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   <news:title>回路設計を学習する（Learning to Design Circuits）</news:title>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>実世界システムにおける効率的かつ頑健な機械学習（Efficient and Robust Machine Learning for Real-World Systems）</news:title>
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   <news:title>ダイバージェンス補正によるエントロピック方策の合成（Composing Entropic Policies using Divergence Correction）</news:title>
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    <news:language>ja</news:language>
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   <news:title>女性向けヘルストラッキングアプリの大規模データによる妊娠予測（Predicting pregnancy using large-scale data from a women’s health tracking mobile application）</news:title>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>補助損失を勾配の類似性で適応させる方法（ADAPTING AUXILIARY LOSSES USING GRADIENT SIMILARITY）</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>カデンス外交のビッグスカイ戦略（A Big Sky Approach to Cadence Diplomacy）</news:title>
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    <news:name>AI Benchmark Research</news:name>
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   <news:title>堅牢性検証の枠組み：質問応答モデルの「意味」と「数値」を分けて測る（Are you tough enough? Framework for Robustness Validation of Machine Comprehension Systems）</news:title>
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    <news:language>ja</news:language>
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   <news:title>決定木のハイパーパラメータ最適化はどこまで効くか（Better Trees: An empirical study on hyperparameter tuning of classification decision tree induction algorithms）</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>データ駆動型デコンボリューションによるクラヒナン乱流の大規模渦シミュレーション（Data-driven deconvolution for large eddy simulations of Kraichnan turbulence）</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>大規模深層学習による銀河カタログ作成（Deep Learning at Scale for the Construction of Galaxy Catalogs in the Dark Energy Survey）</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>MAMLにおける「適応で悪化する」現象の考え方（The effects of negative adaptation in Model-Agnostic Meta-Learning）</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>クラスベース言語モデルとトークンパッシングデコーダによる終端型文脈音声認識（END-TO-END CONTEXTUAL SPEECH RECOGNITION USING CLASS LANGUAGE MODELS AND A TOKEN PASSING DECODER）</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>HEVC における人工参照画像生成による符号化効率向上（HEVC INTER CODING USING DEEP RECURRENT NEURAL NETWORKS AND ARTIFICIAL REFERENCE PICTURES）</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>多段階幾何データ解析（Multiscale Geometric Data Analysis via Laplacian Eigenvector Cascading）</news:title>
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   <news:genres>Blog</news:genres>
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  <lastmod>2026-07-14T05:07:12Z</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>非対応画像間での形状変換による画像翻訳とクロスドメイン検索（An Unpaired Shape Transforming Method for Image Translation and Cross-Domain Retrieval）</news:title>
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   <news:genres>Blog</news:genres>
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  <lastmod>2026-07-14T04:16:05Z</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>情報幾何学に基づく近似ベイズ計算の理解（INFORMATION GEOMETRY FOR APPROXIMATE BAYESIAN COMPUTATION）</news:title>
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   <news:genres>Blog</news:genres>
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  <lastmod>2026-07-14T04:15:57Z</lastmod>
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    <news:language>ja</news:language>
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   <news:title>カーネル行列の固有値に関する相対的収束境界（Relative concentration bounds for the spectrum of kernel matrices）</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>線分検出のための魅力場表現（Learning Attraction Field Representation for Robust Line Segment Detection）</news:title>
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   <news:genres>Blog</news:genres>
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  <lastmod>2026-07-14T04:15:18Z</lastmod>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>教師主導の専門能力開発と探究理解の深化（Teacher-Driven Professional Development and the Pursuit of a Sophisticated Understanding of Inquiry）</news:title>
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   <news:genres>Blog</news:genres>
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  <lastmod>2026-07-14T04:15:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>無ラベルサンプル圧縮とコーナー削除の幾何学的接続（UNLABELED SAMPLE COMPRESSION SCHEMES AND CORNER PEELINGS FOR AMPLE AND MAXIMUM CLASSES）</news:title>
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   <news:genres>Blog</news:genres>
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  <lastmod>2026-07-14T04:14:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>深宇宙観測に対する視野マスクのブラインド復元法（A blind method to recover the mask of a deep galaxy survey）</news:title>
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   <news:genres>Blog</news:genres>
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  <lastmod>2026-07-14T04:14:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>CNNの個別判断を解きほぐす対照的逆伝播（Understanding Individual Decisions of CNNs via Contrastive Backpropagation）</news:title>
   <news:publication_date>2026-07-14T04:14:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-07-14T03:23:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>API文書における概念的相互運用性制約の識別に関する機械学習（How practical is it? Machine Learning for Identifying Conceptual Interoperability Constraints in API Documents）</news:title>
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   <news:genres>Blog</news:genres>
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  <lastmod>2026-07-14T03:23:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>LASSOによるSTEM学習成果の大規模評価（LASSO: Learning About STEM Student Outcomes）</news:title>
   <news:publication_date>2026-07-14T03:23:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-07-14T03:23:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>低計算量・データ並列なEarth Mover’s Distance近似（Low-Complexity Data-Parallel Earth Mover’s Distance Approximations）</news:title>
   <news:publication_date>2026-07-14T03:23:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-07-14T03:22:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単眼ビジョンでの高速自律走行を実現する動的観測器（Vision-Based High Speed Driving with a Deep Dynamic Observer）</news:title>
   <news:publication_date>2026-07-14T03:22:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/711591</loc>
  <lastmod>2026-07-14T03:22:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>k-spaceからの脳領域分割を目指す統合注意ネットワーク（Brain Segmentation from k-space with End-to-end Recurrent Attention Network）</news:title>
   <news:publication_date>2026-07-14T03:22:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-07-14T03:22:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>多変量非対称パワーGARCHモデルの推定（Estimation of multivariate asymmetric power GARCH models）</news:title>
   <news:publication_date>2026-07-14T03:22:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-07-14T03:21:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Calibrate: 事前知識を取り入れた局所的差分プライバシーによる頻度推定とヘビーヒッタ同定（Calibrate: Frequency Estimation and Heavy Hitter Identification with Local Differential Privacy via Incorporating Prior Knowledge）</news:title>
   <news:publication_date>2026-07-14T03:21:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-07-14T02:30:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>OSIRISデータ削減パイプラインの評価と改良（Characterization and Improvement of the OSIRIS Data Reduction Pipeline）</news:title>
   <news:publication_date>2026-07-14T02:30:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/711583</loc>
  <lastmod>2026-07-14T02:30:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>イベントカメラからのカラー映像合成（Learn to See by Events: Color Frame Synthesis from Event and RGB Cameras）</news:title>
   <news:publication_date>2026-07-14T02:30:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711581</loc>
  <lastmod>2026-07-14T02:29:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Point-to-Pose Votingに基づく点群手指姿勢推定（Point-to-Pose Voting based Hand Pose Estimation using Residual Permutation Equivariant Layer）</news:title>
   <news:publication_date>2026-07-14T02:29:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711579</loc>
  <lastmod>2026-07-14T02:29:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非退化集合論的ヤング–バクスター方程式の構造モノイドと代数（THE STRUCTURE MONOID AND ALGEBRA OF A NON-DEGENERATE SET-THEORETIC SOLUTION OF THE YANG–BAXTER EQUATION）</news:title>
   <news:publication_date>2026-07-14T02:29:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711577</loc>
  <lastmod>2026-07-14T02:29:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的重みドロップを用いるモデル剪定（Stochastic Model Pruning via Weight Dropping Away and Back）</news:title>
   <news:publication_date>2026-07-14T02:29:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711575</loc>
  <lastmod>2026-07-14T02:29:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>間接探索によるダークマター検出の現状（TASI Lectures on Indirect Searches For Dark Matter）</news:title>
   <news:publication_date>2026-07-14T02:29:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711573</loc>
  <lastmod>2026-07-14T02:29:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>交通流予測のための動的時空間グラフCNN（Dynamic Spatio-temporal Graph-based CNNs for Traffic Flow Prediction）</news:title>
   <news:publication_date>2026-07-14T02:29:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711571</loc>
  <lastmod>2026-07-14T01:38:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>注意に基づく動画要約（Summarizing Videos with Attention）</news:title>
   <news:publication_date>2026-07-14T01:38:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711569</loc>
  <lastmod>2026-07-14T01:38:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動画の記憶性を構築・解析・予測する（VideoMem: Constructing, Analyzing, Predicting Short-term and Long-term Video Memorability）</news:title>
   <news:publication_date>2026-07-14T01:38:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711567</loc>
  <lastmod>2026-07-14T01:37:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチクラスタ統合RBMによる教師なし特徴学習の実務的解説（Unsupervised Feature Learning Architecture with Multi-clustering Integration RBM）</news:title>
   <news:publication_date>2026-07-14T01:37:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711565</loc>
  <lastmod>2026-07-14T01:37:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スクラッチから訓練する高性能バイナリニューラルネットワーク（Training Competitive Binary Neural Networks from Scratch）</news:title>
   <news:publication_date>2026-07-14T01:37:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711563</loc>
  <lastmod>2026-07-14T01:36:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>肺癌腫瘍領域のボリューム分割における再帰型3D-DenseUNet（Lung Cancer Tumor Region Segmentation Using Recurrent 3D-DenseUNet）</news:title>
   <news:publication_date>2026-07-14T01:36:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711561</loc>
  <lastmod>2026-07-14T01:36:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>汚染された相対比較からの頑健な序数埋め込み (Robust Ordinal Embedding from Contaminated Relative Comparisons)</news:title>
   <news:publication_date>2026-07-14T01:36:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711559</loc>
  <lastmod>2026-07-14T01:36:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>データ由来密度の不確実性管理によるDFT高速化（Managing uncertainty in data-derived densities to accelerate density functional theory）</news:title>
   <news:publication_date>2026-07-14T01:36:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711557</loc>
  <lastmod>2026-07-14T00:44:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>少ない比較で強くなる埋め込み ― 分布的マージンで汎化を高める手法（Less but Better: Generalization Enhancement of Ordinal Embedding via Distributional Margin）</news:title>
   <news:publication_date>2026-07-14T00:44:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711555</loc>
  <lastmod>2026-07-14T00:44:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>堅牢な顔ランドマーク検出を実現する「Stacked Dense U-Nets with Dual Transformers」（Stacked Dense U-Nets with Dual Transformers）</news:title>
   <news:publication_date>2026-07-14T00:44:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711553</loc>
  <lastmod>2026-07-14T00:44:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層畳み込みネットが転倒をどう認識するか―実証的解析の示唆（An empirical study towards understanding how deep convolutional nets recognize falls）</news:title>
   <news:publication_date>2026-07-14T00:44:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711551</loc>
  <lastmod>2026-07-14T00:43:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オープンドメイン質問におけるエンティティ発見とリンクの知識グラフアプローチ (A Knowledge Graph based Solution for Entity Discovery and Linking in Open-domain Questions)</news:title>
   <news:publication_date>2026-07-14T00:43:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711549</loc>
  <lastmod>2026-07-14T00:43:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>入力ごとにフィルタを作るCNNの発想（Learning to Generate Filters for Convolutional Neural Networks）</news:title>
   <news:publication_date>2026-07-14T00:43:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711547</loc>
  <lastmod>2026-07-14T00:42:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>微分方程式解の導関数に関する自動微分と連続感度解析の比較（A Comparison of Automatic Differentiation and Continuous Sensitivity Analysis for Derivatives of Differential Equation Solutions）</news:title>
   <news:publication_date>2026-07-14T00:42:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711545</loc>
  <lastmod>2026-07-14T00:42:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複合的な身体活動を時系列で捉える計算グラフアプローチ（Computational Graph Approach for Detection of Composite Human Activities）</news:title>
   <news:publication_date>2026-07-14T00:42:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711543</loc>
  <lastmod>2026-07-13T23:51:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>医療短文分類のための語クラスタ埋め込みによる意味拡張（Improving Medical Short Text Classification with Semantic Expansion Using Word-Cluster Embedding）</news:title>
   <news:publication_date>2026-07-13T23:51:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711541</loc>
  <lastmod>2026-07-13T23:51:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動的木構造で視覚文脈を組み立てる（Learning to Compose Dynamic Tree Structures for Visual Contexts）</news:title>
   <news:publication_date>2026-07-13T23:51:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711539</loc>
  <lastmod>2026-07-13T23:51:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>一枚の画像から動きを作る技術――一歩ずつ指示を学ぶ（Learning to Take Directions One Step at a Time）</news:title>
   <news:publication_date>2026-07-13T23:51:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711537</loc>
  <lastmod>2026-07-13T23:50:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>解釈可能な分類ルールをMaxSATで学ぶ（MLIC: A MaxSAT-Based framework for learning interpretable classification rules）</news:title>
   <news:publication_date>2026-07-13T23:50:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711535</loc>
  <lastmod>2026-07-13T23:50:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ラベル駆動型深層変形画像レジストレーションの進化（Enhancing Label-Driven Deep Deformable Image Registration with Local Distance Metrics for State-of-the-Art Cardiac Motion Tracking）</news:title>
   <news:publication_date>2026-07-13T23:50:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711533</loc>
  <lastmod>2026-07-13T23:50:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>少数ショット物体検出のための特徴再重み付け（Few-shot Object Detection via Feature Reweighting）</news:title>
   <news:publication_date>2026-07-13T23:50:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711531</loc>
  <lastmod>2026-07-13T23:50:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次元ローカリティセンシティブハッシングによる類似検索の改善（Improving Similarity Search with High-dimensional Locality-sensitive Hashing）</news:title>
   <news:publication_date>2026-07-13T23:50:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711529</loc>
  <lastmod>2026-07-13T22:59:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>少数サンプル知識蒸留による効率的なネットワーク圧縮（Few Sample Knowledge Distillation for Efficient Network Compression）</news:title>
   <news:publication_date>2026-07-13T22:59:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711527</loc>
  <lastmod>2026-07-13T22:59:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>注目機構を強化した逐次推論モデル（Attention Boosted Sequential Inference Model）</news:title>
   <news:publication_date>2026-07-13T22:59:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711525</loc>
  <lastmod>2026-07-13T22:58:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>仮想マシンの動的資源最適化がもたらす現場変革（ADARES: Adaptive Resource Management for Virtual Machines）</news:title>
   <news:publication_date>2026-07-13T22:58:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711523</loc>
  <lastmod>2026-07-13T22:58:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>部分最適デモを活用した協調マルチエージェント学習の実務的示唆（Cooperative Multi-Agent Policy Gradients with Sub-optimal Demonstration）</news:title>
   <news:publication_date>2026-07-13T22:58:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711521</loc>
  <lastmod>2026-07-13T22:57:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>不確実性サンプリングはゼロ・ワン損失に対する前条件付き確率的勾配降下法である（Uncertainty Sampling is Preconditioned Stochastic Gradient Descent on Zero-One Loss）</news:title>
   <news:publication_date>2026-07-13T22:57:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711519</loc>
  <lastmod>2026-07-13T22:57:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>段階的ナレッジ蒸留（An Embarrassingly Simple Approach for Knowledge Distillation）</news:title>
   <news:publication_date>2026-07-13T22:57:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711517</loc>
  <lastmod>2026-07-13T22:57:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>正則化アンサンブルと敵対的学習における転送性（Regularized Ensembles and Transferability in Adversarial Learning）</news:title>
   <news:publication_date>2026-07-13T22:57:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711515</loc>
