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   <news:title>MDLを用いたfrom-below型ブール行列分解の実践的意義（From-Below Boolean Matrix Factorization Algorithm Based on MDL）</news:title>
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   <news:title>配管計装図（P&amp;amp;ID）からの自動情報抽出（Automatic Information Extraction from Piping and Instrumentation Diagrams）</news:title>
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   <news:title>物理層で学習を支援する量子センサーネットワーク（Physical-Layer Supervised Learning Assisted by an Entangled Sensor Network）</news:title>
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   <news:title>曲率正則化による欠損データ復元（Curvature Regularization For Missing Data Recovery）</news:title>
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   <news:title>表象における公平性：ステレオタイプ化を表象的損害として定量化する（Fairness in representation: quantifying stereotyping as a representational harm）</news:title>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>GANのアウト・オブ・サンプル検証（Out-of-Sample Testing for GANs）</news:title>
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   <news:genres>Blog</news:genres>
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   <news:title>世代交代型宇宙船の規模に関する数値的制約（Numerical constraints on the size of generation ships from total energy expenditure on board, annual food production and space farming techniques）</news:title>
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    <news:language>ja</news:language>
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   <news:title>需要側管理への応用を目指す文脈付きバンディットの目標追跡（Target Tracking for Contextual Bandits: Application to Demand Side Management）</news:title>
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   <news:title>ガウス過程回帰のランダム部分抽出を理論的に評価する（On Random Subsampling of Gaussian Process Regression: A Graphon-Based Analysis）</news:title>
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    <news:language>ja</news:language>
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   <news:title>中間特徴を守る複素数ニューラルネットワーク（Interpretable Complex-valued Neural Networks for Privacy Protection）</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>PADAM：適応的勾配法の汎化ギャップを埋める（PADAM: CLOSING THE GENERALIZATION GAP OF ADAPTIVE GRADIENT METHODS IN TRAINING DEEP NEURAL NETWORKS）</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>プレーンテキスト速度で動く安全なマルチパーティ線形回帰（Secure multi-party linear regression at plaintext speed）</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>階層的なクラスタリングで表現学習を一段進める（Hierarchically Clustered Representation Learning）</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>アウトライヤーチャンネル分割による事後量子化の改善（Improving Neural Network Quantization using Outlier Channel Splitting）</news:title>
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   <news:genres>Blog</news:genres>
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   <news:title>反復正則化確率ミラーディセント法による非微分確率最適化の扱い方（An iterative regularized mirror descent method for ill-posed nondiﬀerentiable stochastic optimization）</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>ブラックボックス部分列集合最適化の解法（Black Box Submodular Maximization: Discrete and Continuous Settings）</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>医療費支出における公正回帰の考え方（Fair Regression for Health Care Spending）</news:title>
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   <news:genres>Blog</news:genres>
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    <news:name>AI Benchmark Research</news:name>
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   <news:title>クラウドソース型動画システムにおけるユーザー寄付（User Donations in a Crowdsourced Video System）</news:title>
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   <news:genres>Blog</news:genres>
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   <news:title>データから文を作る技術と「スタイル模倣」の勝ち筋（Data-to-Text Generation with Style Imitation）</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>陽性・未ラベル学習に対する解析的識別器の原理（Principled analytic classifier for positive-unlabeled learning via weighted integral probability metric）</news:title>
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   <news:genres>Blog</news:genres>
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  <news:news>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>ReRAMベースNNアクセラレータのためのフルシステムスタック（FPSA: A Full System Stack Solution for Reconfigurable ReRAM-based NN Accelerator Architecture）</news:title>
   <news:publication_date>2026-07-31T05:25:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-07-31T05:25:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>隠れた低ランク構造を活用した確率的線形バンディット（Stochastic Linear Bandits with Hidden Low Rank Structure）</news:title>
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   <news:genres>Blog</news:genres>
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  <lastmod>2026-07-31T05:25:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>ニューラルネットの「硬さ（Stiffness）」が示す一般化の新視点（Stiffness: A New Perspective on Generalization in Neural Networks）</news:title>
   <news:publication_date>2026-07-31T05:25:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-07-31T05:24:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>活性化空間の形状を位相的に描く（Characterizing the Shape of Activation Space in Deep Neural Networks）</news:title>
   <news:publication_date>2026-07-31T05:24:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/717856</loc>
  <lastmod>2026-07-31T04:33:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>未観測事例の最大相互情報分類のためのCMアルゴリズム（The CM Algorithm for the Maximum Mutual Information Classifications of Unseen Instances）</news:title>
   <news:publication_date>2026-07-31T04:33:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-07-31T04:24:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>計算写真と視覚認識の溝を埋める（Bridging the Gap Between Computational Photography and Visual Recognition）</news:title>
   <news:publication_date>2026-07-31T04:24:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/717852</loc>
  <lastmod>2026-07-31T04:23:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>肝病変分類のためのエンドツーエンド判別型ディープネットワーク（END-TO-END DISCRIMINATIVE DEEP NETWORK FOR LIVER LESION CLASSIFICATION）</news:title>
   <news:publication_date>2026-07-31T04:23:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/717850</loc>
  <lastmod>2026-07-31T04:23:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DAG混合による因果の発見（Causal Discovery with a Mixture of DAGs）</news:title>
   <news:publication_date>2026-07-31T04:23:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/717848</loc>
  <lastmod>2026-07-31T04:22:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>画像検索における透かし（ウォーターマーク）画像を効果的に順位下げするエンドツーエンド手法（An End-to-End Solution for Effectively Demoting Watermarked Images in Image Search）</news:title>
   <news:publication_date>2026-07-31T04:22:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-07-31T04:22:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>IoT製品レビューからソフトウェア要件を読む手法（Do users talk about the software in my product? Analyzing user reviews on IoT products）</news:title>
   <news:publication_date>2026-07-31T04:22:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-07-31T04:22:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>協働タスク仕様のためのベイズ能動学習（Bayesian Active Learning for Collaborative Task Specification Using Equivalence Regions）</news:title>
   <news:publication_date>2026-07-31T04:22:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/717842</loc>
  <lastmod>2026-07-31T03:31:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GANを分解して理解する（Deconstructing Generative Adversarial Networks）</news:title>
   <news:publication_date>2026-07-31T03:31:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
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 <url>
  <loc>https://aibr.jp/archives/717840</loc>
  <lastmod>2026-07-31T03:30:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>前立腺の自動セグメンテーションにおける局所・大域二段階CNN（A novel deep learning-based method for prostate segmentation in T2-weighted magnetic resonance imaging）</news:title>
   <news:publication_date>2026-07-31T03:30:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717838</loc>
  <lastmod>2026-07-31T03:30:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>TUNet：セグメンテーションマップを組み込んだ分類精度向上（TUNet: Incorporating segmentation maps to improve classification）</news:title>
   <news:publication_date>2026-07-31T03:30:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717836</loc>
  <lastmod>2026-07-31T03:30:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>変換同期を学習するニューラル手法（Learning Transformation Synchronization）</news:title>
   <news:publication_date>2026-07-31T03:30:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717834</loc>
  <lastmod>2026-07-31T03:29:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ドメイン適応のための不変表現学習（On Learning Invariant Representation for Domain Adaptation）</news:title>
   <news:publication_date>2026-07-31T03:29:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717832</loc>
  <lastmod>2026-07-31T03:29:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>職業分類における意味表現バイアスの実証研究（Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Setting）</news:title>
   <news:publication_date>2026-07-31T03:29:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717830</loc>
  <lastmod>2026-07-31T03:29:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オフポリシー深層強化学習における共変量シフトの補正（Off-Policy Deep Reinforcement Learning by Bootstrapping the Covariate Shift）</news:title>
   <news:publication_date>2026-07-31T03:29:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717828</loc>
  <lastmod>2026-07-31T02:38:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ADMM-Softmax：多項ロジスティック回帰のためのADMMアプローチ (ADMM-Softmax : An ADMM Approach for Multinomial Logistic Regression)</news:title>
   <news:publication_date>2026-07-31T02:38:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717826</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>電子カルテの自動エンドツーエンド匿名化：高精度だけが指標か（Automatic end-to-end De-identification: Is high accuracy the only metric?）</news:title>
   <news:publication_date>2026-07-31T02:38:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717824</loc>
  <lastmod>2026-07-31T02:38:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オープンソース顔認証評価パッケージの意義（OPEN SOURCE FACE RECOGNITION PERFORMANCE EVALUATION PACKAGE）</news:title>
   <news:publication_date>2026-07-31T02:38:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717822</loc>
  <lastmod>2026-07-31T02:37:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ピクセル単位のセマンティック彩色（Pixelated Semantic Colorization）</news:title>
   <news:publication_date>2026-07-31T02:37:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717820</loc>
  <lastmod>2026-07-31T02:37:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散最適化の99%は時間の無駄である（99% of Distributed Optimization is a Waste of Time: The Issue and How to Fix it）</news:title>
   <news:publication_date>2026-07-31T02:37:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717818</loc>
  <lastmod>2026-07-31T02:37:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>モデル圧縮による母集団リスク改善の情報理論的理解 (Information-Theoretic Understanding of Population Risk Improvement with Model Compression)</news:title>
   <news:publication_date>2026-07-31T02:37:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717816</loc>
  <lastmod>2026-07-31T02:36:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スマートジャマーの識別に波形（ウェーブレット）を使う意義（Identification of Smart Jammers: Learning based Approaches Using Wavelet Representation）</news:title>
   <news:publication_date>2026-07-31T02:36:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717814</loc>
  <lastmod>2026-07-31T01:44:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自然クラスタリングによる表現の分離と学習（Disentangling and Learning Robust Representations with Natural Clustering）</news:title>
   <news:publication_date>2026-07-31T01:44:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717812</loc>
  <lastmod>2026-07-31T01:44:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単眼深度推定の概観（Monocular Depth Estimation: A Survey）</news:title>
   <news:publication_date>2026-07-31T01:44:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717810</loc>
  <lastmod>2026-07-31T01:44:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SGDの一般解析と改善された収束率（SGD: General Analysis and Improved Rates）</news:title>
   <news:publication_date>2026-07-31T01:44:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717808</loc>
  <lastmod>2026-07-31T01:43:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>説明の(不)忠実度と感度に関する研究（On the (In)fidelity and Sensitivity of Explanations）</news:title>
   <news:publication_date>2026-07-31T01:43:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717806</loc>
  <lastmod>2026-07-31T01:43:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>NeuralSampler: Euclidean Point Cloud Auto-Encoder and Sampler（NeuralSampler: Euclidean Point Cloud Auto-Encoder and Sampler）</news:title>
   <news:publication_date>2026-07-31T01:43:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717804</loc>
  <lastmod>2026-07-31T01:43:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>不完全な模倣学習のための自信利用手法（Imitation Learning from Imperfect Demonstration）</news:title>
   <news:publication_date>2026-07-31T01:43:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717802</loc>
  <lastmod>2026-07-31T01:42:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ブラウザで深層学習を動かす限界と可能性（Moving Deep Learning into Web Browser: How Far Can We Go?）</news:title>
   <news:publication_date>2026-07-31T01:42:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717800</loc>
  <lastmod>2026-07-31T00:51:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多成分混合物のNVTフラッシュ計算の高速化（Acceleration of the NVT-flash calculation for multicomponent mixtures using deep neural network models）</news:title>
   <news:publication_date>2026-07-31T00:51:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717798</loc>
  <lastmod>2026-07-31T00:50:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>モバイルIoT機器におけるバイオ特徴量を用いた認証と認可（Authentication and Authorization for Mobile IoT Devices using Bio-features: Recent Advances and Future Trends）</news:title>
   <news:publication_date>2026-07-31T00:50:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717796</loc>
  <lastmod>2026-07-31T00:50:11Z</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>2026-07-31T00:50:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717794</loc>
  <lastmod>2026-07-31T00:49:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>滑らかで強凸な関数の確率的近似：O(1/T)収束率を超えて（Stochastic Approximation of Smooth and Strongly Convex Functions: Beyond the O(1/T) Convergence Rate）</news:title>
   <news:publication_date>2026-07-31T00:49:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717792</loc>
  <lastmod>2026-07-31T00:49:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>不変（Invariant）ネットワークの普遍性について（On the Universality of Invariant Networks）</news:title>
