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   <news:title>BigEarthNetによる大規模リモートセンシング画像理解の基盤革新（BIGEARTHNET: A LARGE-SCALE BENCHMARK ARCHIVE FOR REMOTE SENSING IMAGE UNDERSTANDING）</news:title>
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   <news:title>セルフィッシュなユーザとモバイル群学習における情報鮮度の保証（Can We Achieve Fresh Information with Selfish Users in Mobile Crowd-Learning?）</news:title>
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   <news:title>ほんの少しで十分：分散学習に対する防御回避手法の実態（A Little Is Enough: Circumventing Defenses For Distributed Learning）</news:title>
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   <news:title>Deep Generalized Convolutional Sum-Product Networks（Deep Generalized Convolutional Sum-Product Networks）</news:title>
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   <news:title>不確実な領域における神経調節された目標志向知覚（Neuromodulated Goal-Driven Perception in Uncertain Domains）</news:title>
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   <news:title>学習エージェント間の通信トポロジーを活用した深層強化学習（Leveraging Communication Topologies Between Learning Agents in Deep Reinforcement Learning）</news:title>
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    <news:name>AI Benchmark Research</news:name>
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   <news:title>Data Management in Industry 4.0の概観と課題（Data Management in Industry 4.0: State of the Art and Open Challenges）</news:title>
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   <news:title>過去の修正から学ぶ自動バグ修正の実用性（Getafix: Learning to Fix Bugs Automatically）</news:title>
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   <news:title>機械（深層）学習が人間の学習を理解する手助けをする方法（How Machine (Deep) Learning Helps Us Understand Human Learning: the Value of Big Ideas）</news:title>
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    <news:language>ja</news:language>
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   <news:title>非凸スパース正則化を持つラッソに対するスクリーニングルール（Screening Rules for Lasso with Non-Convex Sparse Regularizers）</news:title>
   <news:publication_date>2026-08-07T18:19:25Z</news:publication_date>
   <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>凸損失関数を外れ値に強くするe指数変換（Making Convex Loss Functions Robust to Outliers using e-Exponentiated Transformation）</news:title>
   <news:publication_date>2026-08-07T18:18:47Z</news:publication_date>
   <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>局所差分プライバシーを用いた分散最適化（Local Differential Privacy in Decentralized Optimization）</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>可微分リザバーコンピューティングの理論的進展（Differentiable Reservoir Computing）</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>交互拡散過程によるグラフ上の半教師あり学習 (Semi-supervised Learning on Graph with an Alternating Diffusion Process)</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>画像超解像の深層学習サーベイ（Deep Learning for Image Super-resolution: A Survey）</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>再決定化情報集合MCTSによるHanabi探索改善（Re-determinizing Information Set Monte Carlo Tree Search in Hanabi）</news:title>
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   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/720598</loc>
  <lastmod>2026-08-07T17:25:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>DC-AL GANによる偽増悪と真の腫瘍増悪の識別（DC-AL GAN: Pseudoprogression and True Tumor Progression of Glioblastoma Multiform Image Classification Based on DCGAN and AlexNet）</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>Res-SE-NetによるResNet改良（RES-SE-NET: BOOSTING PERFORMANCE OF RESNETS BY ENHANCING BRIDGE-CONNECTIONS）</news:title>
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   <news:genres>Blog</news:genres>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>スマートシティにおける短距離通勤者のモード選択（Short-distance commuters in the smart city）</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>メラノーマ検出の自動化に向けたデータ洗浄と増強（Towards Automated Melanoma Detection with Deep Learning: Data Purification and Augmentation）</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>弱監督物体検出のための最小エントロピー潜在モデル（Min-Entropy Latent Model for Weakly Supervised Object Detection）</news:title>
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   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/720588</loc>
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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>アットメートル解像度と90 dB動的レンジ、THz帯域を同時に実現する光学ベクトル解析（Optical vector analysis with attometer resolution, 90‐dB dynamic range and THz bandwidth）</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>PatchNetによる深層パッチ分類の実務的意義（PatchNet: A Tool for Deep Patch Classification）</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>RF信号向けディープ分類器の敵対的事例軽減（Mitigation of Adversarial Examples in RF Deep Classiﬁers Utilizing AutoEncoder Pre-training）</news:title>
   <news:publication_date>2026-08-07T16:31:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-08-07T16:30:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>ドメイン知識と深層学習を組み合わせた短文・非公式メッセージの感情分析（Combination of Domain Knowledge and Deep Learning for Sentiment Analysis of Short and Informal Messages on Social Media）</news:title>
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   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/720580</loc>
  <lastmod>2026-08-07T15:39:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>イエメンのコレラ流行を機械学習で予測する（Forecasting the 2017-2018 Yemen Cholera Outbreak with Machine Learning）</news:title>
   <news:publication_date>2026-08-07T15:39:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/720578</loc>
  <lastmod>2026-08-07T15:39:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>無線(RF)深層学習における敵対的事例：攻撃検知と物理的ロバストネス（Adversarial Examples in RF Deep Learning: Detection of the Attack and its Physical Robustness）</news:title>
   <news:publication_date>2026-08-07T15:39:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-08-07T15:38:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>トピックと数式を同時に扱う新手法の実務的意義（TopicEq: A Joint Topic and Mathematical Equation Model for Scientific Texts）</news:title>
   <news:publication_date>2026-08-07T15:38:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/720574</loc>
  <lastmod>2026-08-07T15:38:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>幸福表現の特徴量に基づく解析手法の提案（CruzAffect: A feature-rich approach to characterize happiness）</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>空間パズル解法に効くヒューリスティック統合法（Heuristics, Answer Set Programming and Markov Decision Process for Solving a Set of Spatial Puzzles）</news:title>
   <news:publication_date>2026-08-07T15:37:54Z</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>ニューラルネットワークにおける変数の有意性検定（Significance Tests for Neural Networks）</news:title>
   <news:publication_date>2026-08-07T15:37:39Z</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>
   </news:publication>
   <news:title>完全微分可能なビームサーチデコーダ（A FULLY DIFFERENTIABLE BEAM SEARCH DECODER）</news:title>
   <news:publication_date>2026-08-07T15:37:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/720566</loc>
  <lastmod>2026-08-07T14:46:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>二層ニューラルネットワークの平均場理論とカーネル極限（Mean-field theory of two-layers neural networks: dimension-free bounds and kernel limit）</news:title>
   <news:publication_date>2026-08-07T14:46:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720564</loc>
  <lastmod>2026-08-07T14:46:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習システムをファイバーバンドルとしてモデル化することで実現する継続学習（Realizing Continual Learning through Modeling a Learning System as a Fiber Bundle）</news:title>
   <news:publication_date>2026-08-07T14:46:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720562</loc>
  <lastmod>2026-08-07T14:45:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>リスク感度のあるカーネル学習のための非パラメトリック合成確率最適化（Nonparametric Compositional Stochastic Optimization for Risk-Sensitive Kernel Learning）</news:title>
   <news:publication_date>2026-08-07T14:45:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720560</loc>
  <lastmod>2026-08-07T14:44:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グループテスティングの情報理論的展開（Group Testing: An Information Theory Perspective）</news:title>
   <news:publication_date>2026-08-07T14:44:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720558</loc>
  <lastmod>2026-08-07T14:44:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>サンプリングに起因するニューラル分類器の情報損失（Information Losses in Neural Classifiers from Sampling）</news:title>
   <news:publication_date>2026-08-07T14:44:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720556</loc>
  <lastmod>2026-08-07T14:44:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的グラフィカルモデルの再調整と再サンプリングの比較（ON RESAMPLING VS. ADJUSTING PROBABILISTIC GRAPHICAL MODELS IN ESTIMATION OF DISTRIBUTION ALGORITHMS）</news:title>
   <news:publication_date>2026-08-07T14:44:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720554</loc>
  <lastmod>2026-08-07T14:43:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>人の知識を符号化して強化学習をウォームスタートする（Encoding Human Domain Knowledge to Warm Start Reinforcement Learning）</news:title>
   <news:publication_date>2026-08-07T14:43:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720552</loc>
  <lastmod>2026-08-07T13:52:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューラルネットワークの堅牢性：確率的かつ実用的アプローチ (Robustness of Neural Networks: A Probabilistic and Practical Approach)</news:title>
   <news:publication_date>2026-08-07T13:52:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720550</loc>
  <lastmod>2026-08-07T13:52:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>逐次適応的サブモジュラリティの枠組み（Adaptive Sequence Submodularity）</news:title>
   <news:publication_date>2026-08-07T13:52:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720548</loc>
  <lastmod>2026-08-07T13:51:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オペレーショナルニューラルネットワーク（Operational Neural Networks）</news:title>
   <news:publication_date>2026-08-07T13:51:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720546</loc>
  <lastmod>2026-08-07T13:50:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>MoMにおける逆行列不要な最近傍評価の意義（Inversion-Free Evaluation of Nearest Neighbors in Method of Moments）</news:title>
   <news:publication_date>2026-08-07T13:50:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720544</loc>
  <lastmod>2026-08-07T13:50:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DeepFaultによるDNNの故障局在化と検査強化（DeepFault: Fault Localization for Deep Neural Networks）</news:title>
   <news:publication_date>2026-08-07T13:50:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720542</loc>