  <lastmod>2026-07-13T22:06:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>視覚的注意を用いた行動複製型自動運転の改善（Visual Attention for Behavioral Cloning in Autonomous Driving）</news:title>
   <news:publication_date>2026-07-13T22:06:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711513</loc>
  <lastmod>2026-07-13T22:06:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模リアルタイムによる食品由来疾患検出（Machine-learned epidemiology: real-time detection of foodborne illness at scale）</news:title>
   <news:publication_date>2026-07-13T22:06:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711511</loc>
  <lastmod>2026-07-13T22:05:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>シンプルなテキストデータ拡張の実務的手法（Text Data Augmentation Made Simple By Leveraging NLP Cloud APIs）</news:title>
   <news:publication_date>2026-07-13T22:05:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711509</loc>
  <lastmod>2026-07-13T22:05:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューラル抽象的テキスト要約とSequence-to-Sequenceモデル（Neural Abstractive Text Summarization with Sequence-to-Sequence Models）</news:title>
   <news:publication_date>2026-07-13T22:05:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711507</loc>
  <lastmod>2026-07-13T22:04:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>エネルギー制約下でのプラットフォーム非依存DNN圧縮（ECC: Platform-Independent Energy-Constrained Deep Neural Network Compression via a Bilinear Regression Model）</news:title>
   <news:publication_date>2026-07-13T22:04:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711505</loc>
  <lastmod>2026-07-13T22:04:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>フレーズ認識で記事推薦を強化する手法（Enriching Article Recommendation with Phrase Awareness）</news:title>
   <news:publication_date>2026-07-13T22:04:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711503</loc>
  <lastmod>2026-07-13T22:04:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>敵対的事例への評価体系とRandom Spikingの提案（Random Spiking and Systematic Evaluation of Defenses Against Adversarial Examples）</news:title>
   <news:publication_date>2026-07-13T22:04:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711501</loc>
  <lastmod>2026-07-13T21:13:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>数学言語を注意深く扱うトランスフォーマー（Attending to Mathematical Language with Transformers）</news:title>
   <news:publication_date>2026-07-13T21:13:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711499</loc>
  <lastmod>2026-07-13T21:12:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>共有潜在表現で進化するゼロショット学習（Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders）</news:title>
   <news:publication_date>2026-07-13T21:12:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711497</loc>
  <lastmod>2026-07-13T21:12:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>InferLine：厳格なレイテンシ目標に対するML推論パイプラインのプロビジョニングと管理（InferLine: ML Prediction Pipeline Provisioning and Management for Tight Latency Objectives）</news:title>
   <news:publication_date>2026-07-13T21:12:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711495</loc>
  <lastmod>2026-07-13T21:12:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>浸水建物の迅速分割を可能にした多解像度・多センサー・多時点融合（Multi3Net: Segmenting Flooded Buildings via Fusion of Multiresolution, Multisensor, and Multitemporal Satellite Imagery）</news:title>
   <news:publication_date>2026-07-13T21:12:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711493</loc>
  <lastmod>2026-07-13T21:12:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>曲率制約経路による多様体クラスタリング（Multiple Manifold Clustering Using Curvature Constrained Path）</news:title>
   <news:publication_date>2026-07-13T21:12:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711491</loc>
  <lastmod>2026-07-13T21:11:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>長期時系列に強靭な分解をもたらす手法の実装と検証（RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series）</news:title>
   <news:publication_date>2026-07-13T21:11:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711489</loc>
  <lastmod>2026-07-13T21:11:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Deep Positronによる低精度推論の挑戦（Deep Positron: A Deep Neural Network Using the Posit Number System）</news:title>
   <news:publication_date>2026-07-13T21:11:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711487</loc>
  <lastmod>2026-07-13T20:20:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数ソースのモーメント一致によるドメイン適応（Moment Matching for Multi-Source Domain Adaptation）</news:title>
   <news:publication_date>2026-07-13T20:20:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711485</loc>
  <lastmod>2026-07-13T20:19:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>脳血管ネットワークのMRA画像セグメンテーション（CEREBROVASCULAR NETWORK SEGMENTATION OF MRA IMAGES WITH DEEP LEARNING）</news:title>
   <news:publication_date>2026-07-13T20:19:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711483</loc>
  <lastmod>2026-07-13T20:19:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>シーン画像に基づく補完型商品推薦（Complete the Look: Scene-based Complementary Product Recommendation）</news:title>
   <news:publication_date>2026-07-13T20:19:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711481</loc>
  <lastmod>2026-07-13T20:18:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチビュー交差監督によるセマンティックセグメンテーション（Multiview Cross-supervision for Semantic Segmentation）</news:title>
   <news:publication_date>2026-07-13T20:18:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711479</loc>
  <lastmod>2026-07-13T20:18:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>操作可能な物体合成のための分解手法（Decompose to manipulate: Manipulable Object Synthesis in 3D Medical Images with Structured Image Decomposition）</news:title>
   <news:publication_date>2026-07-13T20:18:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711477</loc>
  <lastmod>2026-07-13T20:18:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューロモルフィックハードウェアにおけるキーワード検出の効率ベンチマーク（Benchmarking Keyword Spotting Efficiency on Neuromorphic Hardware）</news:title>
   <news:publication_date>2026-07-13T20:18:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711475</loc>
  <lastmod>2026-07-13T20:18:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単一視点からのドメイン適応型3D復元の実用化可能性（Domain-Adaptive Single-View 3D Reconstruction）</news:title>
   <news:publication_date>2026-07-13T20:18:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711473</loc>
  <lastmod>2026-07-13T19:26:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>個別化された心血管応答の学習（Learning Individualized Cardiovascular Responses from Large-scale Wearable Sensors Data）</news:title>
   <news:publication_date>2026-07-13T19:26:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711471</loc>
  <lastmod>2026-07-13T19:16:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ボルツマンジェネレータによる一発サンプリング（Boltzmann Generators – Sampling Equilibrium States of Many-Body Systems with Deep Learning）</news:title>
   <news:publication_date>2026-07-13T19:16:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711469</loc>
  <lastmod>2026-07-13T19:09:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習による粗視化分子力場の構築（Machine Learning of coarse-grained Molecular Dynamics Force Fields）</news:title>
   <news:publication_date>2026-07-13T19:09:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711467</loc>
  <lastmod>2026-07-13T19:08:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>安価で新規性のある航空旅程の学習（Learning Cheap and Novel Flight Itineraries）</news:title>
   <news:publication_date>2026-07-13T19:08:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711465</loc>
  <lastmod>2026-07-13T19:08:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>極値のためのグラフィカルモデル（Graphical Models for Extremes）</news:title>
   <news:publication_date>2026-07-13T19:08:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711463</loc>
  <lastmod>2026-07-13T19:07:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>変化する環境下における最適意思決定モデル（Optimal models of decision-making in dynamic environments）</news:title>
   <news:publication_date>2026-07-13T19:07:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711461</loc>
  <lastmod>2026-07-13T19:06:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>屋内環境の高速3D再構築によるVRシリアスゲームシナリオ生成（RAPID 3D RECONSTRUCTION OF INDOOR ENVIRONMENTS TO GENERATE VIRTUAL REALITY SERIOUS GAMES SCENARIOS）</news:title>
   <news:publication_date>2026-07-13T19:06:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711459</loc>
  <lastmod>2026-07-13T18:15:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深いクイench近似と一般化Cahn–Hilliard系の最適制御（Deep quench approximation and optimal control of general Cahn–Hilliard systems with fractional operators and double obstacle potentials）</news:title>
   <news:publication_date>2026-07-13T18:15:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711457</loc>
  <lastmod>2026-07-13T18:15:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オンデバイスCNN推論の省エネハードウェア（Energy Efficient Hardware for On-Device CNN Inference via Transfer Learning）</news:title>
   <news:publication_date>2026-07-13T18:15:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711455</loc>
  <lastmod>2026-07-13T18:14:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>TensorFlowのCPU並列設定を自動最適化する研究（Auto-tuning TensorFlow Threading Model for CPU Backend）</news:title>
   <news:publication_date>2026-07-13T18:14:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711453</loc>
  <lastmod>2026-07-13T18:13:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>フィードフォワードニューラルネットワークは帰納的バイアスを必要とする（Feed-Forward Neural Networks Need Inductive Bias to Learn Equality Relations）</news:title>
   <news:publication_date>2026-07-13T18:13:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711451</loc>
  <lastmod>2026-07-13T18:13:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフに基づくセキュリティとプライバシー解析（Graph-based Security and Privacy Analytics via Collective Classification with Joint Weight Learning and Propagation）</news:title>
   <news:publication_date>2026-07-13T18:13:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711449</loc>
  <lastmod>2026-07-13T18:13:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習によるポイントクラウドの最適サンプリング（Learning to Sample）</news:title>
   <news:publication_date>2026-07-13T18:13:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711447</loc>
  <lastmod>2026-07-13T18:12:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>位相的特徴を用いた安定したカーディナリティ距離による分類（A Stable Cardinality Distance for Topological Classification）</news:title>
   <news:publication_date>2026-07-13T18:12:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711445</loc>
  <lastmod>2026-07-13T17:21:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的増分近接勾配法がもたらす最適化の見える化（A probabilistic incremental proximal gradient method）</news:title>
   <news:publication_date>2026-07-13T17:21:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711443</loc>
  <lastmod>2026-07-13T17:21:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>厳密なエージェント評価：敵対的手法で致命的失敗を明らかにする（Rigorous Agent Evaluation: An Adversarial Approach to Uncover Catastrophic Failures）</news:title>
   <news:publication_date>2026-07-13T17:21:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711441</loc>
  <lastmod>2026-07-13T17:20:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ソフトパラメータプルーニングによる破滅的忘却の克服（Overcoming Catastrophic Forgetting by Soft Parameter Pruning）</news:title>
   <news:publication_date>2026-07-13T17:20:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711439</loc>
  <lastmod>2026-07-13T17:20:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>遠隔津波予測の高速化を実現する応答関数法（A response function approach for rapid far-field tsunami forecasting）</news:title>
   <news:publication_date>2026-07-13T17:20:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711437</loc>
  <lastmod>2026-07-13T17:20:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフベース深層強化学習によるテキストアドベンチャー攻略（Playing Text-Adventure Games with Graph-Based Deep Reinforcement Learning）</news:title>
   <news:publication_date>2026-07-13T17:20:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711435</loc>
  <lastmod>2026-07-13T17:20:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動画から学ぶ3D人体動態の理解（Learning 3D Human Dynamics from Video）</news:title>
   <news:publication_date>2026-07-13T17:20:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711433</loc>
  <lastmod>2026-07-13T17:19:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>日常生活での歩容（gait）を確率的にモデル化する意義と手法（Probabilistic modelling of gait for remote passive monitoring applications）</news:title>
   <news:publication_date>2026-07-13T17:19:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711431</loc>
  <lastmod>2026-07-13T16:28:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>AutoFocusによる効率的なマルチスケール推論（AutoFocus: Efficient Multi-Scale Inference）</news:title>
   <news:publication_date>2026-07-13T16:28:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711429</loc>
  <lastmod>2026-07-13T16:28:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>並列ダブルグリーディ法によるサブモジュラ最大化（A Parallel Double Greedy Algorithm for Submodular Maximization）</news:title>
   <news:publication_date>2026-07-13T16:28:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711427</loc>
  <lastmod>2026-07-13T16:27:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動画伝播と境界ラベル緩和によるセマンティックセグメンテーションの改善（Improving Semantic Segmentation via Video Propagation and Label Relaxation）</news:title>
   <news:publication_date>2026-07-13T16:27:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711425</loc>
  <lastmod>2026-07-13T16:27:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>法令ネットワークを作る情報抽出の枠組み（Information Extraction Framework to Build Legislation Network）</news:title>
   <news:publication_date>2026-07-13T16:27:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711423</loc>
  <lastmod>2026-07-13T16:27:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>子どもの自然表情データベースの意義と応用（A novel database of children’s spontaneous facial expressions (LIRIS-CSE))</news:title>
   <news:publication_date>2026-07-13T16:27:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711421</loc>
  <lastmod>2026-07-13T16:26:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ベイズ求積法の並列化のためのバッチ選択（Batch Selection for Parallelisation of Bayesian Quadrature）</news:title>
   <news:publication_date>2026-07-13T16:26:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711419</loc>
  <lastmod>2026-07-13T16:26:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多レベルMIMO検出に深層学習を適用する意義（Multilevel MIMO Detection with Deep Learning）</news:title>
   <news:publication_date>2026-07-13T16:26:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711417</loc>
  <lastmod>2026-07-13T15:35:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>探索と活用の最適なバランスを連続時間で定式化する（Exploration versus exploitation in reinforcement learning: a stochastic control approach）</news:title>
   <news:publication_date>2026-07-13T15:35:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711415</loc>
  <lastmod>2026-07-13T15:35:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>地上型ガンマ線天文学における粒子識別にCNNを適用する意義（Particle identification in ground-based gamma-ray astronomy using convolutional neural networks）</news:title>
   <news:publication_date>2026-07-13T15:35:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711413</loc>
  <lastmod>2026-07-13T15:34:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>放射線教育における生成的敵対的ネットワークの新たなパラダイム（Towards generative adversarial networks as a new paradigm for radiology education）</news:title>
   <news:publication_date>2026-07-13T15:34:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711411</loc>
  <lastmod>2026-07-13T15:33:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所選択的並列外れ値アンサンブルの組合せ（Locally Selective Combination in Parallel Outlier Ensembles）</news:title>
   <news:publication_date>2026-07-13T15:33:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711409</loc>
  <lastmod>2026-07-13T15:33:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューラルネットワークに符号化された相転移（Phase transition encoded in neural network）</news:title>
   <news:publication_date>2026-07-13T15:33:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711407</loc>
  <lastmod>2026-07-13T15:33:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>小さなLie理論がロボティクスの状態推定を変えた（A micro Lie theory for state estimation in robotics）</news:title>
   <news:publication_date>2026-07-13T15:33:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711405</loc>
  <lastmod>2026-07-13T15:32:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>未解決の宇宙赤外線背景とX線背景のクロスパワーの探査（Probing the cross-power of unresolved cosmic infrared and X-ray backgrounds）</news:title>
   <news:publication_date>2026-07-13T15:32:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711403</loc>
  <lastmod>2026-07-13T14:41:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>波動伝播の近似解を深層ニューラルネットワークで求める（Approximating the solution to wave propagation using deep neural networks）</news:title>
   <news:publication_date>2026-07-13T14:41:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711401</loc>
  <lastmod>2026-07-13T14:41:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習による帰納法の提案（Towards Machine Learning Induction）</news:title>
   <news:publication_date>2026-07-13T14:41:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711399</loc>
  <lastmod>2026-07-13T14:40:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非接触ワイヤレスセンシングによる継続的ユーザー認証（Continuous User Authentication by Contactless Wireless Sensing）</news:title>
   <news:publication_date>2026-07-13T14:40:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711397</loc>
  <lastmod>2026-07-13T14:40:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>未知の対象ドメインに強い方言識別のためのドメイン注意付き融合（DOMAIN ATTENTIVE FUSION FOR END-TO-END DIALECT IDENTIFICATION WITH UNKNOWN TARGET DOMAIN）</news:title>
   <news:publication_date>2026-07-13T14:40:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711395</loc>
  <lastmod>2026-07-13T14:39:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>経験的機械学習の探索空間を広げる（Expanding search in the space of empirical ML）</news:title>
