an introduction to jet substructure and boosted-object phenomenology)</news:title>
   <news:publication_date>2026-08-01T02:25:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718186</loc>
  <lastmod>2026-08-01T02:24:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>流れの多重スケール問題に対する低次元深層学習（Reduced-order Deep Learning for Flow Dynamics）</news:title>
   <news:publication_date>2026-08-01T02:24:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718184</loc>
  <lastmod>2026-08-01T02:23:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>実戦でのアクティブラーニング性能評価の限界（Limitations of Assessing Active Learning Performance at Runtime）</news:title>
   <news:publication_date>2026-08-01T02:23:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718182</loc>
  <lastmod>2026-08-01T02:22:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>超新星残骸 SN 1987A における衝突なし衝撃波での重イオン加熱（Collisionless shock heating of heavy ions in SN 1987A）</news:title>
   <news:publication_date>2026-08-01T02:22:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718180</loc>
  <lastmod>2026-08-01T02:22:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スパース回帰のランク1凸包化（Rank-One Convexification for Sparse Regression）</news:title>
   <news:publication_date>2026-08-01T02:22:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718178</loc>
  <lastmod>2026-08-01T02:22:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>信頼領域に導かれたPPO（Trust Region-Guided Proximal Policy Optimization）</news:title>
   <news:publication_date>2026-08-01T02:22:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718176</loc>
  <lastmod>2026-08-01T02:22:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>リアルタイム手指ジェスチャー検出と分類（Real-time Hand Gesture Detection and Classification Using Convolutional Neural Networks）</news:title>
   <news:publication_date>2026-08-01T02:22:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718174</loc>
  <lastmod>2026-08-01T01:31:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>リカレントニューラルネットワークのサンプル複雑度と組合せグラフ問題への応用（Sample Complexity Bounds for Recurrent Neural Networks with Application to Combinatorial Graph Problems）</news:title>
   <news:publication_date>2026-08-01T01:31:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718172</loc>
  <lastmod>2026-08-01T01:21:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>信頼できない複数ソースからの頑健学習（Robust Learning from Untrusted Sources）</news:title>
   <news:publication_date>2026-08-01T01:21:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718170</loc>
  <lastmod>2026-08-01T01:21:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>プライバシー保護された個人化推薦のための連合協調フィルタリング（FEDERATED COLLABORATIVE FILTERING FOR PRIVACY-PRESERVING PERSONALIZED RECOMMENDATION SYSTEM）</news:title>
   <news:publication_date>2026-08-01T01:21:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718168</loc>
  <lastmod>2026-08-01T01:21:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GANのユニット解析（On the Units of GANs）</news:title>
   <news:publication_date>2026-08-01T01:21:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718166</loc>
  <lastmod>2026-08-01T01:20:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高品質な自己教師あり深層画像ノイズ除去（High-Quality Self-Supervised Deep Image Denoising）</news:title>
   <news:publication_date>2026-08-01T01:20:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718164</loc>
  <lastmod>2026-08-01T01:20:25Z</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を用いた構造材料特性の最適設計（Structural Material Property Tailoring Using Deep Neural Networks）</news:title>
   <news:publication_date>2026-08-01T01:20:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718162</loc>
  <lastmod>2026-08-01T01:20:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ホット媒質における相転移と中間子・核子構造から学べること（What could be learned about phase transitions, meson and nucleon structure in hot medium from a chiral quark-meson theory?）</news:title>
   <news:publication_date>2026-08-01T01:20:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718160</loc>
  <lastmod>2026-08-01T00:29:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文脈依存の選択関数を学ぶ（Learning Context-Dependent Choice Functions）</news:title>
   <news:publication_date>2026-08-01T00:29:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718158</loc>
  <lastmod>2026-08-01T00:28:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>画像要約における暗黙の多様性（Implicit Diversity in Image Summarization）</news:title>
   <news:publication_date>2026-08-01T00:28:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718156</loc>
  <lastmod>2026-08-01T00:21:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>差分プライベートなMarkov Chain Monte Carloの一般化（Differentially Private Markov Chain Monte Carlo）</news:title>
   <news:publication_date>2026-08-01T00:21:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718154</loc>
  <lastmod>2026-08-01T00:20:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>結合的トラクト分割と方向マッピングによる束特異的トラクトグラフィー（Combined tract segmentation and orientation mapping for bundle-specific tractography）</news:title>