  <lastmod>2026-08-07T13:50:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動的スパース再パラメータ化による畳み込みニューラルネットワークの効率的学習（Parameter Efficient Training of Deep Convolutional Neural Networks by Dynamic Sparse Reparameterization）</news:title>
   <news:publication_date>2026-08-07T13:50:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720540</loc>
  <lastmod>2026-08-07T13:50:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高品質3D顔再構築のためのGAN適合（GANFIT: Generative Adversarial Network Fitting for High Fidelity 3D Face Reconstruction）</news:title>
   <news:publication_date>2026-08-07T13:50:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720538</loc>
  <lastmod>2026-08-07T12:56:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ダークマターから銀河へ：畳み込みネットワークによる写像（From Dark Matter to Galaxies with Convolutional Networks）</news:title>
   <news:publication_date>2026-08-07T12:56:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720536</loc>
  <lastmod>2026-08-07T12:56:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>二次元硬粒子の排除領域の解析（The excluded area of two-dimensional hard particles）</news:title>
   <news:publication_date>2026-08-07T12:56:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720534</loc>
  <lastmod>2026-08-07T12:55:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>若い銀河のハードな電離源を探る — HeIIλ1640放射とその示唆（Exploring Heiiλ1640 emission line properties at z∼2−4）</news:title>
   <news:publication_date>2026-08-07T12:55:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720532</loc>
  <lastmod>2026-08-07T12:55:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>背外側前頭前野の遮断が暗黙的文脈記憶主導の注意を改善する（Disruption of the prefrontal cortex improves implicit contextual memory-guided attention）</news:title>
   <news:publication_date>2026-08-07T12:55:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720530</loc>
  <lastmod>2026-08-07T12:55:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>銀河団の力学質量を深層学習で測る（A Robust and Efficient Deep Learning Method for Dynamical Mass Measurements of Galaxy Clusters）</news:title>
   <news:publication_date>2026-08-07T12:55:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720528</loc>
  <lastmod>2026-08-07T12:54:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>プラズモン色の深層学習による予測（Plasmonic colours predicted by deep learning）</news:title>
   <news:publication_date>2026-08-07T12:54:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720526</loc>
  <lastmod>2026-08-07T12:54:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層畳み込みガウス過程によるベイズ画像分類（Bayesian Image Classification with Deep Convolutional Gaussian Processes）</news:title>
   <news:publication_date>2026-08-07T12:54:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720524</loc>
  <lastmod>2026-08-07T12:02:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習によるスペクトル色あせ補正で血中酸素飽和度を推定する（Estimation of blood oxygenation with learned spectral decoloring for quantitative photoacoustic imaging）</news:title>
   <news:publication_date>2026-08-07T12:02:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720522</loc>
  <lastmod>2026-08-07T12:02:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>リスクスコアの公平性を評価する新指標 xAUC（The Fairness of Risk Scores Beyond Classification: Bipartite Ranking and the xAUC Metric）</news:title>
   <news:publication_date>2026-08-07T12:02:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720520</loc>
  <lastmod>2026-08-07T12:01:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>トリプレット深層距離学習ネットワークによるリモートセンシング画像検索の性能向上 (Enhancing Remote Sensing Image Retrieval with Triplet Deep Metric Learning Network)</news:title>
   <news:publication_date>2026-08-07T12:01:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720518</loc>
  <lastmod>2026-08-07T12:00:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>重い裾（heavy-tailed）カーネルがt-SNE可視化のクラスタ構造を細かく示す（Heavy-tailed kernels reveal a finer cluster structure in t-SNE visualisations）</news:title>
   <news:publication_date>2026-08-07T12:00:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720516</loc>
  <lastmod>2026-08-07T12:00:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>教師あり深層ニューラルネットワークによるオプション価格付けとキャリブレーション（Supervised Deep Neural Networks for Pricing/Calibration of Options）</news:title>
   <news:publication_date>2026-08-07T12:00:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720514</loc>
  <lastmod>2026-08-07T12:00:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模心臓4D MRIの教師なし形状・動態解析が示す臨床的示唆（Unsupervised shape and motion analysis of 3822 cardiac 4D MRIs of UK Biobank）</news:title>
   <news:publication_date>2026-08-07T12:00:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720512</loc>
  <lastmod>2026-08-07T11:59:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>VH過程におけるSMEFTの探索と機械学習の活用（Exploring SMEFT in VH with Machine Learning）</news:title>
   <news:publication_date>2026-08-07T11:59:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720510</loc>
  <lastmod>2026-08-07T11:08:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ラプラシアン固有関数の節点と一般化特異構造（ON NODAL AND GENERALIZED SINGULAR STRUCTURES OF LAPLACIAN EIGENFUNCTIONS AND APPLICATIONS TO INVERSE SCATTERING PROBLEMS）</news:title>
   <news:publication_date>2026-08-07T11:08:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720508</loc>
  <lastmod>2026-08-07T11:07:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>不完全かつノイズのある観測に対する頑健な強化学習（Robust Reinforcement Learning in POMDPs with Incomplete and Noisy Observations）</news:title>
   <news:publication_date>2026-08-07T11:07:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720506</loc>
  <lastmod>2026-08-07T11:06:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>タスクに適応する高速アーキテクチャ推定（Fast Task-Aware Architecture Inference）</news:title>
   <news:publication_date>2026-08-07T11:06:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720504</loc>
  <lastmod>2026-08-07T11:06:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>異種交通環境における深層強化学習ベースの高次運転行動意思決定モデル（Deep Reinforcement Learning Based High-level Driving Behavior Decision-making Model in Heterogeneous Traffic）</news:title>
   <news:publication_date>2026-08-07T11:06:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720502</loc>
  <lastmod>2026-08-07T11:06:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューラル機械翻訳における動的層集約と合意ルーティング（Dynamic Layer Aggregation for Neural Machine Translation with Routing-by-Agreement）</news:title>
   <news:publication_date>2026-08-07T11:06:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720500</loc>
  <lastmod>2026-08-07T11:06:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多変量多項式評価に基づくややホモモルフィック暗号（A Somewhat Homomorphic Encryption Scheme based on Multivariate Polynomial Evaluation）</news:title>
   <news:publication_date>2026-08-07T11:06:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720498</loc>
  <lastmod>2026-08-07T11:05:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>超強結合Jaynes–Cummings模型（Ultrastrong Jaynes-Cummings Model）</news:title>
   <news:publication_date>2026-08-07T11:05:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720496</loc>
  <lastmod>2026-08-07T10:14:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文脈認識型自己注意ネットワーク（Context-Aware Self-Attention Networks）</news:title>
   <news:publication_date>2026-08-07T10:14:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720494</loc>
  <lastmod>2026-08-07T10:14:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>双対性にもとづくコアラージュ学習（Coalgebra Learning via Duality）</news:title>
   <news:publication_date>2026-08-07T10:14:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720492</loc>
  <lastmod>2026-08-07T10:13:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>漸近的に厳密なデータ拡張（Asymptotically exact data augmentation）</news:title>
   <news:publication_date>2026-08-07T10:13:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720490</loc>
  <lastmod>2026-08-07T10:12:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>2段階トランスファー学習による異種ロボット検出と2Dカメラ画像での3D関節推定（Two-Stage Transfer Learning for Heterogeneous Robot Detection and 3D Joint Position Estimation in a 2D Camera Image Using CNN）</news:title>
   <news:publication_date>2026-08-07T10:12:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720488</loc>
  <lastmod>2026-08-07T10:12:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>プロモーター分類におけるSVMとスペクトラル埋め込みの比較（Comparison of SVM and Spectral Embedding in Promoter Biobricks’ Categorizing and Clustering）</news:title>
   <news:publication_date>2026-08-07T10:12:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720486</loc>
  <lastmod>2026-08-07T10:11:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SVMベースの深層積層ネットワーク（SVM-based Deep Stacking Networks）</news:title>
   <news:publication_date>2026-08-07T10:11:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720484</loc>
  <lastmod>2026-08-07T10:11:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>睡眠段階分類のための畳み込みネットワーク（A Convolutional Network for Sleep Stages Classification）</news:title>
   <news:publication_date>2026-08-07T10:11:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720482</loc>
  <lastmod>2026-08-07T09:20:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>制約付きリスクバジェッティング・ポートフォリオ（Constrained Risk Budgeting Portfolios）</news:title>
   <news:publication_date>2026-08-07T09:20:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720480</loc>
  <lastmod>2026-08-07T09:19:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GMMを効率的に学習する深層学習の利点（Efficient Deep Learning of GMMs）</news:title>
   <news:publication_date>2026-08-07T09:19:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720478</loc>
  <lastmod>2026-08-07T09:19:25Z</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スパイクカウント学習則（Deep Spiking Neural Network with Spike Count based Learning Rule）</news:title>
   <news:publication_date>2026-08-07T09:19:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720476</loc>
  <lastmod>2026-08-07T09:19:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>双方向価値学習によるリスク意識型計画（Bi-directional Value Learning for Risk-aware Planning Under Uncertainty: Extended Version）</news:title>
   <news:publication_date>2026-08-07T09:19:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720474</loc>
  <lastmod>2026-08-07T09:18:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>適応的スケーリングを学習するリカレントニューラルネットワーク（Learning to Adaptively Scale Recurrent Neural Networks）</news:title>