   <news:publication_date>2026-07-13T14:39:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711393</loc>
  <lastmod>2026-07-13T14:39:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高解像度画像生成を実現するサブスケールピクセルネットワークと多次元アップスケーリング（GENERATING HIGH FIDELITY IMAGES WITH SUBSCALE PIXEL NETWORKS AND MULTIDIMENSIONAL UPSCALING）</news:title>
   <news:publication_date>2026-07-13T14:39:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711391</loc>
  <lastmod>2026-07-13T14:39:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Natural Option Criticの要点と実務的意義（Natural Option Critic）</news:title>
   <news:publication_date>2026-07-13T14:39:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711389</loc>
  <lastmod>2026-07-13T13:48:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>臨床データのための差分プライバシー対応分散深層学習（Privacy-Preserving Distributed Deep Learning for Clinical Data）</news:title>
   <news:publication_date>2026-07-13T13:48:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711387</loc>
  <lastmod>2026-07-13T13:47:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>不均衡データと費用行列を用いた映画ジャンル推薦の実務応用（UTILIZING IMBALANCED DATA AND CLASSIFICATION COST MATRIX TO PREDICT MOVIE PREFERENCES）</news:title>
   <news:publication_date>2026-07-13T13:47:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711385</loc>
  <lastmod>2026-07-13T13:47:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>行動を「区切って再利用する」学習法の提案 — CompILE: Compositional Imitation Learning and Execution (CompILE: Compositional Imitation Learning and Execution)</news:title>
   <news:publication_date>2026-07-13T13:47:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711383</loc>
  <lastmod>2026-07-13T13:46:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>視覚遠心分離機：モデルに依存しない層化された映像表現（The Visual Centrifuge: Model-Free Layered Video Representations）</news:title>
   <news:publication_date>2026-07-13T13:46:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711381</loc>
  <lastmod>2026-07-13T13:46:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>コンフォーカルレーザ顕微鏡画像による大腸癌検出の実現可能性（Feasibility of Colon Cancer Detection in Confocal Laser Microscopy Images Using Convolution Neural Networks）</news:title>
   <news:publication_date>2026-07-13T13:46:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711379</loc>
  <lastmod>2026-07-13T13:46:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>q-LMF：量子微分に基づくLeast Mean Fourthアルゴリズム（q-LMF: Quantum Calculus-based Least Mean Fourth Algorithm）</news:title>
   <news:publication_date>2026-07-13T13:46:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711377</loc>
  <lastmod>2026-07-13T13:46:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層学習による行列分解の実務的理解（Matrix Factorization via Deep Learning）</news:title>
   <news:publication_date>2026-07-13T13:46:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711375</loc>
  <lastmod>2026-07-13T12:54:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>弱教師ありConvLSTMによる腹腔鏡動画の器具追跡（Weakly Supervised Convolutional LSTM Approach for Tool Tracking in Laparoscopic Videos）</news:title>
   <news:publication_date>2026-07-13T12:54:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711373</loc>
  <lastmod>2026-07-13T12:54:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層注意誘導型ハッシング（Deep Attention-guided Hashing）</news:title>
   <news:publication_date>2026-07-13T12:54:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711371</loc>
  <lastmod>2026-07-13T12:54:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>圧縮分類の考え方と実装可能性（Compressive Classification (Machine Learning without learning)）</news:title>
   <news:publication_date>2026-07-13T12:54:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711369</loc>
  <lastmod>2026-07-13T12:53:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自己導出型信念伝播（Self-Guided Belief Propagation – A Homotopy Continuation Method）</news:title>
   <news:publication_date>2026-07-13T12:53:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711367</loc>
  <lastmod>2026-07-13T12:53:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>生物視覚から学ぶ教師なし階層スパース符号化（From biological vision to unsupervised hierarchical sparse coding）</news:title>
   <news:publication_date>2026-07-13T12:53:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711365</loc>
  <lastmod>2026-07-13T12:53:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>LSTMを用いたAE‑DNN制約による遅延残響抑圧の改善（LSTM based AE-DNN constraint for better late reverb suppression in multi-channel LP formulation）</news:title>
   <news:publication_date>2026-07-13T12:53:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711363</loc>
  <lastmod>2026-07-13T12:52:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>構造化セマンティックモデルによるCTR予測強化（Structured Semantic Model supported Deep Neural Network for Click-Through Rate Prediction）</news:title>
   <news:publication_date>2026-07-13T12:52:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711361</loc>
  <lastmod>2026-07-13T12:01:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>計算資源制約下でのチャンネル単位プルーニング（Channel-wise pruning of neural networks with tapering resource constraint）</news:title>
   <news:publication_date>2026-07-13T12:01:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711359</loc>
  <lastmod>2026-07-13T12:01:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高速キャリブレーターシミュレーションのための生成モデル（Generative Models for Fast Calorimeter Simulation: the LHCb case）</news:title>
   <news:publication_date>2026-07-13T12:01:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711357</loc>
  <lastmod>2026-07-13T12:00:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>命令型プログラムを記号的グラフへ変換するJANUS（JANUS: Fast and Flexible Deep Learning via Symbolic Graph Execution of Imperative Programs）</news:title>
   <news:publication_date>2026-07-13T12:00:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711355</loc>
  <lastmod>2026-07-13T12:00:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Timeceptionによる複雑な行動認識の長期時系列モデリング（Timeception for Complex Action Recognition）</news:title>
   <news:publication_date>2026-07-13T12:00:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711353</loc>
  <lastmod>2026-07-13T12:00:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>補完例を用いた説明学習（Learning to Explain with Complemental Examples）</news:title>
   <news:publication_date>2026-07-13T12:00:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711351</loc>
  <lastmod>2026-07-13T11:59:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>継続的ドメイン適応による医療画像解析の現場適用（Towards Continuous Domain adaptation for Healthcare）</news:title>
   <news:publication_date>2026-07-13T11:59:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711349</loc>
  <lastmod>2026-07-13T11:59:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>希少事象検出のための分離表現学習（Rare Event Detection using Disentangled Representation Learning）</news:title>
   <news:publication_date>2026-07-13T11:59:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711347</loc>
  <lastmod>2026-07-13T11:08:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>歌声分離のための畳み込みニューラルネットワーク（Singing Voice Separation Using a Deep Convolutional Neural Network Trained by Ideal Binary Mask and Cross Entropy）</news:title>
   <news:publication_date>2026-07-13T11:08:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711345</loc>
  <lastmod>2026-07-13T11:07:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>透過率マップと環境照明の共同推定による画像デヘイズ (Image Dehazing via Joint Estimation of Transmittance Map and Environmental Illumination)</news:title>
   <news:publication_date>2026-07-13T11:07:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711343</loc>
  <lastmod>2026-07-13T11:07:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高圧縮ハイパーボリック・グラフェンプラズモンの広帯域全方位負の屈折（Broadband negative refraction of highly squeezed hyperbolic graphene plasmons）</news:title>
   <news:publication_date>2026-07-13T11:07:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711341</loc>
  <lastmod>2026-07-13T11:06:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>不確実性下におけるリスク回避的挙動計画（Risk-averse Behavior Planning for Autonomous Driving under Uncertainty）</news:title>
   <news:publication_date>2026-07-13T11:06:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711339</loc>
  <lastmod>2026-07-13T11:06:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学術用語の分野差を数値化する方法（Quantification and Analysis of Scientific Language Variation Across Research Fields）</news:title>
   <news:publication_date>2026-07-13T11:06:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711337</loc>
  <lastmod>2026-07-13T11:06:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>傾いた大質量惑星と円盤の破断が示す観測的手がかり（Inclined Massive Planets in a Protoplanetary Disc: Gap Opening, Disc Breaking, and Observational Signatures）</news:title>
   <news:publication_date>2026-07-13T11:06:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711335</loc>
  <lastmod>2026-07-13T11:05:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>短時間の音声事象を少数例で学習する注意付き類似性（LEARNING TO MATCH TRANSIENT SOUND EVENTS USING ATTENTIONAL SIMILARITY FOR FEW-SHOT SOUND RECOGNITION）</news:title>
   <news:publication_date>2026-07-13T11:05:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711333</loc>
  <lastmod>2026-07-13T10:14:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>構造化問合せへの自然言語インターフェースの移植性向上（Transferable Natural Language Interface to Structured Queries aided by Adversarial Generation）</news:title>
   <news:publication_date>2026-07-13T10:14:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711331</loc>
  <lastmod>2026-07-13T10:14:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>位置ベースのチャネルデータベースの二段階学習と補間法（A Two-Step Learning and Interpolation Method for Location-Based Channel Database）</news:title>
   <news:publication_date>2026-07-13T10:14:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711329</loc>
  <lastmod>2026-07-13T10:13:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>効率的注意機構：線形複雑性を持つ注意（Efficient Attention: Attention with Linear Complexities）</news:title>
   <news:publication_date>2026-07-13T10:13:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711327</loc>
  <lastmod>2026-07-13T10:12:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>合成データ生成のためのバイン・コピュラモデル学習（Learning Vine Copula Models For Synthetic Data Generation）</news:title>
   <news:publication_date>2026-07-13T10:12:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711325</loc>
  <lastmod>2026-07-13T10:12:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動的チャネルサウンディングによるMU‑MIMOの最適化（Dynamic Sounding for Multi-User MIMO in Wireless LANs）</news:title>
   <news:publication_date>2026-07-13T10:12:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711323</loc>
  <lastmod>2026-07-13T10:12:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>物体間の関係を可視化する動画理解の刷新 — Spatio-Temporal Action Graph Networks（Spatio-Temporal Action Graph Networks）</news:title>
   <news:publication_date>2026-07-13T10:12:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711321</loc>
  <lastmod>2026-07-13T10:12:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ユーザーの修正から学ぶロボットのコスト関数推定（Learning from Extrapolated Corrections）</news:title>
   <news:publication_date>2026-07-13T10:12:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711319</loc>
  <lastmod>2026-07-13T09:20:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>離れた基地局の無線チャネル情報を推定する手法と理論的下限（Inferring Remote Channel State Information: Cramér-Rao Lower Bound and Deep Learning Implementation）</news:title>
   <news:publication_date>2026-07-13T09:20:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711317</loc>
  <lastmod>2026-07-13T09:19:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>車載ネットワークにおける時系列チャネル推定によるビームアライメント（Time-Sequence Channel Inference for Beam Alignment in Vehicular Networks）</news:title>
   <news:publication_date>2026-07-13T09:19:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711315</loc>
  <lastmod>2026-07-13T09:19:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>半教師ありハイパースペクトル画像分類のためのラダーネットワーク（Ladder Networks for Semi-Supervised Hyperspectral Image Classification）</news:title>
   <news:publication_date>2026-07-13T09:19:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711313</loc>
  <lastmod>2026-07-13T09:18:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>バッチサイズを周期的に変えることで学習を「再初期化」する手法（Parameter Re-Initialization through Cyclical Batch Size Schedules）</news:title>
   <news:publication_date>2026-07-13T09:18:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711311</loc>
  <lastmod>2026-07-13T09:18:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>プロトタイプベースのニューラルネットワーク層：ベクトル量子化の導入（Prototype-based Neural Network Layers: Incorporating Vector Quantization）</news:title>
   <news:publication_date>2026-07-13T09:18:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711309</loc>
  <lastmod>2026-07-13T09:18:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>HUBzeroを用いた天体素粒子物理教育プラットフォームの適用（Application of HUBzero platform for the educational process in astroparticle physics）</news:title>
   <news:publication_date>2026-07-13T09:18:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711307</loc>
  <lastmod>2026-07-13T09:18:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>集合の順序を無視する損失関数の提案（Set Cross Entropy: Likelihood-based Permutation Invariant Loss Function for Probability Distributions）</news:title>
   <news:publication_date>2026-07-13T09:18:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711305</loc>
  <lastmod>2026-07-13T08:27:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Zoom-In-to-Check: インスタンス単位判別で動画補間を高精度化する手法（Zoom-In-to-Check: Boosting Video Interpolation via Instance-level Discrimination）</news:title>
   <news:publication_date>2026-07-13T08:27:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711303</loc>
  <lastmod>2026-07-13T08:26:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模事前学習言語モデルによる実用的テキスト分類（Practical Text Classification With Large Pre-Trained Language Models）</news:title>
   <news:publication_date>2026-07-13T08:26:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711301</loc>
  <lastmod>2026-07-13T08:26:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>無線VRで「没入の断絶（Break in Presence）」を減らす分散学習の提案（Federated Echo State Learning for Minimizing Breaks in Presence in Wireless Virtual Reality Networks）</news:title>
   <news:publication_date>2026-07-13T08:26:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711299</loc>
  <lastmod>2026-07-13T08:25:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Twitterベースの交通情報システム（Twitter-based traffic information system based on vector representations for words）</news:title>
   <news:publication_date>2026-07-13T08:25:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711297</loc>
  <lastmod>2026-07-13T08:25:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>敵対的例の分解（Adversarial Example Decomposition）</news:title>
   <news:publication_date>2026-07-13T08:25:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711295</loc>
  <lastmod>2026-07-13T08:24:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>産業用無線センサネットワークのスーパーフレーム最適化（Evolutionary Computing Approach to Optimize Superframe Scheduling on Industrial Wireless Sensor Networks）</news:title>
   <news:publication_date>2026-07-13T08:24:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711293</loc>
  <lastmod>2026-07-13T08:24:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>既存例を「引き出して編集する」発想が示す実務的突破口（A Retrieve-and-Edit Framework for Predicting Structured Outputs）</news:title>
   <news:publication_date>2026-07-13T08:24:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711291</loc>
  <lastmod>2026-07-13T07:33:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ものと背景の融合学習（Learning to Fuse Things and Stuff）</news:title>
   <news:publication_date>2026-07-13T07:33:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711289</loc>
  <lastmod>2026-07-13T07:33:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>説明するNLIデータセット e-SNLIの意義（e-SNLI: Natural Language Inference with Natural Language Explanations）</news:title>
   <news:publication_date>2026-07-13T07:33:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711287</loc>
  <lastmod>2026-07-13T07:33:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Eコマース向けエンドツーエンドニューラルマッチング（EENMF: An End-to-End Neural Matching Framework for E-Commerce Sponsored Search）</news:title>
   <news:publication_date>2026-07-13T07:33:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711285</loc>
  <lastmod>2026-07-13T07:32:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>並列温度付き確率的勾配Hamiltonian Monte Carloによる多峰事後分布のサンプリング（Parallel-tempered Stochastic Gradient Hamiltonian Monte Carlo for Approximate Multimodal Posterior Sampling）</news:title>
   <news:publication_date>2026-07-13T07:32:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711283</loc>
  <lastmod>2026-07-13T07:32:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マニフォールド学習を用いた明示的モデル予測制御への考察（Some manifold learning considerations towards explicit model predictive control）</news:title>
   <news:publication_date>2026-07-13T07:32:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711281</loc>
  <lastmod>2026-07-13T07:32:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>FRAME再考：粒子進化に基づく解釈的視点 (FRAME Revisited: An Interpretation View Based on Particle Evolution)</news:title>
   <news:publication_date>2026-07-13T07:32:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711279</loc>
  <lastmod>2026-07-13T07:31:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>画像分類のためのトレーニング小技集（Bag of Tricks for Image Classification with Convolutional Neural Networks）</news:title>
   <news:publication_date>2026-07-13T07:31:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711277</loc>
  <lastmod>2026-07-13T06:40:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スペクトル正則化による教師なし分離表現の獲得（A Spectral Regularizer for Unsupervised Disentanglement）</news:title>
   <news:publication_date>2026-07-13T06:40:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711275</loc>