   <news:publication_date>2026-07-31T00:49:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717790</loc>
  <lastmod>2026-07-31T00:49:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ノイズ下の音声認識における不確かさを扱うCNN（A Convoloutional Neural Network model based on Neutrosophy for Noisy Speech Recognition）</news:title>
   <news:publication_date>2026-07-31T00:49:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717788</loc>
  <lastmod>2026-07-31T00:48:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>差分可能な特徴選択を実現するConcrete Autoencoders（Concrete Autoencoders for Differentiable Feature Selection and Reconstruction）</news:title>
   <news:publication_date>2026-07-31T00:48:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717786</loc>
  <lastmod>2026-07-30T23:56:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>報酬整形をメタ学習で自動化する手法（Reward Shaping via Meta-Learning）</news:title>
   <news:publication_date>2026-07-30T23:56:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717784</loc>
  <lastmod>2026-07-30T23:48:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ヘテロジニアニティ対応の勾配符号化によるストラグラー耐性（Heterogeneity-aware Gradient Coding for Straggler Tolerance）</news:title>
   <news:publication_date>2026-07-30T23:48:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717782</loc>
  <lastmod>2026-07-30T23:48:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>バッチ拡張で学習を高速化する手法（Augment your batch: better training with larger batches）</news:title>
   <news:publication_date>2026-07-30T23:48:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717780</loc>
  <lastmod>2026-07-30T23:48:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>翌日の見出しを予測するためのTwitter分析（Predicting Tomorrow’s Headline using Today’s Twitter Deliberations）</news:title>
   <news:publication_date>2026-07-30T23:48:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717778</loc>
  <lastmod>2026-07-30T23:47:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>2次元乱流の暗黙的・明示的LESを機械学習で接続する（Connecting implicit and explicit large eddy simulations of two-dimensional turbulence through machine learning）</news:title>
   <news:publication_date>2026-07-30T23:47:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717776</loc>
  <lastmod>2026-07-30T23:47:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散ネットワーク化した深層マルチエージェント強化学習における価値伝搬（Value Propagation for Decentralized Networked Deep Multi-agent Reinforcement Learning）</news:title>
   <news:publication_date>2026-07-30T23:47:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717774</loc>
  <lastmod>2026-07-30T23:47:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>シリケートガラスの剛性予測を高速シミュレーションと機械学習で行う（Prediction of Silicate Glasses’ Stiffness by High-Throughput Molecular Dynamics Simulations and Machine Learning）</news:title>
   <news:publication_date>2026-07-30T23:47:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717772</loc>
  <lastmod>2026-07-30T22:54:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>クラウド基盤の異常検知とランキングを学ぶ（Anomaly detecting and ranking of the cloud computing platform by multi-view learning）</news:title>
   <news:publication_date>2026-07-30T22:54:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフェン中のSi原子ダイナミクスの原子機構（Atomic mechanisms for the Si atom dynamics in graphene: chemical transformations at the edge and in the bulk）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/717768</loc>
  <lastmod>2026-07-30T22:46:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>安静時fMRIから学習した因果影響強度による自閉症スペクトラム障害の診断（Diagnosis of Autism Spectrum Disorder by Causal Influence Strength Learned from Resting-State fMRI Data）</news:title>
   <news:publication_date>2026-07-30T22:46:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717766</loc>
  <lastmod>2026-07-30T22:45:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Fixupによる初期化で正規化を不要にする残差学習（Fixup Initialization: Residual Learning Without Normalization）</news:title>
   <news:publication_date>2026-07-30T22:45:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717764</loc>
  <lastmod>2026-07-30T22:44:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>エンドツーエンド学習のモジュール化（Modularization of End-to-End Learning: Case Study in Arcade Games）</news:title>
   <news:publication_date>2026-07-30T22:44:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717762</loc>
  <lastmod>2026-07-30T22:44:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>対称損失を用いた汚れたラベルからの学習（On Symmetric Losses for Learning from Corrupted Labels）</news:title>
   <news:publication_date>2026-07-30T22:44:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717760</loc>
  <lastmod>2026-07-30T22:44:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>無限地平マルコフ決定過程におけるQ学習の有効性（Q-learning with UCB Exploration is Sample Efficient for Infinite-Horizon MDP）</news:title>
   <news:publication_date>2026-07-30T22:44:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717758</loc>
  <lastmod>2026-07-30T21:52:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習中にモデルを縮小して高速化する方法（PruneTrain: Fast Neural Network Training by Dynamic Sparse Model Reconfiguration）</news:title>
   <news:publication_date>2026-07-30T21:52:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717756</loc>
  <lastmod>2026-07-30T21:52:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ホテル認識を加速する大規模データセット（Hotels-50K: A Global Hotel Recognition Dataset）</news:title>
   <news:publication_date>2026-07-30T21:52:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717754</loc>
  <lastmod>2026-07-30T21:51:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>転移学習による糖尿病網膜症分類の実用性評価（Evaluation of Transfer Learning for Classification of Diabetic Retinopathy）</news:title>
   <news:publication_date>2026-07-30T21:51:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717752</loc>
  <lastmod>2026-07-30T21:51:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>二者間プライベート線形最小二乗法の実用的スキーム（A Practical Scheme for Two-Party Private Linear Least Squares）</news:title>
   <news:publication_date>2026-07-30T21:51:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717750</loc>
  <lastmod>2026-07-30T21:51:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>汎用ハードでのリアルタイム動画要約（Real-time Video Summarization on Commodity Hardware）</news:title>
   <news:publication_date>2026-07-30T21:51:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717748</loc>
  <lastmod>2026-07-30T21:51:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Point-Cloudから画像を生成する新手法の要点（Points2Pix: 3D Point-Cloud to Image Translation using conditional GANs）</news:title>
   <news:publication_date>2026-07-30T21:51:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717746</loc>
  <lastmod>2026-07-30T21:50:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>無駄にされている統計情報を活用して分類器精度を改善する方法 (Money on the Table: Statistical information ignored by softmax can improve classifier accuracy)</news:title>
   <news:publication_date>2026-07-30T21:50:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717744</loc>
  <lastmod>2026-07-30T20:59:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>実用的なリプシッツ・バンディットに向けて（Towards Practical Lipschitz Bandits）</news:title>
   <news:publication_date>2026-07-30T20:59:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717742</loc>
  <lastmod>2026-07-30T20:58:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ナノ構造非晶質シリコンの速度特性を決める原子スケール要因（Atomic-scale factors that control the rate capability of nanostructured amorphous Si for high-energy-density batteries）</news:title>
   <news:publication_date>2026-07-30T20:58:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717740</loc>
  <lastmod>2026-07-30T20:58:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自動運転のデータセット設計と検証の課題（CHALLENGES IN DESIGNING DATASETS AND VALIDATION FOR AUTONOMOUS DRIVING）</news:title>
   <news:publication_date>2026-07-30T20:58:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717738</loc>
  <lastmod>2026-07-30T20:57:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多年にわたる24時間・週7日の起点結点需要推定（Estimating multi-year 24/7 origin-destination demand using high-granular multi-source traffic data）</news:title>
   <news:publication_date>2026-07-30T20:57:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717736</loc>
  <lastmod>2026-07-30T20:57:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>遷移金属上の吸着エネルギーを多層データで精度良く推定する方法（On Deriving Probabilistic Models for Adsorption Energy on Transition Metals using Multi-level ab initio and Experimental Data）</news:title>
   <news:publication_date>2026-07-30T20:57:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717734</loc>
  <lastmod>2026-07-30T20:57:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>差分圧縮による分散学習（Distributed Learning with Compressed Gradient Differences）</news:title>
   <news:publication_date>2026-07-30T20:57:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717732</loc>
  <lastmod>2026-07-30T20:57:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>離散値時系列のクラスタリング（Clustering Discrete-Valued Time Series）</news:title>
   <news:publication_date>2026-07-30T20:57:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717730</loc>
  <lastmod>2026-07-30T20:06:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ラベル無し動画から学ぶ動画表現学習（DistInit: Learning Video Representations Without a Single Labeled Video）</news:title>
   <news:publication_date>2026-07-30T20:06:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717728</loc>
  <lastmod>2026-07-30T20:05:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単純な特徴で効率的に毒性を予測する（Efficient Toxicity Prediction via Simple Features Using Shallow Neural Networks and Decision Trees）</news:title>
   <news:publication_date>2026-07-30T20:05:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717726</loc>
  <lastmod>2026-07-30T20:05:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スペクトラムデータ汚染による敵対的ディープ学習（Spectrum Data Poisoning with Adversarial Deep Learning）</news:title>
   <news:publication_date>2026-07-30T20:05:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717724</loc>
  <lastmod>2026-07-30T20:04:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高効率四接合太陽電池の設計と評価（Novel High Efficiency Quadruple Junction Solar Cell with Current Matching and Optimized Quantum Efficiency）</news:title>
   <news:publication_date>2026-07-30T20:04:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717722</loc>
  <lastmod>2026-07-30T20:04:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>特徴マップ注意による深層学習転移（DELTA: DEEP LEARNING TRANSFER USING FEATURE MAP WITH ATTENTION）</news:title>
   <news:publication_date>2026-07-30T20:04:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717720</loc>
  <lastmod>2026-07-30T20:04:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模画像・信号における分散畳み込み辞書学習（Distributed Convolutional Dictionary Learning (DiCoDiLe): Pattern Discovery in Large Images and Signals）</news:title>
   <news:publication_date>2026-07-30T20:04:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717718</loc>
  <lastmod>2026-07-30T20:04:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GAN生成画像とレタッチ検出の自動化（On Detecting GANs and Retouching based Synthetic Alterations）</news:title>
   <news:publication_date>2026-07-30T20:04:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717716</loc>
  <lastmod>2026-07-30T19:11:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層的思考で捉える多エージェントの有限合理性（Modelling Bounded Rationality in Multi-Agent Interactions by Generalized Recursive Reasoning）</news:title>
   <news:publication_date>2026-07-30T19:11:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717714</loc>
  <lastmod>2026-07-30T19:11:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>段階的画像雨除去ネットワークの簡潔な基準（Progressive Image Deraining Networks: A Better and Simpler Baseline）</news:title>
   <news:publication_date>2026-07-30T19:11:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717712</loc>
  <lastmod>2026-07-30T19:11:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>磁気浮上ハプティックのためのCascade LSTMベース視覚・慣性航法（Cascade LSTM Based Visual-Inertial Navigation for Magnetic Levitation Haptic Interaction）</news:title>
   <news:publication_date>2026-07-30T19:11:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717710</loc>
  <lastmod>2026-07-30T19:09:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的再帰的推論（Probabilistic Recursive Reasoning, PR2）によるマルチエージェント強化学習の刷新（PROBABILISTIC RECURSIVE REASONING FOR MULTI-AGENT REINFORCEMENT LEARNING）</news:title>
   <news:publication_date>2026-07-30T19:09:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717708</loc>
  <lastmod>2026-07-30T19:09:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>フル解像度Atrous Convolutional Neural Networkによる医用画像セグメンテーション（ACNN: a Full Resolution DCNN for Medical Image Segmentation）</news:title>
   <news:publication_date>2026-07-30T19:09:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717706</loc>
  <lastmod>2026-07-30T19:09:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>カーネル誘導による暗黙的生成モデルの安定化（Kernel-Guided Training of Implicit Generative Models with Stability Guarantees）</news:title>
   <news:publication_date>2026-07-30T19:09:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717704</loc>
  <lastmod>2026-07-30T18:16:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>選択的予測を統合した深層ニューラルネットワーク（SelectiveNet: A Deep Neural Network with an Integrated Reject Option）</news:title>
   <news:publication_date>2026-07-30T18:16:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717702</loc>
  <lastmod>2026-07-30T18:06:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>群進化に基づくブラックボックス攻撃の実像（A BLACK-BOX ATTACK ON NEURAL NETWORKS BASED ON SWARM EVOLUTIONARY ALGORITHM）</news:title>
   <news:publication_date>2026-07-30T18:06:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717700</loc>
  <lastmod>2026-07-30T18:06:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>心電図拍における重要な部分列の発見（Discovery of Important Subsequences in Electrocardiogram Beats Using the Nearest Neighbour Algorithm）</news:title>
   <news:publication_date>2026-07-30T18:06:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717698</loc>
  <lastmod>2026-07-30T18:05:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>平面に基づくクラスタリングの一般モデル（A general model for plane-based clustering with loss function）</news:title>
   <news:publication_date>2026-07-30T18:05:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717696</loc>
  <lastmod>2026-07-30T18:05:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>少光子パラメトリック発振器の量子ダイナミクス（Quantum dynamics of a few-photon parametric oscillator）</news:title>
   <news:publication_date>2026-07-30T18:05:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717694</loc>
  <lastmod>2026-07-30T18:05:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スパース進化的ディープラーニング：汎用PCで百万ニューロンを動かす（Sparse evolutionary Deep Learning with over one million artificial neurons on commodity hardware）</news:title>
