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   <news:title>瓦礫塊天体モデルにおけるバイスタティック全波レーダートモグラフィーの検出能力（Bistatic full-wave radar tomography detects deep interior voids, cracks and boulders in a rubble-pile asteroid model）</news:title>
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   <news:title>深層ニューラルネットワークによる最適軌道推定の実務的インパクト（Deep Networks as Approximators of Optimal Transfers）</news:title>
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   <news:title>ProteinNetによるタンパク質構造機械学習の標準化（ProteinNet: a standardized data set for machine learning of protein structure）</news:title>
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   <news:title>組合せ推薦の逐次評価と生成フレームワーク（Sequential Evaluation and Generation Framework for Combinatorial Recommender System）</news:title>
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   <news:title>合体に伴う衝撃波のX線証拠：ZwCl 0008.8+5215のChandra/Suzaku観測 (Evidence for a merger induced shock wave in ZwCl 0008.8+5215 with Chandra and Suzaku)</news:title>
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   <news:title>事前定義された均等分布クラス中心に基づく分類監視オートエンコーダ（A Classification Supervised Auto-Encoder Based on Predefined Evenly-Distributed Class Centroids）</news:title>
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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>既知経路から混合時間を推定する方法（Estimating the Mixing Time of Ergodic Markov Chains）</news:title>
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   <news:genres>Blog</news:genres>
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   <news:title>因果に基づく増分型マルチタッチアトリビューションとRNN（Causally Driven Incremental Multi Touch Attribution Using a Recurrent Neural Network）</news:title>
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   <news:genres>Blog</news:genres>
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    <news:language>ja</news:language>
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   <news:title>環境を操作する自己回帰モデルへの最適攻撃（Optimal Attack against Autoregressive Models by Manipulating the Environment）</news:title>
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   <news:genres>Blog</news:genres>
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    <news:language>ja</news:language>
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   <news:title>マルチアームドバンディット問題とバッチUCB規則（MULTI-ARMED BANDIT PROBLEM AND BATCH UCB RULE）</news:title>
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   <news:genres>Blog</news:genres>
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   <news:title>弱識別モデルに対する期待値最大化法の詳細解析（Sharp Analysis of Expectation-Maximization for Weakly Identifiable Models）</news:title>
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    <news:name>AI Benchmark Research</news:name>
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   <news:title>行動表現を学習する強化学習（Learning Action Representations for Reinforcement Learning）</news:title>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>大規模多言語転移による固有表現認識（Massively Multilingual Transfer for NER）</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>異なるドメイン間での単語埋め込み学習のための単純な正則化アルゴリズム（A Simple Regularization-based Algorithm for Learning Cross-Domain Word Embeddings）</news:title>
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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>文脈をめくる――視覚認識における空間と時間の文脈推論（Lift-the-flap: what, where and when for context reasoning）</news:title>
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    <news:language>ja</news:language>
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   <news:title>連続近似によるバイナリニューラルネットワークの臨界初期化（CRITICAL INITIALISATION IN CONTINUOUS APPROXIMATIONS OF BINARY NEURAL NETWORKS）</news:title>
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   <news:title>勾配最適化器の圧縮手法と実務への示唆（Compressing Gradient Optimizers via Count-Sketches）</news:title>
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   <news:genres>Blog</news:genres>
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    <news:language>ja</news:language>
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   <news:title>文書の日付推定にGCNを使う試み（Dating Documents using Graph Convolution Networks）</news:title>
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   <news:genres>Blog</news:genres>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>オフポリシー評価におけるプライバシー保護（Privacy Preserving Off-Policy Evaluation）</news:title>
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   <news:genres>Blog</news:genres>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>DREAM: 対話型読解の課題とモデル（DREAM: A Challenge Dataset and Models for Dialogue-Based Reading Comprehension）</news:title>
   <news:publication_date>2026-08-01T22:42:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
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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>Open KBの正規化を自動化するCESI（CESI: Canonicalizing Open Knowledge Bases using Embeddings and Side Information）</news:title>
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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>普遍的太陽光発電予測器の提案（A Novel Universal Photovoltaic Energy Predictor）</news:title>
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   <news:genres>Blog</news:genres>
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  <lastmod>2026-08-01T21:50:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>長文を一貫して生成するための多層潜在変数モデル（Towards Generating Long and Coherent Text with Multi-Level Latent Variable Models）</news:title>
   <news:publication_date>2026-08-01T21:50:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
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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>GANの圧縮と知識蒸留による実装可能性の提示（Compressing GANs using Knowledge Distillation）</news:title>
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   <news:genres>Blog</news:genres>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>バイフィデリティデータ支援ニューラルネットワークによる非侵襲型還元モデル（BIFIDELITY DATA-ASSISTED NEURAL NETWORKS IN NONINTRUSIVE REDUCED-ORDER MODELING）</news:title>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
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   <news:title>コラボレーティブ・インテリジェンスに適した深層学習アーキテクチャ（Towards Collaborative Intelligence Friendly Architectures for Deep Learning）</news:title>
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   <news:genres>Blog</news:genres>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>深層トリプレット量子化（Deep Triplet Quantization）</news:title>
   <news:publication_date>2026-08-01T21:48:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
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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>排他ラッソモデルの高速解法：二重ニュートン型前処理付き近接点法（A dual Newton based preconditioned proximal point algorithm for exclusive lasso models）</news:title>
   <news:publication_date>2026-08-01T21:48:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/718466</loc>
  <lastmod>2026-08-01T20:55:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>アグノスティック・フェデレーテッド・ラーニングの本質（Agnostic Federated Learning）</news:title>
   <news:publication_date>2026-08-01T20:55:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718464</loc>
  <lastmod>2026-08-01T20:54:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフ抵抗と対比較学習（Graph Resistance and Learning from Pairwise Comparisons）</news:title>
   <news:publication_date>2026-08-01T20:54:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718462</loc>
  <lastmod>2026-08-01T20:54:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>敵対的攻撃に対して理論的耐性を持つ新しいニューラルネットワーク族（A New Family of Neural Networks Provably Resistant to Adversarial Attacks）</news:title>
   <news:publication_date>2026-08-01T20:54:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718460</loc>
  <lastmod>2026-08-01T20:53:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>正常細胞とがん細胞の識別におけるオートエンコーダのノード重要度（Distinguishing between Normal and Cancer Cells Using Autoencoder Node Saliency）</news:title>
   <news:publication_date>2026-08-01T20:53:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718458</loc>
  <lastmod>2026-08-01T20:53:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>メモリ効率の高い適応最適化が変える訓練スピードとモデル規模（Memory-Efficient Adaptive Optimization）</news:title>
   <news:publication_date>2026-08-01T20:53:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718456</loc>
  <lastmod>2026-08-01T20:52:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習が分子モデリングとシミュレーションを変える（Advances of Machine Learning in Molecular Modeling and Simulation）</news:title>
   <news:publication_date>2026-08-01T20:52:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718454</loc>
  <lastmod>2026-08-01T20:52:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>降下法と局所最小解の実用的示唆（Passed &amp;amp; Spurious: Descent Algorithms and Local Minima in Spiked Matrix-Tensor Models）</news:title>
   <news:publication_date>2026-08-01T20:52:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718452</loc>
  <lastmod>2026-08-01T20:01:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Natural Analystsによる適応的データ解析の再定式化（Natural Analysts in Adaptive Data Analysis）</news:title>
   <news:publication_date>2026-08-01T20:01:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718450</loc>
  <lastmod>2026-08-01T20:01:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>どのFactorization Machineが優れているか：最適保証を伴う理論的答え（Which Factorization Machine Modeling is Better: A Theoretical Answer with Optimal Guarantee）</news:title>
   <news:publication_date>2026-08-01T20:01:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718448</loc>
  <lastmod>2026-08-01T20:00:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Top-k分類の一貫性に関する解析（On the Consistency of Top-k Surrogate Losses）</news:title>
   <news:publication_date>2026-08-01T20:00:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718446</loc>
  <lastmod>2026-08-01T19:59:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>生成的正規化フローのための新しい畳み込み（Emerging Convolutions for Generative Normalizing Flows）</news:title>
   <news:publication_date>2026-08-01T19:59:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718444</loc>
  <lastmod>2026-08-01T19:59:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>経験的性能をモデル化するためのサロゲートはどれが有効か（Which Surrogate Works for Empirical Performance Modelling? A Case Study with Differential Evolution）</news:title>
   <news:publication_date>2026-08-01T19:59:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718442</loc>
  <lastmod>2026-08-01T19:59:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ディープな顔特徴量で「美しさ」を定量化する視点（Understanding Beauty via Deep Facial Features）</news:title>
   <news:publication_date>2026-08-01T19:59:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718440</loc>
  <lastmod>2026-08-01T19:59:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>進化したTransformer（The Evolved Transformer）</news:title>
   <news:publication_date>2026-08-01T19:59:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718438</loc>
  <lastmod>2026-08-01T19:07:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>病理画像の類似検索ツール SMILY（Similar Image Search for Histopathology: SMILY）</news:title>
   <news:publication_date>2026-08-01T19:07:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718436</loc>
