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   <news:title>デバイス別・時空間粒度で見る人間行動予測の実用化（Practical Prediction of Human Movements Across Device Types and Spatiotemporal Granularities）</news:title>
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   <news:title>ニューラルネットワークによる条件付き確率密度推定の実務指針（Conditional Density Estimation with Neural Networks: Best Practices and Benchmarks）</news:title>
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   <news:title>防御の期待効用を直接最大化する学習手法（End-to-End Game-Focused Learning of Adversary Behavior in Security Games）</news:title>
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    <news:language>ja</news:language>
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   <news:title>自然保護区におけるルリノドヒタキの個体数減少の謎を可視化で解く（Addressing The Mystery of Population Decline of The Rose-Crested Blue Pipit In A Nature Preserve Using Data Visualization）</news:title>
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    <news:language>ja</news:language>
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   <news:title>Origins Space Telescopeの遠赤外分光サーベイの予測（Origins Space Telescope: predictions for far-IR spectroscopic surveys）</news:title>
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   <news:genres>Blog</news:genres>
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    <news:name>AI Benchmark Research</news:name>
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   <news:title>構造的教師ありによる非局所文法依存関係の学習改善（Structural Supervision Improves Learning of Non-Local Grammatical Dependencies）</news:title>
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   <news:title>タイのClassStartを用いたオンライン学習受容の要因分析（Investigating factors affecting learner&amp;#039;s perception toward online learning: Evidence from ClassStart Application in Thailand）</news:title>
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    <news:language>ja</news:language>
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   <news:title>ベルヌーイ・レース粒子フィルタ（Bernoulli Race Particle Filters）</news:title>
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    <news:language>ja</news:language>
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   <news:title>標準化損失による深層ニューラルネットワーク学習の高速化（Accelerating Training of Deep Neural Networks with a Standardization Loss）</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>並列コーパスからの転移学習による中国語での認知症検出（Detecting Dementia in Mandarin Chinese using Transfer Learning from a Parallel Corpus）</news:title>
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   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/722680</loc>
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    <news:language>ja</news:language>
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   <news:title>3D時空間グラフ畳み込みによる交通予測の新枠組み（3D Graph Convolutional Networks with Temporal Graphs: A Spatial Information Free Framework For Traffic Forecasting）</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>一枚の静止画からの双方向フローに基づく教師なし動画生成（Unsupervised Bi-directional Flow-based Video Generation from one Snapshot）</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>リカレントニューラルネットワークにおける特徴選択と特徴記憶の理解（Understanding Feature Selection and Feature Memorization in Recurrent Neural Networks）</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>手書きストロークの角点・接線点検出を堅牢にする深層学習手法（Robust corner and tangent point detection for strokes with deep learning approach）</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>MILDNet: 軽量単一スケール深層ランキングアーキテクチャ（MILDNet: A Lightweight Single Scaled Deep Ranking Architecture）</news:title>
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   <news:genres>Blog</news:genres>
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  <lastmod>2026-08-13T08:24:18Z</lastmod>
  <news:news>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>競争的ブリッジ入札における深層ニューラルネットワーク（Competitive Bridge Bidding with Deep Neural Networks）</news:title>
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   <news:genres>Blog</news:genres>
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  <lastmod>2026-08-13T08:23:57Z</lastmod>
  <news:news>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>自己対抗的変分オートエンコーダによる異常検知とガウス異常事前知識（adVAE: a Self-adversarial Variational Autoencoder with Gaussian Anomaly Prior Knowledge for Anomaly Detection）</news:title>
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   <news:genres>Blog</news:genres>
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  <lastmod>2026-08-13T07:32:35Z</lastmod>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>ユーザーレビューに基づく変更ファイルのローカライゼーション (User Review-Based Change File Localization for Mobile Applications)</news:title>
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   <news:genres>Blog</news:genres>
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  <lastmod>2026-08-13T07:32:11Z</lastmod>
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    <news:language>ja</news:language>
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   <news:title>文脈認識型深層学習によるソースコードモデリング（CodeGRU: Context-aware Deep Learning with Gated Recurrent Unit for Source Code Modeling）</news:title>
   <news:publication_date>2026-08-13T07:32:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-08-13T07:32:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>超音波舌画像からの頑健な特徴抽出—Denoising Convolutional Autoencoderの応用（Denoising Convolutional Autoencoder Based B-Mode Ultrasound Tongue Image Feature Extraction）</news:title>
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   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/722660</loc>
  <lastmod>2026-08-13T07:30:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>条件付きカーネル平均埋め込みを用いたベイズ学習による自動化された尤度フリー推論（Bayesian Learning of Conditional Kernel Mean Embeddings for Automatic Likelihood-Free Inference）</news:title>
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   <news:genres>Blog</news:genres>
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  <lastmod>2026-08-13T07:30:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>ブラックボックス問題の可視化枠組み（Solving the Black Box Problem: A Normative Framework for Explainable Artificial Intelligence）</news:title>
   <news:publication_date>2026-08-13T07:30:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-08-13T07:29:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>動画長さで学ぶハイライト検出（Less is More: Learning Highlight Detection from Video Duration）</news:title>
   <news:publication_date>2026-08-13T07:29:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-08-13T07:29:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>Meta-SR: 単一モデルで任意倍率の超解像を可能にする手法（Meta-SR: A Magnification-Arbitrary Network for Super-Resolution）</news:title>
   <news:publication_date>2026-08-13T07:29:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/722652</loc>
  <lastmod>2026-08-13T06:37:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>長期予測可能な車両行動の推定手法：Behavior Interaction Network（Predicting Vehicle Behaviors Over An Extended Horizon Using Behavior Interaction Network）</news:title>
   <news:publication_date>2026-08-13T06:37:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/722650</loc>
  <lastmod>2026-08-13T06:28:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CAD-Netによるリモートセンシング画像の文脈対応物体検出（CAD-Net: A Context-Aware Detection Network for Objects in Remote Sensing Imagery）</news:title>
   <news:publication_date>2026-08-13T06:28:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/722648</loc>
  <lastmod>2026-08-13T06:28:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>都市環境でのオンライン車両軌跡予測の二層フレームワーク（Online Vehicle Trajectory Prediction using Policy Anticipation Network and Optimization-based Context Reasoning）</news:title>
   <news:publication_date>2026-08-13T06:28:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-08-13T06:27:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>Neural Texture Transferによる画像超解像（Image Super-Resolution by Neural Texture Transfer）</news:title>
   <news:publication_date>2026-08-13T06:27:07Z</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>IoTの階層別セキュリティとプライバシーの概観（A survey of security and privacy issues in the Internet of Things from the layered context）</news:title>
   <news:publication_date>2026-08-13T06:27:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-08-13T06:26:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>不完全なデータから生成モデルを学ぶための変分オートデコーダ（Variational Auto-Decoder）</news:title>
   <news:publication_date>2026-08-13T06:26:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/722640</loc>
  <lastmod>2026-08-13T06:26:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>大規模データ回帰のためのMultiple Learning（Multiple Learning for Regression in Big Data）</news:title>
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    <news:name>AI Benchmark Research</news:name>
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    <news:name>AI Benchmark Research</news:name>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 <url>
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    <news:name>AI Benchmark Research</news:name>
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    <news:name>AI Benchmark Research</news:name>
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    <news:name>AI Benchmark Research</news:name>
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    <news:name>AI Benchmark Research</news:name>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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    <news:name>AI Benchmark Research</news:name>
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    <news:name>AI Benchmark Research</news:name>
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    <news:name>AI Benchmark Research</news:name>
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    <news:name>AI Benchmark Research</news:name>
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    <news:name>AI Benchmark Research</news:name>
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    <news:name>AI Benchmark Research</news:name>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 <url>
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  <lastmod>2026-08-13T01:09:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ネットワーク復旧の競合的浸透戦略（Competitive percolation strategies for network recovery）</news:title>
   <news:publication_date>2026-08-13T01:09:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722566</loc>
  <lastmod>2026-08-13T01:09:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>逆問題と機械学習の統一的リプレゼンター定理（A unifying representer theorem for inverse problems and machine learning）</news:title>
   <news:publication_date>2026-08-13T01:09:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722564</loc>
  <lastmod>2026-08-13T01:08:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>産業用ロボット向け完全畳み込みワンショット物体セグメンテーション（Fully Convolutional One–Shot Object Segmentation for Industrial Robotics）</news:title>
   <news:publication_date>2026-08-13T01:08:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722562</loc>
  <lastmod>2026-08-13T01:08:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>OmniDRLによる全周カメラでの歩行者検出の頑健化（OmniDRL: Robust Pedestrian Detection using Deep Reinforcement Learning on Omnidirectional Cameras）</news:title>
   <news:publication_date>2026-08-13T01:08:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722560</loc>
  <lastmod>2026-08-13T01:08:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ソーシャルメディアにおける攻撃的発言検出の実践（Towards NLP with Deep Learning: Convolutional Neural Networks and Recurrent Neural Networks for Offensive Language Identification in Social Media）</news:title>
   <news:publication_date>2026-08-13T01:08:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722558</loc>
  <lastmod>2026-08-13T01:08:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Surrogate出力間の低ランク関係を利活用する構造化予測（Leveraging Low-Rank Relations Between Surrogate Tasks in Structured Prediction）</news:title>
   <news:publication_date>2026-08-13T01:08:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722556</loc>
  <lastmod>2026-08-13T01:08:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DeepGiniによるテスト優先化で実務的なDNN品質向上を狙う（DeepGini: Prioritizing Massive Tests to Enhance the Robustness of Deep Neural Networks）</news:title>
   <news:publication_date>2026-08-13T01:08:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722554</loc>
  <lastmod>2026-08-13T00:16:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>音の振動で注ぎの高さを推定する（Making Sense of Audio Vibration for Liquid Height Estimation in Robotic Pouring）</news:title>
   <news:publication_date>2026-08-13T00:16:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722552</loc>
  <lastmod>2026-08-13T00:16:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>四元数畳み込みニューラルネットワークの要点（Quaternion Convolutional Neural Networks）</news:title>
   <news:publication_date>2026-08-13T00:16:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722550</loc>
  <lastmod>2026-08-13T00:15:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Wasserstein距離に基づく深層敵対的転移学習による機械故障診断（Wasserstein Distance based Deep Adversarial Transfer Learning for Intelligent Fault Diagnosis）</news:title>
   <news:publication_date>2026-08-13T00:15:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722548</loc>
  <lastmod>2026-08-13T00:14:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ワンパスで大規模・欠損混在データを扱う実務的手法の提示（One-Pass Incomplete Multi-view Clustering）</news:title>
   <news:publication_date>2026-08-13T00:14:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722546</loc>
  <lastmod>2026-08-13T00:14:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>不確実な形状補完を踏まえた堅牢な把持計画（Robust Grasp Planning Over Uncertain Shape Completions）</news:title>
   <news:publication_date>2026-08-13T00:14:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722544</loc>
  <lastmod>2026-08-13T00:14:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>都市環境における模倣学習と安全性強化（Deep Imitation Learning for Autonomous Driving in Generic Urban Scenarios with Enhanced Safety）</news:title>
   <news:publication_date>2026-08-13T00:14:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722542</loc>
  <lastmod>2026-08-13T00:14:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>人間の敵対行為を利用したロボット学習（Robot Learning via Human Adversarial Games）</news:title>
   <news:publication_date>2026-08-13T00:14:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722540</loc>
  <lastmod>2026-08-12T23:22:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Variational Bayesが大規模VARの実務を変える（Approximation Properties of Variational Bayes for Vector Autoregressions）</news:title>
   <news:publication_date>2026-08-12T23:22:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722538</loc>
  <lastmod>2026-08-12T23:22:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ロボットの手と目をつなぐ「状態表現」の評価（Evaluation of state representation methods in robot hand-eye coordination learning from demonstration）</news:title>
   <news:publication_date>2026-08-12T23:22:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722536</loc>
  <lastmod>2026-08-12T23:21:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>近似スパース性下の高次元学習と非滑らか推定および正則化ニューラルネットワークへの応用 (High-Dimensional Learning under Approximate Sparsity with Applications to Nonsmooth Estimation and Regularized Neural Networks)</news:title>
   <news:publication_date>2026-08-12T23:21:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722534</loc>
  <lastmod>2026-08-12T23:21:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>量子力学的データ同化の枠組み（Quantum mechanics and data assimilation）</news:title>
   <news:publication_date>2026-08-12T23:21:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722532</loc>
  <lastmod>2026-08-12T23:21:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>汎化可能な近似グラフ分割フレームワーク（GAP: Generalizable Approximate Graph Partitioning Framework）</news:title>
   <news:publication_date>2026-08-12T23:21:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722530</loc>
  <lastmod>2026-08-12T23:21:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>稀な暴力事象の予測に対するアルゴリズム的アプローチ（An Algorithmic Approach to Forecasting Rare Violent Events: An Illustration Based in IPV Perpetration）</news:title>
   <news:publication_date>2026-08-12T23:21:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722528</loc>
  <lastmod>2026-08-12T23:20:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>カバー時間を最小化して探索の選択肢を発見する（Discovering Options for Exploration by Minimizing Cover Time）</news:title>