   <news:publication_date>2026-07-30T18:05:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717692</loc>
  <lastmod>2026-07-30T18:04:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>行動ロバスト強化学習と連続制御への応用（Action Robust Reinforcement Learning and Applications in Continuous Control）</news:title>
   <news:publication_date>2026-07-30T18:04:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717690</loc>
  <lastmod>2026-07-30T17:12:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>データ駆動によるフォトニックシミュレーションの高速化（Data-driven acceleration of photonic simulations）</news:title>
   <news:publication_date>2026-07-30T17:12:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717688</loc>
  <lastmod>2026-07-30T17:12:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>柔軟な契約による大口電力需要学習（Learning Large Electrical Loads via Flexible Contracts with Commitment）</news:title>
   <news:publication_date>2026-07-30T17:12:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717686</loc>
  <lastmod>2026-07-30T17:12:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>重み付き動的ネットワークの非線形時系列リンク予測（GCN-GAN: A Non-linear Temporal Link Prediction Model for Weighted Dynamic Networks）</news:title>
   <news:publication_date>2026-07-30T17:12:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717684</loc>
  <lastmod>2026-07-30T17:11:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>注目領域に導かれるデータ拡張で細分類の精度を高める手法（See Better Before Looking Closer: Weakly Supervised Data Augmentation Network for Fine-Grained Visual Classification）</news:title>
   <news:publication_date>2026-07-30T17:11:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717682</loc>
  <lastmod>2026-07-30T17:11:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SDRとPESQを同時最適化するエンドツーエンド多目的デノイジング（End-to-End Multi-Task Denoising for Joint SDR and PESQ Optimization）</news:title>
   <news:publication_date>2026-07-30T17:11:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717680</loc>
  <lastmod>2026-07-30T17:10:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>制約された空力データセットからの高速ニューラル予測（Fast Neural Network Predictions from Constrained Aerodynamics Datasets）</news:title>
   <news:publication_date>2026-07-30T17:10:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717678</loc>
  <lastmod>2026-07-30T17:10:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>鞍点から抜け出す適応的勾配法（Escaping Saddle Points with Adaptive Gradient Methods）</news:title>
   <news:publication_date>2026-07-30T17:10:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717676</loc>
  <lastmod>2026-07-30T16:18:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>メタメトリック学習による少数ショット学習（Few-shot Learning with Meta Metric Learners）</news:title>
   <news:publication_date>2026-07-30T16:18:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717674</loc>
  <lastmod>2026-07-30T16:18:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフィカルモデルによる差分プライバシー下の推定と推論（Graphical-model based estimation and inference for differential privacy）</news:title>
   <news:publication_date>2026-07-30T16:18:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717672</loc>
  <lastmod>2026-07-30T16:17:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>漸進的ラベル蒸留による入力効率化（PROGRESSIVE LABEL DISTILLATION: LEARNING INPUT-EFFICIENT DEEP NEURAL NETWORKS）</news:title>
   <news:publication_date>2026-07-30T16:17:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717670</loc>
  <lastmod>2026-07-30T16:17:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>VQNet：量子-古典ハイブリッドニューラルネットワークのライブラリ（VQNet: Library for a Quantum-Classical Hybrid Neural Network）</news:title>
   <news:publication_date>2026-07-30T16:17:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717668</loc>
  <lastmod>2026-07-30T16:17:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>最適なk被覆充電問題（Optimal k-Coverage Charging Problem）</news:title>
   <news:publication_date>2026-07-30T16:17:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717666</loc>
  <lastmod>2026-07-30T16:16:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スタッキングと安定性（Stacking and Stability）</news:title>
   <news:publication_date>2026-07-30T16:16:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717664</loc>
  <lastmod>2026-07-30T16:16:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層的文書表現のための言語モデル事前学習（LANGUAGE MODEL PRE-TRAINING FOR HIERARCHICAL DOCUMENT REPRESENTATIONS）</news:title>
   <news:publication_date>2026-07-30T16:16:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717662</loc>
  <lastmod>2026-07-30T15:24:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ラベル無しデータとラベル有りデータによるアクティブラーニングの停止判断の比較 (The Use of Unlabeled Data versus Labeled Data for Stopping Active Learning for Text Classification)</news:title>
   <news:publication_date>2026-07-30T15:24:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717660</loc>
  <lastmod>2026-07-30T15:24:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>予測されたF値変化に基づくアクティブラーニングの停止法（Stopping Active Learning based on Predicted Change of F Measure for Text Classification）</news:title>
   <news:publication_date>2026-07-30T15:24:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717658</loc>
  <lastmod>2026-07-30T15:24:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DeepSZによるニューラルネットワーク圧縮の実務的示唆（DeepSZ: A Novel Framework to Compress Deep Neural Networks by Using Error-Bounded Lossy Compression）</news:title>
   <news:publication_date>2026-07-30T15:24:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717656</loc>
  <lastmod>2026-07-30T15:23:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非常に疎な高次元データの部分空間クラスタリング（Subspace Clustering of Very Sparse High-Dimensional Data）</news:title>
   <news:publication_date>2026-07-30T15:23:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717654</loc>
  <lastmod>2026-07-30T15:23:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散型適応モーメント推定法DADAMの要点（DADAM: A Consensus-based Distributed Adaptive Gradient Method for Online Optimization）</news:title>
   <news:publication_date>2026-07-30T15:23:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717652</loc>
  <lastmod>2026-07-30T15:22:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>限られた学習データでのブラックボックスAPI攻撃に対する生成対抗ネットワーク（Generative Adversarial Networks for Black-Box API Attacks with Limited Training Data）</news:title>
   <news:publication_date>2026-07-30T15:22:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717650</loc>
  <lastmod>2026-07-30T15:22:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動物と植物を形で見分ける視覚分類の研究（A study on general visual categorization of objects into animal and plant groups using global shape descriptors with a focus on category-specific deficits）</news:title>
   <news:publication_date>2026-07-30T15:22:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717648</loc>
  <lastmod>2026-07-30T14:31:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>柔軟な演算子埋め込みによるデータベース機械学習の効率化（Flexible Operator Embeddings via Deep Learning）</news:title>
   <news:publication_date>2026-07-30T14:31:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717646</loc>
  <lastmod>2026-07-30T14:24:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>変換に強いニューラル層の設計：Equivariant Transformer Networks（Equivariant Transformer Networks）</news:title>
   <news:publication_date>2026-07-30T14:24:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717644</loc>
  <lastmod>2026-07-30T14:23:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>意味のあるコード変更を学習する（On Learning Meaningful Code Changes via Neural Machine Translation）</news:title>
   <news:publication_date>2026-07-30T14:23:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717642</loc>
  <lastmod>2026-07-30T14:23:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>移動ソリトンに関するノート：平均曲率流の翻訳的解（Notes on translating solitons for Mean Curvature Flow）</news:title>
   <news:publication_date>2026-07-30T14:23:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717640</loc>
  <lastmod>2026-07-30T14:22:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>最適性が示すカーネル和分類器の統計的効率性（Optimality Implies Kernel Sum Classifiers are Statistically Efficient）</news:title>
   <news:publication_date>2026-07-30T14:22:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717638</loc>
  <lastmod>2026-07-30T14:22:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CT画像による脳室・脳実質・くも膜下腔の3次元自動分割がもたらす診断支援の革新（Automated Segmentation of CT Scans for Normal Pressure Hydrocephalus）</news:title>
   <news:publication_date>2026-07-30T14:22:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717636</loc>
  <lastmod>2026-07-30T14:22:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>相関推定の通信量限界（Communication Complexity of Estimating Correlations）</news:title>
   <news:publication_date>2026-07-30T14:22:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717634</loc>
  <lastmod>2026-07-30T13:30:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>過学習が起きる理由とオンライン学習の挙動（Generalisation dynamics of online learning in over-parameterised neural networks）</news:title>
   <news:publication_date>2026-07-30T13:30:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717632</loc>
  <lastmod>2026-07-30T13:30:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>都市の建設工事が生活の質に与える影響を機械学習で予測する手法（Leveraging Machine Learning Approaches to Predict the Impact of Construction Projects on Urban Quality of Life）</news:title>
   <news:publication_date>2026-07-30T13:30:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717630</loc>
  <lastmod>2026-07-30T13:29:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>太陽周期の急激な終焉が示す太陽内部の姿（What the Sudden Death of Solar Cycles Can Tell us About the Nature of the Solar Interior）</news:title>
   <news:publication_date>2026-07-30T13:29:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717628</loc>
  <lastmod>2026-07-30T13:28:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューラルネットでアーキタイプ空間を見つける方法（Finding Archetypal Spaces Using Neural Networks）</news:title>
   <news:publication_date>2026-07-30T13:28:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717626</loc>
  <lastmod>2026-07-30T13:28:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフェン—ダイアメン相転移の層依存性（Layer dependence of graphene-diamene phase transition in epitaxial and exfoliated few-layer graphene using machine learning）</news:title>
   <news:publication_date>2026-07-30T13:28:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717624</loc>
  <lastmod>2026-07-30T13:28:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>潜在意味パーツに基づくドメイン適応とゼロ・少数ショット認識のための分類器学習（Learning Classifiers for Domain Adaptation, Zero and Few-Shot Recognition Based on Learning Latent Semantic Parts）</news:title>
   <news:publication_date>2026-07-30T13:28:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717622</loc>
  <lastmod>2026-07-30T13:27:43Z</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>2026-07-30T13:27:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717620</loc>
  <lastmod>2026-07-30T12:36:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単語埋め込みの概観と実務的示唆（Word Embeddings: A Survey）</news:title>
   <news:publication_date>2026-07-30T12:36:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717618</loc>
  <lastmod>2026-07-30T12:36:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的非凸最適化におけるステップサイズのオンライン学習のための代理損失（Surrogate Losses for Online Learning of Stepsizes in Stochastic Non-Convex Optimization）</news:title>
   <news:publication_date>2026-07-30T12:36:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717616</loc>
  <lastmod>2026-07-30T12:35:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>測定誤差を伴うデータからの学習：報告漏れへの対処 (Learning Models from Data with Measurement Error: Tackling Underreporting)</news:title>
   <news:publication_date>2026-07-30T12:35:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717614</loc>
  <lastmod>2026-07-30T12:35:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>メモリ制約下で高速化したブースティング（Faster Boosting with Smaller Memory）</news:title>
   <news:publication_date>2026-07-30T12:35:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717612</loc>
  <lastmod>2026-07-30T12:34:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>離散化された勾配フローによる多様体学習（DISCRETIZED GRADIENT FLOW FOR MANIFOLD LEARNING）</news:title>
   <news:publication_date>2026-07-30T12:34:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717610</loc>
  <lastmod>2026-07-30T12:34:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>小規模データで学習する新しい損失関数：コサイン損失（Deep Learning on Small Datasets without Pre-Training using Cosine Loss）</news:title>
   <news:publication_date>2026-07-30T12:34:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717608</loc>
  <lastmod>2026-07-30T12:34:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>生物学的に実装可能な時系列逆伝播の代替（Biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets）</news:title>
   <news:publication_date>2026-07-30T12:34:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717606</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多様性に敏感な条件付き生成的敵対ネットワーク（DIVERSITY-SENSITIVE CONDITIONAL GENERATIVE ADVERSARIAL NETWORKS）</news:title>
   <news:publication_date>2026-07-30T11:43:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717604</loc>
  <lastmod>2026-07-30T11:33:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層ネットワークにおける線形領域の複雑度（Complexity of Linear Regions in Deep Networks）</news:title>
   <news:publication_date>2026-07-30T11:33:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717602</loc>
  <lastmod>2026-07-30T11:33:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>豊富な観測から潜在状態を復元して効率的探索を可能にする手法（Provably efficient RL with Rich Observations via Latent State Decoding）</news:title>
   <news:publication_date>2026-07-30T11:33:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717600</loc>
  <lastmod>2026-07-30T11:32:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>拡散変分オートエンコーダ（Diffusion Variational Autoencoders）</news:title>
   <news:publication_date>2026-07-30T11:32:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717598</loc>
  <lastmod>2026-07-30T11:32:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>運動学的ターゲット質量感度が示すもの（What does kinematical target mass sensitivity in DIS reveal about hadron structure?）</news:title>
   <news:publication_date>2026-07-30T11:32:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717596</loc>
  <lastmod>2026-07-30T11:32:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>集合上の関数表現の限界（On the Limitations of Representing Functions on Sets）</news:title>
   <news:publication_date>2026-07-30T11:32:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717594</loc>
  <lastmod>2026-07-30T11:32:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自己教師あり視覚表現学習の再考（Revisiting Self-Supervised Visual Representation Learning）</news:title>