  <lastmod>2026-08-01T19:07:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>計算流体力学におけるデータ復元と深層イメージプライア（Data recovery in computational fluid dynamics through deep image priors）</news:title>
   <news:publication_date>2026-08-01T19:07:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718434</loc>
  <lastmod>2026-08-01T19:07:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自己内省型機械学習アーキテクチャにおける量子力学の出現（Emergent Quantum Mechanics in an Introspective Machine Learning Architecture）</news:title>
   <news:publication_date>2026-08-01T19:07:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718432</loc>
  <lastmod>2026-08-01T19:06:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>派生商品のポートフォリオ評価におけるガウス過程回帰の適用（Gaussian Process Regression for Derivative Portfolio Modeling and Application to CVA Computations）</news:title>
   <news:publication_date>2026-08-01T19:06:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718430</loc>
  <lastmod>2026-08-01T19:06:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>期待値ベース強化学習と分布ベース強化学習の比較分析（A Comparative Analysis of Expected and Distributional Reinforcement Learning）</news:title>
   <news:publication_date>2026-08-01T19:06:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718428</loc>
  <lastmod>2026-08-01T19:06:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>走査型電子顕微鏡における深層学習を用いた解像度向上（Resolution enhancement in scanning electron microscopy using deep learning）</news:title>
   <news:publication_date>2026-08-01T19:06:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718426</loc>
  <lastmod>2026-08-01T19:05:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DDSLによる幾何学信号の微分可能ラスタライズ（Deep Differentiable Simplex Layer for Learning Geometric Signals）</news:title>
   <news:publication_date>2026-08-01T19:05:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718424</loc>
  <lastmod>2026-08-01T18:13:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>STM/STSデータにおけるネマティック秩序の検出（Detecting nematic order in STM/STS data with artificial intelligence）</news:title>
   <news:publication_date>2026-08-01T18:13:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718422</loc>
  <lastmod>2026-08-01T18:06:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>コラーゲンVI関連筋ジストロフィーの自動診断のための畳み込みニューラルネットワーク（A Convolutional Neural Network for the Automatic Diagnosis of Collagen VI related Muscular Dystrophies）</news:title>
   <news:publication_date>2026-08-01T18:06:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718420</loc>
  <lastmod>2026-08-01T18:05:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>リアルタイム時間分解オゾン予測における深層畳み込みニューラルネットワークの適用（A real-time hourly ozone prediction system using deep convolutional neural network）</news:title>
   <news:publication_date>2026-08-01T18:05:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718418</loc>
  <lastmod>2026-08-01T18:05:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>道路工事現場の注視点の実世界マッピング（REAL-WORLD MAPPING OF GAZE FIXATIONS USING INSTANCE SEGMENTATION FOR ROAD CONSTRUCTION SAFETY APPLICATIONS）</news:title>
   <news:publication_date>2026-08-01T18:05:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718416</loc>
  <lastmod>2026-08-01T18:04:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Metric Gaussian Variational Inferenceの要点と経営への示唆（Metric Gaussian Variational Inference）</news:title>
   <news:publication_date>2026-08-01T18:04:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718414</loc>
  <lastmod>2026-08-01T18:04:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Wilderness Areaデータセットによるクラスタリング評価の再設計（The Wilderness Area Data Set: Adapting the Covertype data set for unsupervised learning）</news:title>
   <news:publication_date>2026-08-01T18:04:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718412</loc>
  <lastmod>2026-08-01T18:04:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>HyperGANによる多様で高性能なニューラルネットワーク生成（HyperGAN: A Generative Model for Diverse, Performant Neural Networks）</news:title>
   <news:publication_date>2026-08-01T18:04:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718410</loc>
  <lastmod>2026-08-01T17:12:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>探索困難問題に対する新たなアプローチ Go-Explore（Go-Explore: a New Approach for Hard-Exploration Problems）</news:title>
   <news:publication_date>2026-08-01T17:12:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718408</loc>
  <lastmod>2026-08-01T17:12:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スライス逆回帰による星の基本パラメータ推定（Sliced Inverse Regression: application to fundamental stellar parameters）</news:title>
   <news:publication_date>2026-08-01T17:12:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718406</loc>
  <lastmod>2026-08-01T17:11:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>限定された仮説集合からのロボット生態学的知覚のブートストラップ（Bootstrapping Robotic Ecological Perception from a Limited Set of Hypotheses Through Interactive Perception）</news:title>
   <news:publication_date>2026-08-01T17:11:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718404</loc>
  <lastmod>2026-08-01T17:11:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>QGPを可視化する電弱プローブの実験的総覧（Shining a Light on the QGP - Electroweak Probes Experimental Summary）</news:title>
   <news:publication_date>2026-08-01T17:11:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718402</loc>
  <lastmod>2026-08-01T17:11:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Successor Features と Generalised Policy Improvement による強化学習の転移（Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement）</news:title>
   <news:publication_date>2026-08-01T17:11:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718400</loc>
  <lastmod>2026-08-01T17:10:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>原理に基づく原子スケール特性の機械学習（Machine-learning of atomic-scale properties based on physical principles）</news:title>
   <news:publication_date>2026-08-01T17:10:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718398</loc>
  <lastmod>2026-08-01T16:19:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自己利益的な群衆を調整する仕組み（Coordinating the Crowd: Inducing Desirable Equilibria in Non-Cooperative Systems）</news:title>
   <news:publication_date>2026-08-01T16:19:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718396</loc>
  <lastmod>2026-08-01T16:12:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所学習のためのスパース結合を用いた直接フィードバックアライメント（Direct Feedback Alignment with Sparse Connections for Local Learning）</news:title>
   <news:publication_date>2026-08-01T16:12:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718394</loc>
  <lastmod>2026-08-01T16:12:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>長期時系列の欠損値を埋める非自己回帰型マルチ解像度補完（NAOMI: Non-Autoregressive Multiresolution Sequence Imputation）</news:title>
   <news:publication_date>2026-08-01T16:12:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718392</loc>
  <lastmod>2026-08-01T16:11:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>内部者脅威検知の分類器群 (Classifier Suites for Insider Threat Detection)</news:title>
   <news:publication_date>2026-08-01T16:11:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718390</loc>
  <lastmod>2026-08-01T16:10:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>フォグ・トゥ・シングス環境を保護するためのアンサンブル学習ベースの侵入検知システム（Securing Fog-to-Things Environment Using Intrusion Detection System Based On Ensemble Learning）</news:title>
   <news:publication_date>2026-08-01T16:10:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718388</loc>
  <lastmod>2026-08-01T16:10:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複雑な3D環境における古典的ナビゲーションと学習型ナビゲーションのベンチマーク（Benchmarking Classic and Learned Navigation in Complex 3D Environments）</news:title>
   <news:publication_date>2026-08-01T16:10:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718386</loc>
  <lastmod>2026-08-01T16:10:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>因果メカニズムの分離を学ぶためのメタ転移目的（A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms）</news:title>
   <news:publication_date>2026-08-01T16:10:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718384</loc>
  <lastmod>2026-08-01T15:18:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>情報ボトルネックによる転移と探索（INFOBOT: Transfer and Exploration via the Information Bottleneck）</news:title>
   <news:publication_date>2026-08-01T15:18:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718382</loc>
  <lastmod>2026-08-01T15:18:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層ニューラルネットワークが抽出する特徴の相関について（On Correlation of Features Extracted by Deep Neural Networks）</news:title>
   <news:publication_date>2026-08-01T15:18:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718380</loc>
  <lastmod>2026-08-01T15:18:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>準クローンネッカー積グラフィカルモデルの学習（Learning Quasi-Kronecker Product Graphical Models）</news:title>
   <news:publication_date>2026-08-01T15:18:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718378</loc>
  <lastmod>2026-08-01T15:16:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>二値重みパーセプトロンの計算的困難さと入力スパース性の利点（Understanding the computational difficulty of a binary-weight perceptron and the advantage of input sparseness）</news:title>
   <news:publication_date>2026-08-01T15:16:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718376</loc>
  <lastmod>2026-08-01T15:16:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>小さなハミング距離で生じる敵対的事例の単純な説明（A Simple Explanation for the Existence of Adversarial Examples with Small Hamming Distance）</news:title>
   <news:publication_date>2026-08-01T15:16:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718374</loc>
  <lastmod>2026-08-01T15:15:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多目的二値線形計画における射影学習（Learning to Project in Multi-Objective Binary Linear Programming）</news:title>
   <news:publication_date>2026-08-01T15:15:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718372</loc>
  <lastmod>2026-08-01T15:15:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>指数機構の長所と落とし穴：Hilbert空間と関数型PCAへの応用（Benefits and Pitfalls of the Exponential Mechanism with Applications to Hilbert Spaces and Functional PCA）</news:title>
   <news:publication_date>2026-08-01T15:15:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718370</loc>
  <lastmod>2026-08-01T14:23:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>視点不変の3次元人体姿勢推定（View Invariant 3D Human Pose Estimation）</news:title>
   <news:publication_date>2026-08-01T14:23:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718368</loc>
  <lastmod>2026-08-01T14:23:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ノイズに強い公平な分類（Noise-tolerant fair classification）</news:title>
   <news:publication_date>2026-08-01T14:23:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718366</loc>
  <lastmod>2026-08-01T14:22:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ディープニューラルネットワークが高次元偏微分方程式の次元の呪いを克服する証明（A proof that rectified deep neural networks overcome the curse of dimensionality in the numerical approximation of semilinear heat equations）</news:title>
   <news:publication_date>2026-08-01T14:22:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718364</loc>
  <lastmod>2026-08-01T14:21:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>UAVの深層強化学習による航行制御とMassive MIMO統合（Deep Reinforcement Learning for UAV Navigation Through Massive MIMO Technique）</news:title>
   <news:publication_date>2026-08-01T14:21:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718362</loc>