   <news:publication_date>2026-08-12T23:20:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722526</loc>
  <lastmod>2026-08-12T22:29:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模半正定値計画問題を分割して解く実践法（Block-Coordinate Minimization for Large SDPs with Block-Diagonal Constraints）</news:title>
   <news:publication_date>2026-08-12T22:29:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722524</loc>
  <lastmod>2026-08-12T22:28:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>表層統計を除去して頑健な表現を学ぶ（Learning Robust Representations by Projecting Superficial Statistics Out）</news:title>
   <news:publication_date>2026-08-12T22:28:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722522</loc>
  <lastmod>2026-08-12T22:28:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>デモンストレーションから学ぶ形式的ポリシー学習（Formal Policy Learning from Demonstrations for Reachability Properties）</news:title>
   <news:publication_date>2026-08-12T22:28:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722520</loc>
  <lastmod>2026-08-12T22:27:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>初心者を現場で導く強化学習によるエコー操作支援（Straight to the point: reinforcement learning for user guidance in ultrasound）</news:title>
   <news:publication_date>2026-08-12T22:27:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722518</loc>
  <lastmod>2026-08-12T22:27:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模商品分類における構造化属性と非構造化属性の統合（Large Scale Product Categorization using Structured and Unstructured Attributes）</news:title>
   <news:publication_date>2026-08-12T22:27:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722516</loc>
  <lastmod>2026-08-12T22:27:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>PuVAEによる敵対的例の浄化（PuVAE: A Variational Autoencoder to Purify Adversarial Examples）</news:title>
   <news:publication_date>2026-08-12T22:27:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722514</loc>
  <lastmod>2026-08-12T22:26:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>タッドポールグラフにおけるオンライン探索の最適解（Online Graph Exploration on a Restricted Graph Class: Optimal Solutions for Tadpole Graphs）</news:title>
   <news:publication_date>2026-08-12T22:26:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722512</loc>
  <lastmod>2026-08-12T21:35:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフ構造操作による分類器攻撃の実態（Attacking Graph-based Classification via Manipulating the Graph Structure）</news:title>
   <news:publication_date>2026-08-12T21:35:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722510</loc>
  <lastmod>2026-08-12T21:27:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Plackett-LuceモデルにおけるPACからインスタンス最適なサンプル複雑度へ（From PAC to Instance-Optimal Sample Complexity in the Plackett-Luce Model）</news:title>
   <news:publication_date>2026-08-12T21:27:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722508</loc>
  <lastmod>2026-08-12T21:27:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GRPモデルによる感覚運動学習（GRP Model for Sensorimotor Learning）</news:title>
   <news:publication_date>2026-08-12T21:27:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722506</loc>
  <lastmod>2026-08-12T21:26:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>共同影響モデリングによる検索行動解析（JIM: Joint Influence Modeling for Collective Search Behavior）</news:title>
   <news:publication_date>2026-08-12T21:26:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722504</loc>
  <lastmod>2026-08-12T21:26:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>URu2Si2における一成分秩序パラメータの発見（One-Component Order Parameter in URu2Si2 Uncovered by Resonant Ultrasound Spectroscopy and Machine Learning）</news:title>
   <news:publication_date>2026-08-12T21:26:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722502</loc>
  <lastmod>2026-08-12T21:25:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非線形時間同期モデルの実務的インパクト（A Nonlinear Model for Time Synchronization）</news:title>
   <news:publication_date>2026-08-12T21:25:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722500</loc>
  <lastmod>2026-08-12T21:25:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Eコマース検索におけるLearning to Rankの応用（On Application of Learning to Rank for E-Commerce Search）</news:title>
   <news:publication_date>2026-08-12T21:25:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722498</loc>
  <lastmod>2026-08-12T20:33:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>トラクリット無監督人物再識別（Unsupervised Tracklet Person Re-Identification）</news:title>
   <news:publication_date>2026-08-12T20:33:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722496</loc>
  <lastmod>2026-08-12T20:33:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-12T20:33:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-12T20:33:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>差分プライバシー下の分散分析の改善（IMPROVED DIFFERENTIALLY PRIVATE ANALYSIS OF VARIANCE）</news:title>
   <news:publication_date>2026-08-12T20:32:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-12T20:32:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722488</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>説明手法の集約による安定で頑健な説明可能性（Aggregating explanation methods for stable and robust explainability）</news:title>
   <news:publication_date>2026-08-12T20:32:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722486</loc>
  <lastmod>2026-08-12T20:31:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-12T20:31:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722484</loc>
  <lastmod>2026-08-12T19:40:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習と幾何を組織して従来の索引を超える（Superseding traditional indexes by orchestrating learning and geometry）</news:title>
   <news:publication_date>2026-08-12T19:40:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722482</loc>
  <lastmod>2026-08-12T19:39:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニュートリノ散乱データに基づくバレンスPDFの非シンギュレットQCD解析（QCD analysis of structure functions in deep inelastic neutrino-nucleon scattering）</news:title>
   <news:publication_date>2026-08-12T19:39:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722480</loc>
  <lastmod>2026-08-12T19:39:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>セマンティック指導によるマルチアテンション局所化とゼロショット学習（Semantic-Guided Multi-Attention Localization for Zero-Shot Learning）</news:title>
   <news:publication_date>2026-08-12T19:39:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722478</loc>
  <lastmod>2026-08-12T19:38:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ミューズ超深宇宙観測によるクエーサー対に伴うLyαネビュラ群の発見（The MUSE Ultra Deep Field (MUDF). I. Discovery of a group of Lyα nebulae associated with a bright z ≈3.23 quasar pair）</news:title>
   <news:publication_date>2026-08-12T19:38:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722476</loc>
  <lastmod>2026-08-12T19:38:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>反復的変分推論による多物体表現学習（Multi-Object Representation Learning with Iterative Variational Inference）</news:title>
   <news:publication_date>2026-08-12T19:38:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722474</loc>
  <lastmod>2026-08-12T19:38:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>準備されたSYKモデルにおける固有状態熱化の解析的研究（Eigenstate Thermalisation in the conformal Sachdev-Ye-Kitaev model: an analytic approach）</news:title>
   <news:publication_date>2026-08-12T19:38:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722472</loc>
  <lastmod>2026-08-12T19:37:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-12T19:37:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722470</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-12T18:45:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-12T18:45:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-12T18:45:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722466</loc>
  <lastmod>2026-08-12T18:44:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数画像の超解像に深層学習を組み合わせる手法（Deep Learning for Multiple-Image Super-Resolution）</news:title>
   <news:publication_date>2026-08-12T18:44:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722464</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>文献コーパスからエネルギー材料の性質・機能を自動抽出するための自然言語処理手法（USING NATURAL LANGUAGE PROCESSING TECHNIQUES TO EXTRACT INFORMATION ON THE PROPERTIES AND FUNCTIONALITIES OF ENERGETIC MATERIALS FROM LARGE TEXT CORPORA）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722462</loc>
  <lastmod>2026-08-12T18:44:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-12T18:44:23Z</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>政策評価における機械学習：因果推論の新しい道（Machine learning in policy evaluation: new tools for causal inference）</news:title>
   <news:publication_date>2026-08-12T18:44:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-12T18:43:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722456</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>街路ビューでの指示に従う学習（Learning to Follow Directions in Street View）</news:title>
   <news:publication_date>2026-08-12T17:52:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722454</loc>
  <lastmod>2026-08-12T17:52:18Z</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>2026-08-12T17:52:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722452</loc>
  <lastmod>2026-08-12T17:51:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>太陽系におけるハドレー循環の角幅の解析的推定（Analytical Estimation of the Width of Hadley Cells in the Solar System）</news:title>
   <news:publication_date>2026-08-12T17:51:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722450</loc>
  <lastmod>2026-08-12T17:51:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722448</loc>
  <lastmod>2026-08-12T17:50:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722446</loc>
  <lastmod>2026-08-12T17:50:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層ネットワークにおけるMAP推定の漸近解析（Asymptotics of MAP Inference in Deep Networks）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>無線通信における敵対的回避攻撃の評価（Evaluating Adversarial Evasion Attacks in the Context of Wireless Communications）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <lastmod>2026-08-12T16:56:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-12T16:56:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722436</loc>
  <lastmod>2026-08-12T16:56:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所化したサポートベクターマシンの定量的ロバスト性（Quantitative Robustness of Localized Support Vector Machines）</news:title>
   <news:publication_date>2026-08-12T16:56:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/722434</loc>
  <lastmod>2026-08-12T16:56:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>FormalZによる正式仕様の学び方を楽しくする試み（Having Fun in Learning Formal Specifications）</news:title>
   <news:publication_date>2026-08-12T16:56:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722432</loc>
  <lastmod>2026-08-12T16:55:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-12T16:55:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-12T16:55:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>形成中惑星の質量推定に深層学習を用いる研究（Using Deep Neural Networks to compute the mass of forming planets）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/722424</loc>
  <lastmod>2026-08-12T15:53:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>太陽系内の星間塵と計算モデルの照合（Interstellar Dust in the Solar System: Model versus In-Situ Spacecraft Data）</news:title>
   <news:publication_date>2026-08-12T15:53:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722422</loc>
  <lastmod>2026-08-12T15:52:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Progress Regression RNNによるオンライン空間時系列行動定位（Progress Regression RNN for Online Spatial-Temporal Action Localization in Unconstrained Videos）</news:title>
   <news:publication_date>2026-08-12T15:52:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722420</loc>
  <lastmod>2026-08-12T15:52:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Machine Learningのための継続的インテグレーション ease.ml/ci（Continuous Integration of Machine Learning Models with ease.ml/ci: Towards a Rigorous Yet Practical Treatment）</news:title>
   <news:publication_date>2026-08-12T15:52:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722418</loc>
  <lastmod>2026-08-12T15:52:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>向き付き準二乗和ネットワークによる証明可能なスケール共変性（Provably scale-covariant networks from oriented quasi quadrature measures in cascade）</news:title>
   <news:publication_date>2026-08-12T15:52:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722416</loc>
  <lastmod>2026-08-12T15:52:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>密集Wi‑Fi環境における分散型AP選択とスティッキネス（Decentralized AP selection using Multi-Armed Bandits: Opportunistic ε-Greedy with Stickiness）</news:title>
   <news:publication_date>2026-08-12T15:52:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722414</loc>
  <lastmod>2026-08-12T15:00:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>乳房X線画像処理のための深層デュアルパスネットワーク（A DEEP DUAL-PATH NETWORK FOR IMPROVED MAMMOGRAM IMAGE PROCESSING）</news:title>
   <news:publication_date>2026-08-12T15:00:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722412</loc>
  <lastmod>2026-08-12T14:53:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>周波数領域トランスフォーマーネットワークによる映像予測（Frequency Domain Transformer Networks for Video Prediction）</news:title>
   <news:publication_date>2026-08-12T14:53:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722410</loc>
  <lastmod>2026-08-12T14:53:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自己中心的バイアスと疑念を持つ認知エージェント（Egocentric Bias and Doubt in Cognitive Agents）</news:title>
   <news:publication_date>2026-08-12T14:53:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722408</loc>
  <lastmod>2026-08-12T14:52:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>密封入札における落札情報漏洩の検出（Stealed-Bid Auctions: Detecting Bid Leakage via Semi-Supervised Learning）</news:title>
   <news:publication_date>2026-08-12T14:52:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722406</loc>
  <lastmod>2026-08-12T14:51:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>半滑らかニュートン法によるSVM最適化の実用化可能性（A Semismooth Newton Method for Support Vector Classification and Regression）</news:title>
   <news:publication_date>2026-08-12T14:51:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722404</loc>
  <lastmod>2026-08-12T14:51:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ヒトのクラウディングは畳み込みニューラルネットワークとは異なる（Crowding in humans is unlike that in convolutional neural networks）</news:title>
   <news:publication_date>2026-08-12T14:51:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722402</loc>
  <lastmod>2026-08-12T14:50:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>最適射影に導かれる転移ハッシングによる画像検索改善（Optimal Projection Guided Transfer Hashing for Image Retrieval）</news:title>
   <news:publication_date>2026-08-12T14:50:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722400</loc>
  <lastmod>2026-08-12T13:58:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マスクスコアリングR-CNN（Mask Scoring R-CNN）</news:title>
   <news:publication_date>2026-08-12T13:58:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722398</loc>
  <lastmod>2026-08-12T13:58:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分子から隠れメッセージを学習して密度汎関数理論を完成させる（Completing density functional theory by machine learning hidden messages from molecules）</news:title>
   <news:publication_date>2026-08-12T13:58:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722396</loc>
  <lastmod>2026-08-12T13:57:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単一画像のモーションブラー除去と6自由度カメラ運動推定（Single Image Deblurring and Camera Motion Estimation with Depth Map）</news:title>
   <news:publication_date>2026-08-12T13:57:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722394</loc>
  <lastmod>2026-08-12T13:56:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>量子チャネル記憶を持つ場合の古典情報率の評価と境界設定（Bounding and Estimating the Classical Information Rate of Quantum Channels with Memory）</news:title>
   <news:publication_date>2026-08-12T13:56:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722392</loc>
  <lastmod>2026-08-12T13:56:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>YouTubeから自動で音声データセットを作る仕組み（KT-Speech-Crawler: Automatic Dataset Construction for Speech Recognition from YouTube Videos）</news:title>
   <news:publication_date>2026-08-12T13:56:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722390</loc>