   <news:publication_date>2026-08-07T09:18:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720472</loc>
  <lastmod>2026-08-07T09:18:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>軽量特徴融合ネットワークによる単一画像超解像（Lightweight Feature Fusion Network for Single Image Super-Resolution）</news:title>
   <news:publication_date>2026-08-07T09:18:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720470</loc>
  <lastmod>2026-08-07T09:18:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ロボットのクラウドオフロード方策（Network Offloading Policies for Cloud Robotics: a Learning-based Approach）</news:title>
   <news:publication_date>2026-08-07T09:18:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720468</loc>
  <lastmod>2026-08-07T08:27:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Lipschitz条件で安定化する生成モデルの理論と実務的示唆（Lipschitz Generative Adversarial Nets）</news:title>
   <news:publication_date>2026-08-07T08:27:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720466</loc>
  <lastmod>2026-08-07T08:27:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層的サンプリングによるネットワークの位相表現学習（Learning Topological Representation for Networks via Hierarchical Sampling）</news:title>
   <news:publication_date>2026-08-07T08:27:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720464</loc>
  <lastmod>2026-08-07T08:26:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>カーネル単位の自動ニューラルネットワーク量子化（AUTOQ: AUTOMATED KERNEL-WISE NEURAL NETWORK QUANTIZATION）</news:title>
   <news:publication_date>2026-08-07T08:26:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720462</loc>
  <lastmod>2026-08-07T08:25:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>医療画像解析における深層学習の展開と課題（Going Deep in Medical Image Analysis: Concepts, Methods, Challenges and Future Directions）</news:title>
   <news:publication_date>2026-08-07T08:25:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720460</loc>
  <lastmod>2026-08-07T08:25:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ProxSARAH による確率的合成非凸最適化の効率化（ProxSARAH: An Efficient Algorithmic Framework for Stochastic Composite Nonconvex Optimization）</news:title>
   <news:publication_date>2026-08-07T08:25:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720458</loc>
  <lastmod>2026-08-07T08:25:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>公開情報を使った犯罪分析の実務的アプローチ（Crime Analysis using Open Source Information）</news:title>
   <news:publication_date>2026-08-07T08:25:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720456</loc>
  <lastmod>2026-08-07T08:25:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分子回転ムービーを高調波で撮る技術（Molecular rotation movie filmed with high-harmonic generation）</news:title>
   <news:publication_date>2026-08-07T08:25:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720454</loc>
  <lastmod>2026-08-07T07:33:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非同期共エージェントネットワークの理論と実装（Asynchronous Coagent Networks）</news:title>
   <news:publication_date>2026-08-07T07:33:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720452</loc>
  <lastmod>2026-08-07T07:33:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>敵対的環境における最大エントロピー深層強化学習による能動的知覚（Active Perception in Adversarial Scenarios using Maximum Entropy Deep Reinforcement Learning）</news:title>
   <news:publication_date>2026-08-07T07:33:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720450</loc>
  <lastmod>2026-08-07T07:33:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>専門知識をニューラルネットに組み込む手法（KINN: Incorporating Expert Knowledge in Neural Networks）</news:title>
   <news:publication_date>2026-08-07T07:33:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720448</loc>
  <lastmod>2026-08-07T07:32:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>バーデの窓における赤化と減光の地図化（Mapping the Interstellar Reddening and Extinction towards Baade’s Window Using Minimum Light Colors of ab-type RR Lyrae Stars）</news:title>
   <news:publication_date>2026-08-07T07:32:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720446</loc>
  <lastmod>2026-08-07T07:32:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>検証可能な安全なオフモデル強化学習（Verifiably Safe Off-Model Reinforcement Learning）</news:title>
   <news:publication_date>2026-08-07T07:32:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720444</loc>
  <lastmod>2026-08-07T07:32:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>都市マイクロ気象のリアルタイム高解像度予測を可能にするSRシミュレーション（Super-Resolution Simulation for Real-Time Prediction of Urban Micrometeorology）</news:title>
   <news:publication_date>2026-08-07T07:32:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720442</loc>
  <lastmod>2026-08-07T07:32:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所的にグラフの最近傍を見つける方法（Finding Nearest Neighbors in graphs locally）</news:title>
   <news:publication_date>2026-08-07T07:32:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720440</loc>
  <lastmod>2026-08-07T06:40:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非コンパクト特徴空間におけるクラス条件付きラベルノイズ下の分類（Classification with unknown class-conditional label noise on non-compact feature spaces）</news:title>
   <news:publication_date>2026-08-07T06:40:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720438</loc>
  <lastmod>2026-08-07T06:39:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>WaveletAEによる風力発電機ブレードの氷結検出（WaveletAE: A Wavelet-enhanced Autoencoder for Wind Turbine Blade Icing Detection）</news:title>
   <news:publication_date>2026-08-07T06:39:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720436</loc>
  <lastmod>2026-08-07T06:39:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>画像ベースGANを用いた時系列データ生成の簡便手法（Quick and Easy Time Series Generation with Established Image-based GANs）</news:title>
   <news:publication_date>2026-08-07T06:39:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720434</loc>
  <lastmod>2026-08-07T06:39:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時間遅延結合系を深い畳み込みニューラルネットへと読み替える発想（Coupled nonlinear delay systems as deep convolutional neural networks）</news:title>
   <news:publication_date>2026-08-07T06:39:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720432</loc>
  <lastmod>2026-08-07T06:39:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>衛星画像から標準地図レイヤを生成するGeoGAN（GeoGAN: A Conditional GAN with Reconstruction and Style Loss to Generate Standard Layer of Maps from Satellite Images）</news:title>
   <news:publication_date>2026-08-07T06:39:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720430</loc>
  <lastmod>2026-08-07T06:38:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DC最適潮流における活性制約集合の分類による高速推定（Learning for DC-OPF: Classifying active sets using neural nets）</news:title>
   <news:publication_date>2026-08-07T06:38:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720428</loc>
  <lastmod>2026-08-07T06:38:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>バッチ正規化を用いるCrossQによるサンプル効率の向上（CROSSQ: BATCH NORMALIZATION IN DEEP REINFORCEMENT LEARNING FOR GREATER SAMPLE EFFICIENCY AND SIMPLICITY）</news:title>
   <news:publication_date>2026-08-07T06:38:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720426</loc>
  <lastmod>2026-08-07T05:47:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>競争下での探索の危険性（The Perils of Exploration under Competition: A Computational Modeling Approach）</news:title>
   <news:publication_date>2026-08-07T05:47:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720424</loc>
  <lastmod>2026-08-07T05:38:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>指数修正ガウス混合モデルと分光データへの応用（Exponentially-Modified Gaussian Mixture Model）</news:title>
   <news:publication_date>2026-08-07T05:38:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-07T05:36:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的枠組みによるQuantum Clusteringの実用化（A Probabilistic framework for Quantum Clustering）</news:title>
   <news:publication_date>2026-08-07T05:36:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>任意長入力に対応するテキスト分類の全畳み込みネットワーク（Fully Convolutional Networks for Text Classification）</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>
   </news:publication>
   <news:title>結び目の双曲体体積を深層学習で予測する（Deep Learning the Hyperbolic Volume of a Knot）</news:title>
   <news:publication_date>2026-08-07T04:44:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-07T04:44:11Z</news:publication_date>
   <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>
   </news:publication>
   <news:title>トラクトグラフィーと機械学習：現状と未解決課題（Tractography and machine learning: Current state and open challenges）</news:title>
   <news:publication_date>2026-08-07T04:43:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ベイズモデル不確実性の簡潔な案内（A Parsimonious Tour of Bayesian Model Uncertainty）</news:title>
   <news:publication_date>2026-08-07T04:43:10Z</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:publication_date>2026-08-07T04:42:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-07T04:42:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>報酬なしでロボットが目標を学ぶ仕組み（Unsupervised Visuomotor Control through Distributional Planning Networks）</news:title>
   <news:publication_date>2026-08-07T04:42:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
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 <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>
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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>ラベル属性と階層構造を統合する階層的ゼロショット画像分類（Integrating Propositional and Relational Label Side Information for Hierarchical Zero-Shot Image Classification）</news:title>
   <news:publication_date>2026-08-07T03:50:23Z</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:title>ハイパー複素数値ホップフィールド型ニューラルネットワークの広範なクラス（A Broad Class of Discrete-Time Hypercomplex-Valued Hopfield Neural Networks）</news:title>
   <news:publication_date>2026-08-07T03:50:17Z</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:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <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>非線形統計的逆学習問題に対するチホノフ正則化の収束解析 (Convergence Analysis of Tikhonov Regularization for Non-Linear Statistical Inverse Learning Problems)</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-07T02:54:55Z</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>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720372</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-07T02:01:04Z</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:title>ソースモデルとターゲットデータを用いた系列ラベリングの転移学習（Transfer Learning for Sequence Labeling Using Source Model and Target Data）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>離散時間ヘッジにおける深層学習の実践と評価（Deep learning for discrete-time hedging in incomplete markets）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>点群のセマンティック・インスタンス分割のための構造認識損失を用いた3Dグラフ埋め込み学習（3D Graph Embedding Learning with a Structure-aware Loss Function for Point Cloud Semantic Instance Segmentation）</news:title>