  <lastmod>2026-08-01T14:21:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的生成モデルによるメタマテリアルの逆設計（Probabilistic representation and inverse design of metamaterials based on a deep generative model with semi-supervised learning strategy）</news:title>
   <news:publication_date>2026-08-01T14:21:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718360</loc>
  <lastmod>2026-08-01T14:21:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>特徴の多様性でGANを安定化する手法（Diversity Regularized Adversarial Learning）</news:title>
   <news:publication_date>2026-08-01T14:21:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718358</loc>
  <lastmod>2026-08-01T14:20:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>回転不変畳み込み記述子による機械学習交換相関汎関数の設計と解析 (Design and Analysis of Machine Learning Exchange-Correlation Functionals via Rotationally Invariant Convolutional Descriptors)</news:title>
   <news:publication_date>2026-08-01T14:20:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718356</loc>
  <lastmod>2026-08-01T13:28:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>一般化テンソルモデルによる再帰型ニューラルネットワークの理論的拡張（Generalized Tensor Models for Recurrent Neural Networks）</news:title>
   <news:publication_date>2026-08-01T13:28:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718354</loc>
  <lastmod>2026-08-01T13:20:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>身体を持つロボットが語彙学習に与える効果（The effect of a physical robot on vocabulary learning）</news:title>
   <news:publication_date>2026-08-01T13:20:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718352</loc>
  <lastmod>2026-08-01T13:19:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>皮膚病変の自動判定を目指す深層学習の実践報告（Automated Skin Lesion Classification Using Ensemble of Deep Neural Networks）</news:title>
   <news:publication_date>2026-08-01T13:19:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718350</loc>
  <lastmod>2026-08-01T13:18:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>テンソライズド埋め込み層が変える語彙表現の圧縮（Tensorized Embedding Layers）</news:title>
   <news:publication_date>2026-08-01T13:18:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718348</loc>
  <lastmod>2026-08-01T13:18:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層アーキタイプ解析の概観（Deep Archetypal Analysis）</news:title>
   <news:publication_date>2026-08-01T13:18:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718346</loc>
  <lastmod>2026-08-01T13:18:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>P300信号検出における再帰型ニューラルネットワークの有用性（Recurrent Neural Networks for P300-based BCI）</news:title>
   <news:publication_date>2026-08-01T13:18:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718344</loc>
  <lastmod>2026-08-01T13:17:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>最小マージン最大化とブースティングの最適化（Optimal Minimal Margin Maximization with Boosting）</news:title>
   <news:publication_date>2026-08-01T13:17:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718342</loc>
  <lastmod>2026-08-01T12:25:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>不完全なフォワードシミュレータを伴うデータ同化のためのアンサンブルカーネル学習（Ensemble-based kernel learning for a class of data assimilation problems with imperfect forward simulators）</news:title>
   <news:publication_date>2026-08-01T12:25:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718340</loc>
  <lastmod>2026-08-01T12:25:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>識別的最小最大類似度/非類似度割当てに基づく非線形変換の共同学習によるクラスタリング（Clustering with Jointly Learned Nonlinear Transforms Over Discriminating Min-Max Similarity/Dissimilarity Assignment）</news:title>
   <news:publication_date>2026-08-01T12:25:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718338</loc>
  <lastmod>2026-08-01T12:25:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分布的に頑健な多目的非負値行列因子分解（Distributionally Robust and Multi-Objective Nonnegative Matrix Factorization）</news:title>
   <news:publication_date>2026-08-01T12:25:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718336</loc>
  <lastmod>2026-08-01T12:24:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多変量時系列のスケーラブルな表現学習（Unsupervised Scalable Representation Learning for Multivariate Time Series）</news:title>
   <news:publication_date>2026-08-01T12:24:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718334</loc>
  <lastmod>2026-08-01T12:23:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GeNetによるメタゲノム解析の再設計（GeNet: Deep Representations for Metagenomics）</news:title>
   <news:publication_date>2026-08-01T12:23:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718332</loc>
  <lastmod>2026-08-01T12:23:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>視覚ベースのステアリング角度推定（Autonomous Cars: Vision based Steering Wheel Angle Estimation）</news:title>
   <news:publication_date>2026-08-01T12:23:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718330</loc>
  <lastmod>2026-08-01T12:23:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>弱いアノテーションから学ぶ高速マッチングモデル（Learning Fast Matching Models from Weak Annotations）</news:title>
   <news:publication_date>2026-08-01T12:23:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718328</loc>
  <lastmod>2026-08-01T11:31:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局面評価関数を学習して探索精度を高める手法（Learning Position Evaluation Functions Used in Monte Carlo Softmax Search）</news:title>
   <news:publication_date>2026-08-01T11:31:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718326</loc>
  <lastmod>2026-08-01T11:31:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>格子型X線干渉計法における画像ドメインのモアレ除去法（Automatic image-domain Moiré artifact reduction method in grating-based x-ray interferometry imaging）</news:title>
   <news:publication_date>2026-08-01T11:31:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718324</loc>
  <lastmod>2026-08-01T11:30:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>PIRセンサーを用いたリアルタイム追跡の新手法（A new PIR-based method for real-time tracking）</news:title>
   <news:publication_date>2026-08-01T11:30:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718322</loc>
  <lastmod>2026-08-01T11:30:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-01T11:30:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/718320</loc>
  <lastmod>2026-08-01T11:29:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-01T11:29:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <lastmod>2026-08-01T11:29:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>外挿を伴う（確率的）勾配降下法の収束に関する考察（On the Convergence of (Stochastic) Gradient Descent with Extrapolation for Non-Convex Optimization）</news:title>
   <news:publication_date>2026-08-01T11:29:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718316</loc>
  <lastmod>2026-08-01T11:29:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率関数降下法が示す統一フレームワーク（Probability Functional Descent）</news:title>
   <news:publication_date>2026-08-01T11:29:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718314</loc>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>二重スパースソフトマックス（Doubly Sparse: Sparse Mixture of Sparse Experts for Efficient Softmax Inference）</news:title>
   <news:publication_date>2026-08-01T10:37:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718312</loc>
  <lastmod>2026-08-01T10:37:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-01T10:37:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718310</loc>
  <lastmod>2026-08-01T10:36:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>微細構造から応力-ひずみを予測する機械学習アプローチ（Predicting the mechanical response of oligocrystals with deep learning）</news:title>
   <news:publication_date>2026-08-01T10:36:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718308</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複雑モデル向けのドメイン差異指標の提案（Domain Discrepancy Measure for Complex Models in Unsupervised Domain Adaptation）</news:title>
   <news:publication_date>2026-08-01T10:36:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718306</loc>
  <lastmod>2026-08-01T10:36:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多クラス分類における拒否（リジェクション）の較正について（On the Calibration of Multiclass Classification with Rejection）</news:title>
   <news:publication_date>2026-08-01T10:36:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718304</loc>
  <lastmod>2026-08-01T10:36:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率分布学習におけるBregmanダイバージェンスの評価（Evaluating Bregman Divergences for Probability Learning from Crowd）</news:title>
   <news:publication_date>2026-08-01T10:36:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718302</loc>
  <lastmod>2026-08-01T10:35:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>特徴連結を用いたマルチビュー部分空間クラスタリング（Feature Concatenation Multi-view Subspace Clustering）</news:title>
   <news:publication_date>2026-08-01T10:35:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718300</loc>
  <lastmod>2026-08-01T09:44:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-01T09:44:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718298</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>位相回復モデルにおける支持集合復元の情報理論的限界（Support Recovery in the Phase Retrieval Model: Information-Theoretic Fundamental Limits）</news:title>
   <news:publication_date>2026-08-01T09:43:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718296</loc>
  <lastmod>2026-08-01T09:43:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>エッジでの計算オフロードに対するアクタークリティック強化学習（An Actor-Critic Reinforcement Learning Method for Computation Offloading with Delay Constraints in Mobile Edge Computing）</news:title>
   <news:publication_date>2026-08-01T09:43:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718294</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層的合成CNNによるスパースビューX線位相トモグラフィ再構成（Robust X-ray Sparse-view Phase Tomography via Hierarchical Synthesis Convolutional Neural Networks）</news:title>
   <news:publication_date>2026-08-01T09:42:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718292</loc>
  <lastmod>2026-08-01T09:42:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフを見て量子優位を予測する（Predicting quantum advantage by quantum walk with convolutional neural networks）</news:title>
   <news:publication_date>2026-08-01T09:42:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718290</loc>
  <lastmod>2026-08-01T09:41:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自動診断向けエンドツーエンド知識ルーティング関係対話システム (End-to-End Knowledge-Routed Relational Dialogue System for Automatic Diagnosis)</news:title>
   <news:publication_date>2026-08-01T09:41:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718288</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-01T09:41:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-01T08:50:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718284</loc>
  <lastmod>2026-08-01T08:50:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>信頼できるスマート道路標識の設計（Reliable Smart Road Signs）</news:title>
   <news:publication_date>2026-08-01T08:50:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718282</loc>
  <lastmod>2026-08-01T08:50:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ダイアディック変換による変分推論の強化（Enhanced Variational Inference with Dyadic Transformation）</news:title>
   <news:publication_date>2026-08-01T08:50:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718280</loc>
  <lastmod>2026-08-01T08:49:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>LiDAR 3D物体検出器の効率的学習のための能動学習（Deep Active Learning for Efficient Training of a LiDAR 3D Object Detector）</news:title>