  <lastmod>2026-08-12T13:56:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Cramer–Wold に基づく非線形ICAの提案（Non-linear ICA based on Cramer-Wold metric）</news:title>
   <news:publication_date>2026-08-12T13:56:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722388</loc>
  <lastmod>2026-08-12T13:55:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>データ効率化された自己教師あり学習によるロボット把持の改善（Improving Data Efficiency of Self-supervised Learning for Robotic Grasping）</news:title>
   <news:publication_date>2026-08-12T13:55:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722386</loc>
  <lastmod>2026-08-12T13:04:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>発症後の急性冠症候群患者のアウトカム駆動クラスタリング（Outcome-Driven Clustering of Acute Coronary Syndrome Patients using Multi-Task Neural Network with Attention）</news:title>
   <news:publication_date>2026-08-12T13:04:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722384</loc>
  <lastmod>2026-08-12T13:03:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>周波間（インターフリークエンシー）信号品質予測とハンドオーバー意思決定の高精度化（Inter-frequency radio signal quality prediction for handover, evaluated in 3GPP LTE）</news:title>
   <news:publication_date>2026-08-12T13:03:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722382</loc>
  <lastmod>2026-08-12T13:03:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>強調付き時刻差分学習は常に有利か（Should All Temporal Difference Learning Use Emphasis?）</news:title>
   <news:publication_date>2026-08-12T13:03:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722372</loc>
  <lastmod>2026-08-12T13:02:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>制約付き合成最適化の近接アルゴリズム（Proximal algorithms for constrained composite optimization, with applications to solving low-rank SDPs）</news:title>
   <news:publication_date>2026-08-12T13:02:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722370</loc>
  <lastmod>2026-08-12T13:01:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散変分ベイズによる拡張物体追跡（Distributed Variational Bayesian Algorithms for Extended Object Tracking）</news:title>
   <news:publication_date>2026-08-12T13:01:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722368</loc>
  <lastmod>2026-08-12T13:01:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>少量データで肺CT画像所見を識別する深層学習の実践（Lung CT Imaging Sign Classification through Deep Learning on Small Data）</news:title>
   <news:publication_date>2026-08-12T13:01:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722366</loc>
  <lastmod>2026-08-12T13:00:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ピラミッド特徴注目ネットワークによる顕著領域検出 (Pyramid Feature Attention Network for Saliency detection)</news:title>
   <news:publication_date>2026-08-12T13:00:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722364</loc>
  <lastmod>2026-08-12T12:09:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>質問応答ペアからのオープン情報抽出（Open Information Extraction from Question-Answer Pairs）</news:title>
   <news:publication_date>2026-08-12T12:09:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722362</loc>
  <lastmod>2026-08-12T12:09:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>強化学習エージェントへのトロイ攻撃の実証（TrojDRL: Trojan Attacks on Deep Reinforcement Learning Agents）</news:title>
   <news:publication_date>2026-08-12T12:09:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722360</loc>
  <lastmod>2026-08-12T12:08:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Deep and Dark Web上の活動の特徴づけ（Characterizing Activity on the Deep and Dark Web）</news:title>
   <news:publication_date>2026-08-12T12:08:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722358</loc>
  <lastmod>2026-08-12T12:07:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>可逆線形埋め込みによる映像外挿 (Video Extrapolation with an Invertible Linear Embedding)</news:title>
   <news:publication_date>2026-08-12T12:07:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722356</loc>
  <lastmod>2026-08-12T12:07:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>人とロボットの対話で伸ばす言語理解（Improving Grounded Natural Language Understanding through Human-Robot Dialog）</news:title>
   <news:publication_date>2026-08-12T12:07:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722354</loc>
  <lastmod>2026-08-12T12:07:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>未ラベルデータで事前学習したコピー拡張アーキテクチャによる文法誤り訂正の改善（Improving Grammatical Error Correction via Pre-Training a Copy-Augmented Architecture with Unlabeled Data）</news:title>
   <news:publication_date>2026-08-12T12:07:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722352</loc>
  <lastmod>2026-08-12T12:07:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>楽器オーディオの統一ニューラルアーキテクチャ（A Unified Neural Architecture for Instrumental Audio Tasks）</news:title>
   <news:publication_date>2026-08-12T12:07:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722350</loc>
  <lastmod>2026-08-12T11:15:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的ブロックモデルのレビューと応用展望（A Review of Stochastic Block Models and Extensions for Graph Clustering）</news:title>
   <news:publication_date>2026-08-12T11:15:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722348</loc>
  <lastmod>2026-08-12T11:14:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高解像度データに基づく局所幾何インデクシングによるスパースマーカーからの顔再構成（Local Geometric Indexing of High Resolution Data for Facial Reconstruction from Sparse Markers）</news:title>
   <news:publication_date>2026-08-12T11:14:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722346</loc>
  <lastmod>2026-08-12T11:14:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単眼画像からの自己教師あり深度・法線推定（Self-supervised Learning for Single View Depth and Surface Normal）</news:title>
   <news:publication_date>2026-08-12T11:14:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722344</loc>
  <lastmod>2026-08-12T11:13:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>プライバシー保護のためのAutoGANに基づく次元削減（AutoGAN-based Dimension Reduction for Privacy Preservation）</news:title>
   <news:publication_date>2026-08-12T11:13:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722342</loc>
  <lastmod>2026-08-12T11:13:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習が至る所にある計算基盤の提案（Learning Everywhere: Pervasive Machine Learning for Effective High-Performance Computation）</news:title>
   <news:publication_date>2026-08-12T11:13:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722340</loc>
  <lastmod>2026-08-12T11:13:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Lipschitz適応と複数学習率によるオンライン学習（Lipschitz Adaptivity with Multiple Learning Rates in Online Learning）</news:title>
   <news:publication_date>2026-08-12T11:13:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722338</loc>
  <lastmod>2026-08-12T11:13:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>行動性スコアに基づく動画要約（Video Summarization via Actionness Ranking）</news:title>
   <news:publication_date>2026-08-12T11:13:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722336</loc>
  <lastmod>2026-08-12T10:22:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>経験に基づく計画ドメインにおける学習されたタスク知識と適用範囲（Learning Task Knowledge and its Scope of Applicability in Experience-Based Planning Domains）</news:title>
   <news:publication_date>2026-08-12T10:22:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722334</loc>
  <lastmod>2026-08-12T10:22:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>画像プライバシー予測の動的マルチモーダル融合（Dynamic Deep Multi-modal Fusion for Image Privacy Prediction）</news:title>
   <news:publication_date>2026-08-12T10:22:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722332</loc>
  <lastmod>2026-08-12T10:21:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>胸部X線画像における肺水腫（肺浮腫）重症度の半教師あり定量化（Semi-supervised Learning for Quantification of Pulmonary Edema in Chest X-Ray Images）</news:title>
   <news:publication_date>2026-08-12T10:21:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722330</loc>
  <lastmod>2026-08-12T10:20:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時系列分類のためのLSTM-FCNの洞察（Insights into LSTM Fully Convolutional Networks for Time Series Classification）</news:title>
   <news:publication_date>2026-08-12T10:20:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722328</loc>
  <lastmod>2026-08-12T10:20:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Tensor Dropoutによる頑健学習の提案（Tensor Dropout for Robust Learning）</news:title>
   <news:publication_date>2026-08-12T10:20:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722326</loc>
  <lastmod>2026-08-12T10:20:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時系列データに対する敵対的攻撃（Adversarial Attacks on Time Series）</news:title>
   <news:publication_date>2026-08-12T10:20:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722324</loc>
  <lastmod>2026-08-12T10:20:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GPS軌跡からの移動手段推定における半教師付きGANの応用（Semi-supervised Generative Adversarial Networks for Travel Mode Inference）</news:title>
   <news:publication_date>2026-08-12T10:20:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722322</loc>
  <lastmod>2026-08-12T09:28:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非線形マルコフ確率場を逆伝播で学習する（Nonlinear Markov Random Fields Learned via Backpropagation）</news:title>
   <news:publication_date>2026-08-12T09:28:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722320</loc>
  <lastmod>2026-08-12T09:28:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>内省学習（Introspection Learning）</news:title>
   <news:publication_date>2026-08-12T09:28:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722318</loc>
  <lastmod>2026-08-12T09:28:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数顔の同時検出と表情リターゲティングの統合（Joint Face Detection and Facial Motion Retargeting for Multiple Faces）</news:title>
   <news:publication_date>2026-08-12T09:28:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722316</loc>
  <lastmod>2026-08-12T09:27:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>顔画像から性的指向を予測する機械学習の再現研究（A Replication Study: Machine Learning Models Are Capable of Predicting Sexual Orientation From Facial Images）</news:title>
   <news:publication_date>2026-08-12T09:27:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722314</loc>
  <lastmod>2026-08-12T09:26:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>散乱媒体を透して高速広視野イメージングを可能にするDEEP法（De-scattering with Excitation Patterning）</news:title>
   <news:publication_date>2026-08-12T09:26:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722312</loc>
  <lastmod>2026-08-12T09:26:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>フラクチャー関数に基づく回折性DISの現象論と回折性パートン分布関数の決定（Phenomenology of diffractive DIS in the framework of fracture functions and determination of diffractive parton distribution functions）</news:title>
   <news:publication_date>2026-08-12T09:26:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722310</loc>
  <lastmod>2026-08-12T09:26:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>UAV画像からの浅海域バチメトリ（SHALLOW WATER BATHYMETRY MAPPING FROM UAV IMAGERY BASED ON MACHINE LEARNING）</news:title>
   <news:publication_date>2026-08-12T09:26:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722308</loc>
  <lastmod>2026-08-12T08:35:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ディープスペースネットワーク（DSN）を用いた電波マグネター観測の実践と意義（Observations of Radio Magnetars with the Deep Space Network）</news:title>
   <news:publication_date>2026-08-12T08:35:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722306</loc>
  <lastmod>2026-08-12T08:35:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>プライベート中心点と半空間の学習（Private Center Points and Learning of Halfspaces）</news:title>
   <news:publication_date>2026-08-12T08:35:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722304</loc>
  <lastmod>2026-08-12T08:34:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高確率一般化境界と均一安定性の最適率への接近（High probability generalization bounds for uniformly stable algorithms with nearly optimal rate）</news:title>
   <news:publication_date>2026-08-12T08:34:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722302</loc>
  <lastmod>2026-08-12T08:33:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>通信における変調検出回避のための敵対的攻撃（Communication without interception: Defense against modulation detection）</news:title>
   <news:publication_date>2026-08-12T08:33:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722300</loc>
  <lastmod>2026-08-12T08:33:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次元ベイズ最適化で低次元特徴空間を使う意義（High-dimensional Bayesian optimization using low-dimensional feature spaces）</news:title>
   <news:publication_date>2026-08-12T08:33:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722298</loc>
  <lastmod>2026-08-12T08:32:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>不連続な多語表現の識別を改善する新手法（Bridging the Gap: Attending to Discontinuity in Identification of Multiword Expressions）</news:title>
   <news:publication_date>2026-08-12T08:32:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722296</loc>
  <lastmod>2026-08-12T08:32:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>屋内ロボット向け物体検出器のカスタマイズ (Customizing Object Detectors for Indoor Robots)</news:title>
   <news:publication_date>2026-08-12T08:32:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722294</loc>
  <lastmod>2026-08-12T07:41:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層生成モデルによる欠損値補完の改善（Improving Missing Data Imputation with Deep Generative Models）</news:title>
   <news:publication_date>2026-08-12T07:41:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722292</loc>
  <lastmod>2026-08-12T07:41:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所関数複雑性に基づく能動学習（Local Function Complexity for Active Learning via Mixture of Gaussian Processes）</news:title>
   <news:publication_date>2026-08-12T07:41:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722290</loc>
  <lastmod>2026-08-12T07:40:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>電子イオン衝突器での半包摂深部非弾性散乱と分布・フラグメンテーション関数（Semi-inclusive Deep-Inelastic Scattering, Parton Distributions and Fragmentation Functions at a Future Electron-Ion Collider）</news:title>
   <news:publication_date>2026-08-12T07:40:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722288</loc>
  <lastmod>2026-08-12T07:39:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Regularity Normalization：神経科学に着想を得た無監督レイヤー間注目（Regularity Normalization: Neuroscience-Inspired Unsupervised Attention across Neural Network Layers）</news:title>
   <news:publication_date>2026-08-12T07:39:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722286</loc>
  <lastmod>2026-08-12T07:39:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>説明から合成へ：デモンストレーション学習のための合成的プログラム誘導（From explanation to synthesis: Compositional program induction for learning from demonstration）</news:title>
   <news:publication_date>2026-08-12T07:39:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722284</loc>
  <lastmod>2026-08-12T07:39:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>決定木の敵対的頑健化（Robust Decision Trees Against Adversarial Examples）</news:title>
   <news:publication_date>2026-08-12T07:39:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722282</loc>
  <lastmod>2026-08-12T07:39:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>経験的リスク推定の集中性に対するWasserstein距離アプローチ (A Wasserstein distance approach for concentration of empirical risk estimates)</news:title>
   <news:publication_date>2026-08-12T07:39:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722280</loc>
  <lastmod>2026-08-12T06:47:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>レベルセット学習による非線形次元削減（Learning nonlinear level sets for dimensionality reduction in function approximation）</news:title>
   <news:publication_date>2026-08-12T06:47:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722278</loc>
  <lastmod>2026-08-12T06:47:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>vitrivrの概念検出とVBS2019での総括 (Deep Learning-based Concept Detection in vitrivr at the Video Browser Showdown 2019 – Final Notes)</news:title>
   <news:publication_date>2026-08-12T06:47:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722276</loc>
  <lastmod>2026-08-12T06:46:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Q学習のアンサンブル手法を社会選択理論で統一する（Unifying Ensemble Methods for Q-learning via Social Choice Theory）</news:title>
   <news:publication_date>2026-08-12T06:46:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722274</loc>
  <lastmod>2026-08-12T06:45:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>勾配ベースのメタ学習に関する証明可能な保証 (Provable Guarantees for Gradient-Based Meta-Learning)</news:title>