   <news:publication_date>2026-07-30T11:32:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717592</loc>
  <lastmod>2026-07-30T10:40:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>LSTMとGRUの動的等方性と平均場理論（Dynamical Isometry and a Mean Field Theory of LSTMs and GRUs）</news:title>
   <news:publication_date>2026-07-30T10:40:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717590</loc>
  <lastmod>2026-07-30T10:40:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>FaceForensics++ による顔画像改ざん検出の標準化（FaceForensics++: Learning to Detect Manipulated Facial Images）</news:title>
   <news:publication_date>2026-07-30T10:40:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717588</loc>
  <lastmod>2026-07-30T10:40:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>教師なし前処理によるクロスバリデーションのバイアス（On the cross-validation bias due to unsupervised preprocessing）</news:title>
   <news:publication_date>2026-07-30T10:40:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717586</loc>
  <lastmod>2026-07-30T10:39:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オプション価格付けとインプライド・ボラティリティの高速推定（Pricing options and computing implied volatilities using neural networks）</news:title>
   <news:publication_date>2026-07-30T10:39:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717584</loc>
  <lastmod>2026-07-30T10:39:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>部分空間に強いワッサースタイン距離（Subspace Robust Wasserstein Distances）</news:title>
   <news:publication_date>2026-07-30T10:39:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717582</loc>
  <lastmod>2026-07-30T10:39:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Skip-GANomaly による異常検知の実務的理解（Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detection）</news:title>
   <news:publication_date>2026-07-30T10:39:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717580</loc>
  <lastmod>2026-07-30T10:38:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>変数分割を緩和した粗い勾配降下法によるスパース・バイナリ活性化ニューラルネットワークの収束（Convergence of a Relaxed Variable Splitting Coarse Gradient Descent Method for Learning Sparse Weight Binarized Activation Neural Networks）</news:title>
   <news:publication_date>2026-07-30T10:38:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717578</loc>
  <lastmod>2026-07-30T09:48:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散SDN制御における最適同期率の学習（Learning the Optimal Synchronization Rates in Distributed SDN Control Architectures）</news:title>
   <news:publication_date>2026-07-30T09:48:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717576</loc>
  <lastmod>2026-07-30T09:47:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自己教師ありで汎化を高めるメタ補助学習（Self-Supervised Generalisation with Meta Auxiliary Learning）</news:title>
   <news:publication_date>2026-07-30T09:47:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717574</loc>
  <lastmod>2026-07-30T09:46:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模分散ネットワークにおける通信効率の高い高速アルゴリズム（Exploring Fast and Communication-Efﬁcient Algorithms in Large-scale Distributed Networks）</news:title>
   <news:publication_date>2026-07-30T09:46:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717572</loc>
  <lastmod>2026-07-30T09:46:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>コアオントロジーに基づく用語クラスタリングの比較（Comparing of Term Clustering Frameworks for Modular Ontology Learning）</news:title>
   <news:publication_date>2026-07-30T09:46:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717570</loc>
  <lastmod>2026-07-30T09:46:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>車載安全通信のための深層学習による送信スケジューラ（Deep Learning-aided Application Scheduler for Vehicular Safety Communication）</news:title>
   <news:publication_date>2026-07-30T09:46:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717568</loc>
  <lastmod>2026-07-30T09:45:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深いピラミッドネットワークによる高密度3D点群再構築（Dense 3D Point Cloud Reconstruction Using a Deep Pyramid Network）</news:title>
   <news:publication_date>2026-07-30T09:45:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717566</loc>
  <lastmod>2026-07-30T09:45:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動的シミュレータにおけるベイジアン代替モデル学習（Bayesian surrogate learning in dynamic simulator-based regression problems）</news:title>
   <news:publication_date>2026-07-30T09:45:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717564</loc>
  <lastmod>2026-07-30T08:54:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>要約統計量の局所次元削減が切り開く尤度フリー推論の実用化（Local dimension reduction of summary statistics for likelihood-free inference）</news:title>
   <news:publication_date>2026-07-30T08:54:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717562</loc>
  <lastmod>2026-07-30T08:53:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>製造部品の表面亀裂検出のための撮像検査システム（Vision-based inspection system employing computer vision &amp;amp; neural networks for detection of fractures in manufactured components）</news:title>
   <news:publication_date>2026-07-30T08:53:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717560</loc>
  <lastmod>2026-07-30T08:53:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>アンサンブル多様性による敵対的堅牢性の向上（Improving Adversarial Robustness via Promoting Ensemble Diversity）</news:title>
   <news:publication_date>2026-07-30T08:53:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717558</loc>
  <lastmod>2026-07-30T08:53:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>相互作用を活かした個別表現予測の強化（Empowering individual trait prediction using interactions）</news:title>
   <news:publication_date>2026-07-30T08:53:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717556</loc>
  <lastmod>2026-07-30T08:52:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>状態正規化リカレントニューラルネットワーク（State-Regularized Recurrent Neural Networks）</news:title>
   <news:publication_date>2026-07-30T08:52:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717554</loc>
  <lastmod>2026-07-30T08:52:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>最小限の深層ネットワークで形状学習とセグメンテーションを同時に行う（Joint shape learning and segmentation for medical images using a minimalistic deep network）</news:title>
   <news:publication_date>2026-07-30T08:52:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/717552</loc>
  <lastmod>2026-07-30T08:51:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多ラベル分類におけるBayesメタ分類器とソフト混同行列分類器の比較（Bayes metaclassifier and Soft-confusion-matrix classifier in the task of multi-label classification）</news:title>
   <news:publication_date>2026-07-30T08:51:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-07-30T08:01:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-07-30T08:00:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>音声の教師なし表現学習（Unsupervised speech representation learning using WaveNet autoencoders）</news:title>
   <news:publication_date>2026-07-30T08:00:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-07-30T08:00:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>印刷/スキャンと異種画像ソース下での顔モーフィング検出（Face morphing detection in the presence of printing/scanning and heterogeneous image sources）</news:title>
   <news:publication_date>2026-07-30T08:00:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-07-30T08:00:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717542</loc>
  <lastmod>2026-07-30T08:00:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ICETによる合金クラスター展開の実務化（ICET – A Python library for constructing and sampling alloy cluster expansions）</news:title>
   <news:publication_date>2026-07-30T08:00:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717540</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>Bethe-Hessianの再考（Revisiting the Bethe-Hessian: Improved Community Detection in Sparse Heterogeneous Graphs）</news:title>
   <news:publication_date>2026-07-30T07:59:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717538</loc>
  <lastmod>2026-07-30T07:59:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的・敵対的環境を同時に最適化するセミバンディット手法（Beating Stochastic and Adversarial Semi-bandits Optimally and Simultaneously）</news:title>
   <news:publication_date>2026-07-30T07:59:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-07-30T07:08:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ノイズ下でのツリー構造ガウスグラフィカルモデルの頑健推定（Robust estimation of tree structured Gaussian Graphical Model）</news:title>
   <news:publication_date>2026-07-30T07:08:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717534</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>仮想条件付き生成対向ネットワーク（Virtual Conditional Generative Adversarial Networks）</news:title>
   <news:publication_date>2026-07-30T07:08:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717532</loc>
  <lastmod>2026-07-30T07:08:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>再帰型ニューラルネットワークによる高速チャネルの過渡シミュレーション高速化（Fast Transient Simulation of High-Speed Channels Using Recurrent Neural Network）</news:title>
   <news:publication_date>2026-07-30T07:08:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717530</loc>
  <lastmod>2026-07-30T07:07:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ロスレスなフェデレーテッド学習フレームワーク SecureBoost（SecureBoost: A Lossless Federated Learning Framework）</news:title>
   <news:publication_date>2026-07-30T07:07:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717528</loc>
  <lastmod>2026-07-30T07:07:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>YouTubeにおける誤解を招くメタデータ検出（Misleading Metadata Detection on YouTube）</news:title>
   <news:publication_date>2026-07-30T07:07:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717526</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>分散型ポリシー反復による協調型マルチエージェント方策の拡張近似（Distributed Policy Iteration for Scalable Approximation of Cooperative Multi-Agent Policies）</news:title>
   <news:publication_date>2026-07-30T07:07:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-07-30T07:07:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>海王星原始円盤の局在形成モデル（Model of Neptune’s protoplanetary disk forming in-situ its surviving regular satellites after Triton’s capture and comparison of the protoplanetary disks of the four gaseous giants）</news:title>
   <news:publication_date>2026-07-30T07:07:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717522</loc>
  <lastmod>2026-07-30T06:16:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層強化学習によるスピンダイナミクス制御（Manipulation of Spin Dynamics by Deep Reinforcement Learning Agent）</news:title>
   <news:publication_date>2026-07-30T06:16:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717520</loc>
  <lastmod>2026-07-30T06:16:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-07-30T06:16:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717518</loc>
  <lastmod>2026-07-30T06:15:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>電荷を扱える機械学習ポテンシャルの進化──eSNAPが切り拓くリチウム窒化物の原子スケール挙動（An Electrostatic Spectral Neighbor Analysis Potential (eSNAP) for Lithium Nitride）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/717516</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>動的ポートフォリオ最適化のためのモデルベース深層強化学習（Model-based Deep Reinforcement Learning for Dynamic Portfolio Optimization）</news:title>
   <news:publication_date>2026-07-30T06:14:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717514</loc>
  <lastmod>2026-07-30T06:14:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>BioBERTによる医療文献の言語表現最適化（BioBERT: a pre-trained biomedical language representation model for biomedical text mining）</news:title>
   <news:publication_date>2026-07-30T06:14:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717512</loc>
  <lastmod>2026-07-30T06:14:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-07-30T06:14:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717510</loc>
  <lastmod>2026-07-30T06:14:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-07-30T06:14:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717508</loc>
  <lastmod>2026-07-30T05:22:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スクラブルにおける評価関数近似（Evaluation Function Approximation for Scrabble）</news:title>
   <news:publication_date>2026-07-30T05:22:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717506</loc>
  <lastmod>2026-07-30T05:22:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-07-30T05:22:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717504</loc>
  <lastmod>2026-07-30T05:22:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-07-30T05:22:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717502</loc>
  <lastmod>2026-07-30T05:21:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Almost Boltzmann Exploration（Almost Boltzmann Exploration）</news:title>
   <news:publication_date>2026-07-30T05:21:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717500</loc>
  <lastmod>2026-07-30T05:21:16Z</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>2026-07-30T05:21:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717498</loc>
  <lastmod>2026-07-30T05:21:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Deep Multimodality Modelによるマルチタスク・マルチビュー学習の実務的意義（Deep Multimodality Model for Multi-task Multi-view Learning）</news:title>
   <news:publication_date>2026-07-30T05:21:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717496</loc>
  <lastmod>2026-07-30T05:20:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>直交統計学習（Orthogonal Statistical Learning）</news:title>
   <news:publication_date>2026-07-30T05:20:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717494</loc>
  <lastmod>2026-07-30T04:29:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多エージェント間通信ゲームにおける言語の自発的現象（Emergent Linguistic Phenomena in Multi-Agent Communication Games）</news:title>
   <news:publication_date>2026-07-30T04:29:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717492</loc>
  <lastmod>2026-07-30T04:28:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-07-30T04:28:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717490</loc>
  <lastmod>2026-07-30T04:28:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>医用画像解析における代理教師学習（SURROGATE SUPERVISION FOR MEDICAL IMAGE ANALYSIS: EFFECTIVE DEEP LEARNING FROM LIMITED QUANTITIES OF LABELED DATA）</news:title>
   <news:publication_date>2026-07-30T04:28:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717488</loc>
  <lastmod>2026-07-30T04:28:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数判別器を用いたGANの多目的学習（Multi–objective training of Generative Adversarial Networks with multiple discriminators）</news:title>
   <news:publication_date>2026-07-30T04:28:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717486</loc>
  <lastmod>2026-07-30T04:28:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>単一クラス畳み込みニューラルネットワーク（One-Class Convolutional Neural Network）</news:title>
   <news:publication_date>2026-07-30T04:28:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717484</loc>