  <lastmod>2026-07-13T06:30:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層的連続時間隠れマルコフモデルとゼロ過剰加速度計データへの応用（Hierarchical Continuous Time Hidden Markov Model, with Application in Zero-Inflated Accelerometer Data）</news:title>
   <news:publication_date>2026-07-13T06:30:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711273</loc>
  <lastmod>2026-07-13T06:29:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>事前定義スパースニューラルネットワークとハードウェア加速（Pre-Defined Sparse Neural Networks with Hardware Acceleration）</news:title>
   <news:publication_date>2026-07-13T06:29:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711271</loc>
  <lastmod>2026-07-13T06:29:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>北天黄道スパーによる太陽系調査（A Northern Ecliptic Survey for Solar System Science）</news:title>
   <news:publication_date>2026-07-13T06:29:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711269</loc>
  <lastmod>2026-07-13T06:28:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>声の異常検出にLSTMを使う意義（Voice Disorder Detection Using Long Short Term Memory (LSTM) Model）</news:title>
   <news:publication_date>2026-07-13T06:28:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711267</loc>
  <lastmod>2026-07-13T06:28:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>星形成を伴うSeyfert 2銀河における塊状円盤風の証拠（Evidence for a clumpy disc-wind in the star forming Seyfert 2 galaxy MCG–03–58–007）</news:title>
   <news:publication_date>2026-07-13T06:28:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711265</loc>
  <lastmod>2026-07-13T06:28:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>対話研究の原点回帰と協調指向（Back to the Future for Dialogue Research: A Position Paper）</news:title>
   <news:publication_date>2026-07-13T06:28:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711263</loc>
  <lastmod>2026-07-13T05:37:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>仮説検証のための逐次実験設計（Sequential Experiment Design for Hypothesis Verification）</news:title>
   <news:publication_date>2026-07-13T05:37:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711261</loc>
  <lastmod>2026-07-13T05:37:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>プランナー過学習の緩和（Mitigating Planner Overfitting in Model-Based Reinforcement Learning）</news:title>
   <news:publication_date>2026-07-13T05:37:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711259</loc>
  <lastmod>2026-07-13T05:36:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>サンプリングベースの非ホロノミック運動計画のための自車ポーズ予測学習（Learning to Predict Ego-Vehicle Poses for Sampling-Based Nonholonomic Motion Planning）</news:title>
   <news:publication_date>2026-07-13T05:36:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711257</loc>
  <lastmod>2026-07-13T05:35:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>無線機を識別するためのORACLE（ORACLE: Optimized Radio classification through Convolutional neuraL nEtworks）</news:title>
   <news:publication_date>2026-07-13T05:35:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711255</loc>
  <lastmod>2026-07-13T05:35:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>シフト不変構造の学習（On learning with shift-invariant structures）</news:title>
   <news:publication_date>2026-07-13T05:35:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711253</loc>
  <lastmod>2026-07-13T05:35:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>生成モデルで探る銀河進化（Exploring galaxy evolution with generative models）</news:title>
   <news:publication_date>2026-07-13T05:35:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711251</loc>
  <lastmod>2026-07-13T05:35:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高効率UV-C薄膜フリップチップLEDの作製技術（Fabrication technology for high light-extraction ultraviolet thin-film flip-chip (UV TFFC) LEDs grown on SiC）</news:title>
   <news:publication_date>2026-07-13T05:35:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711249</loc>
  <lastmod>2026-07-13T04:43:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>連合環境向けベンチマークLEAF（LEAF: A Benchmark for Federated Settings）</news:title>
   <news:publication_date>2026-07-13T04:43:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711247</loc>
  <lastmod>2026-07-13T04:43:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自然言語コマンドによる自動画像編集システム（A System for Automated Image Editing from Natural Language Commands）</news:title>
   <news:publication_date>2026-07-13T04:43:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711245</loc>
  <lastmod>2026-07-13T04:43:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>弱い注釈の体積医用画像から疾患を検出するConv-LSTMのアプローチ（Disease Detection in Weakly Annotated Volumetric Medical Images using a Convolutional LSTM Network）</news:title>
   <news:publication_date>2026-07-13T04:43:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711243</loc>
  <lastmod>2026-07-13T04:43:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>セマンティック画像修復に向けたWasserstein生成モデルの改良（Semantic Image Inpainting Through Improved Wasserstein Generative Adversarial Networks）</news:title>
   <news:publication_date>2026-07-13T04:43:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711241</loc>
  <lastmod>2026-07-13T04:42:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>情報洪水からの蒸留──機械学習研究におけるメタアナリシスとシステマティックレビューの可能性（Distilling Information from a Flood: A Possibility for the Use of Meta-Analysis and Systematic Review in Machine Learning Research）</news:title>
   <news:publication_date>2026-07-13T04:42:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711239</loc>
  <lastmod>2026-07-13T04:42:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>携帯電話データに基づくモビリティ解析の概観（Brief survey of Mobility Analyses based on Mobile Phone Datasets）</news:title>
   <news:publication_date>2026-07-13T04:42:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711237</loc>
  <lastmod>2026-07-13T04:42:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>クラウドソーシングを組み合わせたアクティブラーニングによる駐車標識認識（Crowd Sourcing based Active Learning Approach for Parking Sign Recognition）</news:title>
   <news:publication_date>2026-07-13T04:42:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711235</loc>
  <lastmod>2026-07-13T03:51:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分子最適化をグラフ翻訳として学ぶ手法（LEARNING MULTIMODAL GRAPH-TO-GRAPH TRANSLATION FOR MOLECULAR OPTIMIZATION）</news:title>
   <news:publication_date>2026-07-13T03:51:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711233</loc>
  <lastmod>2026-07-13T03:51:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>再構成を省くCT解釈 — 生データ（シノグラム）で診断する機械学習の可能性（Machine Friendly Machine Learning: Interpretation of Computed Tomography Without Image Reconstruction）</news:title>
   <news:publication_date>2026-07-13T03:51:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711231</loc>
  <lastmod>2026-07-13T03:50:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>並列ホップフィールドネットワークによるQRコードの復元（QR Code Denoising using Parallel Hopfield Networks）</news:title>
   <news:publication_date>2026-07-13T03:50:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711229</loc>
  <lastmod>2026-07-13T03:50:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模ASLデータセットとそのベンチマーク（MS-ASL: A Large-Scale Data Set and Benchmark for Understanding American Sign Language）</news:title>
   <news:publication_date>2026-07-13T03:50:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711227</loc>
  <lastmod>2026-07-13T03:50:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習過程ごと知識を移す手法 Leap（Transferring Knowledge Across Learning Processes）</news:title>
   <news:publication_date>2026-07-13T03:50:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711225</loc>
  <lastmod>2026-07-13T03:49:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ハイブリッドなインスタンスベース転移学習の手法（A Hybrid Instance-based Transfer Learning Method）</news:title>
   <news:publication_date>2026-07-13T03:49:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711223</loc>
  <lastmod>2026-07-13T03:49:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-07-13T03:49:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-07-13T02:58:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711219</loc>
  <lastmod>2026-07-13T02:58:16Z</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>2026-07-13T02:58:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711217</loc>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>脳腫瘍の自動分割と生存予測を目指した3D U-Netアンサンブル手法（Brain Tumor Segmentation using an Ensemble of 3D U-Nets and Overall Survival Prediction using Radiomic Features）</news:title>
   <news:publication_date>2026-07-13T02:58:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-07-13T02:57:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711213</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>量子実験を設計する遺伝的アルゴリズム（Designing quantum experiments with a genetic algorithm）</news:title>
   <news:publication_date>2026-07-13T02:57:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711211</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>量子近似最適化アルゴリズムの実装と性能解析（Quantum Approximate Optimization Algorithm: Performance, Mechanism, and Implementation on Near-Term Devices）</news:title>
   <news:publication_date>2026-07-13T02:57:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711209</loc>
  <lastmod>2026-07-13T02:57:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>感度に基づくニューラルネットワーク説明法（Sensitivity based Neural Networks Explanations）</news:title>
   <news:publication_date>2026-07-13T02:57:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711207</loc>
  <lastmod>2026-07-13T02:06:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-07-13T02:06:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711205</loc>
  <lastmod>2026-07-13T02:05:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>銀河核の核星団が教えること（THE NEXT GENERATION VIRGO CLUSTER SURVEY. XXIII. FUNDAMENTALS OF NUCLEAR STAR CLUSTERS OVER SEVEN DECADES IN GALAXY MASS）</news:title>
   <news:publication_date>2026-07-13T02:05:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711203</loc>
  <lastmod>2026-07-13T02:04:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>再構築防御とプライベートフェデレーテッドラーニング（Protection Against Reconstruction and Its Applications in Private Federated Learning）</news:title>
   <news:publication_date>2026-07-13T02:04:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711201</loc>
  <lastmod>2026-07-13T02:04:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>セマンティックパーシングの概観と実務への示唆（A Survey on Semantic Parsing）</news:title>
   <news:publication_date>2026-07-13T02:04:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711199</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>アンダーソン局在光ファイバーを用いた深層学習セルイメージング（Deep Learning Cell Imaging through Anderson Localizing Optical Fibre）</news:title>
   <news:publication_date>2026-07-13T02:04:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711197</loc>
  <lastmod>2026-07-13T02:03:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>交通システムのための深層強化学習（Deep Reinforcement Learning for Intelligent Transportation Systems）</news:title>
   <news:publication_date>2026-07-13T02:03:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711195</loc>
  <lastmod>2026-07-13T02:03:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>強制的刈り込みによる構造学習（Structure Learning Using Forced Pruning）</news:title>
   <news:publication_date>2026-07-13T02:03:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711193</loc>
  <lastmod>2026-07-13T01:12:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>FoldingZero: 2次元HPモデルにおける自己折りたたみ（FoldingZero: Protein Folding from Scratch in Hydrophobic-Polar Model）</news:title>
   <news:publication_date>2026-07-13T01:12:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711191</loc>
  <lastmod>2026-07-13T01:11:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オンラインで拡張可能かつプライバシー配慮型のグラフ適応学習（Online Graph-Adaptive Learning with Scalability and Privacy）</news:title>
   <news:publication_date>2026-07-13T01:11:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711189</loc>
  <lastmod>2026-07-13T01:11:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習を学ぶ学習：メタラーニングによる自己適応型視覚ナビゲーション（Learning to Learn How to Learn: Self-Adaptive Visual Navigation using Meta-Learning）</news:title>
   <news:publication_date>2026-07-13T01:11:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711187</loc>
  <lastmod>2026-07-13T01:11:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-07-13T01:11:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/711185</loc>
  <lastmod>2026-07-13T01:10:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-07-13T01:10:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711183</loc>
  <lastmod>2026-07-13T01:10:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自己模倣による生成的敵対学習（Generative Adversarial Self-Imitation Learning）</news:title>
   <news:publication_date>2026-07-13T01:10:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711181</loc>
  <lastmod>2026-07-13T01:10:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>IoTに対する世論の長期的変化と主要懸念（A Longitudinal Analysis of the Public Perception of the Opportunities and Challenges of the Internet of Things）</news:title>
   <news:publication_date>2026-07-13T01:10:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711179</loc>
  <lastmod>2026-07-13T00:18:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>視覚メモリによる頑健な経路追従（Visual Memory for Robust Path Following）</news:title>
   <news:publication_date>2026-07-13T00:18:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711177</loc>
  <lastmod>2026-07-13T00:18:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>タイグエン大学天文台建設の意義と実務的検討（CONSTRUCTING THE ASTRONOMICAL OBSERVATORY AT TAY NGUYEN UNIVERSITY, VIETNAM）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711175</loc>
  <lastmod>2026-07-13T00:17:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>フィルター選択がLSSTの外縁太陽系探索に与える影響（The Effects of Filter Choice on Outer Solar System Science with LSST）</news:title>
   <news:publication_date>2026-07-13T00:17:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711173</loc>
  <lastmod>2026-07-13T00:17:30Z</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>2026-07-13T00:17:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711171</loc>
  <lastmod>2026-07-13T00:17:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>観測が非常にノイズだらけのマルチエージェント強化学習（Multi-agent Deep Reinforcement Learning with Extremely Noisy Observations）</news:title>
   <news:publication_date>2026-07-13T00:17:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711169</loc>
  <lastmod>2026-07-13T00:17:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ピクセル単位のドメイン適応による検出器移植の新手法（SPLAT: Semantic Pixel-Level Adaptation Transforms for Detection）</news:title>
   <news:publication_date>2026-07-13T00:17:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711167</loc>
  <lastmod>2026-07-13T00:16:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/711165</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <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>2026-07-12T23:24:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>解釈可能な深層学習の安全性 ― 解釈を標的にする攻撃とその示唆 (Interpretable Deep Learning under Fire)</news:title>
   <news:publication_date>2026-07-12T23:24:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/711159</loc>
  <lastmod>2026-07-12T23:23:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層学習の白箱メンバーシップ推定攻撃とプライバシー評価（Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/711157</loc>
  <lastmod>2026-07-12T23:23:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>タスク駆動型飛行アドホックネットワークの連携枠組み（A Coalition-Based Communication Framework for Task-Driven Flying Ad-Hoc 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>指示条件付き強化敵対学習による多様なプログラム生成（Generating Diverse Programs with Instruction Conditioned Reinforced Adversarial Learning）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>トリプレットによるドメイン整合（Domain Alignment with Triplets）</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>どこで何ができるかを即時に見つける技術（What can I do here? Leveraging Deep 3D saliency and geometry for fast and scalable multiple affordance detection）</news:title>
   <news:publication_date>2026-07-12T22:32:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711149</loc>
  <lastmod>2026-07-12T22:32:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非同期並列Q値反復によるNear‑Optimalサンプル効率（AsyncQVI: Asynchronous-Parallel Q-Value Iteration for Discounted MDPs with Near-Optimal Sample Complexity）</news:title>
   <news:publication_date>2026-07-12T22:32:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711147</loc>
  <lastmod>2026-07-12T22:31:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非遵守バンディットにおけるトンプソン・サンプリング（Thompson Sampling for Noncompliant Bandits）</news:title>
   <news:publication_date>2026-07-12T22:31:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711145</loc>
  <lastmod>2026-07-12T22:31:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>野外単一RGB画像からのスペクトル推定（Towards Spectral Estimation from a Single RGB Image in the Wild）</news:title>
   <news:publication_date>2026-07-12T22:31:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711143</loc>
  <lastmod>2026-07-12T22:31:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>テキストベースゲーム解決のための適応的行動空間生成（Towards Solving Text-based Games by Producing Adaptive Action Spaces）</news:title>
   <news:publication_date>2026-07-12T22:31:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711141</loc>
  <lastmod>2026-07-12T22:31:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>コース成績の早期予測と特徴選択（Early Prediction of Course Grades: Models and Feature Selection）</news:title>
   <news:publication_date>2026-07-12T22:31:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711139</loc>
  <lastmod>2026-07-12T22:30:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散SDNにおけるコントローラ同期の最適化（DQ Scheduler: Deep Reinforcement Learning Based Controller Synchronization in Distributed SDN）</news:title>
   <news:publication_date>2026-07-12T22:30:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711137</loc>
  <lastmod>2026-07-12T21:39:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Deep Inverse Optimization（Deep Inverse Optimization）</news:title>