   <news:publication_date>2026-08-12T06:45:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722272</loc>
  <lastmod>2026-08-12T06:45:18Z</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>2026-08-12T06:45:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722270</loc>
  <lastmod>2026-08-12T06:45:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動的目的を学習するポリシー（Learning Dynamic-Objective Policies from a Class of Optimal Trajectories）</news:title>
   <news:publication_date>2026-08-12T06:45:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722268</loc>
  <lastmod>2026-08-12T06:44:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューラルイメージングパイプラインとフォレンジクスの再考（Neural Imaging Pipelines - the Scourge or Hope of Forensics?）</news:title>
   <news:publication_date>2026-08-12T06:44:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722266</loc>
  <lastmod>2026-08-12T05:52:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>銀河衝突の観測とシミュレーションにおける識別（Identifying Galaxy Mergers in Observations and Simulations with Deep Learning）</news:title>
   <news:publication_date>2026-08-12T05:52:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722264</loc>
  <lastmod>2026-08-12T05:52:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>因子化マルコフ決定過程における</news:title>
   <news:publication_date>2026-08-12T05:52:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722262</loc>
  <lastmod>2026-08-12T05:52:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>LPWAネットワークにおける再送を考慮したチャネル選択の学習（Upper-Confidence Bound for Channel Selection in LPWA Networks with Retransmissions）</news:title>
   <news:publication_date>2026-08-12T05:52:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722260</loc>
  <lastmod>2026-08-12T05:51:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>信頼できる学習内蔵型自律システムの設計（Architecting Dependable Learning-enabled Autonomous Systems: A Survey）</news:title>
   <news:publication_date>2026-08-12T05:51:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722258</loc>
  <lastmod>2026-08-12T05:50:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ツイートが実際に性差別的であるとき（When a Tweet is Actually Sexist）</news:title>
   <news:publication_date>2026-08-12T05:50:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722256</loc>
  <lastmod>2026-08-12T05:50:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>関連性照合のための多解像度グラフアテンションネットワーク（Multiresolution Graph Attention Networks for Relevance Matching）</news:title>
   <news:publication_date>2026-08-12T05:50:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722254</loc>
  <lastmod>2026-08-12T05:50:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多腕同時選択の全バンディット観測下での多腕同定の多項式時間アルゴリズム（Polynomial-time Algorithms for Multiple-arm Identification with Full-bandit Feedback）</news:title>
   <news:publication_date>2026-08-12T05:50:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722252</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>原子核における新しいクォークとグルーオン効果の露呈（Exposing Novel Quark and Gluon Effects in Nuclei）</news:title>
   <news:publication_date>2026-08-12T04:58:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722250</loc>
  <lastmod>2026-08-12T04:58:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>TrIK-SVMによる不定カーネルの新たな分解（TrIK-SVM : an alternative decomposition for kernel methods in Kre˘in spaces）</news:title>
   <news:publication_date>2026-08-12T04:58:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722248</loc>
  <lastmod>2026-08-12T04:58:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>フォグ無線アクセスネットワークにおける強化学習による分散エッジキャッシング（Distributed Edge Caching via Reinforcement Learning in Fog Radio Access Networks）</news:title>
   <news:publication_date>2026-08-12T04:58:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722246</loc>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高解像度向け反復型MVSネットワーク（Recurrent MVSNet for High-resolution Multi-view Stereo Depth Inference）</news:title>
   <news:publication_date>2026-08-12T04:56:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722244</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>ELMによるLED MIMO受信機設計――非線形とクロスLED干渉を同時に扱う（EXTREME LEARNING MACHINE-BASED RECEIVER FOR MIMO LED COMMUNICATIONS）</news:title>
   <news:publication_date>2026-08-12T04:56:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DeepLO: Geometry-Aware Deep LiDAR Odometry（DeepLO: Geometry-Aware Deep LiDAR Odometry）</news:title>
   <news:publication_date>2026-08-12T04:56:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722240</loc>
  <lastmod>2026-08-12T04:56:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自己対戦学習の高速化――KataGoによるGo学習効率の革新（Accelerating Self-Play Learning in Go）</news:title>
   <news:publication_date>2026-08-12T04:56:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722238</loc>
  <lastmod>2026-08-12T04:04:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>事前学習済み言語モデルからの転移学習の極めて単純なアプローチ（An Embarrassingly Simple Approach for Transfer Learning from Pretrained Language Models）</news:title>
   <news:publication_date>2026-08-12T04:04:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722236</loc>
  <lastmod>2026-08-12T04:04:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>属性支援による部分検出と精錬による人物再識別の改善（Attributes-aided Part Detection and Refinement for Person Re-identification）</news:title>
   <news:publication_date>2026-08-12T04:04:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722234</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>Deep Active Subspacesによる高次元不確かさ伝播の可視化と削減（DEEP ACTIVE SUBSPACES - A SCALABLE METHOD FOR HIGH-DIMENSIONAL UNCERTAINTY PROPAGATION）</news:title>
   <news:publication_date>2026-08-12T04:03:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-12T04:03:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>原子構造学習（Atomistic Structure Learning）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722230</loc>
  <lastmod>2026-08-12T04:03:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>生物学的に妥当な深層学習—浅いネットワークでどこまで可能か？ (Biologically plausible deep learning – but how far can we go with shallow networks?)</news:title>
   <news:publication_date>2026-08-12T04:03:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722228</loc>
  <lastmod>2026-08-12T04:03:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>依存構文解析をラベリングで再定式化する意義（Viable Dependency Parsing as Sequence Labeling）</news:title>
   <news:publication_date>2026-08-12T04:03:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722226</loc>
  <lastmod>2026-08-12T04:02:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文脈に基づく文結合の大規模データセット（DISCOFUSE: A Large-Scale Dataset for Discourse-Based Sentence Fusion）</news:title>
   <news:publication_date>2026-08-12T04:02:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722224</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-12T03:11:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722222</loc>
  <lastmod>2026-08-12T03:01:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>EL EmbeddingsによるEL++論理理論のベクトル化（EL Embeddings: Geometric construction of models for the Description Logic EL++）</news:title>
   <news:publication_date>2026-08-12T03:01:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722220</loc>
  <lastmod>2026-08-12T03:01:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>窒化物半導体Ca(Mg1−xZnx)2N2による可変発光とp型導電性（Tunable light-emission through the range 1.8–3.2 eV and p-type conductivity at room temperature for nitride semiconductors, Ca(Mg1−xZnx)2N2）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-12T03:00:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模公開オンラインコースとクラウドコンピューティング（Massive Open Online Courses and Cloud Computing）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722216</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>継続学習における小さなエピソード記憶の効用 (On Tiny Episodic Memories in Continual Learning)</news:title>
   <news:publication_date>2026-08-12T03:00:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722214</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>適応的ガウシアンコピュラABC（Adaptive Gaussian Copula ABC）</news:title>
   <news:publication_date>2026-08-12T03:00:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722212</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-12T02:59:52Z</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>埋め込み事前分布を持つゲート付きコンテキストモデルによる深層画像圧縮（Gated Context Model with Embedded Priors for Deep Image Compression）</news:title>
   <news:publication_date>2026-08-12T02:08:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722208</loc>
  <lastmod>2026-08-12T02:07:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-12T02:07:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>深層量子ニューラルネットワークの効率的学習（Efficient Learning for Deep Quantum Neural Networks）</news:title>
   <news:publication_date>2026-08-12T02:06:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/722198</loc>
  <lastmod>2026-08-12T02:06:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>電波干渉計キャリブレーションの統計的性能（Statistical Performance of Radio Interferometric Calibration）</news:title>
   <news:publication_date>2026-08-12T02:06:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722196</loc>
  <lastmod>2026-08-12T01:14:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>収益最大化オークションで入札を学習する（Learning to bid in revenue-maximizing auctions）</news:title>
   <news:publication_date>2026-08-12T01:14:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722194</loc>
  <lastmod>2026-08-12T01:13:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>遅延フィードバック下における適応ヘッジング（Adaptive Hedging under Delayed Feedback）</news:title>
   <news:publication_date>2026-08-12T01:13:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722192</loc>
  <lastmod>2026-08-12T01:13:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>StyleRemix：ニューラル画像スタイル転送の解釈可能な表現（StyleRemix: An Interpretable Representation for Neural Image Style Transfer）</news:title>
   <news:publication_date>2026-08-12T01:13:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722190</loc>
  <lastmod>2026-08-12T01:12:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>等化正規化によるニューラルネットワークの再パラメータ化（EQUI-NORMALIZATION OF NEURAL NETWORKS）</news:title>
   <news:publication_date>2026-08-12T01:12:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722188</loc>
  <lastmod>2026-08-12T01:12:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>FickleNetの要点と実務的意義（FickleNet: Weakly and Semi-supervised Semantic Image Segmentation using Stochastic Inference）</news:title>
   <news:publication_date>2026-08-12T01:12:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722186</loc>
  <lastmod>2026-08-12T01:12:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>映像で安定動作するCNNの単一フレーム正則化（Single-frame Regularization for Temporally Stable CNNs）</news:title>
   <news:publication_date>2026-08-12T01:12:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722184</loc>
  <lastmod>2026-08-12T01:12:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文章から自動で問いを作る技術の革新（Learning to Generate Questions by Learning What not to Generate）</news:title>
   <news:publication_date>2026-08-12T01:12:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722182</loc>
  <lastmod>2026-08-12T00:20:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>重み付き内積による表現学習の普遍近似（Representation Learning with Weighted Inner Product for Universal Approximation of General Similarities）</news:title>
   <news:publication_date>2026-08-12T00:20:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722180</loc>
  <lastmod>2026-08-12T00:20:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>勾配フロー消滅による非線形固有関数の計算（Computing Nonlinear Eigenfunctions via Gradient Flow Extinction）</news:title>
   <news:publication_date>2026-08-12T00:20:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722178</loc>
  <lastmod>2026-08-12T00:20:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>光ファイバーの非線形性緩和に向けた機械学習検出器の可能性（A Machine Learning-Based Detection Technique for Optical Fiber Nonlinearity Mitigation）</news:title>
   <news:publication_date>2026-08-12T00:20:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722176</loc>
  <lastmod>2026-08-12T00:19:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>制約付きℓp回帰に対する証明可能な近似法（Provable Approximations for Constrained ℓp Regression）</news:title>
   <news:publication_date>2026-08-12T00:19:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722174</loc>
  <lastmod>2026-08-12T00:19:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ソーシャルビッグデータにおける信頼性と意味解析の展望（Social Credibility incorporating Semantic Analysis and Machine Learning）</news:title>
   <news:publication_date>2026-08-12T00:19:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722172</loc>
  <lastmod>2026-08-12T00:19:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ハイパーネットワークによる関数的画像表現（Hypernetwork functional image representation）</news:title>
   <news:publication_date>2026-08-12T00:19:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722170</loc>
  <lastmod>2026-08-12T00:19:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自然画像のノイズ除去学習を地震データ補間に使えるか（Can learning from natural image denoising be used for seismic data interpolation?）</news:title>
   <news:publication_date>2026-08-12T00:19:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722168</loc>
  <lastmod>2026-08-11T23:26:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非凸ペナルティを用いるスパース線形回帰のCV高速化と安定性（Cross validation in sparse linear regression with piecewise continuous nonconvex penalties and its acceleration）</news:title>
   <news:publication_date>2026-08-11T23:26:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722166</loc>
  <lastmod>2026-08-11T23:26:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ドメイン制約付き広告キーワード生成（Domain-Constrained Advertising Keyword Generation）</news:title>
   <news:publication_date>2026-08-11T23:26:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722164</loc>
  <lastmod>2026-08-11T23:26:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>系譜検索のランキング統合（RANKING IN GENEALOGY: SEARCH RESULTS FUSION AT ANCESTRY）</news:title>
   <news:publication_date>2026-08-11T23:26:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722162</loc>
  <lastmod>2026-08-11T23:25:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分布的頑健性に基づく多重カーネル学習の最適化手法（A Distributionally Robust Optimization Method for Adversarial Multiple Kernel Learning）</news:title>
   <news:publication_date>2026-08-11T23:25:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722160</loc>
  <lastmod>2026-08-11T23:25:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチ損失認識に基づくチャネルプルーニング（MULTI-LOSS-AWARE CHANNEL PRUNING OF DEEP NETWORKS）</news:title>
   <news:publication_date>2026-08-11T23:25:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722158</loc>
  <lastmod>2026-08-11T23:25:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>モチーフを利用した拡散ネットワークの時間的ダイナミクスモデル（Leveraging Motifs to Model the Temporal Dynamics of Diffusion Networks）</news:title>
   <news:publication_date>2026-08-11T23:25:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722156</loc>
  <lastmod>2026-08-11T23:25:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>クラスター正則化量子化による深層ネットワーク圧縮（Cluster Regularized Quantization for Deep Networks Compression）</news:title>
   <news:publication_date>2026-08-11T23:25:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722154</loc>
  <lastmod>2026-08-11T22:33:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>コメントの毒性分類における機械学習手法（A Machine Learning Approach to Comment Toxicity Classification）</news:title>
   <news:publication_date>2026-08-11T22:33:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722152</loc>
  <lastmod>2026-08-11T22:22:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ロボット視覚におけるメトリック学習の重要性（The Importance of Metric Learning for Robotic Vision: Open Set Recognition and Active Learning）</news:title>
   <news:publication_date>2026-08-11T22:22:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722150</loc>
  <lastmod>2026-08-11T22:22:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GAN画像フォレンジクスの一般化に関する研究（On the Generalization of GAN Image Forensics）</news:title>
   <news:publication_date>2026-08-11T22:22:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722148</loc>
  <lastmod>2026-08-11T22:22:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>加齢性黄斑変性の進行予測に向けた深層学習アプローチ（A Deep-learning Approach for Prognosis of Age-Related Macular Degeneration Disease using SD-OCT Imaging Biomarkers）</news:title>