   <news:publication_date>2026-08-01T08:49:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718278</loc>
  <lastmod>2026-08-01T08:49:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>バンディットにおける改善されたパス長後悔境界（Improved Path-length Regret Bounds for Bandits）</news:title>
   <news:publication_date>2026-08-01T08:49:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718276</loc>
  <lastmod>2026-08-01T08:49:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>堅牢な深層マルチモーダルセンサ融合（Deep Multi-Modal Sensor Fusion using Fusion Weight Regularization and Target Learning）</news:title>
   <news:publication_date>2026-08-01T08:49:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718274</loc>
  <lastmod>2026-08-01T08:49:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-01T08:49:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718272</loc>
  <lastmod>2026-08-01T07:57:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-01T07:57:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718270</loc>
  <lastmod>2026-08-01T07:47:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-01T07:47:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718268</loc>
  <lastmod>2026-08-01T07:47:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718266</loc>
  <lastmod>2026-08-01T07:46:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-01T07:46:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718264</loc>
  <lastmod>2026-08-01T07:46:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-01T07:46:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718262</loc>
  <lastmod>2026-08-01T07:45:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>組込み機器における深層推論の精度とエネルギーのトレードオフ（Trading-off Accuracy and Energy of Deep Inference on Embedded Systems: A Co-Design Approach）</news:title>
   <news:publication_date>2026-08-01T07:45:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718260</loc>
  <lastmod>2026-08-01T07:45:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-01T07:45:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718258</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
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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:news>
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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:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>連続潜在空間で離散列を生成する新方向性（Latent Normalizing Flows for Discrete Sequences）</news:title>
   <news:publication_date>2026-08-01T06:51:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
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 <url>
  <loc>https://aibr.jp/archives/718248</loc>
  <lastmod>2026-08-01T06:51:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>数値解から厳密解へ：実数領域における線配置の新たな橋渡し（Exact Line Packings from Numerical Solutions）</news:title>
   <news:publication_date>2026-08-01T06:51:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
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 <url>
  <loc>https://aibr.jp/archives/718246</loc>
  <lastmod>2026-08-01T06:51:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スペクトルグラフフィルタの伝達可能性（On the Transferability of Spectral Graph Filters）</news:title>
   <news:publication_date>2026-08-01T06:51:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718244</loc>
  <lastmod>2026-08-01T05:59:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>部分集合サンプリングの再パラメータ化と連続緩和（Reparameterizable Subset Sampling via Continuous Relaxations）</news:title>
   <news:publication_date>2026-08-01T05:59:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718242</loc>
  <lastmod>2026-08-01T05:58:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>超球面プロトタイプネットワーク（Hyperspherical Prototype Networks）</news:title>
   <news:publication_date>2026-08-01T05:58:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718240</loc>
  <lastmod>2026-08-01T05:58:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時間的ネットワークにおけるスペクトル多重スケールコミュニティ検出（Spectral Multi-scale Community Detection in Temporal Networks with an Application）</news:title>
   <news:publication_date>2026-08-01T05:58:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718238</loc>
  <lastmod>2026-08-01T05:58:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時空間トレードオフ：衛星マルチスペクトル時系列における作物分類の最適化（TIME-SPACE TRADEOFF IN DEEP LEARNING MODELS FOR CROP CLASSIFICATION ON SATELLITE MULTI-SPECTRAL IMAGE TIME SERIES）</news:title>
   <news:publication_date>2026-08-01T05:58:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718236</loc>
  <lastmod>2026-08-01T05:58:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>車両追従における安全・効率・快適な速度制御（Safe, Efficient, and Comfortable Velocity Control based on Reinforcement Learning for Autonomous Driving）</news:title>
   <news:publication_date>2026-08-01T05:58:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718234</loc>
  <lastmod>2026-08-01T05:58:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>再学習なしでモデルを修復する（Repairing without Retraining: Avoiding Disparate Impact with Counterfactual Distributions）</news:title>
   <news:publication_date>2026-08-01T05:58:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718232</loc>
  <lastmod>2026-08-01T05:57:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ノイズ下の試験誤差が招く敵対的事例（Adversarial Examples Are a Natural Consequence of Test Error in Noise）</news:title>
   <news:publication_date>2026-08-01T05:57:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718230</loc>
  <lastmod>2026-08-01T05:06:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>連続行動空間の離散化がオンポリシー最適化を変える（Discretizing Continuous Action Space for On-Policy Optimization）</news:title>
   <news:publication_date>2026-08-01T05:06:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718228</loc>
  <lastmod>2026-08-01T05:05:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>訓練データ量がニューラル回答選択モデルに与える影響（Impact of Training Dataset Size on Neural Answer Selection Models）</news:title>
   <news:publication_date>2026-08-01T05:05:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718226</loc>
  <lastmod>2026-08-01T05:05:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>初の低光度クエーサーの発見とその意義（Discovery of the first low-luminosity quasar at z &amp;gt; 7）</news:title>
   <news:publication_date>2026-08-01T05:05:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718224</loc>
  <lastmod>2026-08-01T05:04:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オンライン広告オークションにおける差別制御への取り組み（Toward Controlling Discrimination in Online Ad Auctions）</news:title>
   <news:publication_date>2026-08-01T05:04:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718222</loc>
  <lastmod>2026-08-01T05:04:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SMUVSにおける受動・星形成銀河の星形成効率の違い（The SHMRs of passive and star-forming galaxies in SMUVS）</news:title>
   <news:publication_date>2026-08-01T05:04:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718220</loc>
  <lastmod>2026-08-01T05:04:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>公正な分類のための改良された敵対的学習（Improved Adversarial Learning for Fair Classification）</news:title>
   <news:publication_date>2026-08-01T05:04:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718218</loc>
  <lastmod>2026-08-01T05:03:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非同期バッチベイズ最適化と局所ペナルティの改良（Asynchronous Batch Bayesian Optimisation with Improved Local Penalisation）</news:title>
   <news:publication_date>2026-08-01T05:03:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718216</loc>
  <lastmod>2026-08-01T04:12:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>画像分類データセットに潜む冗長性の発見（Semantic Redundancies in Image-Classification Datasets: The 10% You Don’t Need）</news:title>
   <news:publication_date>2026-08-01T04:12:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718214</loc>
  <lastmod>2026-08-01T04:12:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>リハビリ運動の品質を自動評価する深層学習フレームワーク（A Deep Learning Framework for Assessing Physical Rehabilitation Exercises）</news:title>
   <news:publication_date>2026-08-01T04:12:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718212</loc>
  <lastmod>2026-08-01T04:12:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Deep学習時における虹彩セグメンテーションの影響（Influence of Segmentation on Deep Iris Recognition Performance）</news:title>
   <news:publication_date>2026-08-01T04:12:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718210</loc>
  <lastmod>2026-08-01T04:11:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>変分マッピング粒子フィルタにおけるカーネル埋め込み観測写像（Kernel embedded nonlinear observational mappings in the variational mapping particle filter）</news:title>
   <news:publication_date>2026-08-01T04:11:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718208</loc>
  <lastmod>2026-08-01T04:11:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>判別器の困難度を段階的に上げてGANを安定化する手法（Progressive Augmentation of GANs）</news:title>
   <news:publication_date>2026-08-01T04:11:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718206</loc>
  <lastmod>2026-08-01T04:11:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>幾何学的行列補完と深層条件付き確率場による構造化予測（Geometric Matrix Completion with Deep Conditional Random Fields）</news:title>
   <news:publication_date>2026-08-01T04:11:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718204</loc>
  <lastmod>2026-08-01T04:10:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スライス生成モデルの実務的理解（Sliced generative models）</news:title>
   <news:publication_date>2026-08-01T04:10:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718202</loc>
  <lastmod>2026-08-01T03:19:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>MgNetが示した CNN と多重格子法の統一枠組み（MgNet: A Unified Framework of Multigrid and Convolutional Neural Network）</news:title>
   <news:publication_date>2026-08-01T03:19:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718200</loc>
  <lastmod>2026-08-01T03:19:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>重みの低ランク化が敵対的頑健性に与える影響（On the Effect of Low-Rank Weights on Adversarial Robustness of Neural Networks）</news:title>
   <news:publication_date>2026-08-01T03:19:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718198</loc>
  <lastmod>2026-08-01T03:18:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>線形時間で最適割当を求める手法とその応用（Computing Optimal Assignments in Linear Time for Approximate Graph Matching）</news:title>
   <news:publication_date>2026-08-01T03:18:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718196</loc>
  <lastmod>2026-08-01T03:18:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>短い発話に対する話者検証の品質測定（Quality Measures for Speaker Verification with Short Utterances）</news:title>
   <news:publication_date>2026-08-01T03:18:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718194</loc>
  <lastmod>2026-08-01T03:18:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>メモリ内演算を実現する超省電力アクセラレータの全体像（PUMA: A Programmable Ultra-efficient Memristor-based Accelerator for Machine Learning Inference）</news:title>
   <news:publication_date>2026-08-01T03:18:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718192</loc>
  <lastmod>2026-08-01T03:17:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的Frank‑Wolfe法による複合凸最適化の実用化（Stochastic Frank‑Wolfe for Composite Convex Minimization）</news:title>