   <news:publication_date>2026-07-12T21:39:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711135</loc>
  <lastmod>2026-07-12T21:39:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>敵対的頑健性と一般化の分離（Disentangling Adversarial Robustness and Generalization）</news:title>
   <news:publication_date>2026-07-12T21:39:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711133</loc>
  <lastmod>2026-07-12T21:38:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>顔の主要点で頭向きを推定する手法の意義（NOSE, EYES AND EARS: HEAD POSE ESTIMATION BY LOCATING FACIAL KEYPOINTS）</news:title>
   <news:publication_date>2026-07-12T21:38:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711131</loc>
  <lastmod>2026-07-12T21:38:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>脳領域分割における自信の可視化―Bayesian DNNによるFreeSurfer代替の高速化（Knowing what you know in brain segmentation）</news:title>
   <news:publication_date>2026-07-12T21:38:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711129</loc>
  <lastmod>2026-07-12T21:37:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>未知の複合劣化に対処する画像復元の注意機構（Attention-based Adaptive Selection of Operations for Image Restoration in the Presence of Unknown Combined Distortions）</news:title>
   <news:publication_date>2026-07-12T21:37:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711127</loc>
  <lastmod>2026-07-12T21:37:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>信頼性を考慮した動的NFV配置におけるDeep Q-Learningの実践（Deep Q-Learning for Dynamic Reliability Aware NFV-Based Service Provisioning）</news:title>
   <news:publication_date>2026-07-12T21:37:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711125</loc>
  <lastmod>2026-07-12T21:37:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>低コスト・視覚駆動ロボットアーム制御の実践（CRAVES: Controlling Robotic Arm with a Vision-based Economic System）</news:title>
   <news:publication_date>2026-07-12T21:37:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711123</loc>
  <lastmod>2026-07-12T20:45:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>液体ボーラス療法に対する血圧反応の予測（Predicting Blood Pressure Response to Fluid Bolus Therapy Using Attention-Based Neural Networks for Clinical Interpretability）</news:title>
   <news:publication_date>2026-07-12T20:45:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711121</loc>
  <lastmod>2026-07-12T20:37:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>知覚属性を高めるベイジアン・スタイル生成（Enhancing Perceptual Attributes with Bayesian Style Generation）</news:title>
   <news:publication_date>2026-07-12T20:37:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711119</loc>
  <lastmod>2026-07-12T20:37:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>古典日本文学のための深層学習（Deep Learning for Classical Japanese Literature）</news:title>
   <news:publication_date>2026-07-12T20:37:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711117</loc>
  <lastmod>2026-07-12T20:36:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Care2Vec：自閉・身体障害児の自己ケア分類に挑む深層学習手法（Care2Vec: A Deep learning approach for the classification of self-care problems in physically disabled children）</news:title>
   <news:publication_date>2026-07-12T20:36:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711115</loc>
  <lastmod>2026-07-12T20:35:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ドメイン知識を活かしたデータ駆動型チラー設備の省エネ最適化（Data Driven Chiller Plant Energy Optimization with Domain Knowledge）</news:title>
   <news:publication_date>2026-07-12T20:35:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711113</loc>
  <lastmod>2026-07-12T20:35:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>病院間での放射線レポート分類における半教師あり転移学習の実証（Clinical Document Classification Using Labeled and Unlabeled Data Across Hospitals）</news:title>
   <news:publication_date>2026-07-12T20:35:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711111</loc>
  <lastmod>2026-07-12T20:35:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>HOARD: クラウド上での深層学習訓練を加速する分散データキャッシュ（Hoard: A Distributed Data Caching System to Accelerate Deep Learning Training on the Cloud）</news:title>
   <news:publication_date>2026-07-12T20:35:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711109</loc>
  <lastmod>2026-07-12T19:44:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>光学式センサで行動を識別する機械視覚アプローチ（An Interpretable Machine Vision Approach to Human Activity Recognition using Photoplethysmograph Sensor Data）</news:title>
   <news:publication_date>2026-07-12T19:44:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711107</loc>
  <lastmod>2026-07-12T19:43:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Deep Hierarchical Machineの柔軟な分割統治アーキテクチャ（Deep Hierarchical Machine: a Flexible Divide-and-Conquer Architecture）</news:title>
   <news:publication_date>2026-07-12T19:43:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711105</loc>
  <lastmod>2026-07-12T19:43:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>特徴マップで知識を蒸留する手法の本質（Knowledge Distillation with Feature Maps）</news:title>
   <news:publication_date>2026-07-12T19:43:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711103</loc>
  <lastmod>2026-07-12T19:42:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Deep Slow Feature Analysisに基づく無教師変化検出（Unsupervised Deep Slow Feature Analysis for Change Detection in Multi-Temporal Remote Sensing Images）</news:title>
   <news:publication_date>2026-07-12T19:42:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711101</loc>
  <lastmod>2026-07-12T19:42:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>映像からの運転行動認識の時空間融合CNN（Spatial-temporal Fusion Convolutional Neural Network for Simulated Driving Behavior Recognition）</news:title>
   <news:publication_date>2026-07-12T19:42:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711099</loc>
  <lastmod>2026-07-12T19:42:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>犯罪予測におけるディープラーニングの構造検証（Examining Deep Learning Architectures for Crime Classification and Prediction）</news:title>
   <news:publication_date>2026-07-12T19:42:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711097</loc>
  <lastmod>2026-07-12T19:42:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>資源制約下の深層強化学習（Resource Constrained Deep Reinforcement Learning）</news:title>
   <news:publication_date>2026-07-12T19:42:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711095</loc>
  <lastmod>2026-07-12T18:51:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>冠動脈バイパス術後30日再入院の予測に向けた深層学習アプローチ (Deep Learning Approach for Predicting 30 Day Readmissions after Coronary Artery Bypass Graft Surgery)</news:title>
   <news:publication_date>2026-07-12T18:51:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711093</loc>
  <lastmod>2026-07-12T18:50:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>顔認証に基づくスマートセキュリティ（A Smart Security System with Face Recognition）</news:title>
   <news:publication_date>2026-07-12T18:50:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711091</loc>
  <lastmod>2026-07-12T18:49:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>視覚特徴の変換に向けて（Towards Visual Feature Translation）</news:title>
   <news:publication_date>2026-07-12T18:49:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711089</loc>
  <lastmod>2026-07-12T18:49:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>量子通信プロトコルをプログラム可能な量子コンピュータのベンチマークにする（Quantum communication protocols as a benchmark for programmable quantum computers）</news:title>
   <news:publication_date>2026-07-12T18:49:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711087</loc>
  <lastmod>2026-07-12T18:49:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多クラス・マージン分類器の一般化境界とラデマッハ複雑度（Rademacher Complexity and Generalization Performance of Multi-category Margin Classifiers）</news:title>
   <news:publication_date>2026-07-12T18:49:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711085</loc>
  <lastmod>2026-07-12T18:48:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフ特性のロバストネス計測（Measuring the Robustness of Graph Properties）</news:title>
   <news:publication_date>2026-07-12T18:48:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711083</loc>
  <lastmod>2026-07-12T18:48:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>推奨経路の探索と従属行動のジレンマ（Recommending Paths: Follow or Not Follow?）</news:title>
   <news:publication_date>2026-07-12T18:48:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711081</loc>
  <lastmod>2026-07-12T17:57:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Visual Foresightによる視覚ベースのロボット制御（Visual Foresight: Model-Based Deep Reinforcement Learning for Vision-Based Robotic Control）</news:title>
   <news:publication_date>2026-07-12T17:57:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711079</loc>
  <lastmod>2026-07-12T17:57:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分割学習による医療データの協調学習（Split learning for health: Distributed deep learning without sharing raw patient data）</news:title>
   <news:publication_date>2026-07-12T17:57:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711077</loc>
  <lastmod>2026-07-12T17:57:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>医用画像におけるウィンドウ設定最適化の実践（Practical Window Setting Optimization for Medical Image Deep Learning）</news:title>
   <news:publication_date>2026-07-12T17:57:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711075</loc>
  <lastmod>2026-07-12T17:56:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>新しい超伝導体を見つける深層学習モデル（Deep Learning Model for Finding New Superconductors）</news:title>
   <news:publication_date>2026-07-12T17:56:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711073</loc>
  <lastmod>2026-07-12T17:56:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>患者の治療遅延を予測する時系列特徴ラベリング法（Modeling Treatment Delays for Patients Using Feature Label Pairs in a Time Series）</news:title>
   <news:publication_date>2026-07-12T17:56:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711071</loc>
  <lastmod>2026-07-12T17:55:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>モジュロ観測からの信号再構成 (Signal Reconstruction from Modulo Observations)</news:title>
   <news:publication_date>2026-07-12T17:55:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711069</loc>
  <lastmod>2026-07-12T17:55:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>脱注釈画像の構造分割を実現する敵対的ネットワーク（SUSAN: Segment Unannotated image Structure using Adversarial Network）</news:title>
   <news:publication_date>2026-07-12T17:55:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711067</loc>
  <lastmod>2026-07-12T17:04:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>睡眠/覚醒の非監視識別における隠れマルコフモデル（A Hidden Markov Model Based Unsupervised Algorithm for Sleep/Wake Identification Using Actigraphy）</news:title>
   <news:publication_date>2026-07-12T17:04:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711065</loc>
  <lastmod>2026-07-12T17:04:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ピクセルベースの衣服モデリングフレームワーク（A Pixel-Based Framework for Data-Driven Clothing）</news:title>
   <news:publication_date>2026-07-12T17:04:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711063</loc>
  <lastmod>2026-07-12T17:03:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>画像検索に対する普遍的摂動攻撃の衝撃（Universal Perturbation Attack Against Image Retrieval）</news:title>
   <news:publication_date>2026-07-12T17:03:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711061</loc>
  <lastmod>2026-07-12T17:02:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>希少疾患検出の半教師あり学習とGAN（Semi-supervised Rare Disease Detection Using Generative Adversarial Network）</news:title>
   <news:publication_date>2026-07-12T17:02:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711059</loc>
  <lastmod>2026-07-12T17:02:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>臨床縦断データから読み解くアルツハイマー病の進行と臨床サブタイプ（Learning the progression and clinical subtypes of Alzheimer’s disease from longitudinal clinical data）</news:title>
   <news:publication_date>2026-07-12T17:02:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711057</loc>
  <lastmod>2026-07-12T17:02:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>データセットバイアスを“学習しない”仕組み――医用画像研究における一般化能力の回復（Learning to Unlearn: Building Immunity to Dataset Bias in Medical Imaging Studies）</news:title>
   <news:publication_date>2026-07-12T17:02:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711055</loc>
  <lastmod>2026-07-12T17:02:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>XNet：小規模データ向けX線画像セグメンテーションCNNの実装（XNet: A convolutional neural network implementation for medical X-Ray image segmentation suitable for small datasets）</news:title>
   <news:publication_date>2026-07-12T17:02:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711053</loc>
  <lastmod>2026-07-12T16:10:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>少数ショット自己リマインダーによる破局的忘却の克服（Few-Shot Self Reminder to Overcome Catastrophic Forgetting）</news:title>
   <news:publication_date>2026-07-12T16:10:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711051</loc>
  <lastmod>2026-07-12T16:02:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動画生成モデルの評価指標と課題（Towards Accurate Generative Models of Video: A New Metric &amp;amp; Challenges）</news:title>
   <news:publication_date>2026-07-12T16:02:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711049</loc>
  <lastmod>2026-07-12T16:01:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模バッチによるSGD学習の理論的理解（Towards Theoretical Understanding of Large Batch Training in Stochastic Gradient Descent）</news:title>
   <news:publication_date>2026-07-12T16:01:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711047</loc>
  <lastmod>2026-07-12T16:01:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模スペクトル密度行列の閾値化推定（Large Spectral Density Matrix Estimation by Thresholding）</news:title>
   <news:publication_date>2026-07-12T16:01:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711045</loc>
  <lastmod>2026-07-12T16:00:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>解釈可能なクラスタリングを実現する最適木（Interpretable Clustering via Optimal Trees）</news:title>
   <news:publication_date>2026-07-12T16:00:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711043</loc>
  <lastmod>2026-07-12T16:00:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>フェデレーテッドラーニングにおけるユーザーレベルのプライバシー漏洩（Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning）</news:title>
   <news:publication_date>2026-07-12T16:00:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711041</loc>
  <lastmod>2026-07-12T15:59:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>無線チャネル状態情報の非線形構造活用（Exploiting Wireless Channel State Information Structures Beyond Linear Correlations: A Deep Learning Approach）</news:title>
   <news:publication_date>2026-07-12T15:59:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711039</loc>
  <lastmod>2026-07-12T15:08:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>不規則にサンプリングされた臨床時系列データのモデリング（Modeling Irregularly Sampled Clinical Time Series）</news:title>
   <news:publication_date>2026-07-12T15:08:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711037</loc>
  <lastmod>2026-07-12T15:08:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>反因果学習における一般化（Generalization in anti-causal learning）</news:title>
   <news:publication_date>2026-07-12T15:08:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711035</loc>
  <lastmod>2026-07-12T15:08:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>電子カルテの不均等観測に対処する連続時間隠れマルコフモデル（Modeling disease progression in longitudinal EHR data using continuous-time hidden Markov models）</news:title>
   <news:publication_date>2026-07-12T15:08:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711033</loc>
  <lastmod>2026-07-12T15:07:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>知識駆動型生成サブスペースによる多視点医療データの依存性モデリング（Knowledge-driven generative subspaces for modeling multi-view dependencies in medical data）</news:title>
   <news:publication_date>2026-07-12T15:07:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711031</loc>
  <lastmod>2026-07-12T15:07:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>不完全データからのCT同時再構成を実現する深層網の提案（JSR-NET: A DEEP NETWORK FOR JOINT SPATIAL-RADON DOMAIN CT RECONSTRUCTION FROM INCOMPLETE DATA）</news:title>
   <news:publication_date>2026-07-12T15:07:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711029</loc>
  <lastmod>2026-07-12T15:07:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>探索と排除：最適化された二段階探索によるミリ波ビーム整合（Explore and Eliminate: Optimized Two-Stage Search for Millimeter-Wave Beam Alignment）</news:title>
   <news:publication_date>2026-07-12T15:07:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711027</loc>
  <lastmod>2026-07-12T15:07:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Elastic Boundary Projectionによる3D医療画像分割の新展開（Elastic Boundary Projection for 3D Medical Image Segmentation）</news:title>
   <news:publication_date>2026-07-12T15:07:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711025</loc>
  <lastmod>2026-07-12T14:16:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>建築デザインの機械的分類—機械の眼で見る設計の類似性（Deep Learning Architect: Classification for Architectural Design through the Eye of Artificial Intelligence）</news:title>
   <news:publication_date>2026-07-12T14:16:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711023</loc>
  <lastmod>2026-07-12T14:15:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチタスク学習による12誘導心電図分類の改善（Using Multitask Learning to Improve 12-Lead Electrocardiogram Classification）</news:title>
   <news:publication_date>2026-07-12T14:15:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711021</loc>
  <lastmod>2026-07-12T14:15:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>医療時系列データの教師なし表現学習が臨床予測を高める（Improving Clinical Predictions through Unsupervised Time Series Representation Learning）</news:title>
   <news:publication_date>2026-07-12T14:15:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711019</loc>
  <lastmod>2026-07-12T14:15:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>モデル再利用攻撃が明らかにしたリスク（Model-Reuse Attacks on Deep Learning Systems）</news:title>