   <news:publication_date>2026-08-11T22:22:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722146</loc>
  <lastmod>2026-08-11T22:22:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>予算制約下での因果構造探索を効率化する実験設計（ABCD-Strategy: Budgeted Experimental Design for Targeted Causal Structure Discovery）</news:title>
   <news:publication_date>2026-08-11T22:22:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722144</loc>
  <lastmod>2026-08-11T22:21:34Z</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とガウス過程の深い結びつきが能動学習を加速する（Deeper Connections between Neural Networks and Gaussian Processes Speed-up Active Learning）</news:title>
   <news:publication_date>2026-08-11T22:21:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722142</loc>
  <lastmod>2026-08-11T22:21:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>テキスト分類に必要な語彙サイズの見積り手法（How Large a Vocabulary Does Text Classification Need? A Variational Approach to Vocabulary Selection）</news:title>
   <news:publication_date>2026-08-11T22:21:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722140</loc>
  <lastmod>2026-08-11T21:30:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>治療効果下のサブグループ探索のための機械学習（Machine learning for subgroup discovery under treatment effect）</news:title>
   <news:publication_date>2026-08-11T21:30:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722138</loc>
  <lastmod>2026-08-11T21:30:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散環境におけるビザンチン耐性確率的勾配降下法（Distributed Byzantine Tolerant Stochastic Gradient Descent in the Era of Big Data）</news:title>
   <news:publication_date>2026-08-11T21:30:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722136</loc>
  <lastmod>2026-08-11T21:30:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分 disentangled 表現による自動エンコーディング正則化がもたらす頑健な画像分類（Disentangled Deep Autoencoding Regularization for Robust Image Classification）</news:title>
   <news:publication_date>2026-08-11T21:30:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722134</loc>
  <lastmod>2026-08-11T21:29:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>未知力学系の安全環境と制御器を決める新しいシミュレーション指標（A New Simulation Metric to Determine Safe Environments and Controllers for Systems with Unknown Dynamics）</news:title>
   <news:publication_date>2026-08-11T21:29:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722132</loc>
  <lastmod>2026-08-11T21:29:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>TensorFlow Eagerが変えたプロトタイピングの常識（TensorFlow Eager: A multi-stage, Python-embedded DSL for machine learning）</news:title>
   <news:publication_date>2026-08-11T21:29:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722130</loc>
  <lastmod>2026-08-11T21:28:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューラルによるパケット分類の学習（Neural Packet Classification）</news:title>
   <news:publication_date>2026-08-11T21:28:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722128</loc>
  <lastmod>2026-08-11T21:28:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>映像特徴を時間と意味で豊かにする視覚エンコーディング（Spatio-Temporal Dynamics and Semantic Attribute Enriched Visual Encoding for Video Captioning）</news:title>
   <news:publication_date>2026-08-11T21:28:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722126</loc>
  <lastmod>2026-08-11T20:36:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>FixyNNによるモバイル向け効率的画像認識ハードウェア（FixyNN: Efficient Hardware for Mobile Computer Vision via Transfer Learning）</news:title>
   <news:publication_date>2026-08-11T20:36:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722124</loc>
  <lastmod>2026-08-11T20:36:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>先端ナノ空洞による単一量子エミッタのチップ強化強結合（Tip-enhanced strong coupling spectroscopy, imaging, and control of a single quantum emitter）</news:title>
   <news:publication_date>2026-08-11T20:36:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722122</loc>
  <lastmod>2026-08-11T20:36:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフ埋め込みを敵対的に整合する手法の要点と経営判断への示唆（Deep Adversarial Network Alignment）</news:title>
   <news:publication_date>2026-08-11T20:36:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722120</loc>
  <lastmod>2026-08-11T20:35:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層型コンテンツ配信ネットワークにおける適応キャッシュのための深層強化学習 (Deep Reinforcement Learning for Adaptive Caching in Hierarchical Content Delivery Networks)</news:title>
   <news:publication_date>2026-08-11T20:35:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722118</loc>
  <lastmod>2026-08-11T20:35:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>形式言語の表現：有限オートマタと再帰型ニューラルネットワークの比較（Representing Formal Languages: A Comparison Between Finite Automata and Recurrent Neural Networks）</news:title>
   <news:publication_date>2026-08-11T20:35:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722116</loc>
  <lastmod>2026-08-11T20:35:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ANODE：無条件に正確なメモリ効率の良いニューラルODEの勾配（ANODE: Unconditionally Accurate Memory-Efficient Gradients for Neural ODEs）</news:title>
   <news:publication_date>2026-08-11T20:35:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722114</loc>
  <lastmod>2026-08-11T20:35:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>バッファード確率的変分推論によるVAEの訓練改善（Training Variational Autoencoders with Buffered Stochastic Variational Inference）</news:title>
   <news:publication_date>2026-08-11T20:35:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722112</loc>
  <lastmod>2026-08-11T19:42:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多原因因果推論における未観測交絡の限界と代替手段（On Multi-Cause Causal Inference with Unobserved Confounding: Counterexamples, Impossibility, and Alternatives）</news:title>
   <news:publication_date>2026-08-11T19:42:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722110</loc>
  <lastmod>2026-08-11T19:42:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>順序付き距離計量学習とMDSによる画像ランキング（Ordinal Distance Metric Learning with MDS）</news:title>
   <news:publication_date>2026-08-11T19:42:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722108</loc>
  <lastmod>2026-08-11T19:42:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>データの価値を定量化する効率的手法（Towards Efficient Data Valuation Based on the Shapley Value）</news:title>
   <news:publication_date>2026-08-11T19:42:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722106</loc>
  <lastmod>2026-08-11T19:41:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層Q学習アルゴリズムのボトルネック診断（Diagnosing Bottlenecks in Deep Q-learning Algorithms）</news:title>
   <news:publication_date>2026-08-11T19:41:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722104</loc>
  <lastmod>2026-08-11T19:41:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ベイズ逆問題の「整定性」を緩やかに捉える新視点（On the well-posedness of Bayesian inverse problems）</news:title>
   <news:publication_date>2026-08-11T19:41:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722102</loc>
  <lastmod>2026-08-11T19:41:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層的談話構造学習によるフェイクニュース検出の深化（Learning Hierarchical Discourse-level Structure for Fake News Detection）</news:title>
   <news:publication_date>2026-08-11T19:41:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722100</loc>
  <lastmod>2026-08-11T19:40:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>3D点群オブジェクトのゼロショット学習（Zero-shot Learning of 3D Point Cloud Objects）</news:title>
   <news:publication_date>2026-08-11T19:40:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722098</loc>
  <lastmod>2026-08-11T18:49:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Lidarデータを圧縮して現場で使える地図と位置特定を両立する技術（TENSORMAP: LIDAR-BASED TOPOLOGICAL MAPPING AND LOCALIZATION VIA TENSOR DECOMPOSITIONS）</news:title>
   <news:publication_date>2026-08-11T18:49:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722096</loc>
  <lastmod>2026-08-11T18:49:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非線形力学系の線形埋め込みによる制御（Data-driven approximations of dynamical systems operators for control）</news:title>
   <news:publication_date>2026-08-11T18:49:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722094</loc>
  <lastmod>2026-08-11T18:48:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ターゲット指向ハイパースペクトル分離と一般化Robust PCA（Target–Based Hyperspectral Demixing via Generalized Robust PCA）</news:title>
   <news:publication_date>2026-08-11T18:48:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722092</loc>
  <lastmod>2026-08-11T18:47:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>入院患者の電子カルテからの継続的AKI予測（Continual Prediction from EHR Data for Inpatient Acute Kidney Injury）</news:title>
   <news:publication_date>2026-08-11T18:47:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722090</loc>
  <lastmod>2026-08-11T18:47:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>辞書に基づく一般化されたロバストPCAによるハイパースペクトル分離（A Dictionary-Based Generalization of Robust PCA Part II: Applications to Hyperspectral Demixing）</news:title>
   <news:publication_date>2026-08-11T18:47:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722088</loc>
  <lastmod>2026-08-11T18:47:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>回答しない判断を学習する仕組み（Learning When Not to Answer: A Ternary Reward Structure for Reinforcement Learning based Question Answering）</news:title>
   <news:publication_date>2026-08-11T18:47:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722086</loc>
  <lastmod>2026-08-11T18:46:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複雑ネットワークにおける疾病制御可能性の予測（Prediction of the disease controllability in a complex network using machine learning algorithms）</news:title>
   <news:publication_date>2026-08-11T18:46:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722084</loc>
  <lastmod>2026-08-11T17:54:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>インプリシット・カーネル学習が切り拓く新しいカーネル設計（Implicit Kernel Learning）</news:title>
   <news:publication_date>2026-08-11T17:54:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722082</loc>
  <lastmod>2026-08-11T17:54:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多重精度データから学ぶ複合ニューラルネットワーク（A composite neural network that learns from multi-fidelity data: Application to function approximation and inverse PDE problems）</news:title>
   <news:publication_date>2026-08-11T17:54:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722080</loc>
  <lastmod>2026-08-11T17:53:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>回転座標系の理解を深める映像教材の効果（Improving students’ understanding of rotating frames of reference using videos from different perspectives）</news:title>
   <news:publication_date>2026-08-11T17:53:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722078</loc>
  <lastmod>2026-08-11T17:53:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>信頼できる深層成績予測と不確実性推定（Reliable Deep Grade Prediction with Uncertainty Estimation）</news:title>
   <news:publication_date>2026-08-11T17:53:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722076</loc>
  <lastmod>2026-08-11T17:53:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>RotatEによる知識グラフ埋め込みと実務的示唆（RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space）</news:title>
   <news:publication_date>2026-08-11T17:53:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722074</loc>
  <lastmod>2026-08-11T17:53:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>全結合的低ランク・総変動正則化を用いたDeep MR Fingerprinting（Deep MR Fingerprinting with total-variation and low-rank subspace priors）</news:title>
   <news:publication_date>2026-08-11T17:53:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722072</loc>
  <lastmod>2026-08-11T17:52:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>都市の水・電力需要ネクサスの統合解析（Integrated analysis of the urban water-electricity demand nexus in the Midwestern United States）</news:title>
   <news:publication_date>2026-08-11T17:52:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722070</loc>
  <lastmod>2026-08-11T17:01:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>森林と都市での深層レーザーローカリゼーション（Learning to See the Wood for the Trees: Deep Laser Localization in Urban and Natural Environments on a CPU）</news:title>
   <news:publication_date>2026-08-11T17:01:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722068</loc>
  <lastmod>2026-08-11T17:01:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>進化するグラフ畳み込みネットワーク（EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs）</news:title>
   <news:publication_date>2026-08-11T17:01:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722066</loc>
  <lastmod>2026-08-11T17:00:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Clever Hansを見抜く方法と機械が本当に学んでいることの評価（Unmasking Clever Hans Predictors and Assessing What Machines Really Learn）</news:title>
   <news:publication_date>2026-08-11T17:00:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722064</loc>
  <lastmod>2026-08-11T17:00:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層ニューラルネットワークにおけるモデル不確実性の測定のための変分推論（Variational Inference to Measure Model Uncertainty in Deep Neural Networks）</news:title>
   <news:publication_date>2026-08-11T17:00:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722062</loc>
  <lastmod>2026-08-11T17:00:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Attentionは説明にならない（Attention is not Explanation）</news:title>
   <news:publication_date>2026-08-11T17:00:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722060</loc>
  <lastmod>2026-08-11T16:59:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>不確かさを持つ線形システムの収束性（Convergence in uncertain linear systems）</news:title>
   <news:publication_date>2026-08-11T16:59:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722058</loc>
  <lastmod>2026-08-11T16:59:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模な部分集合最適化を高速化するメモ化フレームワーク（A Memoization Framework for Scaling Submodular Optimization to Large Scale Problems）</news:title>
   <news:publication_date>2026-08-11T16:59:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722056</loc>
  <lastmod>2026-08-11T16:08:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-11T16:08:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722054</loc>
  <lastmod>2026-08-11T16:08:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-11T16:08:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722052</loc>
  <lastmod>2026-08-11T16:08:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的予測を逐次学習で統合する手法（Online Learning with Continuous Ranked Probability Score）</news:title>
   <news:publication_date>2026-08-11T16:08:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722050</loc>
  <lastmod>2026-08-11T16:06:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>次の十年における機械学習の役割（The Role of Machine Learning in the Next Decade of Cosmology）</news:title>
   <news:publication_date>2026-08-11T16:06:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722048</loc>
  <lastmod>2026-08-11T16:06:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>異常過渡現象の起源を探るHI観測の示唆（On the nature of the unusual transient AT 2018cow from Hi observations of its host galaxy）</news:title>
   <news:publication_date>2026-08-11T16:06:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722046</loc>
  <lastmod>2026-08-11T16:06:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>頻出k-merの高速近似とメタゲノミクスへの応用（Fast Approximation of Frequent k-mers and Applications to Metagenomics）</news:title>
   <news:publication_date>2026-08-11T16:06:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722044</loc>
  <lastmod>2026-08-11T16:06:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模グラフの彩色をAlphaGoZeroで学ぶ（Coloring Big Graphs With AlphaGoZero）</news:title>
   <news:publication_date>2026-08-11T16:06:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722042</loc>
  <lastmod>2026-08-11T15:14:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>コンテキスト化単語表現を用いたマルチタスク学習による拡張固有表現認識（Multi-Task Learning with Contextualized Word Representations for Extended Named Entity Recognition）</news:title>
   <news:publication_date>2026-08-11T15:14:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722040</loc>
  <lastmod>2026-08-11T15:14:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高赤方偏移でガス分率が高い主系列銀河の観測結果（High Gas Fraction in a CO-Selected Main-Sequence Galaxy at z &amp;gt; 3）</news:title>
   <news:publication_date>2026-08-11T15:14:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722038</loc>