  <lastmod>2026-07-30T04:27:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スペクトラルクラスタリングに公正性制約を組み込む理論的保証（Guarantees for Spectral Clustering with Fairness Constraints）</news:title>
   <news:publication_date>2026-07-30T03:35:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717478</loc>
  <lastmod>2026-07-30T03:35:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>任意サンプリングを用いたSAGAの一般化（SAGA with Arbitrary Sampling）</news:title>
   <news:publication_date>2026-07-30T03:35:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717476</loc>
  <lastmod>2026-07-30T03:34:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>パリレン系メモリスタで多段階抵抗変化を実現する研究（Parylene Based Memristive Devices with Multilevel Resistive Switching for Neuromorphic Applications）</news:title>
   <news:publication_date>2026-07-30T03:34:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717474</loc>
  <lastmod>2026-07-30T03:34:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>助けるマルチアームバンディット（The Assistive Multi-Armed Bandit）</news:title>
   <news:publication_date>2026-07-30T03:34:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717472</loc>
  <lastmod>2026-07-30T03:33:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>特徴抽出と方策学習の分離がもたらす効果（Decoupling Feature Extraction from Policy Learning: Assessing Benefits of State Representation Learning in Goal Based Robotics）</news:title>
   <news:publication_date>2026-07-30T03:33:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717470</loc>
  <lastmod>2026-07-30T03:33:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>公平性リスク測度（Fairness Risk Measures）</news:title>
   <news:publication_date>2026-07-30T03:33:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717468</loc>
  <lastmod>2026-07-30T03:33:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習による四足ロボットの俊敏な運動技能獲得（Learning Agile and Dynamic Motor Skills for Legged Robots）</news:title>
   <news:publication_date>2026-07-30T03:33:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717466</loc>
  <lastmod>2026-07-30T02:41:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>リアルタイム呼吸運動予測に向けたLSTMニューラルネットワークの試み（Towards Real-Time Respiratory Motion Prediction based on Long Short-Term Memory Neural Networks）</news:title>
   <news:publication_date>2026-07-30T02:41:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717464</loc>
  <lastmod>2026-07-30T02:32:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>独立に獲得可能な報酬関数の学習（Learning Independently-Obtainable Reward Functions）</news:title>
   <news:publication_date>2026-07-30T02:32:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717462</loc>
  <lastmod>2026-07-30T02:32:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューラルネットワークのアブレーション研究入門（Ablation Studies in Artificial Neural Networks）</news:title>
   <news:publication_date>2026-07-30T02:32:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717460</loc>
  <lastmod>2026-07-30T02:31:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動的計測スケジューリングによるイベント予測（Dynamic Measurement Scheduling for Event Forecasting Using Deep RL）</news:title>
   <news:publication_date>2026-07-30T02:31:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717458</loc>
  <lastmod>2026-07-30T02:31:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>建設現場で走る自律ロボットのための軽量リアルタイム画面分割（Real-time Scene Segmentation Using a Light Deep Neural Network Architecture for Autonomous Robot Navigation on Construction Sites）</news:title>
   <news:publication_date>2026-07-30T02:31:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717456</loc>
  <lastmod>2026-07-30T02:31:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>フェアなk中心クラスタリングによるデータ要約（Fair k-Center Clustering for Data Summarization）</news:title>
   <news:publication_date>2026-07-30T02:31:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717454</loc>
  <lastmod>2026-07-30T02:31:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>太陽フレア大気の逆問題を解く可逆ニューラルネットワーク（RADYNVERSION: Learning to Invert a Solar Flare Atmosphere with Invertible Neural Networks）</news:title>
   <news:publication_date>2026-07-30T02:31:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717452</loc>
  <lastmod>2026-07-30T01:40:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>AutoShuffleNetによる順列行列の自動学習（AutoShuffleNet: Learning Permutation Matrices via an Exact Lipschitz Continuous Penalty in Deep Convolutional Neural Networks）</news:title>
   <news:publication_date>2026-07-30T01:40:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717450</loc>
  <lastmod>2026-07-30T01:39:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ヒンディー語—英語ニュースの並列コーパス自動生成の試み（Automatic Parallel Corpus Creation for Hindi-English News Translation Task）</news:title>
   <news:publication_date>2026-07-30T01:39:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717448</loc>
  <lastmod>2026-07-30T01:39:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>地震波データから井戸ログ特性を推定する再帰型ニューラルネットワークの応用（Petrophysical Property Estimation from Seismic Data Using Recurrent Neural Networks）</news:title>
   <news:publication_date>2026-07-30T01:39:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717446</loc>
  <lastmod>2026-07-30T01:38:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習型アンサッツの近似能力（Approximating power of machine-learning ansatz for quantum many-body states）</news:title>
   <news:publication_date>2026-07-30T01:38:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717444</loc>
  <lastmod>2026-07-30T01:38:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューラル線形バンディットと忘却対策（Neural Linear Bandits: Overcoming Catastrophic Forgetting through Likelihood Matching）</news:title>
   <news:publication_date>2026-07-30T01:38:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717442</loc>
  <lastmod>2026-07-30T01:38:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単純スケーリングとSNR適応を用いた学習済み信念伝播デコーディング（Learned Belief-Propagation Decoding with Simple Scaling and SNR Adaptation）</news:title>
   <news:publication_date>2026-07-30T01:38:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717440</loc>
  <lastmod>2026-07-30T01:38:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフ熱混合モデルによる信号分離とグラフ推定（GRAPH HEAT MIXTURE MODEL LEARNING）</news:title>
   <news:publication_date>2026-07-30T01:38:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717438</loc>
  <lastmod>2026-07-30T00:46:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>過学習しない過剰パラメータ化二層ネットの最適化と一般化の精密解析 (Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks)</news:title>
   <news:publication_date>2026-07-30T00:46:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717436</loc>
  <lastmod>2026-07-30T00:46:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非凸・非滑らか最適化における摂動付き近接降下法（Perturbed Proximal Descent to Escape Saddle Points for Non-convex and Non-smooth Objective Functions）</news:title>
   <news:publication_date>2026-07-30T00:46:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717434</loc>
  <lastmod>2026-07-30T00:45:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>TAP方程式のメモリーフリー動力学（Memory-free dynamics for the TAP equations of Ising models）</news:title>
   <news:publication_date>2026-07-30T00:45:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717432</loc>
  <lastmod>2026-07-30T00:45:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>層の幅が最適化にもたらす決定的影響（Width Provably Matters in Optimization for Deep Linear Neural Networks）</news:title>
   <news:publication_date>2026-07-30T00:45:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717430</loc>
  <lastmod>2026-07-30T00:44:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>解釈可能な因果保証付きモデルの学習（Learning Interpretable Models with Causal Guarantees）</news:title>
   <news:publication_date>2026-07-30T00:44:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717428</loc>
  <lastmod>2026-07-30T00:44:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GstLALによるコンパクトバイナリ合体探索手法（The GstLAL Search Analysis Methods for Compact Binary Mergers）</news:title>
   <news:publication_date>2026-07-30T00:44:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717426</loc>
  <lastmod>2026-07-30T00:44:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>堅牢性と精度の理論的トレードオフ（Theoretically Principled Trade-off between Robustness and Accuracy）</news:title>
   <news:publication_date>2026-07-30T00:44:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717424</loc>
  <lastmod>2026-07-29T23:53:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的変換による一般化監督学習の枠組み（GENERAL SUPERVISION VIA PROBABILISTIC TRANSFORMATIONS）</news:title>
   <news:publication_date>2026-07-29T23:53:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717422</loc>
  <lastmod>2026-07-29T23:44:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>有限ビット量化下の非パラメトリック推論（Nonparametric Inference under B-bits Quantization）</news:title>
   <news:publication_date>2026-07-29T23:44:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717420</loc>
  <lastmod>2026-07-29T23:44:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動的意思決定における公平性アルゴリズム（Algorithms for Fairness in Sequential Decision Making）</news:title>
   <news:publication_date>2026-07-29T23:44:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717418</loc>
  <lastmod>2026-07-29T23:44:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散性IM/DDチャネルに対する双方向RNNを用いたエンドツーエンド最適化伝送（End-to-end optimized transmission over dispersive intensity-modulated channels using bidirectional recurrent neural networks）</news:title>
   <news:publication_date>2026-07-29T23:44:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717416</loc>
  <lastmod>2026-07-29T23:43:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ほぼ確定的な力学系における方策勾配推定のサンプル複雑度（Sample Complexity of Estimating the Policy Gradient for Nearly Deterministic Dynamical Systems）</news:title>
   <news:publication_date>2026-07-29T23:43:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717414</loc>
  <lastmod>2026-07-29T23:43:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>プログラム合成によるニューロシンボリック生成モデルの学習 (Learning Neurosymbolic Generative Models via Program Synthesis)</news:title>
   <news:publication_date>2026-07-29T23:43:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717412</loc>
  <lastmod>2026-07-29T23:43:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>セミ・アンマーク学習: 超希薄ラベルによるクラスタリングと分類（Semi-Unsupervised Learning: Clustering and Classifying using Ultra-Sparse Labels）</news:title>
   <news:publication_date>2026-07-29T23:43:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717410</loc>
  <lastmod>2026-07-29T22:51:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>全天サーベイで遠隔太陽系天体を深く探す方法（Enabling Deep All-Sky Searches of Outer Solar System Objects）</news:title>
   <news:publication_date>2026-07-29T22:51:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717408</loc>
  <lastmod>2026-07-29T22:51:43Z</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>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 <url>
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    <news:name>AI Benchmark Research</news:name>
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    <news:name>AI Benchmark Research</news:name>
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 <url>
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    <news:name>AI Benchmark Research</news:name>
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 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
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 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 <url>
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    <news:name>AI Benchmark Research</news:name>
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 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
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 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
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 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 <url>
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    <news:name>AI Benchmark Research</news:name>
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 <url>
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    <news:name>AI Benchmark Research</news:name>
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 <url>
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  <news:news>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
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 </url>
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    <news:name>AI Benchmark Research</news:name>
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   <news: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>
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 </url>
 <url>
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    <news:name>AI Benchmark Research</news:name>
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 </url>
 <url>
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    <news:name>AI Benchmark Research</news:name>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 </url>
 <url>
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  <news:news>
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    <news:name>AI Benchmark Research</news:name>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 <url>
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    <news:name>AI Benchmark Research</news:name>
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    <news:name>AI Benchmark Research</news:name>
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    <news:language>ja</news:language>
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   <news:title>交通シーンにおける3次元バックボーンネットワーク（Three-dimensional Backbone Network for 3D Object Detection in Traffic Scenes）</news:title>
   <news:publication_date>2026-07-29T18:21:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717334</loc>
  <lastmod>2026-07-29T18:21:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>超臨界状態で見いだされた固体に似た波動の存在（The nature of collective excitations and their crossover at extreme supercritical conditions）</news:title>
   <news:publication_date>2026-07-29T18:21:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717332</loc>
  <lastmod>2026-07-29T18:21:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深絞り工程におけるゼロショット学習の応用（A Zero-Shot Learning application in Deep Drawing process using Hyper-Process Model）</news:title>
   <news:publication_date>2026-07-29T18:21:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717330</loc>
  <lastmod>2026-07-29T18:20:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>情報鮮度（Age of Information）を減らす強化学習アプローチ（Reinforcement Learning to Minimize Age of Information）</news:title>
   <news:publication_date>2026-07-29T18:20:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717328</loc>