   <news:publication_date>2026-07-12T14:15:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711017</loc>
  <lastmod>2026-07-12T14:14:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Neural Rejuvenationによる深層ネットワーク訓練の効率化（Neural Rejuvenation: Improving Deep Network Training by Enhancing Computational Resource Utilization）</news:title>
   <news:publication_date>2026-07-12T14:14:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711015</loc>
  <lastmod>2026-07-12T14:14:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DeepLiDAR：法線指向の屋外深度予測（Deep Surface Normal Guided Depth Prediction for Outdoor Scene from Sparse LiDAR Data and Single Color Image）</news:title>
   <news:publication_date>2026-07-12T14:14:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711013</loc>
  <lastmod>2026-07-12T14:14:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>生成モデルと自己アンサンブリングによる無監督ドメイン適応（Unsupervised Domain Adaptation using Generative Models and Self-ensembling）</news:title>
   <news:publication_date>2026-07-12T14:14:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711011</loc>
  <lastmod>2026-07-12T13:22:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>心電図データによるリスク層別化の新流儀（Multiple Instance Learning for ECG Risk Stratification）</news:title>
   <news:publication_date>2026-07-12T13:22:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711009</loc>
  <lastmod>2026-07-12T13:22:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>アンチドートデータで推薦の偏向と不公平を是正する（Fighting Fire with Fire: Using Antidote Data to Improve Polarization and Fairness of Recommender Systems）</news:title>
   <news:publication_date>2026-07-12T13:22:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711007</loc>
  <lastmod>2026-07-12T13:22:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>頭上カメラと外部固定視点による人物位置同定（Ego-Downward and Ambient Video based Person Location Association）</news:title>
   <news:publication_date>2026-07-12T13:22:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711005</loc>
  <lastmod>2026-07-12T13:21:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>アンカーボックス最適化による物体検出の精度向上（Anchor Box Optimization for Object Detection）</news:title>
   <news:publication_date>2026-07-12T13:21:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711003</loc>
  <lastmod>2026-07-12T13:21:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-07-12T13:21:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/711001</loc>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>プーリングによる介入確率の個人化（Personalizing Intervention Probabilities By Pooling）</news:title>
   <news:publication_date>2026-07-12T13:20:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710999</loc>
  <lastmod>2026-07-12T13:20:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Autodockにおける自動ハイパーパラメータ選択（Automatic hyperparameter selection in Autodock）</news:title>
   <news:publication_date>2026-07-12T13:20:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710997</loc>
  <lastmod>2026-07-12T12:29:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ソフトマックス版ベルマン演算子の再考（Revisiting the Softmax Bellman Operator: New Benefits and New Perspective）</news:title>
   <news:publication_date>2026-07-12T12:29:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710995</loc>
  <lastmod>2026-07-12T12:19:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>公平性の確率的検証と集中不等式の応用（Probabilistic Verification of Fairness Properties via Concentration）</news:title>
   <news:publication_date>2026-07-12T12:19:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710993</loc>
  <lastmod>2026-07-12T12:18:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>伝播と生成を分離した映像予測（Disentangling Propagation and Generation for Video Prediction）</news:title>
   <news:publication_date>2026-07-12T12:18:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710991</loc>
  <lastmod>2026-07-12T12:17:49Z</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利用者の表現学習（Learning Representations of Social Media Users）</news:title>
   <news:publication_date>2026-07-12T12:17:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710989</loc>
  <lastmod>2026-07-12T12:17:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オミクスとMRIを統合してアルツハイマー病の希少遺伝マーカーを特定する（Integrating omics and MRI data with kernel-based tests and CNNs to identify rare genetic markers for Alzheimer’s disease）</news:title>
   <news:publication_date>2026-07-12T12:17:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710987</loc>
  <lastmod>2026-07-12T12:17:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>磁気共鳴弾性写真法へのCNNによる二重目的アプローチ（Dual Objective Approach Using A Convolutional Neural Network for Magnetic Resonance Elastography）</news:title>
   <news:publication_date>2026-07-12T12:17:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710985</loc>
  <lastmod>2026-07-12T12:17:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Deep Cosine Metric Learning for Person Re-Identification（Deep Cosine Metric Learning for Person Re-Identification）</news:title>
   <news:publication_date>2026-07-12T12:17:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710983</loc>
  <lastmod>2026-07-12T11:25:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>健康データ向けユニーク関連情報に基づく特徴選択（Feature Selection Based on Unique Relevant Information for Health Data）</news:title>
   <news:publication_date>2026-07-12T11:25:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710981</loc>
  <lastmod>2026-07-12T11:17:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>効率的な生涯学習とA-GEMの実用性（EFFICIENT LIFELONG LEARNING WITH A-GEM）</news:title>
   <news:publication_date>2026-07-12T11:17:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710979</loc>
  <lastmod>2026-07-12T11:16:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>加齢黄斑変性の多目的ディープラーニングによる画像分類（A multi-task deep learning model for the classification of Age-related Macular Degeneration）</news:title>
   <news:publication_date>2026-07-12T11:16:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710977</loc>
  <lastmod>2026-07-12T11:16:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時系列データにおける臨床共変量の補完（Imputation of Clinical Covariates in Time Series）</news:title>
   <news:publication_date>2026-07-12T11:16:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710975</loc>
  <lastmod>2026-07-12T11:15:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Snorkel DryBellによる弱い教師あり学習の実運用（Snorkel DryBell: A Case Study in Deploying Weak Supervision at Industrial Scale）</news:title>
   <news:publication_date>2026-07-12T11:15:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710973</loc>
  <lastmod>2026-07-12T11:15:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>個別化医療における計算EEG：パーキンソン病を対象とした検討（Computational EEG in Personalized Medicine: A study in Parkinson’s Disease）</news:title>
   <news:publication_date>2026-07-12T11:15:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710971</loc>
  <lastmod>2026-07-12T11:15:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層表現が知覚品質特徴になる理由（Why Are Deep Representations Good Perceptual Quality Features?）</news:title>
   <news:publication_date>2026-07-12T11:15:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710969</loc>
  <lastmod>2026-07-12T10:23:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>サイバー犯罪のサプライチェーン自動検出への接近（Towards Automatic Discovery of Cybercrime Supply Chains）</news:title>
   <news:publication_date>2026-07-12T10:23:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710967</loc>
  <lastmod>2026-07-12T10:15:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>交通最適化におけるメタモデルの性能検証（Investigating performance of neural networks and gradient boosting models approximating microscopic traffic simulations in traffic optimization tasks）</news:title>
   <news:publication_date>2026-07-12T10:15:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710965</loc>
  <lastmod>2026-07-12T10:15:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動的スペックルを利用した超解像アコースト光学トモグラフィー（Acousto-optic tomography beyond the acoustic diffraction-limit using speckle decorrelation）</news:title>
   <news:publication_date>2026-07-12T10:15:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710963</loc>
  <lastmod>2026-07-12T10:14:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Kikiチャレンジの危険動画検出（Kiki Kills: Identifying Dangerous Challenge Videos from Social Media）</news:title>
   <news:publication_date>2026-07-12T10:14:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710961</loc>
  <lastmod>2026-07-12T10:13:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>線形バンディットにおける最良行動の迅速同定（Quick Best Action Identification in Linear Bandit Problems）</news:title>
   <news:publication_date>2026-07-12T10:13:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710959</loc>
  <lastmod>2026-07-12T10:13:40Z</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24時間以内退院予測（Predicting Inpatient Discharge Prioritization With Electronic Health Records）</news:title>
   <news:publication_date>2026-07-12T10:13:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710957</loc>
  <lastmod>2026-07-12T10:13:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>不安定なCNNアクセラレータのための訓練法 — FPGAを事例とした実践的アプローチ (Training for ‘Unstable’ CNN Accelerator: A Case Study on FPGA)</news:title>
   <news:publication_date>2026-07-12T10:13:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710955</loc>
  <lastmod>2026-07-12T09:21:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>再帰的ベイズ剪定によるCNN高速化（Accelerate CNN via Recursive Bayesian Pruning）</news:title>
   <news:publication_date>2026-07-12T09:21:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710953</loc>
  <lastmod>2026-07-12T09:21:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>MDU‑Net：多段階で密に接続されたU‑Netによる医用画像セグメンテーション（MDU‑Net: multi‑scale densely connected U‑Net for biomedical image segmentation）</news:title>
   <news:publication_date>2026-07-12T09:21:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710951</loc>
  <lastmod>2026-07-12T09:21:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>それは私のものです！ロボットが所有関係とルールを学ぶ方法（That&amp;#039;s Mine! Learning Ownership Relations and Norms for Robots）</news:title>
   <news:publication_date>2026-07-12T09:21:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710949</loc>
  <lastmod>2026-07-12T09:20:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分布データの整合を変える一手（Regularized Wasserstein Means for Aligning Distributional Data）</news:title>
   <news:publication_date>2026-07-12T09:20:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710947</loc>
  <lastmod>2026-07-12T09:20:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ハイブリッドアナログ-デジタル大規模MIMOにおける機械学習を用いた高分解能到来方向測定とロバストなDM（Machine-Learning-based High-resolution DOA Measurement and Robust DM for Hybrid Analog-Digital Massive MIMO Transceiver）</news:title>
   <news:publication_date>2026-07-12T09:20:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710945</loc>
  <lastmod>2026-07-12T09:20:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>バッチ正規化された残差ネットワークにおける勾配伝播の解析（Analysis on Gradient Propagation in Batch Normalized Residual Networks）</news:title>
   <news:publication_date>2026-07-12T09:20:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710943</loc>
  <lastmod>2026-07-12T09:20:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ウェブ教育動画の推論学習（Learning to Reason towards Understanding Web Instructional Videos）</news:title>
   <news:publication_date>2026-07-12T09:20:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710941</loc>
  <lastmod>2026-07-12T08:27:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GAN-EMによる生成モデルを用いたEM学習フレームワーク（GAN-EM: GAN based EM Learning Framework）</news:title>
   <news:publication_date>2026-07-12T08:27:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710939</loc>
  <lastmod>2026-07-12T08:27:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>イメージスコア：有用なサンプルの選択方法（Image Score: How to Select Useful Samples）</news:title>
   <news:publication_date>2026-07-12T08:27:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710937</loc>
  <lastmod>2026-07-12T08:27:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>点群とビューの関係を学習するPVRNet（PVRNet: Point-View Relation Neural Network for 3D Shape Recognition）</news:title>
   <news:publication_date>2026-07-12T08:27:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710935</loc>
  <lastmod>2026-07-12T08:27:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模タスクとハードウェアに直接適合するニューラルアーキテクチャ探索（ProxylessNAS）</news:title>
   <news:publication_date>2026-07-12T08:27:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710933</loc>
  <lastmod>2026-07-12T08:26:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>畳み込みニューラルネットと動的計画法のエンドツーエンド学習（End-to-end learning of convolutional neural net and dynamic programming for left ventricle segmentation）</news:title>
   <news:publication_date>2026-07-12T08:26:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710931</loc>
  <lastmod>2026-07-12T08:26:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>反復的再構成による弱い空間制約（Iterative Reorganization with Weak Spatial Constraints: Solving Arbitrary Jigsaw Puzzles for Unsupervised Representation Learning）</news:title>
   <news:publication_date>2026-07-12T08:26:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710929</loc>
  <lastmod>2026-07-12T08:26:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>視点移動で自分の居場所を作る仕組み：ECO（Egocentric Cognitive Map）</news:title>
   <news:publication_date>2026-07-12T08:26:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710927</loc>
  <lastmod>2026-07-12T07:35:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層ニューラルネットワークの勾配変動について（On variation of gradients of deep neural networks）</news:title>
   <news:publication_date>2026-07-12T07:35:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710925</loc>
  <lastmod>2026-07-12T07:35:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>プラン認識駆動注意による視覚認識の改善（Plan-Recognition-Driven Attention Modeling for Visual Recognition）</news:title>
   <news:publication_date>2026-07-12T07:35:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710923</loc>
  <lastmod>2026-07-12T07:34:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>個別化人工膵臓コントローラのインシリコ危険度解析（In-silico Risk Analysis of Personalized Artificial Pancreas Controllers via Rare-event Simulation）</news:title>
   <news:publication_date>2026-07-12T07:34:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710921</loc>
  <lastmod>2026-07-12T07:33:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高齢者歩行の安定性予測のためのLSTMネットワーク（LSTM-based Network for Human Gait Stability Prediction in an Intelligent Robotic Rollator）</news:title>
   <news:publication_date>2026-07-12T07:33:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710919</loc>
  <lastmod>2026-07-12T07:33:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>子どもの関与（エンゲージメント）を深層学習で推定する多視点アプローチ（A Deep Learning Approach for Multi-View Engagement Estimation of Children in a Child-Robot Joint Attention task）</news:title>
   <news:publication_date>2026-07-12T07:33:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710917</loc>
  <lastmod>2026-07-12T07:33:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SentiNetによる局所的普遍攻撃の検出（SentiNet: Detecting Localized Universal Attacks Against Deep Learning Systems）</news:title>
   <news:publication_date>2026-07-12T07:33:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710915</loc>
  <lastmod>2026-07-12T07:32:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>AnyThreat：インサイダ脅威検出への機会主義的知識発見アプローチ（AnyThreat: An Opportunistic Knowledge Discovery Approach to Insider Threat Detection）</news:title>
   <news:publication_date>2026-07-12T07:32:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710913</loc>
  <lastmod>2026-07-12T06:42:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自信のある分布外例を生成して堅牢な分類器を作る（Building robust classifiers through generation of confident out of distribution examples）</news:title>
   <news:publication_date>2026-07-12T06:42:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710911</loc>
  <lastmod>2026-07-12T06:41:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>U-net圧縮と知識蒸留の実務的示唆（On Compressing U-net Using Knowledge Distillation）</news:title>
   <news:publication_date>2026-07-12T06:41:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710909</loc>
  <lastmod>2026-07-12T06:41:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ライブトラフィックを使った分類器の堅牢化（Improving robustness of classifiers by training against live traffic）</news:title>
   <news:publication_date>2026-07-12T06:41:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710907</loc>
  <lastmod>2026-07-12T06:41:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>質問を通じて生涯学習で画像にキャプションを付ける（Learning to Caption Images through a Lifetime by Asking Questions）</news:title>
   <news:publication_date>2026-07-12T06:41:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710905</loc>
  <lastmod>2026-07-12T06:41:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>InGANによる自然画像の“DNA”の獲得とリマッピング (InGAN: Capturing and Remapping the “DNA” of a Natural Image)</news:title>
   <news:publication_date>2026-07-12T06:41:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710903</loc>
  <lastmod>2026-07-12T06:40:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層的な目的関数で学習する遺伝的アルゴリズム（Hierarchical Genetic Algorithms with evolving objective functions）</news:title>
   <news:publication_date>2026-07-12T06:40:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710901</loc>
  <lastmod>2026-07-12T06:40:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層を模倣学習で発見する（DISCOVERING HIERARCHIES USING IMITATION LEARNING FROM HIERARCHY AWARE POLICIES）</news:title>
   <news:publication_date>2026-07-12T06:40:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710899</loc>
  <lastmod>2026-07-12T05:49:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>EHRとEKGに基づく予測モデルの安定性評価（Measuring the Stability of EHR- and EKG-based Predictive Models）</news:title>
   <news:publication_date>2026-07-12T05:49:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710897</loc>