  <lastmod>2026-08-11T15:14:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ネットワークに基づく病気遺伝子予測（Network-based methods for disease-gene prediction）</news:title>
   <news:publication_date>2026-08-11T15:14:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722036</loc>
  <lastmod>2026-08-11T15:13:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>発話レベル集約による野外環境での話者認識（UTTERANCE-LEVEL AGGREGATION FOR SPEAKER RECOGNITION IN THE WILD）</news:title>
   <news:publication_date>2026-08-11T15:13:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722034</loc>
  <lastmod>2026-08-11T15:12:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>伝送系の故障を想定したオンライン適応学習による電圧安定性評価（Adaptive Online Learning with Momentum for Contingency-based Voltage Stability Assessment）</news:title>
   <news:publication_date>2026-08-11T15:12:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722032</loc>
  <lastmod>2026-08-11T15:12:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>摂動履歴による探索（Perturbed-History Exploration in Stochastic Multi-Armed Bandits）</news:title>
   <news:publication_date>2026-08-11T15:12:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722030</loc>
  <lastmod>2026-08-11T15:12:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>細粒度意見抽出のためのマルチモーダル映画レビューコーパス（A multimodal movie review corpus for fine-grained opinion mining）</news:title>
   <news:publication_date>2026-08-11T15:12:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722028</loc>
  <lastmod>2026-08-11T14:21:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>反強磁性薄膜におけるドメイン壁移動の分散制御（Controllable dispersion of domain wall movement in antiferromagnetic thin films at finite temperatures）</news:title>
   <news:publication_date>2026-08-11T14:21:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722026</loc>
  <lastmod>2026-08-11T14:12:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>バンド行列演算子と自動微分時代のガウス・マルコフモデル（Banded Matrix Operators for Gaussian Markov Models in the Automatic Differentiation Era）</news:title>
   <news:publication_date>2026-08-11T14:12:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722024</loc>
  <lastmod>2026-08-11T14:12:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>直接的量子相関からの回復可能性（Recoverability from direct quantum correlations）</news:title>
   <news:publication_date>2026-08-11T14:12:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722022</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>マルチドメイン特徴学習による視覚的場所認識の新展開（A Multi-Domain Feature Learning Method for Visual Place Recognition）</news:title>
   <news:publication_date>2026-08-11T14:11:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722020</loc>
  <lastmod>2026-08-11T14:11:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文法に基づく機械学習システムの指向的テスト（Grammar Based Directed Testing of Machine Learning Systems）</news:title>
   <news:publication_date>2026-08-11T14:11:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722018</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>Graph Neural Processes（Towards Bayesian Graph Neural Networks）</news:title>
   <news:publication_date>2026-08-11T14:10:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722016</loc>
  <lastmod>2026-08-11T14:10:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>表データから臨床情報を取り出すためのフレームワーク（A framework for information extraction from tables in biomedical literature）</news:title>
   <news:publication_date>2026-08-11T14:10:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722014</loc>
  <lastmod>2026-08-11T13:18:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>STAR-Netによる動作認識と時空間活性化再投影（STAR-Net: Action Recognition using Spatio-Temporal Activation Reprojection）</news:title>
   <news:publication_date>2026-08-11T13:18:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722012</loc>
  <lastmod>2026-08-11T13:18:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>携帯型スパイロメトリにおける気流信号ベースの自動咳検出（Automatic cough detection based on airflow signals for portable spirometry system）</news:title>
   <news:publication_date>2026-08-11T13:18:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722010</loc>
  <lastmod>2026-08-11T13:17:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次元離散分布に対する厳密な適合度検定の体系 (A Family of Exact Goodness-of-Fit Tests for High-Dimensional Discrete Distributions)</news:title>
   <news:publication_date>2026-08-11T13:17:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722008</loc>
  <lastmod>2026-08-11T13:17:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オプションの自律発見と終了条件の批評（The Termination Critic）</news:title>
   <news:publication_date>2026-08-11T13:17:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722006</loc>
  <lastmod>2026-08-11T13:17:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>因果影響図で読み解くエージェントのインセンティブ（Understanding Agent Incentives using Causal Influence Diagrams: Part I: Single Decision Settings）</news:title>
   <news:publication_date>2026-08-11T13:17:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722004</loc>
  <lastmod>2026-08-11T13:17:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>完全分散型ベイズ最適化と確率的方策（Fully Distributed Bayesian Optimization with Stochastic Policies）</news:title>
   <news:publication_date>2026-08-11T13:17:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722002</loc>
  <lastmod>2026-08-11T13:16:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層的強化学習における局所方策での計画保証（Planning in Hierarchical Reinforcement Learning: Guarantees for Using Local Policies）</news:title>
   <news:publication_date>2026-08-11T13:16:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722000</loc>
  <lastmod>2026-08-11T12:24:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>順序モデルにおける意図的バックドアの設計（DESIGN OF INTENTIONAL BACKDOORS IN SEQUENTIAL MODELS）</news:title>
   <news:publication_date>2026-08-11T12:24:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721998</loc>
  <lastmod>2026-08-11T12:24:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>事象予測に基づく行動プリミティブの自律的同定と目標指向呼び出し（AUTONOMOUS IDENTIFICATION AND GOAL-DIRECTED INVOCATION OF EVENT-PREDICTIVE BEHAVIORAL PRIMITIVES）</news:title>
   <news:publication_date>2026-08-11T12:24:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721996</loc>
  <lastmod>2026-08-11T12:23:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>合成データだけで物体検出を学ばせる（An Annotation Saved is an Annotation Earned: Using Fully Synthetic Training for Object Instance Detection）</news:title>
   <news:publication_date>2026-08-11T12:23:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721994</loc>
  <lastmod>2026-08-11T12:22:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>意味関係に基づく物体追跡（Semantic Relational Object Tracking）</news:title>
   <news:publication_date>2026-08-11T12:22:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721992</loc>
  <lastmod>2026-08-11T12:22:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>摂動理論に基づく特徴選択（A Feature Selection Based on Perturbation Theory）</news:title>
   <news:publication_date>2026-08-11T12:22:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721990</loc>
  <lastmod>2026-08-11T12:22:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>頂点畳み込みネットワークによるグラフ分類の学習（Learning Vertex Convolutional Networks for Graph Classification）</news:title>
   <news:publication_date>2026-08-11T12:22:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721988</loc>
  <lastmod>2026-08-11T12:22:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>構造情報を用いた特徴選択のためのフューズドラッソ（Fused Lasso for Feature Selection using Structural Information）</news:title>
   <news:publication_date>2026-08-11T12:22:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721986</loc>
  <lastmod>2026-08-11T11:30:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動画行動認識における時間情報と特徴融合の革新（Information Fused Temporal Transformation Network）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-11T11:30:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721982</loc>
  <lastmod>2026-08-11T11:29:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>サンプリングバイアス下における二群間効果推定（Effect Inference from Two-Group Data with Sampling Bias）</news:title>
   <news:publication_date>2026-08-11T11:29:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721980</loc>
  <lastmod>2026-08-11T11:29:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Region Deformer Networksを用いた単眼無監督深度推定（Region Deformer Networks for Unsupervised Depth Estimation from Unconstrained Monocular Videos）</news:title>
   <news:publication_date>2026-08-11T11:29:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721978</loc>
  <lastmod>2026-08-11T11:28:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>トップ粒子識別の機械学習の全景（The Machine Learning Landscape of Top Taggers）</news:title>
   <news:publication_date>2026-08-11T11:28:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721976</loc>
  <lastmod>2026-08-11T11:28:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模な敵対的データに効率的に対応するオンラインカーネル学習（EFFICIENT ONLINE LEARNING WITH KERNELS FOR ADVERSARIAL LARGE SCALE PROBLEMS）</news:title>
   <news:publication_date>2026-08-11T11:28:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721974</loc>
  <lastmod>2026-08-11T11:28:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>HexaGANによる実世界分類問題への統合的対処（HexaGAN: Generative Adversarial Nets for Real World Classification）</news:title>
   <news:publication_date>2026-08-11T11:28:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721972</loc>
  <lastmod>2026-08-11T10:36:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>3D顔形状の分離表現学習（Disentangled Representation Learning for 3D Face Shape）</news:title>
   <news:publication_date>2026-08-11T10:36:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721970</loc>
  <lastmod>2026-08-11T10:36:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>海草（シーグラス）検出とマッピングのための撮像と分類技術（Imaging and Classification Techniques for Seagrass Mapping and Monitoring: A Comprehensive Survey）</news:title>
   <news:publication_date>2026-08-11T10:36:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721968</loc>
  <lastmod>2026-08-11T10:35:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>仮定・拡張・学習：ランダムラベルとデータ拡張による教師なし少数ショットメタ学習（Assume, Augment and Learn: Unsupervised Few-Shot Meta-Learning via Random Labels and Data Augmentation）</news:title>
   <news:publication_date>2026-08-11T10:35:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721966</loc>
  <lastmod>2026-08-11T10:35:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>太陽光発電の翌日時間別出力予測（Day-Ahead Hourly Forecasting of Power Generation from Photovoltaic Plants）</news:title>
   <news:publication_date>2026-08-11T10:35:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721964</loc>
  <lastmod>2026-08-11T10:34:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文脈ベクトルは単語ベクトルの半分の次元での反射である（Context Vectors are Reflections of Word Vectors in Half the Dimensions）</news:title>
   <news:publication_date>2026-08-11T10:34:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721962</loc>
  <lastmod>2026-08-11T10:34:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>3D人間姿勢推定の弱教師あり再投影ネットワーク（RepNet: Weakly Supervised Training of an Adversarial Reprojection Network for 3D Human Pose Estimation）</news:title>
   <news:publication_date>2026-08-11T10:34:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721960</loc>
  <lastmod>2026-08-11T10:34:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>シンボリック伝播による深層ニューラルネットワーク検証の高精度化と高速化（Analyzing Deep Neural Networks with Symbolic Propagation: Towards Higher Precision and Faster Verification）</news:title>
   <news:publication_date>2026-08-11T10:34:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721958</loc>
  <lastmod>2026-08-11T09:42:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>FliPerClassによるTESSデータの自動分類の実用性（FliPerClass: In search of solar-like pulsators among TESS targets）</news:title>
   <news:publication_date>2026-08-11T09:42:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721956</loc>
  <lastmod>2026-08-11T09:42:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>少ないデータで学ぶ：条件付きPGGANによる脳転移検出のデータ拡張（Learning More with Less: Conditional PGGAN-based Data Augmentation for Brain Metastases Detection Using Highly-Rough Annotation on MR Images）</news:title>
   <news:publication_date>2026-08-11T09:42:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721954</loc>
  <lastmod>2026-08-11T09:41:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>点群におけるインスタンスと意味の相互分割（Associatively Segmenting Instances and Semantics in Point Clouds）</news:title>
   <news:publication_date>2026-08-11T09:41:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721952</loc>
  <lastmod>2026-08-11T09:41:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>超音波センサの現実的環境シミュレーション（Realistic Ultrasonic Environment Simulation Using Conditional Generative Adversarial Networks）</news:title>
   <news:publication_date>2026-08-11T09:41:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721950</loc>
  <lastmod>2026-08-11T09:41:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習率に動的境界を設けた適応的勾配法（ADAPTIVE GRADIENT METHODS WITH DYNAMIC BOUND OF LEARNING RATE）</news:title>
   <news:publication_date>2026-08-11T09:41:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721948</loc>
  <lastmod>2026-08-11T09:40:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチスケールQuasi-RNNによる次アイテム推薦の要点解説（Multi-Scale Quasi-RNN for Next Item Recommendation）</news:title>
   <news:publication_date>2026-08-11T09:40:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721946</loc>
  <lastmod>2026-08-11T08:49:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>専門家の知見を少ないデータで活かす方法（Human-in-the-loop Active Covariance Learning for Improving Prediction in Small Data Sets）</news:title>
   <news:publication_date>2026-08-11T08:49:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721944</loc>
  <lastmod>2026-08-11T08:48:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>評価可能なゲーム戦略においてメタ解釈学習は深層強化学習を超えられるか（Can Meta-Interpretive Learning outperform Deep Reinforcement Learning of Evaluable Game strategies?）</news:title>
   <news:publication_date>2026-08-11T08:48:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721942</loc>
  <lastmod>2026-08-11T08:48:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>宇宙ニュートリノで探る強い相互作用の振る舞い（Probing strong dynamics with cosmic neutrinos）</news:title>
   <news:publication_date>2026-08-11T08:48:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721940</loc>
  <lastmod>2026-08-11T08:48:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>運転操作予測のドメイン適応型RNN（Robust and Subject-Independent Driving Manoeuvre Anticipation through Domain-Adversarial Recurrent Neural Networks）</news:title>
   <news:publication_date>2026-08-11T08:48:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721938</loc>
  <lastmod>2026-08-11T08:48:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>LaSOによる多ラベル少数例学習のためのラベル集合操作ネットワーク（LaSO: Label-Set Operations networks for multi-label few-shot learning）</news:title>
   <news:publication_date>2026-08-11T08:48:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721936</loc>
  <lastmod>2026-08-11T08:48:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>リンク属性を適切に組み込むGCN（GCN-LASE: Towards Adequately Incorporating Link Attributes in Graph Convolutional Networks）</news:title>
   <news:publication_date>2026-08-11T08:48:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721934</loc>
  <lastmod>2026-08-11T08:47:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>生成的視覚対話システムの学習手法（Generative Visual Dialogue System via Weighted Likelihood Estimation）</news:title>
   <news:publication_date>2026-08-11T08:47:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721932</loc>
  <lastmod>2026-08-11T07:55:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>パラメータ不要なオンラインロジスティック回帰に対する対数後悔の理論（Logarithmic Regret for Parameter-Free Online Logistic Regression）</news:title>
   <news:publication_date>2026-08-11T07:55:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721930</loc>
  <lastmod>2026-08-11T07:55:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>再帰畳み込みによる圧縮とコスト可変化（Recurrent Convolution for Compact and Cost-Adjustable Neural Networks）</news:title>
   <news:publication_date>2026-08-11T07:55:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721928</loc>
  <lastmod>2026-08-11T07:55:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>BMSTとテールバイティング畳み込み符号の統計学習支援復号（Statistical Learning Aided Decoding of BMST of Tail-Biting Convolutional Code）</news:title>