  <lastmod>2026-07-29T18:19:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多階層文脈による深い推論で顕著領域検出を高精度化する（Deep Reasoning with Multi-Scale Context for Salient Object Detection）</news:title>
   <news:publication_date>2026-07-29T18:19:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717326</loc>
  <lastmod>2026-07-29T17:28:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Jensenの力が照らす皮質非同期状態の統計力学（Jensen’s force and the statistical mechanics of cortical asynchronous states）</news:title>
   <news:publication_date>2026-07-29T17:28:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717324</loc>
  <lastmod>2026-07-29T17:28:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>交差エントロピーと低ランク特徴が招く敵対的事例の本質（Cross-Entropy Loss and Low-Rank Features Have Responsibility for Adversarial Examples）</news:title>
   <news:publication_date>2026-07-29T17:28:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717322</loc>
  <lastmod>2026-07-29T17:28:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グローバルな2変量相互作用を不確かさ付きで学習する手法（Learning Global Pairwise Interactions with Bayesian Neural Networks）</news:title>
   <news:publication_date>2026-07-29T17:28:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717320</loc>
  <lastmod>2026-07-29T17:27:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>取得関数の局所最適化に関する考察（On Local Optimizers of Acquisition Functions in Bayesian Optimization）</news:title>
   <news:publication_date>2026-07-29T17:27:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717318</loc>
  <lastmod>2026-07-29T17:27:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>RCT要旨からのPICO要素抽出（Extracting PICO elements from RCT abstracts using 1-2gram analysis and multitask classification）</news:title>
   <news:publication_date>2026-07-29T17:27:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717316</loc>
  <lastmod>2026-07-29T17:27:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>セミスーパーバイズド・セマンティックマッチング（Semi-Supervised Semantic Matching）</news:title>
   <news:publication_date>2026-07-29T17:27:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717314</loc>
  <lastmod>2026-07-29T17:26:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オンティーチャーのセンサを使わず人を模倣するロボット学習（Teaching robots to imitate a human with no on-teacher sensors. What are the key challenges?）</news:title>
   <news:publication_date>2026-07-29T17:26:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717312</loc>
  <lastmod>2026-07-29T16:35:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>次元を超えて隠れ要因を分離する手法の革新（Overcomplete Independent Component Analysis via SDP）</news:title>
   <news:publication_date>2026-07-29T16:35:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717310</loc>
  <lastmod>2026-07-29T16:34:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>黄色超巨星V509 Casの拡張大気（Extended atmosphere of the yellow hypergiant V509 Cas in 1996–2018）</news:title>
   <news:publication_date>2026-07-29T16:34:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717308</loc>
  <lastmod>2026-07-29T16:33:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習とビッグデータが変える無線通信の視点（When Machine Learning Meets Big Data: A Wireless Communication Perspective）</news:title>
   <news:publication_date>2026-07-29T16:33:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717306</loc>
  <lastmod>2026-07-29T16:32:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューラルIRとグラフ埋め込みが出会うとき（Neural IR Meets Graph Embedding: A Ranking Model for Product Search）</news:title>
   <news:publication_date>2026-07-29T16:32:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717304</loc>
  <lastmod>2026-07-29T16:32:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>道路交通における映像監視を用いた異常検知の概説（Anomaly Detection in Road Traffic Using Visual Surveillance: A Survey）</news:title>
   <news:publication_date>2026-07-29T16:32:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717302</loc>
  <lastmod>2026-07-29T16:31:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時系列予測におけるTemporal Logistic Neural Bag-of-Features（Temporal Logistic Neural Bag-of-Features for Financial Time series Forecasting leveraging Limit Order Book Data）</news:title>
   <news:publication_date>2026-07-29T16:31:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717300</loc>
  <lastmod>2026-07-29T16:31:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>属性付きグラフ上の深層学習（Deep Learning on Attributed Graphs）</news:title>
   <news:publication_date>2026-07-29T16:31:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717298</loc>
  <lastmod>2026-07-29T15:39:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>組合せQ学習による『ドゥーディーズー（Dou Di Zhu）』攻略（Combinational Q-Learning for Dou Di Zhu）</news:title>
   <news:publication_date>2026-07-29T15:39:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717296</loc>
  <lastmod>2026-07-29T15:39:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>連邦深層強化学習（Federated Deep Reinforcement Learning）</news:title>
   <news:publication_date>2026-07-29T15:39:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717294</loc>
  <lastmod>2026-07-29T15:38:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>重い裾（ヘビーテイル）が示すニューラルネットの実力（Heavy-Tailed Universality Predicts Trends in Test Accuracies for Very Large Pre-Trained Deep Neural Networks）</news:title>
   <news:publication_date>2026-07-29T15:38:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717292</loc>
  <lastmod>2026-07-29T15:38:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多段階精度のベイズ最適化とMax-value Entropy Searchの並列化（Multi-fidelity Bayesian Optimization with Max-value Entropy Search and its parallelization）</news:title>
   <news:publication_date>2026-07-29T15:38:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717290</loc>
  <lastmod>2026-07-29T15:38:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GANの極端圧縮を現実化したQGAN（QGAN: Quantized Generative Adversarial Networks）</news:title>
   <news:publication_date>2026-07-29T15:38:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717288</loc>
  <lastmod>2026-07-29T15:38:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所内容のベクトル表現と局所運動の行列表現によるV1のシンプル細胞学習 (Learning V1 Simple Cells with Vector Representation of Local Content and Matrix Representation of Local Motion)</news:title>
   <news:publication_date>2026-07-29T15:38:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717286</loc>
  <lastmod>2026-07-29T15:37:52Z</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伝統的およびヘビーテール自己正則化（Traditional and Heavy-Tailed Self Regularization in Neural Network Models）</news:title>
   <news:publication_date>2026-07-29T15:37:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717284</loc>
  <lastmod>2026-07-29T14:46:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>交差種間画像翻訳を可能にするマルチブランチ識別器（Generative Adversarial Network with Multi-Branch Discriminator for Cross-Species Image-to-Image Translation）</news:title>
   <news:publication_date>2026-07-29T14:46:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717282</loc>
  <lastmod>2026-07-29T14:35:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>フォローアップクエリ解析の新手法（FANDA: A Novel Approach to Perform Follow-up Query Analysis）</news:title>
   <news:publication_date>2026-07-29T14:35:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717280</loc>
  <lastmod>2026-07-29T14:35:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模バッチでLSTMを高速学習する方法（Large-Batch Training for LSTM and Beyond）</news:title>
   <news:publication_date>2026-07-29T14:35:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717278</loc>
  <lastmod>2026-07-29T14:34:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>軸受（ベアリング）故障診断における深層学習の包括的レビュー（Deep Learning Algorithms for Bearing Fault Diagnostics – A Comprehensive Review）</news:title>
   <news:publication_date>2026-07-29T14:34:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717276</loc>
  <lastmod>2026-07-29T14:34:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ノード信頼度を組み込むグラフ畳み込みネットワーク（Confidence-based Graph Convolutional Networks for Semi-Supervised Learning）</news:title>
   <news:publication_date>2026-07-29T14:34:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717274</loc>
  <lastmod>2026-07-29T14:34:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自己注意ネットワークを用いた教師なし画像間翻訳（Unsupervised Image-to-Image Translation with Self-Attention Networks）</news:title>
   <news:publication_date>2026-07-29T14:34:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717272</loc>
  <lastmod>2026-07-29T14:34:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層ネットワークヘシアンのスペクトルに現れる外れ値における三層階層構造の計測（Measurements of Three-Level Hierarchical Structure in the Outliers in the Spectrum of Deepnet Hessians）</news:title>
   <news:publication_date>2026-07-29T14:34:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717270</loc>
  <lastmod>2026-07-29T13:42:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>緊急時ツイートから位置参照を抽出する深層学習（Location reference identification from tweets during emergencies: A deep learning approach）</news:title>
   <news:publication_date>2026-07-29T13:42:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717268</loc>
  <lastmod>2026-07-29T13:42:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>映画を用いたトポグラフィック独立成分分析の可視化（Visualizing Topographic Independent Component Analysis with Movies）</news:title>
   <news:publication_date>2026-07-29T13:42:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717266</loc>
  <lastmod>2026-07-29T13:41:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次元ロバストM推定：任意の汚染と重い裾（High Dimensional Robust M-Estimation: Arbitrary Corruption and Heavy Tails）</news:title>
   <news:publication_date>2026-07-29T13:41:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717264</loc>
  <lastmod>2026-07-29T13:40:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習が導く無線ネットワークの最適化（Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks）</news:title>
   <news:publication_date>2026-07-29T13:40:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717262</loc>
  <lastmod>2026-07-29T13:40:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>軌道正規化勾配による分散最適化（Trajectory Normalized Gradients for Distributed Optimization）</news:title>
   <news:publication_date>2026-07-29T13:40:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717260</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>SARと光学リモートセンシング画像の相互変換（Reciprocal Translation between SAR and Optical Remote Sensing Images with Cascaded-Residual Adversarial Networks）</news:title>
   <news:publication_date>2026-07-29T13:40:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717258</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>領域分解と組み立てに基づく物体検出（Object Detection based on Region Decomposition and Assembly）</news:title>
   <news:publication_date>2026-07-29T13:39:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717256</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>自律走行車が介入し人へ返すべき時（When is it right and good for an intelligent autonomous vehicle to take over control (and hand it back)?)</news:title>
   <news:publication_date>2026-07-29T12:48:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717254</loc>
  <lastmod>2026-07-29T12:48:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>半教師付き画像間翻訳の実践的意義（Semi-Supervised Image-to-Image Translation）</news:title>
   <news:publication_date>2026-07-29T12:48:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717252</loc>
  <lastmod>2026-07-29T12:48:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>外れ値を含むk中心クラスタリングに対する貪欲法とコアセット構築（Greedy Strategy Works for k-Center Clustering with Outliers and Coreset Construction）</news:title>
   <news:publication_date>2026-07-29T12:48:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717250</loc>
  <lastmod>2026-07-29T12:47:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Bottom-up Broadcast Neural Networkによる音楽ジャンル分類の再定義（Bottom-up Broadcast Neural Network For Music Genre Classification）</news:title>
   <news:publication_date>2026-07-29T12:47:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717248</loc>
  <lastmod>2026-07-29T12:47:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>画像と特徴の相乗的適応による医用画像のクロスモダリティ適応（Synergistic Image and Feature Adaptation: Towards Cross-Modality Domain Adaptation for Medical Image Segmentation）</news:title>
   <news:publication_date>2026-07-29T12:47:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717246</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ストリーミング音楽における逐次スキップ予測とFew-shot学習（Sequential Skip Prediction with Few-shot in Streamed Music Contents）</news:title>
   <news:publication_date>2026-07-29T12:47:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717244</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>ICU内死亡予測の可視化可能な深層学習（ISeeU: Visually interpretable deep learning for mortality prediction inside the ICU）</news:title>
   <news:publication_date>2026-07-29T12:46:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717242</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>自律レーシングにおける学習モデル予測制御のアプローチ（Learning How to Autonomously Race a Car: a Predictive Control Approach）</news:title>
   <news:publication_date>2026-07-29T11:55:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717240</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-07-29T11:54:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717238</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>データ幾何から生成・整合する手法（Generating and Aligning from Data Geometries with Generative Adversarial Networks）</news:title>
   <news:publication_date>2026-07-29T11:54:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717236</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>正則化された線形オートエンコーダの損失地形（Loss Landscapes of Regularized Linear Autoencoders）</news:title>
   <news:publication_date>2026-07-29T11:53:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717234</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>木構造アンサンブルによる異常検知の実効性（Effectiveness of Tree-based Ensembles for Anomaly Discovery: Insights, Batch and Streaming Active Learning）</news:title>
   <news:publication_date>2026-07-29T11:53:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717232</loc>
  <lastmod>2026-07-29T11:53:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>完全確率的プライマル・デュアルアルゴリズム（A Fully Stochastic Primal-Dual Algorithm）</news:title>
   <news:publication_date>2026-07-29T11:53:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717230</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>近似勾配符号化の根本限界（Fundamental Limits of Approximate Gradient Coding）</news:title>
   <news:publication_date>2026-07-29T11:53:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>メタ強化学習からの因果推論（Causal Reasoning from Meta-reinforcement Learning）</news:title>
   <news:publication_date>2026-07-29T11:01:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717226</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>CNNの層ごとの独立学習で訓練の「更新ロック」を解く（Decoupled Greedy Learning of CNNs）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717224</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717222</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-07-29T11:00:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717220</loc>