   <news:publication_date>2026-08-01T00:20:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718152</loc>
  <lastmod>2026-08-01T00:19:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューラルネットワークによる関数の近似（Approximation of Functions by Neural Networks）</news:title>
   <news:publication_date>2026-08-01T00:19:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718150</loc>
  <lastmod>2026-08-01T00:18:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>リソース効率的な判定ベース不可視攻撃（RED-Attack: Resource Efficient Decision-based Imperceptible Attack for Machine Learning）</news:title>
   <news:publication_date>2026-08-01T00:18:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718148</loc>
  <lastmod>2026-08-01T00:18:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オンライン学習によるランキング最適化（Optimizing Ranking Models in an Online Setting）</news:title>
   <news:publication_date>2026-08-01T00:18:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718146</loc>
  <lastmod>2026-07-31T23:26:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチモデル・マルチタイプフィッティングの学習（Learning for Multi-Model and Multi-Type Fitting）</news:title>
   <news:publication_date>2026-07-31T23:26:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718144</loc>
  <lastmod>2026-07-31T23:25:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オンライン配車サービスの短期需要予測とリカレントニューラルネットワーク（Short-Term Demand Forecasting for Online Car-Hailing Services Using Recurrent Neural Networks）</news:title>
   <news:publication_date>2026-07-31T23:25:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718142</loc>
  <lastmod>2026-07-31T23:25:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>対角-巡回（Diagonal-Circulant）ニューラルネットワークの理解と訓練（Understanding and Training Deep Diagonal Circulant Neural Networks）</news:title>
   <news:publication_date>2026-07-31T23:25:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718140</loc>
  <lastmod>2026-07-31T23:24:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>音声に視覚スタイル転送を適用する試み（Applying Visual Domain Style Transfer and Texture Synthesis Techniques to Audio - Insights and Challenges）</news:title>
   <news:publication_date>2026-07-31T23:24:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718138</loc>
  <lastmod>2026-07-31T23:24:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>メッセージのスタンス分類に対する半教師ありグラフ手法（Semi-supervised Graph-based Stance Classification）</news:title>
   <news:publication_date>2026-07-31T23:24:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718136</loc>
  <lastmod>2026-07-31T23:24:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多段階生成モデルによるマルチエージェントモデルベース強化学習（Multi-Agent Reinforcement Learning with Multi-Step Generative Models）</news:title>
   <news:publication_date>2026-07-31T23:24:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718134</loc>
  <lastmod>2026-07-31T23:24:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>明示的な位相事前知識を用いた深層学習ベースの画像セグメンテーション（Explicit topological priors for deep-learning based image segmentation using persistent homology）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718132</loc>
  <lastmod>2026-07-31T22:32:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>異種情報ネットワークの表現学習をイベント埋め込みで改善する（Representation Learning for Heterogeneous Information Networks via Embedding Events）</news:title>
   <news:publication_date>2026-07-31T22:32:33Z</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>
   </news:publication>
   <news:title>全身CTを用いた骨年齢推定の深層階層特徴学習（Automatic Whole-body Bone Age Assessment Using Deep Hierarchical Features）</news:title>
   <news:publication_date>2026-07-31T22:32:19Z</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>2D切片からの3次元多孔質メディア再構築（Reconstruction of 3D Porous Media From 2D Slices）</news:title>
   <news:publication_date>2026-07-31T22:32:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-07-31T22:31:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718124</loc>
  <lastmod>2026-07-31T22:31:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチカーネル活性化関数の定式化と事例研究（Multikernel activation functions: formulation and a case study）</news:title>
   <news:publication_date>2026-07-31T22:31:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718122</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>部分的に可換なネットワークと近似ベイズ計算における要約統計量学習（Partially Exchangeable Networks and Architectures for Learning Summary Statistics in Approximate Bayesian Computation）</news:title>
   <news:publication_date>2026-07-31T22:30:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-07-31T22:30:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>MRI画像からのアルツハイマー病検出 ― 転移学習とBellCNNの比較（Detection of Alzheimers Disease from MRI using Convolutional Neural Networks, Exploring Transfer Learning And BellCNN）</news:title>
   <news:publication_date>2026-07-31T22:30:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-07-31T21:39:23Z</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>
   </news:publication>
   <news:title>プッシュプル層によるCNNの頑健性向上（A Push-Pull Layer Improves Robustness of Convolutional Neural Networks）</news:title>