   <news:publication_date>2026-08-11T07:55:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721926</loc>
  <lastmod>2026-08-11T07:54:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>テキスト表現学習のためのセマンティック・ヒルベルト空間（Semantic Hilbert Space for Text Representation Learning）</news:title>
   <news:publication_date>2026-08-11T07:54:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721924</loc>
  <lastmod>2026-08-11T07:54:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>BoostGANによる横顔かつ遮蔽された顔の正面化と認識（BoostGAN for Occlusive Profile Face Frontalization and Recognition）</news:title>
   <news:publication_date>2026-08-11T07:54:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721922</loc>
  <lastmod>2026-08-11T07:54:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単一画像からの分片平面3D再構築（Single-Image Piece-wise Planar 3D Reconstruction via Associative Embedding）</news:title>
   <news:publication_date>2026-08-11T07:54:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721920</loc>
  <lastmod>2026-08-11T07:54:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>二次分解可能な部分モジュラ関数の最適化（Quadratic Decomposable Submodular Function Minimization）</news:title>
   <news:publication_date>2026-08-11T07:54:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721918</loc>
  <lastmod>2026-08-11T07:03:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>関数空間における粒子最適化法が切り拓くベイズニューラルネットワークの新地平（Function Space Particle Optimization for Bayesian Neural Networks）</news:title>
   <news:publication_date>2026-08-11T07:03:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721916</loc>
  <lastmod>2026-08-11T06:57:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>方向性埋め込みに基づく半教師あり鳥鳴き分割（Directional Embedding Based Semi-supervised Framework For Bird Vocalization Segmentation）</news:title>
   <news:publication_date>2026-08-11T06:57:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721914</loc>
  <lastmod>2026-08-11T06:56:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>希薄データセットでのガラスのヤング率予測（Predicting Young’s Modulus of Glasses with Sparse Datasets using Machine Learning）</news:title>
   <news:publication_date>2026-08-11T06:56:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/721912</loc>
  <lastmod>2026-08-11T06:56:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Interaction-aware Factorization Machinesによる推薦の精度向上（Interaction-aware Factorization Machines for Recommender Systems）</news:title>
   <news:publication_date>2026-08-11T06:56:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721910</loc>
  <lastmod>2026-08-11T06:55:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的需要対応型交通のオンライン予測最適化フレームワーク（Online Predictive Optimization Framework for Stochastic Demand-Responsive Transit Services）</news:title>
   <news:publication_date>2026-08-11T06:55:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721908</loc>
  <lastmod>2026-08-11T06:55:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>構造化データに対する機能的透明性の導入（Functional Transparency for Structured Data: a Game-Theoretic Approach）</news:title>
   <news:publication_date>2026-08-11T06:55:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721906</loc>
  <lastmod>2026-08-11T06:55:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Deep Variational Koopmanモデルによる不確実性に配慮した動力学モデリング（Deep Variational Koopman Models: Inferring Koopman Observations for Uncertainty-Aware Dynamics Modeling and Control）</news:title>
   <news:publication_date>2026-08-11T06:55:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721904</loc>
  <lastmod>2026-08-11T06:03:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>事前に変異解析を行う故障局所化手法の提案（Ahead of Time Mutation Based Fault Localisation using Statistical Inference）</news:title>
   <news:publication_date>2026-08-11T06:03:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721902</loc>
  <lastmod>2026-08-11T06:02:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ハーモニックな非対応画像変換（Harmonic Unpaired Image-to-Image Translation）</news:title>
   <news:publication_date>2026-08-11T06:02:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721900</loc>
  <lastmod>2026-08-11T06:02:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Saec: 類似性を利用した埋め込み圧縮によるレコメンド最適化（Saec: Similarity-Aware Embedding Compression in Recommendation Systems）</news:title>
   <news:publication_date>2026-08-11T06:02:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721898</loc>
  <lastmod>2026-08-11T06:02:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>トンネル効果を使った超省電力シリコンニューロン（Band-to-Band Tunneling based Ultra-Energy Efficient Silicon Neuron）</news:title>
   <news:publication_date>2026-08-11T06:02:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721896</loc>
  <lastmod>2026-08-11T06:01:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>トポロジカルベイズ最適化とパーシステンス図（Topological Bayesian Optimization with Persistence Diagrams）</news:title>
   <news:publication_date>2026-08-11T06:01:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721894</loc>
  <lastmod>2026-08-11T06:01:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ベイズ積分を用いた自動モデル選択（Automated Model Selection with Bayesian Quadrature）</news:title>
   <news:publication_date>2026-08-11T06:01:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721892</loc>
  <lastmod>2026-08-11T06:01:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文体（スタイロメトリ）を文法で捉える合成的アプローチ（Syntactic Recurrent Neural Network for Authorship Attribution）</news:title>
   <news:publication_date>2026-08-11T06:01:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721890</loc>
  <lastmod>2026-08-11T05:10:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>部分ラベルで学習するマルチラベル分類のための深層畳み込みネットワーク学習（Learning a Deep ConvNet for Multi-label Classification with Partial Labels）</news:title>
   <news:publication_date>2026-08-11T05:10:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721888</loc>
  <lastmod>2026-08-11T05:10:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ろうそく足チャートと深層畳み込みで株価を予測する手法（Using Deep Learning Neural Networks and Candlestick Chart Representation to Predict Stock Market）</news:title>
   <news:publication_date>2026-08-11T05:10:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721886</loc>
  <lastmod>2026-08-11T05:10:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散学習と最適化における線形収束の維持（On Maintaining Linear Convergence of Distributed Learning and Optimization under Limited Communication）</news:title>
   <news:publication_date>2026-08-11T05:10:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721884</loc>
  <lastmod>2026-08-11T05:09:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>圧縮動画における多フレーム品質改善の新手法（MFQE 2.0: A New Approach for Multi-frame Quality Enhancement on Compressed Video）</news:title>
   <news:publication_date>2026-08-11T05:09:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721882</loc>
  <lastmod>2026-08-11T05:09:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層注意を用いた構造可視化可能な文書エンコーダ（Interpretable Structure-aware Document Encoders with Hierarchical Attention）</news:title>
   <news:publication_date>2026-08-11T05:09:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721880</loc>
  <lastmod>2026-08-11T05:09:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CHASE-CIによるワークフロー駆動分散機械学習（Workflow-Driven Distributed Machine Learning in CHASE-CI）</news:title>
   <news:publication_date>2026-08-11T05:09:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721878</loc>
  <lastmod>2026-08-11T05:08:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>他者を解釈するロボットの自己起点学習（Beyond the Self: Using Grounded Affordances to Interpret and Describe Others’ Actions）</news:title>
   <news:publication_date>2026-08-11T05:08:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721876</loc>
  <lastmod>2026-08-11T04:17:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>暗黙的に再帰的なCNNを学習するためのパラメータ共有（LEARNING IMPLICITLY RECURRENT CNNS THROUGH PARAMETER SHARING）</news:title>
   <news:publication_date>2026-08-11T04:17:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721874</loc>
  <lastmod>2026-08-11T04:17:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフアルゴリズムの難解事例を学習する新手法（Learning to Sample Hard Instances for Graph Algorithms）</news:title>
   <news:publication_date>2026-08-11T04:17:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721872</loc>
  <lastmod>2026-08-11T04:16:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GANを用いた高速復元と収束保証を備えた射影器（GAN-based Projector for Faster Recovery with Convergence Guarantees in Linear Inverse Problems）</news:title>
   <news:publication_date>2026-08-11T04:16:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721870</loc>
  <lastmod>2026-08-11T04:16:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>リアルタイム資源スライシングのための高速最適化（Optimal and Fast Real-time Resources Slicing with Deep Dueling Neural Networks）</news:title>
   <news:publication_date>2026-08-11T04:16:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721868</loc>
  <lastmod>2026-08-11T04:16:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>欠測値を直接扱う最適クラスタリングの提案（Optimal clustering with missing values）</news:title>
   <news:publication_date>2026-08-11T04:16:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721866</loc>
  <lastmod>2026-08-11T04:16:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的ブレグマン並列双方向乗数法が変える分散最適化（Stochastic Bregman Parallel Direction Method of Multipliers）</news:title>
   <news:publication_date>2026-08-11T04:16:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721864</loc>
  <lastmod>2026-08-11T04:15:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ポリグロット文脈表現が切り拓く越境学習の実利（Polyglot Contextual Representations Improve Crosslingual Transfer）</news:title>
   <news:publication_date>2026-08-11T04:15:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721862</loc>
  <lastmod>2026-08-11T03:23:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>市場経済モデルによるCR-IoTの資源配分（Market-Based Model in CR-IoT: A Q-Probabilistic Multi-agent Learning Approach）</news:title>
   <news:publication_date>2026-08-11T03:23:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721860</loc>
  <lastmod>2026-08-11T03:13:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>反対称性RNN：再帰ニューラルネットワークの力学系的考察（ANTISYMMETRICRNN: A DYNAMICAL SYSTEM VIEW ON RECURRENT NEURAL NETWORKS）</news:title>
   <news:publication_date>2026-08-11T03:13:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721858</loc>
  <lastmod>2026-08-11T03:13:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフ処理アクセラレータの展望と課題（A Survey on Graph Processing Accelerators: Challenges and Opportunities）</news:title>
   <news:publication_date>2026-08-11T03:13:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721856</loc>
  <lastmod>2026-08-11T03:12:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>臨床ノートからのコホート選定のためのタスク特化辞書の開発と利用（Developing and Using Special-Purpose Lexicons for Cohort Selection from Clinical Notes）</news:title>
   <news:publication_date>2026-08-11T03:12:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721854</loc>
  <lastmod>2026-08-11T03:12:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチスケールガウス過程によるレベルセット推定（Multiscale Gaussian Process Level Set Estimation）</news:title>
   <news:publication_date>2026-08-11T03:12:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721852</loc>
  <lastmod>2026-08-11T03:12:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>イベント駆動型ビデオフレーム合成（Event-driven Video Frame Synthesis）</news:title>
   <news:publication_date>2026-08-11T03:12:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721850</loc>
  <lastmod>2026-08-11T03:12:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ソーシャルメディアにおける攻撃的投稿の種類と対象を同時に予測する手法（Predicting the Type and Target of Offensive Posts in Social Media）</news:title>
   <news:publication_date>2026-08-11T03:12:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721848</loc>
  <lastmod>2026-08-11T02:19:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>NAS-Bench-101が拓く再現性あるNeural Architecture Searchの世界（NAS-Bench-101: Towards Reproducible Neural Architecture Search）</news:title>
   <news:publication_date>2026-08-11T02:19:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721846</loc>
  <lastmod>2026-08-11T02:19:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>部分観測下での多エージェント相互作用の確率的予測（Stochastic Prediction of Multi-Agent Interactions from Partial Observations）</news:title>
   <news:publication_date>2026-08-11T02:19:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721844</loc>
  <lastmod>2026-08-11T02:19:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>TraVeLGANによる画像変換の新展開（TraVeLGAN: Image-to-image Translation by Transformation Vector Learning）</news:title>
   <news:publication_date>2026-08-11T02:19:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721842</loc>
  <lastmod>2026-08-11T02:18:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>低線量CTの深層学習によるノイズ除去（Deep Learning for Low-Dose CT Denoising）</news:title>
   <news:publication_date>2026-08-11T02:18:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721840</loc>
  <lastmod>2026-08-11T02:18:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ハチドリ並みの機敏さを小型ロボで実現する（Learning Extreme Hummingbird Maneuvers on Flapping Wing Robots）</news:title>
   <news:publication_date>2026-08-11T02:18:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721838</loc>
  <lastmod>2026-08-11T02:18:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>羽ばたき飛行ロボットの高精度シミュレーションがもたらす変化（Flappy Hummingbird: An Open Source Dynamic Simulation of Flapping Wing Robots and Animals）</news:title>
   <news:publication_date>2026-08-11T02:18:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721836</loc>
  <lastmod>2026-08-11T02:17:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>一般化されたIntersection over Union（Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression）</news:title>
   <news:publication_date>2026-08-11T02:17:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721834</loc>
  <lastmod>2026-08-11T01:25:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ビン分割散布図の評価と改善（On Binscatter）</news:title>
   <news:publication_date>2026-08-11T01:25:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721832</loc>
  <lastmod>2026-08-11T01:25:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>訓練データ選択の情報損失理論的解析（Analyzing Data Selection Techniques with Tools from the Theory of Information Losses）</news:title>
   <news:publication_date>2026-08-11T01:25:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721830</loc>
  <lastmod>2026-08-11T01:25:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模ネットワークにおける短期道路交通予測（Short-term Road Traffic Prediction based on Deep Cluster at Large-scale Networks）</news:title>
   <news:publication_date>2026-08-11T01:25:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721828</loc>
  <lastmod>2026-08-11T01:23:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オープンソース深層学習フレームワークの詳細比較（A Detailed Comparative Study of Open Source Deep Learning Frameworks）</news:title>
   <news:publication_date>2026-08-11T01:23:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721826</loc>
  <lastmod>2026-08-11T01:23:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ロバスト性を超えた仕様検証（Specifications Beyond Robustness）</news:title>
   <news:publication_date>2026-08-11T01:23:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721824</loc>
  <lastmod>2026-08-11T01:23:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>不完全データから学習するGANフレームワーク（MISGAN: LEARNING FROM INCOMPLETE DATA WITH GENERATIVE ADVERSARIAL NETWORKS）</news:title>
   <news:publication_date>2026-08-11T01:23:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721822</loc>
  <lastmod>2026-08-11T01:23:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>教師なし学習に基づく長期スーパーピクセルトラッキング（Unsupervised learning-based long-term superpixel tracking）</news:title>
   <news:publication_date>2026-08-11T01:23:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721815</loc>
  <lastmod>2026-08-11T00:31:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>PolyDroidによるモバイルアプリ最適化（PolyDroid: Learning-Driven Specialization of Mobile Applications）</news:title>
   <news:publication_date>2026-08-11T00:31:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721813</loc>
  <lastmod>2026-08-11T00:31:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>BERTで読み解く噂の立場判定（Determining the Rumour Stance with Pre-Trained Deep Bidirectional Transformers）</news:title>