   <news:publication_date>2026-08-07T01:08:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720350</loc>
  <lastmod>2026-08-07T01:07:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフ正則化による画像セマンティック埋め込み（Graph-RISE: Graph-Regularized Image Semantic Embedding）</news:title>
   <news:publication_date>2026-08-07T01:07:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720348</loc>
  <lastmod>2026-08-07T01:06:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>HyPLCによるPLCとハイブリッドプログラムの双方向翻訳（HyPLC: Hybrid Programmable Logic Controller Program Translation for Verification）</news:title>
   <news:publication_date>2026-08-07T01:06:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720346</loc>
  <lastmod>2026-08-07T01:06:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非漸近的なMonte Carlo Tree Searchの解析（Non-Asymptotic Analysis of Monte Carlo Tree Search）</news:title>
   <news:publication_date>2026-08-07T01:06:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720344</loc>
  <lastmod>2026-08-07T01:06:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>視覚共追跡における長短メモリのバランス最適化（Long and Short Memory Balancing in Visual Co-Tracking Using Q-Learning）</news:title>
   <news:publication_date>2026-08-07T01:06:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720337</loc>
  <lastmod>2026-08-07T00:14:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次元確率偏微分方程式をシミュレータ不要で解く（Simulator-free Solution of High-dimensional Stochastic Elliptic Partial Differential Equations using Deep Neural Networks）</news:title>
   <news:publication_date>2026-08-07T00:14:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720335</loc>
  <lastmod>2026-08-07T00:14:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>最大一般エントロピーを達成するオフポリシーActor-Criticと効果的な環境探索（Off-Policy Actor-Critic for Maximum General Entropy and Effective Environment Exploration）</news:title>
   <news:publication_date>2026-08-07T00:14:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720333</loc>
  <lastmod>2026-08-07T00:13:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大型電波観測における光学的赤方偏移推定手法の比較（A Comparison of Photometric Redshift Techniques for Large Radio Surveys）</news:title>
   <news:publication_date>2026-08-07T00:13:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720331</loc>
  <lastmod>2026-08-07T00:12:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>MSEとCCCの多対多写像（The Many-to-Many Mapping Between the Concordance Correlation Coefficient, and the Mean Square Error）</news:title>
   <news:publication_date>2026-08-07T00:12:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720329</loc>
  <lastmod>2026-08-07T00:12:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>転移学習が医用画像解析にもたらす示唆（Transfusion: Understanding Transfer Learning for Medical Imaging）</news:title>
   <news:publication_date>2026-08-07T00:12:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720327</loc>
  <lastmod>2026-08-07T00:12:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>データ相関を考慮したワイヤレスVRの資源管理（Data Correlation-Aware Resource Management in Wireless Virtual Reality）</news:title>
   <news:publication_date>2026-08-07T00:12:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720325</loc>
  <lastmod>2026-08-07T00:11:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>変形する軟組織を深層強化学習で操作する意義（Manipulating Soft Tissues by Deep Reinforcement Learning for Autonomous Robotic Surgery）</news:title>
   <news:publication_date>2026-08-07T00:11:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720323</loc>
  <lastmod>2026-08-06T23:20:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>屋内物体操作行為の分割による人間工学的リスク予測に向けて (Toward Ergonomic Risk Prediction via Segmentation of Indoor Object Manipulation Actions Using Spatiotemporal Convolutional Networks)</news:title>
   <news:publication_date>2026-08-06T23:20:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720321</loc>
  <lastmod>2026-08-06T23:19:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>部分的順序情報を用いたガウス有向非巡回グラフの推定と乳牛データへの応用（Estimation of Gaussian directed acyclic graphs using partial ordering information with an application to dairy cattle data）</news:title>
   <news:publication_date>2026-08-06T23:19:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720319</loc>
  <lastmod>2026-08-06T23:19:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>量子言語処理（Quantum Language Processing）</news:title>
   <news:publication_date>2026-08-06T23:19:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720317</loc>
  <lastmod>2026-08-06T23:18:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>原子スケールシミュレーションにおける教師なし機械学習の位置づけ（Perspective: Unsupervised machine learning in atomistic simulations, between predictions and understanding）</news:title>
   <news:publication_date>2026-08-06T23:18:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720315</loc>
  <lastmod>2026-08-06T23:18:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多波長衛星画像とWasserstein GANを用いた半教師ありマルチタスク学習による貧困予測（Semi-Supervised Multitask Learning on Multispectral Satellite Images Using Wasserstein Generative Adversarial Networks (GANs) for Predicting Poverty）</news:title>
   <news:publication_date>2026-08-06T23:18:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720313</loc>
  <lastmod>2026-08-06T23:18:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>バイオ医用画像における機械学習の課題と解法（Machine Learning on Biomedical Images: Interactive Learning, Transfer Learning, Class Imbalance, and Beyond）</news:title>
   <news:publication_date>2026-08-06T23:18:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720311</loc>
  <lastmod>2026-08-06T23:17:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ダストが明かすミニネプチューン誕生の証拠（Dust Unveils the Formation of a Mini‑Neptune Planet in a Protoplanetary Ring）</news:title>
   <news:publication_date>2026-08-06T23:17:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720309</loc>
  <lastmod>2026-08-06T22:25:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的ニューラルアーキテクチャ探索が変える設計実務（Probabilistic Neural Architecture Search）</news:title>
   <news:publication_date>2026-08-06T22:25:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720307</loc>
  <lastmod>2026-08-06T22:25:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ImageNet分類器はImageNetに対して一般化するか（Do ImageNet Classifiers Generalize to ImageNet?）</news:title>
   <news:publication_date>2026-08-06T22:25:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720305</loc>
  <lastmod>2026-08-06T22:25:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>開放量子多体系のニューラルネットワークによる動力学表現（Neural-Network Approach to Dissipative Quantum Many-Body Dynamics）</news:title>
   <news:publication_date>2026-08-06T22:25:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720303</loc>
  <lastmod>2026-08-06T22:24:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>木構造のトレース再構成が示す新しい展望（Reconstructing Trees from Traces）</news:title>
   <news:publication_date>2026-08-06T22:24:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720301</loc>
  <lastmod>2026-08-06T22:24:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフ構造化再帰型ニューラルネットワークとスパース化による疫学予測（A Study on Graph-Structured Recurrent Neural Networks and Sparsification with Application to Epidemic Forecasting）</news:title>
   <news:publication_date>2026-08-06T22:24:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720299</loc>
  <lastmod>2026-08-06T22:24:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Anytime Tail Averaging（Anytime Tail Averaging）</news:title>
   <news:publication_date>2026-08-06T22:24:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720297</loc>
  <lastmod>2026-08-06T22:23:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>農業コミュニティのツイートから州レベルの農業センチメントを予測する研究（Predicting State-Level Agricultural Sentiment with Tweets from Farming Communities）</news:title>
   <news:publication_date>2026-08-06T22:23:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720295</loc>
  <lastmod>2026-08-06T21:32:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>超低消費電力で動く埋め込み型ゲーティッド再帰ユニットの提案（AN OPTIMIZED RECURRENT UNIT FOR ULTRA-LOW-POWER KEYWORD SPOTTING）</news:title>
   <news:publication_date>2026-08-06T21:32:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720293</loc>
  <lastmod>2026-08-06T21:32:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>無限幅・有界ノルムのReLUネットワークは関数空間でどう見えるか（How do infinite width bounded norm networks look in function space?）</news:title>
   <news:publication_date>2026-08-06T21:32:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720291</loc>
  <lastmod>2026-08-06T21:31:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>損切りを学ぶ：故障許容的制御と最適停止の考え方（Cutting Your Losses: Learning Fault-Tolerant Control and Optimal Stopping under Adverse Risk）</news:title>
   <news:publication_date>2026-08-06T21:31:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720289</loc>
  <lastmod>2026-08-06T21:30:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ATMSeerによるAutoMLの可視化と制御性向上（ATMSeer: Increasing Transparency and Controllability in Automated Machine Learning）</news:title>
   <news:publication_date>2026-08-06T21:30:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720287</loc>
  <lastmod>2026-08-06T21:30:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Wasserstein バリセントリックによるモデルアンサンブリング（Wasserstein Barycenter Model Ensembling）</news:title>
   <news:publication_date>2026-08-06T21:30:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720285</loc>
  <lastmod>2026-08-06T21:30:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ボリューム・トゥイーニング・ネットワークによる3D医療画像非教師ありエンドツーエンド登録（Unsupervised 3D End-to-End Medical Image Registration with Volume Tweening Network）</news:title>
   <news:publication_date>2026-08-06T21:30:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720283</loc>
  <lastmod>2026-08-06T21:29:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>幾何学的概念の差分プライバート学習（Differentially Private Learning of Geometric Concepts）</news:title>
   <news:publication_date>2026-08-06T21:29:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720281</loc>
  <lastmod>2026-08-06T20:38:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>異なるデータ分布をまたぐ学習の設計思想（ADAGE: Agnostic Domain Generalization and Adaptation）</news:title>