   <news:publication_date>2026-07-31T21:39:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718114</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>catch22による時系列特徴量の簡潔化（catch22: CAnonical Time-series CHaracteristics）</news:title>
   <news:publication_date>2026-07-31T21:38:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718112</loc>
  <lastmod>2026-07-31T21:37:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-07-31T21:37:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718110</loc>
  <lastmod>2026-07-31T21:37:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複雑な文の自動生成（Divide and Generate: Neural Generation of Complex Sentences）</news:title>
   <news:publication_date>2026-07-31T21:37:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718108</loc>
  <lastmod>2026-07-31T21:37:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Deep500が切り開く大規模ディープラーニングの公平で再現可能な評価基盤（A Modular Benchmarking Infrastructure for High-Performance and Reproducible Deep Learning）</news:title>
   <news:publication_date>2026-07-31T21:37:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718106</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>アーティキュレーテッドソフトロボットアームの高速精密位置追従のための反復学習制御（Iterative Learning Control for Fast and Accurate Position Tracking with an Articulated Soft Robotic Arm）</news:title>
   <news:publication_date>2026-07-31T21:36:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718104</loc>
  <lastmod>2026-07-31T20:45:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層非対称距離埋め込みによる教師なし人物再識別（Unsupervised Person Re-identification by Deep Asymmetric Metric Embedding）</news:title>
   <news:publication_date>2026-07-31T20:45:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718102</loc>
  <lastmod>2026-07-31T20:45:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>射出成形の熱画像から最終形状を予測するGAN活用（Generative Adversarial Networks for geometric surfaces prediction in injection molding: Performance analysis with Discrete Modal Decomposition）</news:title>
   <news:publication_date>2026-07-31T20:45:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718100</loc>
  <lastmod>2026-07-31T20:45:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>J-PARC Muon g-2/EDM 実験向けシリコンストリップ検出器用フロントエンドASICの試作（Prototype Front-end ASIC for Silicon-strip Detectors）</news:title>
   <news:publication_date>2026-07-31T20:45:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718098</loc>
  <lastmod>2026-07-31T20:44:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ベイズ不均衡影響指標（Bayes Imbalance Impact Index）</news:title>
   <news:publication_date>2026-07-31T20:44:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718096</loc>
  <lastmod>2026-07-31T20:44:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ファッション認識のための二流マルチタスクネットワーク（TWO-STREAM MULTI-TASK NETWORK FOR FASHION RECOGNITION）</news:title>
   <news:publication_date>2026-07-31T20:44:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718094</loc>
  <lastmod>2026-07-31T20:44:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>微粒度な人間の移動予測と端末内文脈データの体系的分析（A Systematic Analysis of Fine-Grained Human Mobility Prediction with On-Device Contextual Data）</news:title>
   <news:publication_date>2026-07-31T20:44:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718092</loc>
  <lastmod>2026-07-31T20:44:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深い電波天の詳細：3-GHz VLA-COSMOS における多成分電波源の解析（A closer look at the deep radio sky: Multi-component radio sources at 3-GHz VLA-COSMOS）</news:title>
   <news:publication_date>2026-07-31T20:44:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718090</loc>
  <lastmod>2026-07-31T19:53:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ヘッセ行列スペクトルによるニューラルネット最適化の考察（An Investigation into Neural Net Optimization via Hessian Eigenvalue Density）</news:title>
   <news:publication_date>2026-07-31T19:53:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718088</loc>
  <lastmod>2026-07-31T19:52:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>未ラベルデータから正例を取り出す新しいPU学習（Revisiting Sample Selection Approach to Positive-Unlabeled Learning: Turning Unlabeled Data into Positive rather than Negative）</news:title>
   <news:publication_date>2026-07-31T19:52:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718086</loc>
  <lastmod>2026-07-31T19:52:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>木星の金属水素領域における非圧縮トーションアルヴェン振動（Anelastic torsional oscillations in Jupiter’s metallic hydrogen region）</news:title>
   <news:publication_date>2026-07-31T19:52:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718084</loc>
  <lastmod>2026-07-31T19:51:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>柔軟物体の動的制御を深層学習で実現する方法（Dynamic Manipulation of Flexible Objects with Torque Sequence Using a Deep Neural Network）</news:title>
   <news:publication_date>2026-07-31T19:51:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718082</loc>
  <lastmod>2026-07-31T19:51:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GANを用いたVSRのゼロショット学習（Harnessing GANs for Zero-Shot Learning of New Classes in Visual Speech Recognition）</news:title>