  <lastmod>2026-07-29T11:00:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>真実的なデータサイエンス（Veridical Data Science）</news:title>
   <news:publication_date>2026-07-29T11:00:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717218</loc>
  <lastmod>2026-07-29T11:00:12Z</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>2026-07-29T11:00:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717216</loc>
  <lastmod>2026-07-29T11:00:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文脈付きバンディットの探索をメタ学習する手法（Meta-Learning for Contextual Bandit Exploration）</news:title>
   <news:publication_date>2026-07-29T11:00:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717214</loc>
  <lastmod>2026-07-29T10:08:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-07-29T10:08:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717212</loc>
  <lastmod>2026-07-29T10:08:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>TransferTransfoによる会話生成の転移学習（TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents）</news:title>
   <news:publication_date>2026-07-29T10:08:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717210</loc>
  <lastmod>2026-07-29T10:07:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチエージェント強化学習競技の意義と設計（The Multi-Agent Reinforcement Learning in MalmÖ (MARLÖ) Competition）</news:title>
   <news:publication_date>2026-07-29T10:07:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717208</loc>
  <lastmod>2026-07-29T10:06:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>強化学習におけるディスティレーションとPPOの共演（Distillation Strategies for Proximal Policy Optimization）</news:title>
   <news:publication_date>2026-07-29T10:06:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717206</loc>
  <lastmod>2026-07-29T10:06:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフニューラルネットワークによるSDN向けネットワークモデル化と最適化の可能性（Unveiling the potential of Graph Neural Networks for network modeling and optimization in SDN）</news:title>
   <news:publication_date>2026-07-29T10:06:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717204</loc>
  <lastmod>2026-07-29T10:06:34Z</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/717202</loc>
  <lastmod>2026-07-29T10:06:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模マルチモーダルデータと分離可能リスク評価による1年死亡率予測（A Large-scale Multimodal Study for Predicting Mortality Risk Using Minimal and Low Parameter Models and Separable Risk Assessment）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717200</loc>
  <lastmod>2026-07-29T09:15:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非可逆な勝負を学び続ける仕組み（Open-ended Learning in Symmetric Zero-sum Games）</news:title>
   <news:publication_date>2026-07-29T09:15:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717198</loc>
  <lastmod>2026-07-29T09:13:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>モデル関数に基づく条件付き勾配法とArmijo様ラインサーチ（Model Function Based Conditional Gradient Method with Armijo-like Line Search）</news:title>
   <news:publication_date>2026-07-29T09:13:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717196</loc>
  <lastmod>2026-07-29T09:05:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <lastmod>2026-07-29T09:04:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-07-29T09:04:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717190</loc>
  <lastmod>2026-07-29T09:03:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時系列予測のための独立ベイズフィルタ学習（Recurrent Neural Filters: Learning Independent Bayesian Filtering Steps for Time Series Prediction）</news:title>
   <news:publication_date>2026-07-29T09:03:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717188</loc>
  <lastmod>2026-07-29T09:03:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ガウス過程の平均関数をメタ学習する（Meta-Learning Mean Functions for Gaussian Processes）</news:title>
   <news:publication_date>2026-07-29T09:03:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717186</loc>
  <lastmod>2026-07-29T08:12:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>質問包含（Question Entailment）に基づく質問応答アプローチ（A QUESTION-ENTAILMENT APPROACH TO QUESTION ANSWERING）</news:title>
   <news:publication_date>2026-07-29T08:12:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717184</loc>
  <lastmod>2026-07-29T08:11:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>パラメータ化量子回路による生成モデルの堅牢な実装（Robust Implementation of Generative Modeling with Parametrized Quantum Circuits）</news:title>
   <news:publication_date>2026-07-29T08:11:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717182</loc>
  <lastmod>2026-07-29T08:11:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>協調オンライン学習：隣人と情報を共有して学ぶ仕組み（Cooperative Online Learning: Keeping your Neighbors Updated）</news:title>
   <news:publication_date>2026-07-29T08:11:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717180</loc>
  <lastmod>2026-07-29T08:11:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>協調するAIを学ぶ——非定常な協働環境での学習戦略（Learning to Collaborate in Markov Decision Processes）</news:title>
   <news:publication_date>2026-07-29T08:11:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717178</loc>
  <lastmod>2026-07-29T08:11:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>直交行列のためのハミルトニアンモンテカルロ（Hamiltonian Monte-Carlo for Orthogonal Matrices）</news:title>
   <news:publication_date>2026-07-29T08:11:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717176</loc>
  <lastmod>2026-07-29T08:10:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>下からの物体検出：極点と中心点を結ぶ（Bottom-up Object Detection by Grouping Extreme and Center Points）</news:title>
   <news:publication_date>2026-07-29T08:10:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717174</loc>
  <lastmod>2026-07-29T08:10:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>タンデムに学ぶタンパク質の構造と動的挙動の同時決定（A practical guide to the simultaneous determination of protein structure and dynamics using metainference）</news:title>
   <news:publication_date>2026-07-29T08:10:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717172</loc>
  <lastmod>2026-07-29T07:19:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多段階加速確率的勾配法の普遍最適化（A Universally Optimal Multistage Accelerated Stochastic Gradient Method）</news:title>
   <news:publication_date>2026-07-29T07:19:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717170</loc>
  <lastmod>2026-07-29T07:18:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>重大事象に強い時系列差分学習の設計（Robust Temporal Difference Learning for Critical Domains）</news:title>
   <news:publication_date>2026-07-29T07:18:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717168</loc>
  <lastmod>2026-07-29T07:17:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>入力と表現の相互情報量を最大化するオートエンコーダ（An information theoretic approach to the autoencoder）</news:title>
   <news:publication_date>2026-07-29T07:17:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717166</loc>
  <lastmod>2026-07-29T07:17:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オンライン変化点検出を高速化する新手法の実用化可能性（Stein Variational Online Changepoint Detection with Applications to Hawkes Processes and Neural Networks）</news:title>
   <news:publication_date>2026-07-29T07:17:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717164</loc>
  <lastmod>2026-07-29T07:17:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>一晩で星の変動を描く発想（Characterizing Variable Stars in a Single Night with LSST）</news:title>
   <news:publication_date>2026-07-29T07:17:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717162</loc>
  <lastmod>2026-07-29T07:17:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>近似活性化を用いたメモリ効率的バックプロパゲーション（Backprop with Approximate Activations for Memory-efficient Network Training）</news:title>
   <news:publication_date>2026-07-29T07:17:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717160</loc>
  <lastmod>2026-07-29T07:16:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>感情と皮肉の同時判定がもたらす実務的インパクト（Sentiment and Sarcasm Classification with Multitask Learning）</news:title>
   <news:publication_date>2026-07-29T07:16:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717158</loc>
  <lastmod>2026-07-29T06:25:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>型付きグラフネットワーク（Typed Graph Networks）</news:title>
   <news:publication_date>2026-07-29T06:25:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717156</loc>
  <lastmod>2026-07-29T06:25:20Z</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>2026-07-29T06:25:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717154</loc>
  <lastmod>2026-07-29T06:25:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>軽量暗号で実現するプライベート分散機械学習（PD-ML-Lite: Private Distributed Machine Learning from Lightweight Cryptography）</news:title>
   <news:publication_date>2026-07-29T06:25:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717152</loc>
  <lastmod>2026-07-29T06:24:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>縮約モデルと生成モデルを組み合わせた重要度サンプリング推定器の提案（Coupling the Reduced-Order Model and the Generative Model for an Importance Sampling Estimator）</news:title>
   <news:publication_date>2026-07-29T06:24:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717150</loc>
  <lastmod>2026-07-29T06:23:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CTCModelによるKerasでの時系列ラベリング拡張（CTCModel: a Keras Model for Connectionist Temporal Classification）</news:title>
   <news:publication_date>2026-07-29T06:23:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717148</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次元変数の交互作用検出に向けたスパース主ヘッセ行列法（High-Dimensional Interactions Detection with Sparse Principal Hessian Matrix）</news:title>
   <news:publication_date>2026-07-29T06:23:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717146</loc>
  <lastmod>2026-07-29T06:23:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>データ希少な侵入検知を強化する深層敵対的学習とデータ拡張（Deep Adversarial Learning in Intrusion Detection: A Data Augmentation Enhanced Framework）</news:title>
   <news:publication_date>2026-07-29T06:23:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717144</loc>
  <lastmod>2026-07-29T05:31:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>IoT時代の無線通信における機械学習の総覧（Machine Learning for Wireless Communications in the Internet of Things: A Comprehensive Survey）</news:title>
   <news:publication_date>2026-07-29T05:31:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717142</loc>
  <lastmod>2026-07-29T05:31:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>フリーエネルギー原理が脳について教えること（What does the free energy principle tell us about the brain?）</news:title>
   <news:publication_date>2026-07-29T05:31:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717140</loc>
  <lastmod>2026-07-29T05:31:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>エンドツーエンド自然言語生成の最前線評価（Evaluating the State-of-the-Art of End-to-End Natural Language Generation: The E2E NLG Challenge）</news:title>
   <news:publication_date>2026-07-29T05:31:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717138</loc>
  <lastmod>2026-07-29T05:30:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>光学リモートセンシング画像における空間周波数チャネル特徴を用いた物体検出フレームワーク（ORSIm Detector: A Novel Object Detection Framework in Optical Remote Sensing Imagery Using Spatial-Frequency Channel Features）</news:title>
   <news:publication_date>2026-07-29T05:30:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717136</loc>
  <lastmod>2026-07-29T05:30:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模次元解析によるマージンベース分類法の理論的評価 (Large dimensional analysis of general margin based classification methods)</news:title>
   <news:publication_date>2026-07-29T05:30:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717134</loc>
  <lastmod>2026-07-29T05:30:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>畳み込みニューラルネットワークによる地震データの補間と雑音除去（Interpolation and Denoising of Seismic Data using Convolutional Neural Networks）</news:title>
   <news:publication_date>2026-07-29T05:30:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717132</loc>
  <lastmod>2026-07-29T05:29:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>U2-Netによる網膜OCTの光受容体層セグメンテーションと不確実性可視化（U2-NET: A BAYESIAN U-NET MODEL WITH EPISTEMIC UNCERTAINTY FEEDBACK FOR PHOTORECEPTOR LAYER SEGMENTATION IN PATHOLOGICAL OCT SCANS）</news:title>
   <news:publication_date>2026-07-29T05:29:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717130</loc>
  <lastmod>2026-07-29T04:38:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自然言語ベースの制約なしサービス合成（Unrestricted Natural Language-based Service Composition through Sentence Embeddings）</news:title>
   <news:publication_date>2026-07-29T04:38:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717128</loc>
  <lastmod>2026-07-29T04:38:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>増分主成分分析の実装と連続性補正（Incremental Principal Component Analysis: Exact implementation and continuity corrections）</news:title>
   <news:publication_date>2026-07-29T04:38:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717126</loc>
  <lastmod>2026-07-29T04:37:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>位相復元の一意性と安定性（PHASE RETRIEVAL: UNIQUENESS AND STABILITY）</news:title>
   <news:publication_date>2026-07-29T04:37:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717124</loc>
  <lastmod>2026-07-29T04:37:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>LTLモデル検査結果の機械学習による予測（Predicting the Results of LTL Model Checking using Multiple Machine Learning Algorithms）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717122</loc>
  <lastmod>2026-07-29T04:37:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>中国語感情分析のための音韻強化テキスト表現と強化学習（Phonetic-enriched Text Representation for Chinese Sentiment Analysis with Reinforcement Learning）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <lastmod>2026-07-29T04:37:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>光励起状態の機械学習による特徴付け（Characterization of photoexcited states in the half-filled one-dimensional extended Hubbard model assisted by machine learning）</news:title>
   <news:publication_date>2026-07-29T04:37:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717118</loc>
  <lastmod>2026-07-29T04:37:16Z</lastmod>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>船舶自動操縦のための強化学習：サンプル効率とモデル予測制御の統合（Reinforcement Learning Boat Autopilot: A Sample-efficient and Model Predictive Control based Approach）</news:title>
   <news:publication_date>2026-07-29T04:37:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717116</loc>
  <lastmod>2026-07-29T03:46:06Z</lastmod>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717114</loc>