  <lastmod>2026-07-12T05:49:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>心電図と心臓生理の確率モデル（A Probabilistic Model of Cardiac Physiology and Electrocardiograms）</news:title>
   <news:publication_date>2026-07-12T05:49:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710895</loc>
  <lastmod>2026-07-12T05:49:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動物ナビゲーションのための脳電気刺激（Brain Electrical Stimulation for Animal Navigation）</news:title>
   <news:publication_date>2026-07-12T05:49:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710893</loc>
  <lastmod>2026-07-12T05:48:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>都市環境のデータ駆動型大気質評価（Data-driven Air Quality Characterisation for Urban Environments: a Case Study）</news:title>
   <news:publication_date>2026-07-12T05:48:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710891</loc>
  <lastmod>2026-07-12T05:48:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DeCoILFNetが変えるFPGA上のConvNet実装戦略（DeCoILFNet: Depth Concatenation and Inter-Layer Fusion based ConvNet Accelerator）</news:title>
   <news:publication_date>2026-07-12T05:48:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710889</loc>
  <lastmod>2026-07-12T05:48:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>FineFool：注目（アテンション）で捉える微細物体輪郭攻撃（FineFool: Fine Object Contour Attack via Attention）</news:title>
   <news:publication_date>2026-07-12T05:48:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710887</loc>
  <lastmod>2026-07-12T05:47:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>連続的な画像検索スペクトルを横断する深層動的モデル（Traversing the Continuous Spectrum of Image Retrieval with Deep Dynamic Models）</news:title>
   <news:publication_date>2026-07-12T05:47:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710885</loc>
  <lastmod>2026-07-12T04:56:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>パッチでは足りない状況 — HardFails: ソフトウェアから悪用されうるハードウェア不具合（When a Patch is Not Enough — HardFails: Software-Exploitable Hardware Bugs）</news:title>
   <news:publication_date>2026-07-12T04:56:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710883</loc>
  <lastmod>2026-07-12T04:56:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>エンティティとイベントの共同ニューラルモデリング（One for All: Neural Joint Modeling of Entities and Events）</news:title>
   <news:publication_date>2026-07-12T04:56:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710881</loc>
  <lastmod>2026-07-12T04:56:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Racial Faces in-the-Wild と IMAN が示したフェアネス改善の道筋（Racial Faces in-the-Wild: Reducing Racial Bias by Information Maximization Adaptation Network）</news:title>
   <news:publication_date>2026-07-12T04:56:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710879</loc>
  <lastmod>2026-07-12T04:55:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>NTX：浮動小数点の汎用リダクション向け省電力ストリーミングアクセラレータ（NTX: An Energy-efficient Streaming Accelerator for Floating-point Generalized Reduction Workloads in 22 nm FD-SOI）</news:title>
   <news:publication_date>2026-07-12T04:55:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710877</loc>
  <lastmod>2026-07-12T04:55:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチパーティ対話における談話構造解析の順序的深層モデル（A Deep Sequential Model for Discourse Parsing on Multi-Party Dialogues）</news:title>
   <news:publication_date>2026-07-12T04:55:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710875</loc>
  <lastmod>2026-07-12T04:55:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>残差ネットワークの確率的訓練：微分方程式の視点（Stochastic Training of Residual Networks: a Differential Equation Viewpoint）</news:title>
   <news:publication_date>2026-07-12T04:55:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710873</loc>
  <lastmod>2026-07-12T04:55:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>損失関数とターゲット表現が敵対的ロバスト性に与える影響（Effects of Loss Functions And Target Representations on Adversarial Robustness）</news:title>
   <news:publication_date>2026-07-12T04:55:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710871</loc>
  <lastmod>2026-07-12T04:04:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ランクプロジェクションツリーによる多層ニューラルネットワーク解釈（Rank Projection Trees for Multilevel Neural Network Interpretation）</news:title>
   <news:publication_date>2026-07-12T04:04:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710869</loc>
  <lastmod>2026-07-12T04:03:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>在宅高齢者の歩容可視化による見守り革新（Vision-Based Gait Analysis for Senior Care）</news:title>
   <news:publication_date>2026-07-12T04:03:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710867</loc>
  <lastmod>2026-07-12T04:03:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>クエリと広告のカテゴリー類似度を近似する新手法（Approximating Categorical Similarity in Sponsored Search Relevance）</news:title>
   <news:publication_date>2026-07-12T04:03:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710865</loc>
  <lastmod>2026-07-12T04:03:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SwishNet：高速で軽量な1次元畳み込みネットワークによる音声・音楽・雑音の分類と分割（SwishNet: A Fast Convolutional Neural Network for Speech, Music and Noise Classification and Segmentation）</news:title>
   <news:publication_date>2026-07-12T04:03:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710863</loc>
  <lastmod>2026-07-12T04:02:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>航空画像における回転物体検出のためのRoIトランスフォーマ（Learning RoI Transformer for Detecting Oriented Objects in Aerial Images）</news:title>
   <news:publication_date>2026-07-12T04:02:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710861</loc>
  <lastmod>2026-07-12T04:02:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>離散的敵対的攻撃とサブモジュラ最適化（Discrete Adversarial Attacks and Submodular Optimization）</news:title>
   <news:publication_date>2026-07-12T04:02:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710859</loc>
  <lastmod>2026-07-12T04:02:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>衛星夜間光データから経済成長を定量化する動的ネットワークと表現学習アプローチ（A Dynamic Network and Representation Learning Approach for Quantifying Economic Growth from Satellite Imagery）</news:title>
   <news:publication_date>2026-07-12T04:02:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710857</loc>
  <lastmod>2026-07-12T03:10:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>網膜血管の動脈・静脈自動識別とセグメンテーション（AUTOMATED SEGMENTATION AND CLASSIFICATION OF ARTERIOLES AND VENULES USING CASCADING DILATED CONVOLUTIONAL NEURAL NETWORKS）</news:title>
   <news:publication_date>2026-07-12T03:10:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710855</loc>
  <lastmod>2026-07-12T03:10:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>認知的画像インペインティングのための深層インセプション生成ネットワーク (Deep Inception Generative Network for Cognitive Image Inpainting)</news:title>
   <news:publication_date>2026-07-12T03:10:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710853</loc>
  <lastmod>2026-07-12T03:10:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>サブグラフ観測から連結成分数を推定するスペクトル手法（Number of Connected Components in a Graph: Estimation via Counting Patterns）</news:title>
   <news:publication_date>2026-07-12T03:10:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710851</loc>
  <lastmod>2026-07-12T03:09:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次離散のCFDデータ欠損を復元する深層学習と動力学学習（Recovering missing CFD data for high-order discretizations using deep neural networks and dynamics learning）</news:title>
   <news:publication_date>2026-07-12T03:09:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710849</loc>
  <lastmod>2026-07-12T03:09:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>プライバシー志向ユーザーのプロファイル取得法（How to Profile Privacy-Conscious Users in Recommender Systems）</news:title>
   <news:publication_date>2026-07-12T03:09:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710847</loc>
  <lastmod>2026-07-12T03:09:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スナップショット蒸留：1世代での教師―生徒最適化（Snapshot Distillation: Teacher-Student Optimization in One Generation）</news:title>
   <news:publication_date>2026-07-12T03:09:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710845</loc>
  <lastmod>2026-07-12T03:09:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>AlGaN/AlGaN深紫外発光ダイオードの量子井戸数最適化（Optimization of quantum well number of AlGaN/AlGaN deep-ultraviolet light-emitting diodes）</news:title>
   <news:publication_date>2026-07-12T03:09:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710843</loc>
  <lastmod>2026-07-12T02:17:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>セントーラス銀河団コアにおける金属の塵への取り込みの可能性（Possible depletion of metals into dust grains in the core of the Centaurus cluster of galaxies）</news:title>
   <news:publication_date>2026-07-12T02:17:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710841</loc>
  <lastmod>2026-07-12T02:17:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>衛星画像で道路品質を等級付けする（Assigning a Grade: Accurate Measurement of Road Quality Using Satellite Imagery）</news:title>
   <news:publication_date>2026-07-12T02:17:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710839</loc>
  <lastmod>2026-07-12T02:17:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SKA1-Lowによる再電離期（EoR）深宇宙観測の候補野事前選定（Pre-selection of the Candidate Fields for Deep Imaging of the Epoch of Reionization with SKA1-Low）</news:title>
   <news:publication_date>2026-07-12T02:17:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710837</loc>
  <lastmod>2026-07-12T02:16:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>データベース接続ログにおける異常検知のためのビッグデータアーキテクチャ（A Big Data Architecture for the Detection of Anomalies within Database Connection Logs）</news:title>
   <news:publication_date>2026-07-12T02:16:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710835</loc>
  <lastmod>2026-07-12T02:16:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動的マルチストリーム映像要約（Multi-stream dynamic video Summarization）</news:title>
   <news:publication_date>2026-07-12T02:16:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710833</loc>
  <lastmod>2026-07-12T02:15:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Explore-Exploitによる対話的オンライン学習の実装と意義（Explore-Exploit: A Framework for Interactive and Online Learning）</news:title>
   <news:publication_date>2026-07-12T02:15:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710831</loc>
  <lastmod>2026-07-12T02:15:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ネットワーク接続ログの異常検知（Anomaly Detection for Network Connection Logs）</news:title>
   <news:publication_date>2026-07-12T02:15:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710829</loc>
  <lastmod>2026-07-12T01:24:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>RpropニューラルネットワークによるPV最大電力点追従と短絡電流制限（A Rprop-Neural-Network-Based PV Maximum Power Point Tracking Algorithm with Short-Circuit Current Limitation）</news:title>
   <news:publication_date>2026-07-12T01:24:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710827</loc>
  <lastmod>2026-07-12T01:23:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>出力層付近に限定したベイズ化で不確実性を捉える（The Relevance of Bayesian Layer Positioning to Model Uncertainty in Deep Bayesian Active Learning）</news:title>
   <news:publication_date>2026-07-12T01:23:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710825</loc>
  <lastmod>2026-07-12T01:23:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>超キャパシタ（ウルトラキャパシタ）向けニューラルネットワーク最適制御の実装可能性（A Neural-Network-Based Optimal Control of Ultra-Capacitors with System Uncertainties）</news:title>
   <news:publication_date>2026-07-12T01:23:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710823</loc>
  <lastmod>2026-07-12T01:22:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>臨床時系列データを用いた予測の落とし穴（Leveraging Clinical Time-Series Data for Prediction: A Cautionary Tale）</news:title>
   <news:publication_date>2026-07-12T01:22:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710821</loc>
  <lastmod>2026-07-12T01:22:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ゾーン・クリギングによる回帰と分類（Regression and Classification by Zonal Kriging）</news:title>
   <news:publication_date>2026-07-12T01:22:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710819</loc>
  <lastmod>2026-07-12T01:22:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>EvoMSA: 感情分析のための多言語進化的アプローチ (EvoMSA: A Multilingual Evolutionary Approach for Sentiment Analysis)</news:title>
   <news:publication_date>2026-07-12T01:22:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710817</loc>
  <lastmod>2026-07-12T01:22:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>RNN方策の有限状態表現の学習（Learning Finite State Representations of Recurrent Policy Networks）</news:title>
   <news:publication_date>2026-07-12T01:22:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710814</loc>
  <lastmod>2026-07-12T00:31:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>畳み込みニューラルネットワークにおける暗黙のフィルタ単位スパース化（On Implicit Filter Level Sparsity in Convolutional Neural Networks）</news:title>
   <news:publication_date>2026-07-12T00:31:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710812</loc>
  <lastmod>2026-07-12T00:31:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>3D半教師あり学習の不確実性配慮マルチビュー共同学習（3D Semi-Supervised Learning with Uncertainty-Aware Multi-View Co-Training）</news:title>
   <news:publication_date>2026-07-12T00:31:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710810</loc>
  <lastmod>2026-07-12T00:30:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>逐次埋め込みによるテキストクラスタリング（Sequential Embedding Induced Text Clustering）</news:title>
   <news:publication_date>2026-07-12T00:30:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710808</loc>
  <lastmod>2026-07-12T00:30:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>仮想現実学習環境におけるセキュリティ・プライバシー・安全性のリスク評価 (Security, Privacy and Safety Risk Assessment for Virtual Reality Learning Environment Applications)</news:title>
   <news:publication_date>2026-07-12T00:30:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710806</loc>
  <lastmod>2026-07-12T00:29:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Deep Steinの無偏リスク推定器を活用した教師なしX線画像のデノイズ（Leveraging Deep Stein’s Unbiased Risk Estimator for Unsupervised X-ray Denoising）</news:title>
   <news:publication_date>2026-07-12T00:29:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710804</loc>
  <lastmod>2026-07-12T00:29:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単一画像から複数照明を分離する学習法（Learning to Separate Multiple Illuminants in a Single Image）</news:title>
   <news:publication_date>2026-07-12T00:29:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710802</loc>
  <lastmod>2026-07-12T00:29:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>VC次元を超えるラベルなし圧縮スキーム（Unlabeled Compression Schemes Exceeding the VC-dimension）</news:title>
   <news:publication_date>2026-07-12T00:29:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710800</loc>
  <lastmod>2026-07-11T23:38:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>敵対的視点から見るフェデレーテッドラーニング（Analyzing Federated Learning through an Adversarial Lens）</news:title>
   <news:publication_date>2026-07-11T23:38:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710798</loc>
  <lastmod>2026-07-11T23:36:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>FU Ori天体の円盤風モデル（Disc wind models for FU Ori objects）</news:title>
   <news:publication_date>2026-07-11T23:36:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710796</loc>
  <lastmod>2026-07-11T23:27:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューラルネットワークにおけるスパース符号化の不確実性伝播（Uncertainty propagation in neural networks for sparse coding）</news:title>
   <news:publication_date>2026-07-11T23:27:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710794</loc>
  <lastmod>2026-07-11T23:27:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>シャッフリングによるプライバシー増幅（Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity）</news:title>
   <news:publication_date>2026-07-11T23:27:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710792</loc>
  <lastmod>2026-07-11T23:26:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高速かつ柔軟な屋内シーン合成（Fast and Flexible Indoor Scene Synthesis via Deep Convolutional Generative Models）</news:title>
   <news:publication_date>2026-07-11T23:26:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710790</loc>
  <lastmod>2026-07-11T23:26:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューラルネットワークによるサーフェスコード用デコーダ比較（Comparing neural network based decoders for the surface code）</news:title>
   <news:publication_date>2026-07-11T23:26:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710788</loc>
  <lastmod>2026-07-11T23:26:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>コミュニティ変化の検出手法（Testing Changes in Communities for the Stochastic Block Model）</news:title>
   <news:publication_date>2026-07-11T23:26:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710786</loc>
  <lastmod>2026-07-11T22:34:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マイクロ流体デバイスの流れ形状設計に深層強化学習を用いる（Flow Shape Design for Microfluidic Devices Using Deep Reinforcement Learning）</news:title>
   <news:publication_date>2026-07-11T22:34:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710784</loc>
  <lastmod>2026-07-11T22:34:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多峰性分布の効率的サンプリング手法と理論的裏付け（Simulated Tempering Langevin Monte Carlo II: An Improved Proof using Soft Markov Chain Decomposition）</news:title>
   <news:publication_date>2026-07-11T22:34:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710782</loc>
  <lastmod>2026-07-11T22:33:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>政府支出パターンの対応分析（Correspondence Analysis of Government Expenditure Patterns）</news:title>
   <news:publication_date>2026-07-11T22:33:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710780</loc>
  <lastmod>2026-07-11T22:32:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スーパー神岡観測器における二核子・核子崩壊の探索（Dinucleon and Nucleon Decay to Two-Body Final States with no Hadrons in Super-Kamiokande）</news:title>
   <news:publication_date>2026-07-11T22:32:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710778</loc>