   <news:publication_date>2026-07-31T19:51:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-07-31T19:51:27Z</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>2026-07-31T19:51:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718078</loc>
  <lastmod>2026-07-31T19:51:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>検出したランドマークの品質を学習で検証する手法（Learning to Validate the Quality of Detected Landmarks）</news:title>
   <news:publication_date>2026-07-31T19:51:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718076</loc>
  <lastmod>2026-07-31T18:59:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単眼深度推定のための注意に基づく文脈集約ネットワーク（Attention-based Context Aggregation Network for Monocular Depth Estimation）</news:title>
   <news:publication_date>2026-07-31T18:59:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-07-31T18:59:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718072</loc>
  <lastmod>2026-07-31T18:59:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718070</loc>
  <lastmod>2026-07-31T18:58:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>画像分類におけるCNNとカプセルネットワークの汎化評価（Evaluating Generalization Ability of Convolutional Neural Networks and Capsule Networks for Image Classification via Top-2 Classification）</news:title>
   <news:publication_date>2026-07-31T18:58:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718068</loc>
  <lastmod>2026-07-31T18:58:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-07-31T18:58:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <lastmod>2026-07-31T18:58:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>DEEP-DUST: ソウルの微粒子濃度予測にLSTMを用いる（DEEP-DUST: PREDICTING CONCENTRATIONS OF FINE DUST IN SEOUL）</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>
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  <loc>https://aibr.jp/archives/718062</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>スパース最小二乗ローレンクカーネル機械（Sparse Least Squares Low Rank Kernel Machines）</news:title>
   <news:publication_date>2026-07-31T18:06:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/718060</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>相対局所距離に基づく人物構造の発見（Discovering Underlying Person Structure Pattern with Relative Local Distance）</news:title>
   <news:publication_date>2026-07-31T18:06:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/718058</loc>
  <lastmod>2026-07-31T18:05:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフラプラシアン回帰器の一様収束に関する最大原理の議論（A Maximum Principle Argument for the Uniform Convergence of Graph Laplacian Regressors）</news:title>
   <news:publication_date>2026-07-31T18:05:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/718056</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>メトリック制約最適化の並列射影法（A Parallel Projection Method for Metric Constrained Optimization）</news:title>
   <news:publication_date>2026-07-31T18:05:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/718054</loc>
  <lastmod>2026-07-31T18:04:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所エントロピーとヒート正則化の変分的解析（VARIATIONAL CHARACTERIZATIONS OF LOCAL ENTROPY AND HEAT REGULARIZATION IN DEEP LEARNING）</news:title>
   <news:publication_date>2026-07-31T18:04:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718052</loc>
  <lastmod>2026-07-31T18:04:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>変数選択を伴う二値分類の能動学習（Active learning for binary classification with variable selection）</news:title>
   <news:publication_date>2026-07-31T18:04:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718050</loc>
  <lastmod>2026-07-31T18:04:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>一般化された公平性を組み込む学習法の提案（General Fair Empirical Risk Minimization）</news:title>
   <news:publication_date>2026-07-31T18:04:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718048</loc>
  <lastmod>2026-07-31T17:12:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Schatten–von Neumann 演算子の学習可能性（Learning Schatten–von Neumann Operators）</news:title>
   <news:publication_date>2026-07-31T17:12:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718046</loc>
  <lastmod>2026-07-31T17:12:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Cloud-NetによるLandsat 8画像の雲検出（CLOUD-NET: AN END-TO-END CLOUD DETECTION ALGORITHM FOR LANDSAT 8 IMAGERY）</news:title>
   <news:publication_date>2026-07-31T17:12:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
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 <url>
  <loc>https://aibr.jp/archives/718044</loc>
  <lastmod>2026-07-31T17:11:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CaRENetsによる暗号化医用画像の効率的同報推論（CaRENets: Compact and Resource-Efficient CNN for Homomorphic Inference on Encrypted Medical Images）</news:title>
   <news:publication_date>2026-07-31T17:11:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718042</loc>
  <lastmod>2026-07-31T17:11:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>画像と疎なレンジから推定する密な深度の確率分布（Dense Depth Posterior from Single Image and Sparse Range）</news:title>
   <news:publication_date>2026-07-31T17:11:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718040</loc>