   <news:publication_date>2026-08-06T20:38:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720279</loc>
  <lastmod>2026-08-06T20:36:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニュース駆動型株価予測の説明可能なニューラルネットワーク（Explainable Text-Driven Neural Network for Stock Prediction）</news:title>
   <news:publication_date>2026-08-06T20:36:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720277</loc>
  <lastmod>2026-08-06T20:36:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ゲーティッド画像から高密度深度を得るGated2Depth（Gated2Depth: Real-Time Dense Lidar From Gated Images）</news:title>
   <news:publication_date>2026-08-06T20:36:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/720275</loc>
  <lastmod>2026-08-06T20:35:39Z</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>2026-08-06T20:35:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720273</loc>
  <lastmod>2026-08-06T20:35:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>低ランク位相復元の証明可能な手法（Provable Low Rank Phase Retrieval）</news:title>
   <news:publication_date>2026-08-06T20:35:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720271</loc>
  <lastmod>2026-08-06T20:35:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深い発散に基づくクラスタリング手法（Deep Divergence-Based Approach to Clustering）</news:title>
   <news:publication_date>2026-08-06T20:35:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/720269</loc>
  <lastmod>2026-08-06T19:43:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>図形問題（Diagrammatic Reasoning）の自動化は可能か？（Can We Automate Diagrammatic Reasoning?）</news:title>
   <news:publication_date>2026-08-06T19:43:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/720267</loc>
  <lastmod>2026-08-06T19:43:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次元データに効く部分サンプリングNewton法の理論的検証（Do Subsampled Newton Methods Work for High-Dimensional Data?）</news:title>
   <news:publication_date>2026-08-06T19:43:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720265</loc>
  <lastmod>2026-08-06T19:42:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>P2Pレンディング市場におけるLSTMを用いた貸倒率予測（Risk Prediction of Peer-to-Peer Lending Market by a LSTM Model with Macroeconomic Factor）</news:title>
   <news:publication_date>2026-08-06T19:42:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720263</loc>
  <lastmod>2026-08-06T19:41:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-06T19:41:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720261</loc>
  <lastmod>2026-08-06T19:41:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ランダム初期化されたReLUネットワークにおけるサンプル分散の減衰（Sample Variance Decay in Randomly Initialized ReLU Networks）</news:title>
   <news:publication_date>2026-08-06T19:41:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720259</loc>
  <lastmod>2026-08-06T19:41:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多様な生データから学ぶ3D顔モデリング（3D Face Modeling From Diverse Raw Scan Data）</news:title>
   <news:publication_date>2026-08-06T19:41:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720257</loc>
  <lastmod>2026-08-06T19:40:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>温暖から高温の水素優勢大気を素早く解析するための簡略化化学スキーム（A reduced chemical scheme for modelling warm to hot hydrogen-dominated atmospheres）</news:title>
   <news:publication_date>2026-08-06T19:40:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720255</loc>
  <lastmod>2026-08-06T18:49:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>軌跡データから化学反応ネットワークを学習する（Learning chemical reaction networks from trajectory data）</news:title>
   <news:publication_date>2026-08-06T18:49:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720253</loc>
  <lastmod>2026-08-06T18:48:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>対話応答における知識選択の学習（Learning to Select Knowledge for Response Generation in Dialog Systems）</news:title>
   <news:publication_date>2026-08-06T18:48:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720251</loc>
  <lastmod>2026-08-06T18:48:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ソフトウェアモジュールクラスタリングと適応型ファジーTLBO（Software Module Clustering based on the Fuzzy Adaptive Teaching Learning based Optimization Algorithm）</news:title>
   <news:publication_date>2026-08-06T18:48:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720249</loc>
  <lastmod>2026-08-06T18:47:23Z</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>2026-08-06T18:47:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720247</loc>
  <lastmod>2026-08-06T18:47:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>牛の再識別におけるマルチビュー埋め込み（Multi-views Embedding for Cattle Re-identification）</news:title>
   <news:publication_date>2026-08-06T18:47:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720245</loc>
  <lastmod>2026-08-06T18:47:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>骨テクスチャ解析による股関節レントゲンからの変形性関節症発症予測（Bone Texture Analysis for Prediction of Incident Radiographic Hip Osteoarthritis Using Machine Learning）</news:title>
   <news:publication_date>2026-08-06T18:47:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720243</loc>
  <lastmod>2026-08-06T18:46:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>どのニューラルネットワーク構造が人工文法学習で人間に近い振る舞いを示すか（Which Neural Network Architecture matches Human Behavior in Artificial Grammar Learning?）</news:title>
   <news:publication_date>2026-08-06T18:46:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720241</loc>
  <lastmod>2026-08-06T17:54:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>不規則ドメイン上の信号分類（Classifying Signals on Irregular Domains via Convolutional Cluster Pooling）</news:title>
   <news:publication_date>2026-08-06T17:54:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720239</loc>
  <lastmod>2026-08-06T17:48:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SHEAR-netによる単一プッシュ超音波せん断波弾性イメージングの実用化可能性（SHEAR-net: An End-to-End Deep Learning Approach for Single Push Ultrasound Shear Wave Elasticity Imaging）</news:title>
   <news:publication_date>2026-08-06T17:48:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720237</loc>
  <lastmod>2026-08-06T17:47:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>土壌LIBSスペクトルの汎化学習による微量元素予測（Machine Learning Allows Calibration Models to Predict Trace Element Concentration in Soil with Generalized LIBS Spectra）</news:title>
   <news:publication_date>2026-08-06T17:47:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720235</loc>
  <lastmod>2026-08-06T17:46:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>正確なスパース最適化の下限となる凸計画法（Lower Bound Convex Programs for Exact Sparse Optimization）</news:title>
   <news:publication_date>2026-08-06T17:46:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720233</loc>
  <lastmod>2026-08-06T17:46:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-06T17:46:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720231</loc>
  <lastmod>2026-08-06T17:46:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層型グラフ畳み込みネットワークによる半教師付きノード分類（Hierarchical Graph Convolutional Networks for Semi-supervised Node Classification）</news:title>
   <news:publication_date>2026-08-06T17:46:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720229</loc>
  <lastmod>2026-08-06T16:54:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習における非凸最適化：勾配、確率性、鞍点（On Nonconvex Optimization for Machine Learning: Gradients, Stochasticity, and Saddle Points）</news:title>
   <news:publication_date>2026-08-06T16:54:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720227</loc>
  <lastmod>2026-08-06T16:53:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>モバイル向けDNNの構造的ベイズ圧縮（Structured Bayesian Compression for DNNs in Connected Healthcare）</news:title>
   <news:publication_date>2026-08-06T16:53:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720225</loc>
  <lastmod>2026-08-06T16:52:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自己教師あり学習で手術器具を自動ラベル化する手法（Self-Supervised Surgical Tool Segmentation using Kinematic Information）</news:title>
   <news:publication_date>2026-08-06T16:52:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720223</loc>
  <lastmod>2026-08-06T16:52:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ユークリッドカーネルの表現力とカーネル学習の効率性（On the Expressive Power of Kernel Methods and the Efficiency of Kernel Learning by Association Schemes）</news:title>
   <news:publication_date>2026-08-06T16:52:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720221</loc>
  <lastmod>2026-08-06T16:52:05Z</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>2026-08-06T16:52:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720219</loc>
  <lastmod>2026-08-06T16:51:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-06T16:51:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720217</loc>
  <lastmod>2026-08-06T16:51:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/720215</loc>
  <lastmod>2026-08-06T15:59:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>特徴次元に最適化されたパラメトリックQ学習（Sample-Optimal Parametric Q-Learning Using Linearly Additive Features）</news:title>
   <news:publication_date>2026-08-06T15:59:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720213</loc>
  <lastmod>2026-08-06T15:59:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-06T15:59:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/720211</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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  <lastmod>2026-08-06T15:58:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>半教師あり学習における効率的な交差検証の近似（Efficient Cross-Validation for Semi-Supervised Learning）</news:title>
   <news:publication_date>2026-08-06T15:58:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/720203</loc>
  <lastmod>2026-08-06T15:05:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>幅広いニューラルネットワークのスケーリング限界（Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation）</news:title>
   <news:publication_date>2026-08-06T15:05:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720201</loc>
  <lastmod>2026-08-06T15:05:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>セッション内の連続的スキップ予測（Session-based Sequential Skip Prediction via Recurrent Neural Networks）</news:title>
   <news:publication_date>2026-08-06T15:05:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720199</loc>
  <lastmod>2026-08-06T15:04:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>集合ベースの顔認識を変えた多プロトタイプ学習（Multi-Prototype Networks for Unconstrained Set-based Face Recognition）</news:title>
   <news:publication_date>2026-08-06T15:04:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720197</loc>