   <news:publication_date>2026-08-01T03:17:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718190</loc>
  <lastmod>2026-08-01T03:17:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>予知保全による太陽光発電所の異常予測（PREDICTIVE MAINTENANCE IN PHOTOVOLTAIC PLANTS WITH A BIG DATA APPROACH）</news:title>
   <news:publication_date>2026-08-01T03:17:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718188</loc>
  <lastmod>2026-08-01T02:25:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ジェット内部の観察：ジェットサブストラクチャーとブースト対象の現象学入門 (Looking inside jets: an introduction to jet substructure and boosted-object phenomenology)</news:title>
   <news:publication_date>2026-08-01T02:25:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718186</loc>
  <lastmod>2026-08-01T02:24:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>流れの多重スケール問題に対する低次元深層学習（Reduced-order Deep Learning for Flow Dynamics）</news:title>
   <news:publication_date>2026-08-01T02:24:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718184</loc>
  <lastmod>2026-08-01T02:23:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>実戦でのアクティブラーニング性能評価の限界（Limitations of Assessing Active Learning Performance at Runtime）</news:title>
   <news:publication_date>2026-08-01T02:23:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718182</loc>
  <lastmod>2026-08-01T02:22:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>超新星残骸 SN 1987A における衝突なし衝撃波での重イオン加熱（Collisionless shock heating of heavy ions in SN 1987A）</news:title>
   <news:publication_date>2026-08-01T02:22:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718180</loc>
  <lastmod>2026-08-01T02:22:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スパース回帰のランク1凸包化（Rank-One Convexification for Sparse Regression）</news:title>
   <news:publication_date>2026-08-01T02:22:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718178</loc>
  <lastmod>2026-08-01T02:22:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>信頼領域に導かれたPPO（Trust Region-Guided Proximal Policy Optimization）</news:title>
   <news:publication_date>2026-08-01T02:22:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718176</loc>
  <lastmod>2026-08-01T02:22:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>リアルタイム手指ジェスチャー検出と分類（Real-time Hand Gesture Detection and Classification Using Convolutional Neural Networks）</news:title>
   <news:publication_date>2026-08-01T02:22:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718174</loc>
  <lastmod>2026-08-01T01:31:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>リカレントニューラルネットワークのサンプル複雑度と組合せグラフ問題への応用（Sample Complexity Bounds for Recurrent Neural Networks with Application to Combinatorial Graph Problems）</news:title>
   <news:publication_date>2026-08-01T01:31:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718172</loc>
  <lastmod>2026-08-01T01:21:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>信頼できない複数ソースからの頑健学習（Robust Learning from Untrusted Sources）</news:title>
   <news:publication_date>2026-08-01T01:21:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718170</loc>
  <lastmod>2026-08-01T01:21:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>プライバシー保護された個人化推薦のための連合協調フィルタリング（FEDERATED COLLABORATIVE FILTERING FOR PRIVACY-PRESERVING PERSONALIZED RECOMMENDATION SYSTEM）</news:title>
   <news:publication_date>2026-08-01T01:21:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718168</loc>
  <lastmod>2026-08-01T01:21:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GANのユニット解析（On the Units of GANs）</news:title>
   <news:publication_date>2026-08-01T01:21:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718166</loc>
  <lastmod>2026-08-01T01:20:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高品質な自己教師あり深層画像ノイズ除去（High-Quality Self-Supervised Deep Image Denoising）</news:title>
   <news:publication_date>2026-08-01T01:20:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718164</loc>
  <lastmod>2026-08-01T01:20:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層ニューラルネットワークを用いた構造材料特性の最適設計（Structural Material Property Tailoring Using Deep Neural Networks）</news:title>
   <news:publication_date>2026-08-01T01:20:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718162</loc>
  <lastmod>2026-08-01T01:20:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ホット媒質における相転移と中間子・核子構造から学べること（What could be learned about phase transitions, meson and nucleon structure in hot medium from a chiral quark-meson theory?）</news:title>
   <news:publication_date>2026-08-01T01:20:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718160</loc>
  <lastmod>2026-08-01T00:29:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文脈依存の選択関数を学ぶ（Learning Context-Dependent Choice Functions）</news:title>
   <news:publication_date>2026-08-01T00:29:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718158</loc>
  <lastmod>2026-08-01T00:28:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>画像要約における暗黙の多様性（Implicit Diversity in Image Summarization）</news:title>
   <news:publication_date>2026-08-01T00:28:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718156</loc>
  <lastmod>2026-08-01T00:21:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>差分プライベートなMarkov Chain Monte Carloの一般化（Differentially Private Markov Chain Monte Carlo）</news:title>
   <news:publication_date>2026-08-01T00:21:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718154</loc>
  <lastmod>2026-08-01T00:20:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>結合的トラクト分割と方向マッピングによる束特異的トラクトグラフィー（Combined tract segmentation and orientation mapping for bundle-specific tractography）</news:title>
   <news:publication_date>2026-08-01T00:20:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718152</loc>
  <lastmod>2026-08-01T00:19:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューラルネットワークによる関数の近似（Approximation of Functions by Neural Networks）</news:title>
   <news:publication_date>2026-08-01T00:19:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718150</loc>
  <lastmod>2026-08-01T00:18:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>リソース効率的な判定ベース不可視攻撃（RED-Attack: Resource Efficient Decision-based Imperceptible Attack for Machine Learning）</news:title>
   <news:publication_date>2026-08-01T00:18:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718148</loc>
  <lastmod>2026-08-01T00:18:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オンライン学習によるランキング最適化（Optimizing Ranking Models in an Online Setting）</news:title>
   <news:publication_date>2026-08-01T00:18:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718146</loc>
  <lastmod>2026-07-31T23:26:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチモデル・マルチタイプフィッティングの学習（Learning for Multi-Model and Multi-Type Fitting）</news:title>
   <news:publication_date>2026-07-31T23:26:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718144</loc>
  <lastmod>2026-07-31T23:25:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オンライン配車サービスの短期需要予測とリカレントニューラルネットワーク（Short-Term Demand Forecasting for Online Car-Hailing Services Using Recurrent Neural Networks）</news:title>
   <news:publication_date>2026-07-31T23:25:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718142</loc>
  <lastmod>2026-07-31T23:25:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>対角-巡回（Diagonal-Circulant）ニューラルネットワークの理解と訓練（Understanding and Training Deep Diagonal Circulant Neural Networks）</news:title>
   <news:publication_date>2026-07-31T23:25:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718140</loc>
  <lastmod>2026-07-31T23:24:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-07-31T23:24:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718138</loc>
  <lastmod>2026-07-31T23:24:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>メッセージのスタンス分類に対する半教師ありグラフ手法（Semi-supervised Graph-based Stance Classification）</news:title>
   <news:publication_date>2026-07-31T23:24:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718136</loc>
  <lastmod>2026-07-31T23:24:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多段階生成モデルによるマルチエージェントモデルベース強化学習（Multi-Agent Reinforcement Learning with Multi-Step Generative Models）</news:title>
   <news:publication_date>2026-07-31T23:24:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718134</loc>
  <lastmod>2026-07-31T23:24:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>明示的な位相事前知識を用いた深層学習ベースの画像セグメンテーション（Explicit topological priors for deep-learning based image segmentation using persistent homology）</news:title>
   <news:publication_date>2026-07-31T23:24:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718132</loc>
  <lastmod>2026-07-31T22:32:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>異種情報ネットワークの表現学習をイベント埋め込みで改善する（Representation Learning for Heterogeneous Information Networks via Embedding Events）</news:title>
   <news:publication_date>2026-07-31T22:32:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718130</loc>
  <lastmod>2026-07-31T22:32:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>全身CTを用いた骨年齢推定の深層階層特徴学習（Automatic Whole-body Bone Age Assessment Using Deep Hierarchical Features）</news:title>
   <news:publication_date>2026-07-31T22:32:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718128</loc>
  <lastmod>2026-07-31T22:32:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>2D切片からの3次元多孔質メディア再構築（Reconstruction of 3D Porous Media From 2D Slices）</news:title>
   <news:publication_date>2026-07-31T22:32:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718126</loc>
  <lastmod>2026-07-31T22:31:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>表現転移による差分プライバシー対応ドラッグ感受性予測（Representation Transfer for Differentially Private Drug Sensitivity Prediction）</news:title>
   <news:publication_date>2026-07-31T22:31:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718124</loc>
  <lastmod>2026-07-31T22:31:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチカーネル活性化関数の定式化と事例研究（Multikernel activation functions: formulation and a case study）</news:title>
   <news:publication_date>2026-07-31T22:31:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718122</loc>
  <lastmod>2026-07-31T22:30:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>部分的に可換なネットワークと近似ベイズ計算における要約統計量学習（Partially Exchangeable Networks and Architectures for Learning Summary Statistics in Approximate Bayesian Computation）</news:title>
   <news:publication_date>2026-07-31T22:30:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718120</loc>
  <lastmod>2026-07-31T22:30:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>MRI画像からのアルツハイマー病検出 ― 転移学習とBellCNNの比較（Detection of Alzheimers Disease from MRI using Convolutional Neural Networks, Exploring Transfer Learning And BellCNN）</news:title>
   <news:publication_date>2026-07-31T22:30:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718118</loc>
  <lastmod>2026-07-31T21:39:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>近似スペクトラルクラスタリングとサンプリングによる高速化（Approximating Spectral Clustering via Sampling: a Review）</news:title>
   <news:publication_date>2026-07-31T21:39:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718116</loc>
  <lastmod>2026-07-31T21:39:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>プッシュプル層によるCNNの頑健性向上（A Push-Pull Layer Improves Robustness of Convolutional Neural Networks）</news:title>
   <news:publication_date>2026-07-31T21:39:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718114</loc>