  <lastmod>2026-07-11T22:32:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>天の川平面とバルジをWFDで覆うべき理由（The Plane’s The Thing: The Case for Wide-Fast-Deep Coverage of the Galactic Plane and Bulge）</news:title>
   <news:publication_date>2026-07-11T22:32:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710776</loc>
  <lastmod>2026-07-11T22:32:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>生成モデルでメタグレーティング設計を加速する（Freeform Diffractive Metagrating Design Based on Generative Adversarial Networks）</news:title>
   <news:publication_date>2026-07-11T22:32:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710774</loc>
  <lastmod>2026-07-11T22:32:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動画認識の高速化を実現する適応的フレーム選択（AdaFrame: Adaptive Frame Selection for Fast Video Recognition）</news:title>
   <news:publication_date>2026-07-11T22:32:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710772</loc>
  <lastmod>2026-07-11T21:41:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GANの暗黙の仮定を読み解く（On the Implicit Assumptions of GANs）</news:title>
   <news:publication_date>2026-07-11T21:41:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710770</loc>
  <lastmod>2026-07-11T21:41:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Komondor: 次世代高密度WLAN向け無線ネットワークシミュレータ（Komondor: a Wireless Network Simulator for Next-Generation High-Density WLANs）</news:title>
   <news:publication_date>2026-07-11T21:41:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710768</loc>
  <lastmod>2026-07-11T21:40:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ドープされたハバード模型のスナップショット分類に機械学習を用いる研究（Classifying Snapshots of the Doped Hubbard Model with Machine Learning）</news:title>
   <news:publication_date>2026-07-11T21:40:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710766</loc>
  <lastmod>2026-07-11T21:40:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチスケールで非線形力学を階層的に捉える手法（Tree-Structured Recurrent Switching Linear Dynamical Systems for Multi-Scale Modeling）</news:title>
   <news:publication_date>2026-07-11T21:40:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710764</loc>
  <lastmod>2026-07-11T21:39:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Sym-parameterを用いた混合ドメイン画像変換（Sym-parameterized Dynamic Inference for Mixed-Domain Image Translation）</news:title>
   <news:publication_date>2026-07-11T21:39:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710762</loc>
  <lastmod>2026-07-11T21:39:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>セマンティックレイアウトからの多様な画像生成（Diverse Image Synthesis from Semantic Layouts via Conditional IMLE）</news:title>
   <news:publication_date>2026-07-11T21:39:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710760</loc>
  <lastmod>2026-07-11T21:39:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CNN-Cert: 畳み込みニューラルネットワークの堅牢性を証明する効率的フレームワーク（CNN-Cert: An Efficient Framework for Certifying Robustness of Convolutional Neural Networks）</news:title>
   <news:publication_date>2026-07-11T21:39:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710758</loc>
  <lastmod>2026-07-11T20:47:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スムーズド解析による教師なし学習の安定化（Smoothed Analysis in Unsupervised Learning via Decoupling）</news:title>
   <news:publication_date>2026-07-11T20:47:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710756</loc>
  <lastmod>2026-07-11T20:47:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層学習でゲノムの構造カバレッジを広げる方法（Deep learning extends de novo protein modelling coverage of genomes using iteratively predicted structural constraints）</news:title>
   <news:publication_date>2026-07-11T20:47:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710754</loc>
  <lastmod>2026-07-11T20:46:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>教師なし分離表現学習に対する一般的な仮定への挑戦（Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations）</news:title>
   <news:publication_date>2026-07-11T20:46:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710752</loc>
  <lastmod>2026-07-11T20:46:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>TOUCHDOWN: 実世界ストリートビューでの自然言語ナビゲーションと空間推論（TOUCHDOWN: Natural Language Navigation and Spatial Reasoning in Visual Street Environments）</news:title>
   <news:publication_date>2026-07-11T20:46:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710750</loc>
  <lastmod>2026-07-11T20:45:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複素数ニューラルネットワークの実用性評価（Evaluation of Complex-Valued Neural Networks on Real-Valued Classification Tasks）</news:title>
   <news:publication_date>2026-07-11T20:45:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710748</loc>
  <lastmod>2026-07-11T20:45:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>薄膜気体の収縮に伴うマイクロバブル発生の高忠実度シミュレーション（High Fidelity Simulations of Micro-Bubble Shedding from Retracting Thin Gas Films in the Context of Liquid-Liquid Impact）</news:title>
   <news:publication_date>2026-07-11T20:45:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710746</loc>
  <lastmod>2026-07-11T20:45:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>低温での対数的抵抗上昇が示すd波秩序の兆候（Logarithmic Upturn in Low-Temperature Electronic Transport as a Signature of d-Wave Order in Cuprate Superconductors）</news:title>
   <news:publication_date>2026-07-11T20:45:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710744</loc>
  <lastmod>2026-07-11T19:54:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多クラス・マルチインスタンス学習の確率的最尤解法（A Multiclass Multiple Instance Learning Method with Exact Likelihood）</news:title>
   <news:publication_date>2026-07-11T19:54:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710742</loc>
  <lastmod>2026-07-11T19:54:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフを使った多視点正準相関分析の実践（Graph Multiview Canonical Correlation Analysis）</news:title>
   <news:publication_date>2026-07-11T19:54:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710740</loc>
  <lastmod>2026-07-11T19:53:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層強化学習を用いたトーリック符号の量子誤り訂正（Quantum error correction for the toric code using deep reinforcement learning）</news:title>
   <news:publication_date>2026-07-11T19:53:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710738</loc>
  <lastmod>2026-07-11T19:52:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>InverseRenderNetによる単一画像からの逆レンダリング（InverseRenderNet: Learning single image inverse rendering）</news:title>
   <news:publication_date>2026-07-11T19:52:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710736</loc>
  <lastmod>2026-07-11T19:52:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ベイズ的敵対的球面とノイズのない設定における推論（Bayesian Adversarial Spheres: Bayesian Inference and Adversarial Examples in a Noiseless Setting）</news:title>
   <news:publication_date>2026-07-11T19:52:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710734</loc>
  <lastmod>2026-07-11T19:52:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>重尾分布に基づくロバストなベイズ型クラスタ数推定（Robust Bayesian Cluster Enumeration Based on the t Distribution）</news:title>
   <news:publication_date>2026-07-11T19:52:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710732</loc>
  <lastmod>2026-07-11T19:52:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>代表サンプル選定を高速化する手法の要点（Iterative Projection and Matching: Finding Structure-preserving Representatives and Its Application to Computer Vision）</news:title>
   <news:publication_date>2026-07-11T19:52:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710730</loc>
  <lastmod>2026-07-11T19:01:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>手術室での顔検出の改善（Face Detection in the Operating Room: Comparison of State-of-the-art Methods and a Self-supervised Approach）</news:title>
   <news:publication_date>2026-07-11T19:01:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710728</loc>
  <lastmod>2026-07-11T19:00:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>増分的シーン合成（Incremental Scene Synthesis）</news:title>
   <news:publication_date>2026-07-11T19:00:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710726</loc>
  <lastmod>2026-07-11T19:00:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>生存時間解析における治療強度選択への深い潜在変数モデルの応用 (A Deep Latent-Variable Model Application to Select Treatment Intensity in Survival Analysis)</news:title>
   <news:publication_date>2026-07-11T19:00:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710724</loc>
  <lastmod>2026-07-11T18:59:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>タプルマックス損失による言語識別の最適化（Tuplemax Loss for Language Identification）</news:title>
   <news:publication_date>2026-07-11T18:59:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710722</loc>
  <lastmod>2026-07-11T18:59:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>近傍銀河の星状潮汐ストリームを深掘りする観測サーベイ（The Stellar Tidal Stream Survey）</news:title>
   <news:publication_date>2026-07-11T18:59:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710720</loc>
  <lastmod>2026-07-11T18:59:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ソフトウェア工学における知識管理の体系的レビュー（Knowledge Management in Software Engineering: A Systematic Review）</news:title>
   <news:publication_date>2026-07-11T18:59:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710718</loc>
  <lastmod>2026-07-11T18:59:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>クラスタリングによる回帰とメトロポリス–ヘイスティングス（Regression by clustering using Metropolis-Hastings）</news:title>
   <news:publication_date>2026-07-11T18:59:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710716</loc>
  <lastmod>2026-07-11T18:07:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>医療文書とマルチモーダル学習による院内死亡率予測の精度改善（Improving Hospital Mortality Prediction with Medical Named Entities and Multimodal Learning）</news:title>
   <news:publication_date>2026-07-11T18:07:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710714</loc>
  <lastmod>2026-07-11T18:07:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>データセットやタスク間での表現の転移性（On the Transferability of Representations in Neural Networks Between Datasets and Tasks）</news:title>
   <news:publication_date>2026-07-11T18:07:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710712</loc>
  <lastmod>2026-07-11T18:07:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>BCCNetによるラベル合成とニューラルネットワーク同時学習（BCCNet: Bayesian classifier combination neural network）</news:title>
   <news:publication_date>2026-07-11T18:07:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710710</loc>
  <lastmod>2026-07-11T18:06:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>電子カルテを使った機械学習：薬剤不遵守の予測モデルと特徴利用（Machine Learning on Electronic Health Records: Models and Features Usages to predict Medication Non-Adherence）</news:title>
   <news:publication_date>2026-07-11T18:06:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710708</loc>
  <lastmod>2026-07-11T18:06:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>人間校正フィードバックによる反事実学習（Counterfactual Learning from Human Proofreading Feedback for Semantic Parsing）</news:title>
   <news:publication_date>2026-07-11T18:06:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710706</loc>
  <lastmod>2026-07-11T18:05:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>異種データがもたらす認知症検出の汎化力（The Effect of Heterogeneous Data for Alzheimer’s Disease Detection from Speech）</news:title>
   <news:publication_date>2026-07-11T18:05:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710704</loc>
  <lastmod>2026-07-11T18:05:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所差分プライバシー下での離散分布学習におけるランダマイズド・レスポンスの最適性（Locally Differentially-Private Randomized Response for Discrete Distribution Learning）</news:title>
   <news:publication_date>2026-07-11T18:05:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710702</loc>
  <lastmod>2026-07-11T17:14:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>銀河群におけるMONDの優位なサンプル（MOND in galaxy groups: A superior sample）</news:title>
   <news:publication_date>2026-07-11T17:14:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710700</loc>
  <lastmod>2026-07-11T17:13:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ImageNet訓練済みCNNはテクスチャに偏る──形状バイアスを高める意義（IMAGENET-TRAINED CNNS ARE BIASED TOWARDS TEXTURE; INCREASING SHAPE BIAS IMPROVES ACCURACY AND ROBUSTNESS）</news:title>
   <news:publication_date>2026-07-11T17:13:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710698</loc>
  <lastmod>2026-07-11T17:13:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自動列車連結のための機械学習による障害物検知（Machine Learning Based Obstacle Detection for Automatic Train Pairing）</news:title>
   <news:publication_date>2026-07-11T17:13:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710696</loc>
  <lastmod>2026-07-11T17:13:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>21 cm信号のパラメータ空間を最適にサンプリングする方法（Working towards an optimal sampling of the 21 cm signal parameter space）</news:title>
   <news:publication_date>2026-07-11T17:13:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710694</loc>
  <lastmod>2026-07-11T17:12:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ApolloCar3Dによる自動運転向け3D車両理解の大転換（ApolloCar3D: A Large 3D Car Instance Understanding Benchmark for Autonomous Driving）</news:title>
   <news:publication_date>2026-07-11T17:12:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710692</loc>
  <lastmod>2026-07-11T17:12:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非一様フーリエデータからのエッジ検出とスパースベイズ学習（Detecting edges from non-uniform Fourier data via sparse Bayesian learning）</news:title>
   <news:publication_date>2026-07-11T17:12:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710690</loc>
  <lastmod>2026-07-11T17:11:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>術後合併症リスクの早期層別化（Early Stratification of Patients at Risk for Postoperative Complications after Elective Colectomy）</news:title>
   <news:publication_date>2026-07-11T17:11:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710688</loc>
  <lastmod>2026-07-11T16:20:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>反復残差CNNによるバースト撮影の画質復元（Iterative Residual CNNs for Burst Photography Applications）</news:title>
   <news:publication_date>2026-07-11T16:20:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710686</loc>
  <lastmod>2026-07-11T16:20:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>音楽における意味関係を掘る：Word2Vecを使った音楽表現の探索（From Context to Concept: Exploring Semantic Relationships in Music with Word2Vec）</news:title>
   <news:publication_date>2026-07-11T16:20:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710684</loc>
  <lastmod>2026-07-11T16:20:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GPU容量を超える大規模モデルのデータ並列分散学習（Data-parallel distributed training of very large models beyond GPU capacity）</news:title>
   <news:publication_date>2026-07-11T16:20:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710682</loc>
  <lastmod>2026-07-11T16:18:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>三次テンソル積による推論学習（Learning to Reason with Third-Order Tensor Products）</news:title>
   <news:publication_date>2026-07-11T16:18:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710680</loc>
  <lastmod>2026-07-11T16:18:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>FLIPERでTESSの振動星を自動分類する（FLIPER: Classifying TESS Pulsating Stars）</news:title>
   <news:publication_date>2026-07-11T16:18:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710678</loc>
  <lastmod>2026-07-11T16:18:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>顔表情認識における二層注意と二段階マルチタスク学習（Two-level Attention with Two-stage Multi-task Learning for Facial Emotion Recognition）</news:title>
   <news:publication_date>2026-07-11T16:18:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710676</loc>
  <lastmod>2026-07-11T16:18:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高コストなブラックボックス最適化の新戦略（Global optimization of expensive black-box models based on asynchronous hybrid-criterion with interval reduction）</news:title>
   <news:publication_date>2026-07-11T16:18:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710674</loc>
  <lastmod>2026-07-11T15:26:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>強相互作用領域へクエンチされたボース気体の初期ダイナミクス（Early-time dynamics of Bose gases quenched into the strongly interacting regime）</news:title>
   <news:publication_date>2026-07-11T15:26:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710672</loc>
  <lastmod>2026-07-11T15:26:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>周期性信号の異常検出における位相分類手法（A Machine-Learning Phase Classification Scheme for Anomaly Detection in Signals with Periodic Characteristics）</news:title>
   <news:publication_date>2026-07-11T15:26:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710670</loc>
  <lastmod>2026-07-11T15:26:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非包含部分列が突きつける自然言語推論の弱点（Non-entailed subsequences as a challenge for natural language inference）</news:title>
   <news:publication_date>2026-07-11T15:26:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710668</loc>
  <lastmod>2026-07-11T15:24:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ロシア・ドイツの天体粒子データライフサイクル構想（Russian–German Astroparticle Data Life Cycle Initiative）</news:title>
   <news:publication_date>2026-07-11T15:24:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710666</loc>
  <lastmod>2026-07-11T15:24:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>QUIC実装の相互運用性を導くテスト（Interoperability-Guided Testing of QUIC Implementations using Symbolic Execution）</news:title>
   <news:publication_date>2026-07-11T15:24:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710664</loc>
  <lastmod>2026-07-11T15:24:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>近似画像処理パイプラインからのディープニューラルネットワークのブートストラップ（Bootstrapping Deep Neural Networks from Approximate Image Processing Pipelines）</news:title>
   <news:publication_date>2026-07-11T15:24:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/710662</loc>
  <lastmod>2026-07-11T15:24:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>画像の非線形拡散問題のためのネットワーク（Networks for Nonlinear Diffusion Problems in Imaging）</news:title>
   <news:publication_date>2026-07-11T15:24:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
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