  <lastmod>2026-08-06T15:03:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>一様収束（ユニフォームコンバージェンス）は深層学習の汎化を説明できないかもしれない（Uniform convergence may be unable to explain generalization in deep learning）</news:title>
   <news:publication_date>2026-08-06T15:03:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720195</loc>
  <lastmod>2026-08-06T15:03:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>候補者選別を学ぶ（Learning to Screen）</news:title>
   <news:publication_date>2026-08-06T15:03:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720193</loc>
  <lastmod>2026-08-06T15:03:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>生物輸送モデルのための偏微分方程式学習（Learning partial differential equations for biological transport models from noisy spatiotemporal data）</news:title>
   <news:publication_date>2026-08-06T15:03:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720191</loc>
  <lastmod>2026-08-06T15:02:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>安定インスタンスによる頑健なマルチインスタンス学習（Robust Multi-Instance Learning with Stable Instances）</news:title>
   <news:publication_date>2026-08-06T15:02:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720189</loc>
  <lastmod>2026-08-06T14:11:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>フィード配信推薦における長期エンゲージメント最適化（Reinforcement Learning to Optimize Long-term User Engagement in Recommender Systems）</news:title>
   <news:publication_date>2026-08-06T14:11:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720187</loc>
  <lastmod>2026-08-06T14:11:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>独立欠損を許容するイジングモデルの学習（Learning Ising Models with Independent Failures）</news:title>
   <news:publication_date>2026-08-06T14:11:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720185</loc>
  <lastmod>2026-08-06T14:11:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>精密な3D細胞分割法（ACCURATE 3D CELL SEGMENTATION USING DEEP FEATURE AND CRF REFINEMENT）</news:title>
   <news:publication_date>2026-08-06T14:11:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720183</loc>
  <lastmod>2026-08-06T14:10:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチモーダル関心関連アイテム類似度モデル（Multimodal Interest-Related Item Similarity for Top-N Recommendation）</news:title>
   <news:publication_date>2026-08-06T14:10:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720181</loc>
  <lastmod>2026-08-06T14:10:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>衛星画像で食料安全保障を予測するCNNと衛星タスク化（Predicting Food Security Outcomes Using CNNs for Satellite Tasking）</news:title>
   <news:publication_date>2026-08-06T14:10:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720179</loc>
  <lastmod>2026-08-06T14:10:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>視神経乳頭と杯の自動分割（Automated Segmentation of the Optic Disk and Cup using Dual-Stage Fully Convolutional Networks）</news:title>
   <news:publication_date>2026-08-06T14:10:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720177</loc>
  <lastmod>2026-08-06T14:10:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>視覚と特徴量に基づく制御方策を同時学習する手法（Simultaneously Learning Vision and Feature-based Control Policies for Real-world Ball-in-a-Cup）</news:title>
   <news:publication_date>2026-08-06T14:10:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720175</loc>
  <lastmod>2026-08-06T13:18:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自己適応型単一および複数照明推定フレームワーク（Self-adaptive Single and Multi-illuminant Estimation Framework based on Deep Learning）</news:title>
   <news:publication_date>2026-08-06T13:18:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720173</loc>
  <lastmod>2026-08-06T13:18:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>神経回路網モデルと深層学習—生物学者のための入門 (Neural network models and deep learning – a primer for biologists)</news:title>
   <news:publication_date>2026-08-06T13:18:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720171</loc>
  <lastmod>2026-08-06T13:18:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ハイパーパラメータ選択のための差分記述長（Differential Description Length for Hyperparameter Selection in Machine Learning）</news:title>
   <news:publication_date>2026-08-06T13:18:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720169</loc>
  <lastmod>2026-08-06T13:17:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>記憶と一般化の境界：過剰パラメータ化下のアイデンティティ課題（Identity Crisis: Memorization and Generalization under Extreme Overparameterization）</news:title>
   <news:publication_date>2026-08-06T13:17:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720167</loc>
  <lastmod>2026-08-06T13:17:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>生成モデルのモードカバレッジ再考（Rethinking Generative Mode Coverage: A Pointwise Guaranteed Approach）</news:title>
   <news:publication_date>2026-08-06T13:17:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720165</loc>
  <lastmod>2026-08-06T13:16:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>概算モデルを用いたロボット学習（Using Approximate Models in Robot Learning）</news:title>
   <news:publication_date>2026-08-06T13:16:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720163</loc>
  <lastmod>2026-08-06T13:16:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>線形回帰のプライバシーと有用性のトレードオフ（Privacy-Utility Trade-off of Linear Regression under Random Projections and Additive Noise）</news:title>
   <news:publication_date>2026-08-06T13:16:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720161</loc>
  <lastmod>2026-08-06T12:23:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>勾配を小さくすることの複雑さ（The Complexity of Making the Gradient Small in Stochastic Convex Optimization）</news:title>
   <news:publication_date>2026-08-06T12:23:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720159</loc>
  <lastmod>2026-08-06T12:23:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>中程度の過学習化で収束を保証する浅いニューラルネットワークの理論（Towards moderate overparameterization: global convergence guarantees for training shallow neural networks）</news:title>
   <news:publication_date>2026-08-06T12:23:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720157</loc>
  <lastmod>2026-08-06T12:22:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>画像のフォレンジック類似性による改ざん検出の新潮流（Forensic Similarity for Digital Images）</news:title>
   <news:publication_date>2026-08-06T12:22:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720155</loc>
  <lastmod>2026-08-06T12:21:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>心臓・胸部CTにおける直接的な冠動脈石灰化スコア算出（Direct Automatic Coronary Calcium Scoring in Cardiac and Chest CT）</news:title>
   <news:publication_date>2026-08-06T12:21:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720153</loc>
  <lastmod>2026-08-06T12:21:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>構造化信号の重ね合わせから生成モデルを学ぶ—GANを用いたデノイズとデミキシング（Learning Generative Models of Structured Signals from Their Superposition Using GANs with Application to Denoising and Demixing）</news:title>
   <news:publication_date>2026-08-06T12:21:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720151</loc>
  <lastmod>2026-08-06T12:21:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>PLIT：植物トランスクリプトームで長鎖非コードRNAを同定するアラインメント不要ツール（PLIT: An alignment-free computational tool for identification of long non-coding RNAs in plant transcriptomic datasets）</news:title>
   <news:publication_date>2026-08-06T12:21:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720149</loc>
  <lastmod>2026-08-06T12:21:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率単体上の外れ値耐性推定における信頼領域とミニマックス速度（Confidence regions and minimax rates in outlier-robust estimation on the probability simplex）</news:title>
   <news:publication_date>2026-08-06T12:21:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720147</loc>
  <lastmod>2026-08-06T11:29:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>調整可能な損失関数 α-loss が示す実務上の示唆（A Tunable Loss Function for Binary Classification）</news:title>
   <news:publication_date>2026-08-06T11:29:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720145</loc>
  <lastmod>2026-08-06T11:28:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>低メモリ環境での適応的前処理を可能にする「Extreme Tensoring」（Extreme Tensoring for Low-Memory Preconditioning）</news:title>
   <news:publication_date>2026-08-06T11:28:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720143</loc>
  <lastmod>2026-08-06T11:28:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>クラウドソースによるノイズ下のPAC学習の実務的意義（Crowdsourced PAC Learning under Classification Noise）</news:title>
   <news:publication_date>2026-08-06T11:28:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720141</loc>
  <lastmod>2026-08-06T11:28:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時系列データ学習における遅延埋め込みとPrecision Annealing（Machine Learning of Time Series Using Time-delay Embedding and Precision Annealing）</news:title>
   <news:publication_date>2026-08-06T11:28:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720139</loc>
  <lastmod>2026-08-06T11:28:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習理論とサポートベクターマシンの入門（LEARNING THEORY AND SUPPORT VECTOR MACHINES - A PRIMER）</news:title>
   <news:publication_date>2026-08-06T11:28:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720137</loc>
  <lastmod>2026-08-06T11:28:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>報酬と制約を動的に両立させる連続制御学習の実務的インパクト（Value constrained model-free continuous control）</news:title>
   <news:publication_date>2026-08-06T11:28:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720135</loc>
  <lastmod>2026-08-06T10:37:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>細粒度GPU共有のためのSalus（Salus: Fine-Grained GPU Sharing Primitives for Deep Learning Applications）</news:title>
   <news:publication_date>2026-08-06T10:37:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720133</loc>
  <lastmod>2026-08-06T10:36:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高解像度衛星画像の屋根セグメンテーションにおける漸進的生成対抗ネットワークの有効性（Progressively Growing GANs for High Resolution Semantic Segmentation of Satellite Images）</news:title>
   <news:publication_date>2026-08-06T10:36:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720131</loc>
  <lastmod>2026-08-06T10:36:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>対照的変分オートエンコーダによる顕在特徴強調（Contrastive Variational Autoencoder Enhances Salient Features）</news:title>
   <news:publication_date>2026-08-06T10:36:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720129</loc>
  <lastmod>2026-08-06T10:35:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>低表面輝度銀河の形成と進化（The formation and evolution of low-surface-brightness galaxies）</news:title>