  <lastmod>2026-07-31T21:38:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>catch22による時系列特徴量の簡潔化（catch22: CAnonical Time-series CHaracteristics）</news:title>
   <news:publication_date>2026-07-31T21:38:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718112</loc>
  <lastmod>2026-07-31T21:37:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>バランスド・シミラリティによる最適離散オンラインハッシング（Towards Optimal Discrete Online Hashing with Balanced Similarity）</news:title>
   <news:publication_date>2026-07-31T21:37:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718110</loc>
  <lastmod>2026-07-31T21:37:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複雑な文の自動生成（Divide and Generate: Neural Generation of Complex Sentences）</news:title>
   <news:publication_date>2026-07-31T21:37:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718108</loc>
  <lastmod>2026-07-31T21:37:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Deep500が切り開く大規模ディープラーニングの公平で再現可能な評価基盤（A Modular Benchmarking Infrastructure for High-Performance and Reproducible Deep Learning）</news:title>
   <news:publication_date>2026-07-31T21:37:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718106</loc>
  <lastmod>2026-07-31T21:36:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-07-31T21:36:47Z</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>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 <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>2026-07-31T20:44:52Z</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:publication_date>2026-07-31T20:44:44Z</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>2026-07-31T20:44:35Z</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>2026-07-31T20:44:26Z</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>2026-07-31T19:53:02Z</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>2026-07-31T19:52:25Z</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>2026-07-31T19:52:09Z</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>2026-07-31T19:51:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GANを用いたVSRのゼロショット学習（Harnessing GANs for Zero-Shot Learning of New Classes in Visual Speech Recognition）</news:title>
   <news:publication_date>2026-07-31T19:51:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
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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>2026-07-31T19:51:27Z</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>2026-07-31T19:51:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/718076</loc>
  <lastmod>2026-07-31T18:59:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-07-31T18:59:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-07-31T18:59:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-07-31T18:58:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-07-31T18:58:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-07-31T18:58:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718062</loc>
  <lastmod>2026-07-31T18:06:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スパース最小二乗ローレンクカーネル機械（Sparse Least Squares Low Rank Kernel Machines）</news:title>
   <news:publication_date>2026-07-31T18:06:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718060</loc>
  <lastmod>2026-07-31T18:06:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718058</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/718056</loc>
  <lastmod>2026-07-31T18:05:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-07-31T18:05:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718054</loc>
  <lastmod>2026-07-31T18:04:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-07-31T18:04:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-07-31T18:04:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718050</loc>
  <lastmod>2026-07-31T18:04:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-07-31T18:04:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718048</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Schatten–von Neumann 演算子の学習可能性（Learning Schatten–von Neumann Operators）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-07-31T17:12:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Cloud-NetによるLandsat 8画像の雲検出（CLOUD-NET: AN END-TO-END CLOUD DETECTION ALGORITHM FOR LANDSAT 8 IMAGERY）</news:title>
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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>CaRENetsによる暗号化医用画像の効率的同報推論（CaRENets: Compact and Resource-Efficient CNN for Homomorphic Inference on Encrypted Medical Images）</news:title>
   <news:publication_date>2026-07-31T17:11:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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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>2026-07-31T17:11:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>多値の保護属性を考慮した公正な深層クラスタリング（Towards Fair Deep Clustering With Multi-State Protected Variables）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/718034</loc>
  <lastmod>2026-07-31T16:19:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>異種ネットワークにおけるモチーフ解析の一般化（Heterogeneous Network Motifs）</news:title>
   <news:publication_date>2026-07-31T16:19:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718032</loc>
  <lastmod>2026-07-31T16:18:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Lyapunovに基づく連続制御の安全な方策最適化（Lyapunov-based Safe Policy Optimization for Continuous Control）</news:title>
   <news:publication_date>2026-07-31T16:18:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718030</loc>
  <lastmod>2026-07-31T16:17:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>デモによるクロスドメイン画像操作（Cross-Domain Image Manipulation by Demonstration）</news:title>
   <news:publication_date>2026-07-31T16:17:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718028</loc>
  <lastmod>2026-07-31T16:17:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GPU上での推論を効率化するOoO VLIW JITコンパイラ（The OoO VLIW JIT Compiler for GPU Inference）</news:title>
   <news:publication_date>2026-07-31T16:17:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718026</loc>
  <lastmod>2026-07-31T16:17:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習ライフサイクルにおける被害源のフレームワーク（A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle）</news:title>
   <news:publication_date>2026-07-31T16:17:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718024</loc>
  <lastmod>2026-07-31T16:16:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>共感的で社会的に優雅な運転に向けた相互作用モデルと運動計画（How Shall I Drive? Interaction Modeling and Motion Planning towards Empathetic and Socially-Graceful Driving）</news:title>
   <news:publication_date>2026-07-31T16:16:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718022</loc>
  <lastmod>2026-07-31T16:16:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Fogネットワークの強化学習による負荷分散管理（Managing Fog Networks using Reinforcement Learning Based Load Balancing Algorithm）</news:title>
   <news:publication_date>2026-07-31T16:16:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718020</loc>
  <lastmod>2026-07-31T15:23:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>過去を忘れて局所を繰り返す準ニュートン法（Quasi-Newton Methods for Machine Learning: Forget the Past, Just Sample）</news:title>
   <news:publication_date>2026-07-31T15:23:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718018</loc>
  <lastmod>2026-07-31T15:23:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフフィルタリングによるラベル効率な半教師あり学習（Label Efficient Semi-Supervised Learning via Graph Filtering）</news:title>
   <news:publication_date>2026-07-31T15:23:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718016</loc>
  <lastmod>2026-07-31T15:23:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>表面波乱流における浮遊体の統計解析（Statistics of single and multiple floaters in experiments of surface wave turbulence）</news:title>
   <news:publication_date>2026-07-31T15:23:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718014</loc>
  <lastmod>2026-07-31T15:22:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ジェンダー差別的ツイートに込められた感情を読む（How is Your Mood When Writing Sexist tweets? Detecting the Emotion Type and Intensity of Emotion Using Natural Language Processing Techniques）</news:title>
   <news:publication_date>2026-07-31T15:22:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718012</loc>
  <lastmod>2026-07-31T15:22:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多様性訓練によるアンサンブルの対敵的堅牢性向上（Improving Adversarial Robustness of Ensembles with Diversity Training）</news:title>
   <news:publication_date>2026-07-31T15:22:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718010</loc>
  <lastmod>2026-07-31T15:22:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>心拍異常検出における敵対的オーバーサンプリング（Heartbeat Anomaly Detection using Adversarial Oversampling）</news:title>
   <news:publication_date>2026-07-31T15:22:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718008</loc>
  <lastmod>2026-07-31T15:22:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ブラックボックス選択後の推論（Inference after Black Box Selection）</news:title>
   <news:publication_date>2026-07-31T15:22:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718006</loc>
  <lastmod>2026-07-31T14:31:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>リー群オートエンコーダの概観（Lie Group Auto-Encoder）</news:title>
   <news:publication_date>2026-07-31T14:31:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718004</loc>
  <lastmod>2026-07-31T14:30:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>RNNに対する敵対的事例への防御法（Defense Methods Against Adversarial Examples for Recurrent Neural Networks）</news:title>
   <news:publication_date>2026-07-31T14:30:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718002</loc>
  <lastmod>2026-07-31T14:29:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>事前学習がモデルの頑健性と不確かさを高める（Using Pre-Training Can Improve Model Robustness and Uncertainty）</news:title>
   <news:publication_date>2026-07-31T14:29:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718000</loc>
  <lastmod>2026-07-31T14:29:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>協同畳み込みネットワークが切り開く細粒度認識（CoCoNet: A Collaborative Convolutional Network）</news:title>
   <news:publication_date>2026-07-31T14:29:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717998</loc>
  <lastmod>2026-07-31T14:28:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大画像生成の新潮流：TGANが切り開くテンソル空間での生成（TGAN: Deep Tensor Generative Adversarial Nets for Large Image Generation）</news:title>
   <news:publication_date>2026-07-31T14:28:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717996</loc>
  <lastmod>2026-07-31T14:28:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スパイキングニューラルネットワークにおける代理勾配学習 (Surrogate Gradient Learning in Spiking Neural Networks)</news:title>
   <news:publication_date>2026-07-31T14:28:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717994</loc>
  <lastmod>2026-07-31T14:28:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>潜在変数の任意領域での厳密推論（Exact Inference with Latent Variables in an Arbitrary Domain）</news:title>
   <news:publication_date>2026-07-31T14:28:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717992</loc>
  <lastmod>2026-07-31T13:36:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模データでの厳密なベイズ推論を可能にするスケーラブルMetropolis–Hastings（Scalable Metropolis–Hastings for Exact Bayesian Inference with Large Datasets）</news:title>