   <news:publication_date>2026-08-11T00:31:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721811</loc>
  <lastmod>2026-08-11T00:30:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層ニューラルネットワークにおけるスパース化の現状（The State of Sparsity in Deep Neural Networks）</news:title>
   <news:publication_date>2026-08-11T00:30:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721809</loc>
  <lastmod>2026-08-11T00:29:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Eコマース価格システムの異常検知（Anomaly Detection for an E-commerce Pricing System）</news:title>
   <news:publication_date>2026-08-11T00:29:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721807</loc>
  <lastmod>2026-08-11T00:29:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>画像分類パイプラインにおける誤差寄与の定量化（Quantifying error contributions of computational steps, algorithms and hyperparameter choices in image classification pipelines）</news:title>
   <news:publication_date>2026-08-11T00:29:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721805</loc>
  <lastmod>2026-08-11T00:29:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>MOSDEFサーベイに見る高赤方偏移星形成銀河の運動学と構造進化（The MOSDEF Survey: Kinematic and Structural Evolution of Star-Forming Galaxies at 1.4 ≤ z ≤ 3.8）</news:title>
   <news:publication_date>2026-08-11T00:29:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721803</loc>
  <lastmod>2026-08-11T00:29:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>初期宇宙における「休止」銀河の実在確認（Passive galaxies in the early Universe: ALMA confirmation of z ∼3–5 candidates in the CANDELS GOODS-South field）</news:title>
   <news:publication_date>2026-08-11T00:29:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721801</loc>
  <lastmod>2026-08-10T23:37:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>消光と銀河コンフォーミティの一般的手法 (A general approach to quenching and galactic conformity)</news:title>
   <news:publication_date>2026-08-10T23:37:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721799</loc>
  <lastmod>2026-08-10T23:37:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>FEELVOSによる高速エンドツーエンド埋め込み学習で変わる動画物体セグメンテーション（FEELVOS: Fast End-to-End Embedding Learning for Video Object Segmentation）</news:title>
   <news:publication_date>2026-08-10T23:37:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721797</loc>
  <lastmod>2026-08-10T23:36:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>条件不変なマルチビュー場所認識の実務的インパクト（Condition-Invariant Multi-View Place Recognition）</news:title>
   <news:publication_date>2026-08-10T23:36:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721795</loc>
  <lastmod>2026-08-10T23:36:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>流入境界に基づくNavier–Stokes数値波槽：平坦底と傾斜底上の波伝播に関する検証と妥当性検証（An inflow-boundary-based Navier-Stokes wave tank: verification and validation for waves propagating over flat and inclined bottoms）</news:title>
   <news:publication_date>2026-08-10T23:36:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721793</loc>
  <lastmod>2026-08-10T23:36:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GQA：実世界の視覚的推論のための新データセット（GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question Answering）</news:title>
   <news:publication_date>2026-08-10T23:36:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721791</loc>
  <lastmod>2026-08-10T23:36:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>物体の深層特徴のみを用いた画像記述（Using Deep Features of Only Objects to Describe Images）</news:title>
   <news:publication_date>2026-08-10T23:36:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721789</loc>
  <lastmod>2026-08-10T23:35:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>合成ノイズによる機械翻訳の堅牢化（Improving Robustness of Machine Translation with Synthetic Noise）</news:title>
   <news:publication_date>2026-08-10T23:35:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721784</loc>
  <lastmod>2026-08-10T22:44:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>頭蓋内高血圧の早期予測を目指す多階層波形解析（Forecasting intracranial hypertension using multi-scale waveform metrics）</news:title>
   <news:publication_date>2026-08-10T22:44:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721782</loc>
  <lastmod>2026-08-10T22:44:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文脈依存型単語埋め込みの言語間整合とゼロショット構文解析への応用（Cross-Lingual Alignment of Contextual Word Embeddings, with Applications to Zero-shot Dependency Parsing）</news:title>
   <news:publication_date>2026-08-10T22:44:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721780</loc>
  <lastmod>2026-08-10T22:43:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>視覚質問応答のためのマルチモーダル関係推論（MUREL: Multimodal Relational Reasoning for Visual Question Answering）</news:title>
   <news:publication_date>2026-08-10T22:43:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721778</loc>
  <lastmod>2026-08-10T22:43:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>近似k近傍探索における適応的推定の考え方（Adaptive Estimation for Approximate k-Nearest-Neighbor Computations）</news:title>
   <news:publication_date>2026-08-10T22:43:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721776</loc>
  <lastmod>2026-08-10T22:42:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>重み付き損失関数と異種信号のための行列デノイジング（Matrix denoising for weighted loss functions and heterogeneous signals）</news:title>
   <news:publication_date>2026-08-10T22:42:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721774</loc>
  <lastmod>2026-08-10T22:42:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非凸特異値しきい値によるロジスティック主成分分析（Logistic principal component analysis via non-convex singular value thresholding）</news:title>
   <news:publication_date>2026-08-10T22:42:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721772</loc>
  <lastmod>2026-08-10T22:42:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>MedMentions：UMLSで注釈された大規模バイオ医療コーパスの公開（MedMentions: A Large Biomedical Corpus Annotated with UMLS Concepts）</news:title>
   <news:publication_date>2026-08-10T22:42:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721770</loc>
  <lastmod>2026-08-10T21:51:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>長距離屋内ナビゲーションを実現するPRM-RL（Long-Range Indoor Navigation with PRM-RL）</news:title>
   <news:publication_date>2026-08-10T21:51:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721768</loc>
  <lastmod>2026-08-10T21:50:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>S-TRIGGER：自己トリガー型生成リプレイによる継続的状態表現学習（S-TRIGGER: Continual State Representation Learning via Self-Triggered Generative Replay）</news:title>
   <news:publication_date>2026-08-10T21:50:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721766</loc>
  <lastmod>2026-08-10T21:50:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>加法的パラメータ分解による順序に頑健な継続学習（Scalable and Order‑Robust Continual Learning with Additive Parameter Decomposition）</news:title>
   <news:publication_date>2026-08-10T21:50:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721764</loc>
  <lastmod>2026-08-10T21:49:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>統計的サンプリングと新規特徴選択によるてんかん発作検出の実用性（Epileptic seizure classification using statistical sampling and a novel feature selection algorithm）</news:title>
   <news:publication_date>2026-08-10T21:49:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721762</loc>
  <lastmod>2026-08-10T21:49:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>超低消費エネルギーのCTFベースシナプスと寄生リーク抑制（Ultra-low Energy charge trap flash based synapse enabled by parasitic leakage mitigation）</news:title>
   <news:publication_date>2026-08-10T21:49:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news: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>格子変調によるマルチマーの伝播制御（Manipulating multimer propagation using lattice modulation）</news:title>
   <news:publication_date>2026-08-10T21:48:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721756</loc>
  <lastmod>2026-08-10T20:57:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-10T20:57:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721754</loc>
  <lastmod>2026-08-10T20:49:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-10T20:49:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721752</loc>
  <lastmod>2026-08-10T20:49:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習された変換によるワンショット医療画像セグメンテーションのデータ拡張（Data augmentation using learned transformations for one-shot medical image segmentation）</news:title>
   <news:publication_date>2026-08-10T20:49:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721750</loc>
  <lastmod>2026-08-10T20:48:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-10T20:48:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721748</loc>
  <lastmod>2026-08-10T20:47:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>コンパクトファジーモデル構築のための分散ルール導出アルゴリズムCFM-BD（CFM-BD: a distributed rule induction algorithm for building Compact Fuzzy Models in Big Data classification problems）</news:title>
   <news:publication_date>2026-08-10T20:47:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-10T20:47:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>半教師付き・弱教師付き階層テキスト分類のための効率的パス予測 (Efficient Path Prediction for Semi-Supervised and Weakly Supervised Hierarchical Text Classification)</news:title>
   <news:publication_date>2026-08-10T20:47:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-10T20:47:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>履歴を重視する視覚対話学習（Making History Matter: History-Advantage Sequence Training for Visual Dialog）</news:title>
   <news:publication_date>2026-08-10T20:47:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721742</loc>
  <lastmod>2026-08-10T19:54:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>重み付きパーソナライズ行列因子分解によるマルチラベルネットワーク分類（Multi-Label Network Classification via Weighted Personalized Factorizations）</news:title>
   <news:publication_date>2026-08-10T19:54:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721740</loc>
  <lastmod>2026-08-10T19:54:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Wasserstein-Wassersteinオートエンコーダ（Wasserstein-Wasserstein Auto-Encoders）</news:title>
   <news:publication_date>2026-08-10T19:54:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721738</loc>
  <lastmod>2026-08-10T19:53:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>パーソナライズされた仮想教育アシスタント（A Virtual Teaching Assistant for Personalized Learning）</news:title>
   <news:publication_date>2026-08-10T19:53:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-10T19:53:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-10T19:52:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-10T19:52:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721732</loc>
  <lastmod>2026-08-10T19:52:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>明示的文脈条件付けを用いた関係抽出（Relation Extraction using Explicit Context Conditioning）</news:title>
   <news:publication_date>2026-08-10T19:52:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721730</loc>
  <lastmod>2026-08-10T19:52:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-10T19:52:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721728</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-10T19:01:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721726</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-10T18:52:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721724</loc>
  <lastmod>2026-08-10T18:52:18Z</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>2026-08-10T18:52:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721722</loc>
  <lastmod>2026-08-10T18:52:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-10T18:52:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721720</loc>
  <lastmod>2026-08-10T18:51:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高解像度表現学習による人物姿勢推定の刷新（Deep High-Resolution Representation Learning for Human Pose Estimation）</news:title>
   <news:publication_date>2026-08-10T18:51:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721718</loc>
  <lastmod>2026-08-10T18:51:15Z</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>2026-08-10T18:51:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721716</loc>
  <lastmod>2026-08-10T18:51:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-10T18:51:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721714</loc>
  <lastmod>2026-08-10T17:58:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-10T17:58:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721712</loc>
  <lastmod>2026-08-10T17:58:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>実生活下における鏡像系の皮質活動（Cortical Mirror-System Activation During Real-Life Game Playing: An Intracranial Electroencephalography (EEG) Study）</news:title>
   <news:publication_date>2026-08-10T17:58:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721710</loc>
  <lastmod>2026-08-10T17:57:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>応答の多様性を高める周波数対応交差エントロピー損失（Improving Neural Response Diversity with Frequency-Aware Cross-Entropy Loss）</news:title>
   <news:publication_date>2026-08-10T17:57:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721708</loc>
  <lastmod>2026-08-10T17:57:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>宇宙の塵に覆われた星形成銀河の統計的性質—Herschelデータの多波長de-blend解析 (A multi-wavelength de-blended Herschel view of the statistical properties of dusty star-forming galaxies across cosmic time)</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721706</loc>
  <lastmod>2026-08-10T17:56:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数領域を同時に学習する短答自動採点（Joint Multi-Domain Learning for Automatic Short Answer Grading）</news:title>
   <news:publication_date>2026-08-10T17:56:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721704</loc>
  <lastmod>2026-08-10T17:56:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自動運転におけるコーナーケース検出の実装と評価（Towards Corner Case Detection for Autonomous Driving）</news:title>
   <news:publication_date>2026-08-10T17:56:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721702</loc>
  <lastmod>2026-08-10T17:56:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GFCN：並列フローに基づく新しいグラフ畳み込みネットワーク（GFCN: A New Graph Convolutional Network Based on Parallel Flows）</news:title>
   <news:publication_date>2026-08-10T17:56:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721700</loc>
  <lastmod>2026-08-10T17:05:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>医療画像解析におけるクラウドソーシングの概観（A Survey of Crowdsourcing in Medical Image Analysis）</news:title>
   <news:publication_date>2026-08-10T17:05:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721698</loc>
  <lastmod>2026-08-10T17:04:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オンラインクラスタリングバンディットの改良アルゴリズム（Improved Algorithm on Online Clustering of Bandits）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721696</loc>
  <lastmod>2026-08-10T17:04:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>不確かな穴への迅速なペグ挿入（Quickly Inserting Pegs into Uncertain Holes using Multi-view Images and Deep Network Trained on Synthetic Data）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <lastmod>2026-08-10T17:04:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>長読みに基づくウイルスゲノム進化の未来的方法（Futuristic methods in virus genome evolution using the Third-Generation DNA sequencing and artificial neural networks）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <lastmod>2026-08-10T17:04:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチグリッド偏微分方程式（PDE）ソルバーの最適化を学習する（Learning to Optimize Multigrid PDE Solvers）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
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    <news:name>AI Benchmark Research</news:name>
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   <news:publication_date>2026-08-10T17:03:10Z</news:publication_date>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-10T16:09:29Z</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:genres>Blog</news:genres>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-10T15:09:41Z</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:genres>Blog</news:genres>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/721664</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-10T15:08:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/721662</loc>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/721660</loc>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-10T15:07:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
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