   <news:publication_date>2026-08-06T10:35:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720127</loc>
  <lastmod>2026-08-06T10:35:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>集団のパラメータ学習の最尤推定（Maximum Likelihood Estimation for Learning Populations of Parameters）</news:title>
   <news:publication_date>2026-08-06T10:35:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720125</loc>
  <lastmod>2026-08-06T10:35:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>少数ショット学習のための無限混合プロトタイプ（Infinite Mixture Prototypes for Few-Shot Learning）</news:title>
   <news:publication_date>2026-08-06T10:35:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720123</loc>
  <lastmod>2026-08-06T10:34:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>フォーナックス矮小球状銀河における第六の星団の再発見（REDISCOVERY OF THE SIXTH STAR CLUSTER IN THE FORNAX DWARF SPHEROIDAL GALAXY）</news:title>
   <news:publication_date>2026-08-06T10:34:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720121</loc>
  <lastmod>2026-08-06T09:42:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ACTRCEによる経験強化――言語で目標を与えて希薄報酬問題を突破する手法（Augmenting Experience via Teacher’s Advice）</news:title>
   <news:publication_date>2026-08-06T09:42:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720119</loc>
  <lastmod>2026-08-06T09:42:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>超電導磁気トラップ中の冷分子間衝突の観測（Collisions between cold molecules in a superconducting magnetic trap）</news:title>
   <news:publication_date>2026-08-06T09:42:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720117</loc>
  <lastmod>2026-08-06T09:42:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ケプラー光度で観測された赤色巨星連星の潮汐と軌道円化（OBSERVATIONS OF TIDES AND CIRCULARIZATION IN RED-GIANT BINARIES FROM KEPLER PHOTOMETRY）</news:title>
   <news:publication_date>2026-08-06T09:42:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720115</loc>
  <lastmod>2026-08-06T09:41:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Binary Stochastic Filteringによる特徴選択とニューラルネットワーク縮小の実務的意義（Binary Stochastic Filtering: a Method for Neural Network Size Minimization and Supervised Feature Selection）</news:title>
   <news:publication_date>2026-08-06T09:41:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720113</loc>
  <lastmod>2026-08-06T09:41:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>獣医病理領域で示された領域同定の優位性（Deep learning algorithms out-perform veterinary pathologists in detecting the mitotically most active tumor region）</news:title>
   <news:publication_date>2026-08-06T09:41:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720111</loc>
  <lastmod>2026-08-06T09:40:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ベイズによる変化点のオンライン予測 (Bayesian Online Prediction of Change Points)</news:title>
   <news:publication_date>2026-08-06T09:40:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720109</loc>
  <lastmod>2026-08-06T08:48:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Fast-SCNNによる高速セマンティックセグメンテーション（Fast-SCNN: Fast Semantic Segmentation Network）</news:title>
   <news:publication_date>2026-08-06T08:48:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720107</loc>
  <lastmod>2026-08-06T08:47:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>外側銀河円盤のA・F星の全空間運動の研究（A study of full space motions of outer Galactic disk A and F stars in two deep pencil-beams）</news:title>
   <news:publication_date>2026-08-06T08:47:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720105</loc>
  <lastmod>2026-08-06T08:47:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>プライバシーの代償：差分プライバシー下での最適収束率（THE COST OF PRIVACY: OPTIMAL RATES OF CONVERGENCE FOR PARAMETER ESTIMATION WITH DIFFERENTIAL PRIVACY）</news:title>
   <news:publication_date>2026-08-06T08:47:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720103</loc>
  <lastmod>2026-08-06T08:46:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューラルネットワークの「容量配分」解析が示す設計原理（Capacity allocation analysis of neural networks: A tool for principled architecture design）</news:title>
   <news:publication_date>2026-08-06T08:46:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720101</loc>
  <lastmod>2026-08-06T08:46:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>クエリごとのばらつきを埋めるランキング設計（A Domain Generalization Perspective on Listwise Context Modeling）</news:title>
   <news:publication_date>2026-08-06T08:46:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720099</loc>
  <lastmod>2026-08-06T08:45:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Extended 2Dコンセンサスによる海馬セグメンテーションの実践的意義（Extended 2D Consensus Hippocampus Segmentation）</news:title>
   <news:publication_date>2026-08-06T08:45:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720097</loc>
  <lastmod>2026-08-06T08:45:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>教師ラベルを汚染しないCNNへの新しいバックドア攻撃（A NEW BACKDOOR ATTACK IN CNNS BY TRAINING SET CORRUPTION WITHOUT LABEL POISONING）</news:title>
   <news:publication_date>2026-08-06T08:45:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720095</loc>
  <lastmod>2026-08-06T07:53:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>潜在連続時間確率力学系の解釈可能な学習（Interpretable continuous-time latent stochastic dynamical models）</news:title>
   <news:publication_date>2026-08-06T07:53:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720093</loc>
  <lastmod>2026-08-06T07:53:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>顧客対応チャットボットの回答再ランキングに機械読解を使う意義（Machine Reading Comprehension for Answer Re-Ranking in Customer Support Chatbots）</news:title>
   <news:publication_date>2026-08-06T07:53:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720091</loc>
  <lastmod>2026-08-06T07:53:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>結合学習をEnd-to-Endで訓練すべきか（To Ensemble or Not Ensemble: When does End-To-End Training Fail?）</news:title>
   <news:publication_date>2026-08-06T07:53:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720089</loc>
  <lastmod>2026-08-06T07:52:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフベースのIoTマルウェア検出に対する敵対的学習の検証（Examining Adversarial Learning against Graph-based IoT Malware Detection Systems）</news:title>
   <news:publication_date>2026-08-06T07:52:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720087</loc>
  <lastmod>2026-08-06T07:52:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>TensorSCONE：Intel SGXを用いた安全なTensorFlowフレームワーク（TensorSCONE: A Secure TensorFlow Framework using Intel SGX）</news:title>
   <news:publication_date>2026-08-06T07:52:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720085</loc>
  <lastmod>2026-08-06T07:52:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>検証コード認識における能動学習と深層学習の併用（Verification Code Recognition Based on Active and Deep Learning）</news:title>
   <news:publication_date>2026-08-06T07:52:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720083</loc>
  <lastmod>2026-08-06T07:51:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ピアノの多声音楽自動書き起こしにおけるマルチタスク学習の実践（Multitask Learning for Polyphonic Piano Transcription, a Case Study）</news:title>
   <news:publication_date>2026-08-06T07:51:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720081</loc>
  <lastmod>2026-08-06T07:00:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>資源配分のための適応的確率最適化アルゴリズム（An adaptive stochastic optimization algorithm for resource allocation）</news:title>
   <news:publication_date>2026-08-06T07:00:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720079</loc>
  <lastmod>2026-08-06T07:00:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>画像コントラストの深層ネットワークでの顕在化（Manifestation of Image Contrast in Deep Networks）</news:title>
   <news:publication_date>2026-08-06T07:00:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720077</loc>
  <lastmod>2026-08-06T06:59:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Apollo：あらゆるリードを使える普遍的アセンブリ研磨アルゴリズム（Apollo: A Universal Assembly Polishing Algorithm）</news:title>
   <news:publication_date>2026-08-06T06:59:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720075</loc>
  <lastmod>2026-08-06T06:59:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Gaussian Mean Fieldが示す学習情報の上限による正則化（Gaussian Mean Field Regularizes by Limiting Learned Information）</news:title>
   <news:publication_date>2026-08-06T06:59:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720073</loc>
  <lastmod>2026-08-06T06:59:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>車載向けキーワードスポッティングにおけるマルチソース融合（An In-Vehicle Keyword Spotting System with Multi-Source Fusion for Vehicle Applications）</news:title>
   <news:publication_date>2026-08-06T06:59:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720071</loc>
  <lastmod>2026-08-06T06:58:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ビッグデータ賞受賞手法による電力グリッド流量の高速・高信頼予測（Winning the Big Data Technologies Horizon Prize: Fast and reliable forecasting of electricity grid traffic by identification of recurrent fluctuations）</news:title>
   <news:publication_date>2026-08-06T06:58:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720069</loc>
  <lastmod>2026-08-06T06:58:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>有向非巡回グラフを扱うハイパーボリックディスク埋め込み（Hyperbolic Disk Embeddings for Directed Acyclic Graphs）</news:title>
   <news:publication_date>2026-08-06T06:58:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720067</loc>
  <lastmod>2026-08-06T06:07:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>優先付けしないオートエンコーダによる画像生成（UNPRIORITIZED AUTOENCODER FOR IMAGE GENERATION）</news:title>
   <news:publication_date>2026-08-06T06:07:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720065</loc>
  <lastmod>2026-08-06T06:07:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>近傍矮小銀河の化学進化を示す惑星状星雲とH II領域の知見（What do planetary nebulae and H ii regions reveal about the chemical evolution of nearby dwarf galaxies?）</news:title>
   <news:publication_date>2026-08-06T06:07:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-06T06:06:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-06T06:06:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-06T06:06:27Z</lastmod>
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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>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-06T06:06:21Z</news:publication_date>
   <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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    <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:genres>Blog</news:genres>