   <news:publication_date>2026-07-31T13:36:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717990</loc>
  <lastmod>2026-07-31T13:36:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非同期加速近接確率勾配法による分散有限和最適化（ASYNCHRONOUS ACCELERATED PROXIMAL STOCHASTIC GRADIENT FOR STRONGLY CONVEX DISTRIBUTED FINITE SUMS）</news:title>
   <news:publication_date>2026-07-31T13:36:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717988</loc>
  <lastmod>2026-07-31T13:35:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ユーティリティを保持する安全なプライベートデータ公開（Utility Preserving Secure Private Data Release）</news:title>
   <news:publication_date>2026-07-31T13:35:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717986</loc>
  <lastmod>2026-07-31T13:35:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模ビジュアルカタログの対話的閲覧を実現する手法（Multi-modal dialog for browsing large visual catalogs using exploration-exploitation paradigm in a joint embedding space）</news:title>
   <news:publication_date>2026-07-31T13:35:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717984</loc>
  <lastmod>2026-07-31T13:34:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニュートラルネットワークにおける活性化適応（Activation Adaptation in Neural Networks）</news:title>
   <news:publication_date>2026-07-31T13:34:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717982</loc>
  <lastmod>2026-07-31T13:34:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>合成コーパスに基づくトピックモデル評価の新枠組み（A new evaluation framework for topic modeling algorithms based on synthetic corpora）</news:title>
   <news:publication_date>2026-07-31T13:34:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717980</loc>
  <lastmod>2026-07-31T12:43:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>誤差フィードバックがSignSGDの問題を解決する（Error Feedback Fixes SignSGD and other Gradient Compression Schemes）</news:title>
   <news:publication_date>2026-07-31T12:43:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717978</loc>
  <lastmod>2026-07-31T12:43:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層ニューラルネットワークの変数重要度で「見える化」する方法（Interpreting Deep Neural Networks Through Variable Importance）</news:title>
   <news:publication_date>2026-07-31T12:43:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717976</loc>
  <lastmod>2026-07-31T12:42:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ネットワーク構造データにおける総変動最小化による半教師あり学習（Semi-supervised Learning in Network-Structured Data via Total Variation Minimization）</news:title>
   <news:publication_date>2026-07-31T12:42:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717974</loc>
  <lastmod>2026-07-31T12:41:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深さは余計な局所最適を作らない（Depth creates no more spurious local minima）</news:title>
   <news:publication_date>2026-07-31T12:41:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717972</loc>
  <lastmod>2026-07-31T12:41:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>テキスト分類のための圧縮非常に深い畳み込みニューラルネットワーク（Squeezed Very Deep Convolutional Neural Networks for Text Classification）</news:title>
   <news:publication_date>2026-07-31T12:41:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717970</loc>
  <lastmod>2026-07-31T12:41:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>群ロボットによるボディガード行動の自律学習（The Emergence of Complex Bodyguard Behavior Through Multi-Agent Reinforcement Learning）</news:title>
   <news:publication_date>2026-07-31T12:41:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717968</loc>
  <lastmod>2026-07-31T12:41:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ロボットボディガードの報酬設計が示すもの（Designing a Multi-Objective Reward Function for Creating Teams of Robotic Bodyguards Using Deep Reinforcement Learning）</news:title>
   <news:publication_date>2026-07-31T12:41:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717966</loc>
  <lastmod>2026-07-31T11:49:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>類推現象の説明：単語埋め込みの理解に向けて (Analogies Explained: Towards Understanding Word Embeddings)</news:title>
   <news:publication_date>2026-07-31T11:49:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717964</loc>
  <lastmod>2026-07-31T11:48:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>屋外ウェブカメラを活用した局所特徴記述子の学習（Leveraging Outdoor Webcams for Local Descriptor Learning）</news:title>
   <news:publication_date>2026-07-31T11:48:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717962</loc>
  <lastmod>2026-07-31T11:48:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>異なるビデオ異常検知ドメインから転移された特徴埋め込みの汎化（Generalization of feature embeddings transferred from different video anomaly detection domains）</news:title>
   <news:publication_date>2026-07-31T11:48:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717960</loc>
  <lastmod>2026-07-31T11:47:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>欠損画像データ補完のためのCollaGAN（CollaGAN: Collaborative GAN for Missing Image Data Imputation）</news:title>
   <news:publication_date>2026-07-31T11:47:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717958</loc>
  <lastmod>2026-07-31T11:47:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Additive Margin SincNetによる話者認識の改善（Additive Margin SincNet for Speaker Recognition）</news:title>
   <news:publication_date>2026-07-31T11:47:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717956</loc>
  <lastmod>2026-07-31T11:47:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>RefCurv：小児の参照曲線作成を現場に近づけるソフトウェア（RefCurv: A Software for the Construction of Pediatric Reference Curves）</news:title>
   <news:publication_date>2026-07-31T11:47:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717954</loc>
  <lastmod>2026-07-31T11:47:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>属性誘導スケッチ生成（Attribute-Guided Sketch Generation）</news:title>
   <news:publication_date>2026-07-31T11:47:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717952</loc>
  <lastmod>2026-07-31T10:54:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>フェアウォッシングの危険性（Fairwashing: the risk of rationalization）</news:title>
   <news:publication_date>2026-07-31T10:54:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717950</loc>
  <lastmod>2026-07-31T10:54:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Decode and Transfer: 条件付き敵対的生成ネットワークによる新しいステガノアリシス手法（Decode and Transfer: A New Steganalysis Technique via Conditional Generative Adversarial Networks）</news:title>
   <news:publication_date>2026-07-31T10:54:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717948</loc>
  <lastmod>2026-07-31T10:54:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>無秩序なしに起こる極性ガスの準局在化（Disorderless quasi-localization of polar gases in one-dimensional lattices）</news:title>
   <news:publication_date>2026-07-31T10:54:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717946</loc>
  <lastmod>2026-07-31T10:53:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>惑星系間の類似性を定量化する新たな指標（A new metric to quantify the similarity between planetary systems - application to dimensionality reduction using T-SNE）</news:title>
   <news:publication_date>2026-07-31T10:53:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717944</loc>
  <lastmod>2026-07-31T10:53:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時間離散化に強くする深層Q学習の設計（Making Deep Q-learning Methods Robust to Time Discretization）</news:title>
   <news:publication_date>2026-07-31T10:53:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717942</loc>
  <lastmod>2026-07-31T10:52:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非報酬環境におけるカリキュラム学習と模倣による物体制御（CLIC: Curriculum Learning and Imitation for object Control in non-rewarding environments）</news:title>
   <news:publication_date>2026-07-31T10:52:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717940</loc>
  <lastmod>2026-07-31T10:52:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>潜在条件付きガウス変数を持つイジングモデル（Ising Models with Latent Conditional Gaussian Variables）</news:title>
   <news:publication_date>2026-07-31T10:52:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717938</loc>
  <lastmod>2026-07-31T10:01:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ベイズ的差分プライバシーによる機械学習の実務的な改善（Bayesian Differential Privacy for Machine Learning）</news:title>
   <news:publication_date>2026-07-31T10:01:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717936</loc>
  <lastmod>2026-07-31T10:01:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>空間的交絡を考慮した異質な因果効果の推定（Inferring Heterogeneous Causal Effects in Presence of Spatial Confounding）</news:title>
   <news:publication_date>2026-07-31T10:01:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717934</loc>
  <lastmod>2026-07-31T10:00:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>パーソナライズ対話生成と多様な性格表現（Personalized Dialogue Generation with Diversified Traits）</news:title>
   <news:publication_date>2026-07-31T10:00:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717932</loc>
  <lastmod>2026-07-31T10:00:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>条件付き独立性の検定と条件予測影響（Testing Conditional Independence in Supervised Learning Algorithms）</news:title>
   <news:publication_date>2026-07-31T10:00:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717930</loc>
  <lastmod>2026-07-31T09:59:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>シフトコンパクト性を巡る新たな視点（Beyond Erdős-Kunen-Mauldin: Singular sets with shift-compactness properties）</news:title>
   <news:publication_date>2026-07-31T09:59:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717928</loc>
  <lastmod>2026-07-31T09:59:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>科学論文の引用影響を多次元で評価する枠組み（A multi-dimensional framework for characterizing the citation impact of scientific publications）</news:title>
   <news:publication_date>2026-07-31T09:59:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717926</loc>
  <lastmod>2026-07-31T09:08:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ローカル低表面輝度銀河のハロー質量推定（Halo mass estimates from the Globular Cluster populations of 175 Low Surface Brightness Galaxies in the Fornax Cluster）</news:title>
   <news:publication_date>2026-07-31T09:08:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717924</loc>
  <lastmod>2026-07-31T09:08:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Deep Learning Volatility（A deep neural network perspective on pricing and calibration in (rough) volatility models）</news:title>
   <news:publication_date>2026-07-31T09:08:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717922</loc>
  <lastmod>2026-07-31T09:08:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>サポート特徴機械（Support Feature Machines: Support Vectors are not enough）</news:title>
   <news:publication_date>2026-07-31T09:08:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717920</loc>
  <lastmod>2026-07-31T09:07:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オンライン動画配信の新作コンテンツ人気予測に対するハイブリッド機械学習アプローチ (HYBRID MACHINE LEARNING APPROACH TO POPULARITY PREDICTION OF NEWLY RELEASED CONTENTS FOR ONLINE VIDEO STREAMING SERVICE)</news:title>
   <news:publication_date>2026-07-31T09:07:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717918</loc>
  <lastmod>2026-07-31T09:07:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>NW環太平洋帯における地震発生の統計的性質と動態（Statistical Nature and Dynamics of Seismogenesis in the NW Circum-Pacific Belt）</news:title>
   <news:publication_date>2026-07-31T09:07:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717916</loc>