  <lastmod>2026-07-31T17:11:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>適応的サンプリングによる条件付き設計（Conditioning by adaptive sampling for robust design）</news:title>
   <news:publication_date>2026-07-31T17:11:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718038</loc>
  <lastmod>2026-07-31T17:10:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多値の保護属性を考慮した公正な深層クラスタリング（Towards Fair Deep Clustering With Multi-State Protected Variables）</news:title>
   <news:publication_date>2026-07-31T17:10:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718036</loc>
  <lastmod>2026-07-31T17:10:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層制約クラスタリングの枠組み（A Framework for Deep Constrained Clustering - Algorithms and Advances）</news:title>
   <news:publication_date>2026-07-31T17:10:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718034</loc>
  <lastmod>2026-07-31T16:19:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>異種ネットワークにおけるモチーフ解析の一般化（Heterogeneous Network Motifs）</news:title>
   <news:publication_date>2026-07-31T16:19:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718032</loc>
  <lastmod>2026-07-31T16:18:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Lyapunovに基づく連続制御の安全な方策最適化（Lyapunov-based Safe Policy Optimization for Continuous Control）</news:title>
   <news:publication_date>2026-07-31T16:18:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718030</loc>
  <lastmod>2026-07-31T16:17:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>デモによるクロスドメイン画像操作（Cross-Domain Image Manipulation by Demonstration）</news:title>
   <news:publication_date>2026-07-31T16:17:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718028</loc>
  <lastmod>2026-07-31T16:17:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GPU上での推論を効率化するOoO VLIW JITコンパイラ（The OoO VLIW JIT Compiler for GPU Inference）</news:title>
   <news:publication_date>2026-07-31T16:17:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718026</loc>
  <lastmod>2026-07-31T16:17:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習ライフサイクルにおける被害源のフレームワーク（A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle）</news:title>
   <news:publication_date>2026-07-31T16:17:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718024</loc>
  <lastmod>2026-07-31T16:16:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>共感的で社会的に優雅な運転に向けた相互作用モデルと運動計画（How Shall I Drive? Interaction Modeling and Motion Planning towards Empathetic and Socially-Graceful Driving）</news:title>
   <news:publication_date>2026-07-31T16:16:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718022</loc>
  <lastmod>2026-07-31T16:16:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Fogネットワークの強化学習による負荷分散管理（Managing Fog Networks using Reinforcement Learning Based Load Balancing Algorithm）</news:title>
   <news:publication_date>2026-07-31T16:16:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718020</loc>
  <lastmod>2026-07-31T15:23:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>過去を忘れて局所を繰り返す準ニュートン法（Quasi-Newton Methods for Machine Learning: Forget the Past, Just Sample）</news:title>
   <news:publication_date>2026-07-31T15:23:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718018</loc>
  <lastmod>2026-07-31T15:23:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフフィルタリングによるラベル効率な半教師あり学習（Label Efficient Semi-Supervised Learning via Graph Filtering）</news:title>
   <news:publication_date>2026-07-31T15:23:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718016</loc>
  <lastmod>2026-07-31T15:23:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>表面波乱流における浮遊体の統計解析（Statistics of single and multiple floaters in experiments of surface wave turbulence）</news:title>
   <news:publication_date>2026-07-31T15:23:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718014</loc>
  <lastmod>2026-07-31T15:22:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ジェンダー差別的ツイートに込められた感情を読む（How is Your Mood When Writing Sexist tweets? Detecting the Emotion Type and Intensity of Emotion Using Natural Language Processing Techniques）</news:title>
   <news:publication_date>2026-07-31T15:22:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718012</loc>
  <lastmod>2026-07-31T15:22:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多様性訓練によるアンサンブルの対敵的堅牢性向上（Improving Adversarial Robustness of Ensembles with Diversity Training）</news:title>
   <news:publication_date>2026-07-31T15:22:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718010</loc>
  <lastmod>2026-07-31T15:22:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>心拍異常検出における敵対的オーバーサンプリング（Heartbeat Anomaly Detection using Adversarial Oversampling）</news:title>
   <news:publication_date>2026-07-31T15:22:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718008</loc>
  <lastmod>2026-07-31T15:22:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ブラックボックス選択後の推論（Inference after Black Box Selection）</news:title>
   <news:publication_date>2026-07-31T15:22:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
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