  <lastmod>2026-07-29T03:45:57Z</lastmod>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>価値関数最適化における信頼領域とカルマンフィルタの融合（Trust Region Value Optimization using Kalman Filtering）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717106</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>ヒンディー語における文脈に基づく語義解析（Context based Analysis of Lexical Semantics for Hindi Language）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/717104</loc>
  <lastmod>2026-07-29T03:44:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ベイズネットワークとハイブリッド量子古典学習による因果解明（Bayesian Networks based Hybrid Quantum-Classical Machine Learning Approach to Elucidate Gene Regulatory Pathways）</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>Mixture Density RNNが未来をどう予測するか（How do Mixture Density RNNs Predict the Future?）</news:title>
   <news:publication_date>2026-07-29T02:52:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717100</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>肺結節診断の進化：古典的手法から深層学習支援の意思決定へ（Evolving the pulmonary nodules diagnosis from classical approaches to deep learning-aided decision support）</news:title>
   <news:publication_date>2026-07-29T02:49:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717098</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>
  <loc>https://aibr.jp/archives/717096</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>ホモモルフィックセンシングの実務的インパクト（Homomorphic Sensing）</news:title>
   <news:publication_date>2026-07-29T02:48:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717094</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>
   </news:publication>
   <news:title>制約付き・多目的マルコフ決定過程における強化学習の最前線（Reinforcement Learning for Constrained and Multi-Objective Markov Decision Processes）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/717090</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>ピーク制約付きマルコフ決定過程の強化学習（Reinforcement Learning of Markov Decision Processes with Peak Constraints）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717088</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>学習表現を伴うランダムフォレストによるセマンティックセグメンテーション（Random Forest with Learned Representations for Semantic Segmentation）</news:title>
   <news:publication_date>2026-07-29T01:56:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717086</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>アスペクトに着目した評価予測モデル（AspeRa: Aspect-based Rating Prediction Model）</news:title>
   <news:publication_date>2026-07-29T01:56:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717084</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>知識グラフ強化推薦のマルチタスク特徴学習（Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-07-29T01:55:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717080</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-07-29T01:55:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717078</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>構造的スパース正則化によるフィルタ剪定で小型ConvNetへ（Towards Compact ConvNets via Structure-Sparsity Regularized Filter Pruning）</news:title>
   <news:publication_date>2026-07-29T01:54:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717076</loc>
  <lastmod>2026-07-29T01:54:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>若い星の深部を覗くX線観測の示唆（A deep X-ray view of the Class I YSO Elias 29 with XMM-Newton and NuSTAR）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717074</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>深紫外での1.5フェムト秒パルス合成（Harmonic concatenation of 1.5-femtosecond-pulses in the deep ultraviolet）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717072</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>確率的勾配決定木（Stochastic Gradient Trees）</news:title>
   <news:publication_date>2026-07-29T01:03:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717070</loc>
  <lastmod>2026-07-29T01:03:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>見出し生成における自己注意型モデルの応用（Self-Attentive Model for Headline Generation）</news:title>
   <news:publication_date>2026-07-29T01:03:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717068</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717066</loc>
  <lastmod>2026-07-29T01:02:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ノイズ付き医療画像におけるロバスト学習（ROBUST LEARNING AT NOISY LABELED MEDICAL IMAGES: APPLIED TO SKIN LESION CLASSIFICATION）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717064</loc>
  <lastmod>2026-07-29T01:01:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ダイナミック・オートエンコーダによる深層クラスタリングの刷新（DEEP CLUSTERING WITH A DYNAMIC AUTOENCODER: FROM RECONSTRUCTION TOWARDS CENTROIDS CONSTRUCTION）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/717056</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>ODNによるオープンセット行動認識の要点と実務的示唆（ODN: Opening the Deep Network for Open-Set Action Recognition）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/717054</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>メンバーシップクエリによる決定木の適応的厳密学習（Adaptive Exact Learning of Decision Trees from Membership Queries）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/717052</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-07-28T22:15:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-07-28T22:15:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-07-28T21:22:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-07-28T21:22:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-07-28T21:20:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>衝突検査から学習するコンフィギュレーション空間信念モデル（Learning Configuration Space Belief Model from Collision Checks for Motion Planning）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717004</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-07-28T20:29:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717002</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>単一ディープ反事実後悔最小化（Single Deep Counterfactual Regret Minimization）</news:title>
   <news:publication_date>2026-07-28T20:28:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717000</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>音声認識におけるSelf-AttentionによるCTCネットワーク（SELF-ATTENTION NETWORKS FOR CONNECTIONIST TEMPORAL CLASSIFICATION IN SPEECH RECOGNITION）</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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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 </url>
 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 </url>
 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 <url>
  <loc>https://aibr.jp/archives/716976</loc>
  <lastmod>2026-07-28T18:41:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ICLabelによるEEG独立成分分類の自動化（ICLabel: An automated electroencephalographic independent component classifier, dataset, and website）</news:title>
   <news:publication_date>2026-07-28T18:41:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716974</loc>
  <lastmod>2026-07-28T18:41:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>転倒から自律回復する四足歩行ロボット制御（Robust Recovery Controller for a Quadrupedal Robot using Deep Reinforcement Learning）</news:title>
   <news:publication_date>2026-07-28T18:41:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716972</loc>
  <lastmod>2026-07-28T18:41:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DQNターゲットの多段階強化学習理解（Understanding Multi-Step Deep Reinforcement Learning: A Systematic Study of the DQN Target）</news:title>
   <news:publication_date>2026-07-28T18:41:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716970</loc>
  <lastmod>2026-07-28T18:40:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>制約付きトポロジ設計最適化のための新しいCGAN技術（A New CGAN Technique for Constrained Topology Design Optimization）</news:title>
   <news:publication_date>2026-07-28T18:40:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716968</loc>
  <lastmod>2026-07-28T18:40:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>正則化重み付きチェビシェフ近似による支持サイズ推定（Regularized Weighted Chebyshev Approximations for Support Estimation）</news:title>
   <news:publication_date>2026-07-28T18:40:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716966</loc>
  <lastmod>2026-07-28T18:40:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非漸近解析によるフラクショナル・ランジュバン・モンテカルロの示唆（Non-Asymptotic Analysis of Fractional Langevin Monte Carlo for Non-Convex Optimization）</news:title>
   <news:publication_date>2026-07-28T18:40:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716964</loc>
  <lastmod>2026-07-28T18:39:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ベイズ加法回帰木と汎用BARTモデル（Bayesian additive regression trees and the General BART model）</news:title>
   <news:publication_date>2026-07-28T18:39:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716962</loc>
  <lastmod>2026-07-28T17:48:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数グラフの構造不変表現を学ぶ（Multiple Graph Adversarial Learning）</news:title>
   <news:publication_date>2026-07-28T17:48:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716960</loc>
  <lastmod>2026-07-28T17:30:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフ観測に対する特徴ベクトル型RBFネットワークの正確な再定式化（An Exact Reformulation of Feature-Vector-based Radial-Basis-Function Networks for Graph-based Observations）</news:title>
   <news:publication_date>2026-07-28T17:30:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716958</loc>
  <lastmod>2026-07-28T17:30:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>歩行者属性認識の総説（Pedestrian Attribute Recognition: A Survey）</news:title>
   <news:publication_date>2026-07-28T17:30:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716956</loc>
  <lastmod>2026-07-28T17:30:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>個々の経験と情報伝達による集団学習と採餌行動（Collective learning from individual experiences and information transfer during group foraging）</news:title>
   <news:publication_date>2026-07-28T17:30:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716954</loc>
  <lastmod>2026-07-28T17:29:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的モメンタム法の加速線形収束（Accelerated Linear Convergence of Stochastic Momentum Methods in Wasserstein Distances）</news:title>
   <news:publication_date>2026-07-28T17:29:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716952</loc>
  <lastmod>2026-07-28T17:29:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>一人称視点と三人称視点を組み合わせた交差点分類（Use of First and Third Person Views for Deep Intersection Classification）</news:title>
   <news:publication_date>2026-07-28T17:29:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716950</loc>
  <lastmod>2026-07-28T17:28:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層学習と部分語単位による詩生成（Deep learning and sub-word-unit approach in written art generation）</news:title>
   <news:publication_date>2026-07-28T17:28:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716948</loc>
  <lastmod>2026-07-28T16:37:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Multiple Sclerosisの病変と脳構造を同時に分割する深層学習（Simultaneous lesion and brain segmentation in Multiple Sclerosis using deep neural networks）</news:title>
   <news:publication_date>2026-07-28T16:37:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716946</loc>
  <lastmod>2026-07-28T16:36:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>通信負荷を抑える新手法 CAMR（Coded Aggregated MapReduce）</news:title>
   <news:publication_date>2026-07-28T16:36:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716944</loc>
  <lastmod>2026-07-28T16:36:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層学習における連結なサブレベル集合（On Connected Sublevel Sets in Deep Learning）</news:title>
   <news:publication_date>2026-07-28T16:36:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716942</loc>
  <lastmod>2026-07-28T16:35:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>屋内空間のレジアビリティ（判読性）を定量化する手法（Quantifying Legibility of Indoor Spaces Using Deep Convolutional Neural Networks: Case Studies in Train Stations）</news:title>
   <news:publication_date>2026-07-28T16:35:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716940</loc>
  <lastmod>2026-07-28T16:35:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>長期依存性を扱うための非飽和型再帰ユニット（Towards Non-saturating Recurrent Units for Modelling Long-term Dependencies）</news:title>
   <news:publication_date>2026-07-28T16:35:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716938</loc>
  <lastmod>2026-07-28T16:34:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的近接点法の非漸近収束率の更新（New nonasymptotic convergence rates of stochastic proximal point algorithm for stochastic convex optimization）</news:title>
   <news:publication_date>2026-07-28T16:34:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716936</loc>
  <lastmod>2026-07-28T16:34:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフィカルモデルの同定検定の下限（Lower bounds for testing graphical models: colorings and antiferromagnetic Ising models）</news:title>
   <news:publication_date>2026-07-28T16:34:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716934</loc>
  <lastmod>2026-07-28T15:43:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Top-Kワークロードにおけるホット・コールド階層配置の最適化（Adapting The Secretary Hiring Problem for Optimal Hot-Cold Tier Placement under Top-K Workloads）</news:title>
   <news:publication_date>2026-07-28T15:43:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716932</loc>
  <lastmod>2026-07-28T15:43:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>更新を絞ることで長期依存を扱う：Gaussian-gated LSTMの概観（REDUCING STATE UPDATES VIA GAUSSIAN-GATED LSTMS）</news:title>
   <news:publication_date>2026-07-28T15:43:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716930</loc>
  <lastmod>2026-07-28T15:42:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自動特徴量エンジニアリングと選択を提供するautofeatライブラリ（The autofeat Python Library for Automated Feature Engineering and Selection）</news:title>
   <news:publication_date>2026-07-28T15:42:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716928</loc>
  <lastmod>2026-07-28T15:42:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>クロスリンガル言語モデル事前学習（Cross-lingual Language Model Pretraining）</news:title>
   <news:publication_date>2026-07-28T15:42:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716926</loc>
  <lastmod>2026-07-28T15:42:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数の動的グラフをオンライン推定する手法（Online Estimation of Multiple Dynamic Graphs in Pattern Sequences）</news:title>
   <news:publication_date>2026-07-28T15:42:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716924</loc>
  <lastmod>2026-07-28T15:41:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>セントロメア衛星DNAを識別するdna-brnn（Identifying centromeric satellites with dna-brnn）</news:title>
   <news:publication_date>2026-07-28T15:41:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/716922</loc>
  <lastmod>2026-07-28T15:41:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>モーダルクラスタリングの漸近理論と帯域幅選択（Modal clustering asymptotics with applications to bandwidth selection）</news:title>
   <news:publication_date>2026-07-28T15:41:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
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