  <lastmod>2026-07-31T09:07:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>畳み込みニューラルネットワークにおけるオフチップメモリアクセス削減の単純手法（A Simple Method to Reduce Off-chip Memory Accesses on Convolutional Neural Networks）</news:title>
   <news:publication_date>2026-07-31T09:07:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717914</loc>
  <lastmod>2026-07-31T09:07:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分類器ではなく「排除器（eliminator）」で不確実性を扱う視点（Neural eliminators and classifiers）</news:title>
   <news:publication_date>2026-07-31T09:07:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717912</loc>
  <lastmod>2026-07-31T08:16:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>サブミリ波銀河の同定を機械学習で進める方法（A machine learning approach for identifying the counterparts of submillimetre galaxies and applications to the GOODS-North field）</news:title>
   <news:publication_date>2026-07-31T08:16:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717910</loc>
  <lastmod>2026-07-31T08:15:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>TuckERによる知識グラフ補完のためのテンソル因子分解（TuckER: Tensor Factorization for Knowledge Graph Completion）</news:title>
   <news:publication_date>2026-07-31T08:15:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717908</loc>
  <lastmod>2026-07-31T08:15:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Learning Mean-Field Games（Learning Mean-Field Games）</news:title>
   <news:publication_date>2026-07-31T08:15:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717906</loc>
  <lastmod>2026-07-31T08:15:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ドキュメントのノイズ除去をGANで学ぶ（Learning to Clean: A GAN Perspective）</news:title>
   <news:publication_date>2026-07-31T08:15:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717904</loc>
  <lastmod>2026-07-31T08:15:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <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>
   <news:publication_date>2026-07-31T08:15:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717902</loc>
  <lastmod>2026-07-31T08:14:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <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>
   <news:publication_date>2026-07-31T08:14:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717900</loc>
  <lastmod>2026-07-31T08:14:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <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>
   <news:publication_date>2026-07-31T08:14:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717898</loc>
  <lastmod>2026-07-31T07:22:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <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>
   <news:publication_date>2026-07-31T07:22:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717896</loc>
  <lastmod>2026-07-31T07:22:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <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>
   <news:publication_date>2026-07-31T07:22:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717894</loc>
  <lastmod>2026-07-31T07:21:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GANのアウト・オブ・サンプル検証（Out-of-Sample Testing for GANs）</news:title>
   <news:publication_date>2026-07-31T07:21:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717892</loc>
  <lastmod>2026-07-31T07:21:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <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>
   <news:publication_date>2026-07-31T07:21:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717890</loc>
  <lastmod>2026-07-31T07:21:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <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>
   <news:publication_date>2026-07-31T07:21:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717888</loc>
  <lastmod>2026-07-31T07:21:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-07-31T07:21:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717886</loc>
  <lastmod>2026-07-31T07:20:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <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>
   <news:publication_date>2026-07-31T07:20:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717884</loc>
  <lastmod>2026-07-31T06:29:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <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>
   <news:publication_date>2026-07-31T06:29:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717882</loc>
  <lastmod>2026-07-31T06:29:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <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>
   <news:publication_date>2026-07-31T06:29:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717880</loc>
  <lastmod>2026-07-31T06:28:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層的なクラスタリングで表現学習を一段進める（Hierarchically Clustered Representation Learning）</news:title>
   <news:publication_date>2026-07-31T06:28:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717878</loc>
  <lastmod>2026-07-31T06:27:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <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>
   <news:publication_date>2026-07-31T06:27:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717876</loc>
  <lastmod>2026-07-31T06:27:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <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>
   <news:publication_date>2026-07-31T06:27:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717874</loc>
  <lastmod>2026-07-31T06:27:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ブラックボックス部分列集合最適化の解法（Black Box Submodular Maximization: Discrete and Continuous Settings）</news:title>
   <news:publication_date>2026-07-31T06:27:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717872</loc>
  <lastmod>2026-07-31T06:27:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <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>
   <news:publication_date>2026-07-31T06:27:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717870</loc>
  <lastmod>2026-07-31T05:35:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>クラウドソー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>
   <news:publication_date>2026-07-31T05:35:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717868</loc>
  <lastmod>2026-07-31T05:27:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <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>
   <news:publication_date>2026-07-31T05:27:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717866</loc>
  <lastmod>2026-07-31T05:26:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <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>
   <news:publication_date>2026-07-31T05:26:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717864</loc>
  <lastmod>2026-07-31T05:25:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <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>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717862</loc>
  <lastmod>2026-07-31T05:25:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <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>
   <news:publication_date>2026-07-31T05:25:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717860</loc>
  <lastmod>2026-07-31T05:25:15Z</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>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717858</loc>
  <lastmod>2026-07-31T05:24:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <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>
  </news:news>
 </url>
 <url>
  <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>
   </news:publication>
   <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>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717854</loc>
  <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>
  </news:news>
 </url>
 <url>
  <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>
  </news:news>
 </url>
 <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>
  </news:news>
 </url>
 <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>
   </news:publication>
   <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>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717846</loc>
  <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>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717844</loc>
  <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>
  </news:news>
 </url>
 <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>
 </url>
 <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>
  <lastmod>2026-07-31T02:38:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>電子カルテの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>
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   <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>
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   <news:publication_date>2026-07-31T02:36:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <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>
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   <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>
   <news:title>プライバシー保持型ランダム化ガシップアルゴリズム（A Privacy Preserving Randomized Gossip Algorithm via Controlled Noise Insertion）</news:title>
   <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>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/717778</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>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>
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  <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>
  <loc>https://aibr.jp/archives/717770</loc>
  <lastmod>2026-07-30T22:46:32Z</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>
   <news:publication_date>2026-07-30T22:46:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </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>
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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/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>
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  <loc>https://aibr.jp/archives/717672</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>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>
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 <url>
  <loc>https://aibr.jp/archives/717670</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>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>
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 <url>
  <loc>https://aibr.jp/archives/717668</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>最適な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>
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 <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>
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   <news:publication_date>2026-07-30T15:24:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </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>
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   <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>
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 </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>
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   <news:publication_date>2026-07-30T15:22:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
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