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 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-06T05:14:06Z</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>2026-08-06T05:14:00Z</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: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>2026-08-06T05:13:38Z</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>2026-08-06T04:22:49Z</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>PPGから呼吸波を抽出する深層学習の実用性（RespNet: A deep learning model for extraction of respiration from photoplethysmogram）</news:title>
   <news:publication_date>2026-08-06T04:22:14Z</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>2026-08-06T04:21:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-06T04:21:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <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>2026-08-06T03:27:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720013</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720007</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>2026-08-06T02:35:25Z</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>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ネットワーク埋め込みで未観測交絡を補正する手法（Using Embeddings to Correct for Unobserved Confounding in Networks）</news:title>
   <news:publication_date>2026-08-06T01:40:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/719987</loc>
  <lastmod>2026-08-06T01:40:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自律走行のための安全な深層強化学習フレームワーク（WiseMove: A Framework for Safe Deep Reinforcement Learning for Autonomous Driving）</news:title>
   <news:publication_date>2026-08-06T01:40:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
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 <url>
  <loc>https://aibr.jp/archives/719985</loc>
  <lastmod>2026-08-06T01:40:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>属性誘導型顔画像検索における深層クロスモーダルハッシュと誤り訂正符号の併用（USING DEEP CROSS MODAL HASHING AND ERROR CORRECTING CODES FOR IMPROVING THE EFFICIENCY OF ATTRIBUTE GUIDED FACIAL IMAGE RETRIEVAL）</news:title>
   <news:publication_date>2026-08-06T01:40:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719983</loc>
  <lastmod>2026-08-06T00:49:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>暗号分野における機械学習の応用（Applications of Machine Learning in Cryptography: A Survey）</news:title>
   <news:publication_date>2026-08-06T00:49:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719981</loc>
  <lastmod>2026-08-06T00:49:07Z</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EMのダイバージェンス動機づけと隠れ変数モデルの結合（Divergence-Based Motivation for Online EM and Combining Hidden Variable Models）</news:title>
   <news:publication_date>2026-08-06T00:49:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
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 <url>
  <loc>https://aibr.jp/archives/719979</loc>
  <lastmod>2026-08-06T00:48:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>残差ネットワークは恒等写像からの摂動を学習する（On Residual Networks Learning a Perturbation from Identity）</news:title>
   <news:publication_date>2026-08-06T00:48:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719977</loc>
  <lastmod>2026-08-06T00:48:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>BERTに口ができた：BERTをMarkov Random Field言語モデルとして見る（BERT has a Mouth, and It Must Speak: BERT as a Markov Random Field Language Model）</news:title>
   <news:publication_date>2026-08-06T00:48:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719975</loc>
  <lastmod>2026-08-06T00:47:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Yelpの料理画像識別とそのビジネス的意義（Yelp Food Identification via Image Feature Extraction and Classification）</news:title>
   <news:publication_date>2026-08-06T00:47:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719973</loc>
  <lastmod>2026-08-06T00:47:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的生成深層学習による分子設計の新展開（Probabilistic Generative Deep Learning for Molecular Design）</news:title>
   <news:publication_date>2026-08-06T00:47:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719971</loc>
  <lastmod>2026-08-06T00:47:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Psi-Net：形状と境界を意識した共同マルチタスク深層ネットワークによる医用画像セグメンテーション（Psi-Net: Shape and boundary aware joint multi-task deep network for medical image segmentation）</news:title>
   <news:publication_date>2026-08-06T00:47:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719969</loc>
  <lastmod>2026-08-05T23:55:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>AKARI NEP-Deepデータに基づくfuzzy SVMによるAGN候補選別（AGN selection in the AKARI NEP deep field with the fuzzy SVM algorithm）</news:title>
   <news:publication_date>2026-08-05T23:55:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719967</loc>
  <lastmod>2026-08-05T23:55:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>乱雑相互作用系における競合秩序の多面的機械学習（Multi-faceted machine learning of competing orders in disordered interacting systems）</news:title>
   <news:publication_date>2026-08-05T23:55:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719965</loc>
  <lastmod>2026-08-05T23:55:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多体系量子の効率的変分シミュレーションを可能にする深層自己回帰モデル（Deep autoregressive models for the efficient variational simulation of many-body quantum systems）</news:title>
   <news:publication_date>2026-08-05T23:55:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719963</loc>
  <lastmod>2026-08-05T23:54:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ランキングの公平性のための方策学習（Policy Learning for Fairness in Ranking）</news:title>
   <news:publication_date>2026-08-05T23:54:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719961</loc>
  <lastmod>2026-08-05T23:54:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オンライン学習者行動のデータ駆動型非教師ありクラスタリング（DATA-DRIVEN UNSUPERVISED CLUSTERING OF ONLINE LEARNER BEHAVIOUR）</news:title>
   <news:publication_date>2026-08-05T23:54:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719959</loc>
  <lastmod>2026-08-05T23:54:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械は惑星衝突の結果を学べるか（Can a machine learn the outcome of planetary collisions?）</news:title>
   <news:publication_date>2026-08-05T23:54:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719957</loc>
  <lastmod>2026-08-05T23:03:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ゲージ等変畳み込みネットワークとイコサヘドロンCNN（Gauge Equivariant Convolutional Networks and the Icosahedral CNN）</news:title>
   <news:publication_date>2026-08-05T23:03:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719955</loc>
  <lastmod>2026-08-05T22:54:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>波が運ぶ拡散力学：表面波が粒子拡散を加速する仕組み（Surface waves enhance particle dispersion）</news:title>
   <news:publication_date>2026-08-05T22:54:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719953</loc>
  <lastmod>2026-08-05T22:54:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>StarCraftを用いた協調型マルチエージェント強化学習の標準化（The StarCraft Multi-Agent Challenge）</news:title>
   <news:publication_date>2026-08-05T22:54:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719951</loc>
  <lastmod>2026-08-05T22:54:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>イベント認証と画像再利用検出の深層学習手法（Deep Learning Methods for Event Verification and Image Repurposing Detection）</news:title>
   <news:publication_date>2026-08-05T22:54:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719949</loc>
  <lastmod>2026-08-05T22:53:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>KTBoost：カーネルと木を組み合わせるブースティング（KTBoost: Combined Kernel and Tree Boosting）</news:title>
   <news:publication_date>2026-08-05T22:53:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719947</loc>
  <lastmod>2026-08-05T22:53:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Real Time Recurrent Learningの最適近似とその意義（Optimal Kronecker-Sum Approximation of Real Time Recurrent Learning）</news:title>
   <news:publication_date>2026-08-05T22:53:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719945</loc>
  <lastmod>2026-08-05T22:53:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>全体チェーン推薦（Whole-Chain Recommendations）</news:title>
   <news:publication_date>2026-08-05T22:53:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719943</loc>
  <lastmod>2026-08-05T22:01:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>判別器の一般化と安定化を促す勾配ペナルティ（Improving Generalization and Stability of Generative Adversarial Networks）</news:title>
   <news:publication_date>2026-08-05T22:01:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719941</loc>
  <lastmod>2026-08-05T21:50:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>前向き・後向き確率微分方程式を用いた深層確率最適制御の学習（Learning Deep Stochastic Optimal Control Policies using Forward-Backward SDEs）</news:title>
   <news:publication_date>2026-08-05T21:50:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719939</loc>
  <lastmod>2026-08-05T21:50:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Interaction-Transformationを用いた進化的シンボリック回帰（Interaction-Transformation Evolutionary Algorithm for Symbolic Regression）</news:title>
   <news:publication_date>2026-08-05T21:50:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719937</loc>
  <lastmod>2026-08-05T21:49:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Deep Node Rankingによるネットワークノード埋め込みと分類（Deep Node Ranking for Neuro-symbolic Structural Node Embedding and Classification）</news:title>
   <news:publication_date>2026-08-05T21:49:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719935</loc>
  <lastmod>2026-08-05T21:49:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>小データ環境での物理認識確率的機械学習による粗視化（A physics-aware, probabilistic machine learning framework for coarse-graining high-dimensional systems in the Small Data regime）</news:title>
   <news:publication_date>2026-08-05T21:49:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719933</loc>
  <lastmod>2026-08-05T21:49:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>実世界の携帯データに対する頑健な予測モデル（A Machine Learning based Robust Prediction Model for Real-life Mobile Phone Data）</news:title>
   <news:publication_date>2026-08-05T21:49:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719931</loc>
  <lastmod>2026-08-05T21:48:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフに基づくマルウェア解析と検出（Analyzing, Comparing, and Detecting Emerging Malware: A Graph-based Approach）</news:title>
   <news:publication_date>2026-08-05T21:48:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719929</loc>
  <lastmod>2026-08-05T20:56:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Net2Vis — 論文向けCNNアーキテクチャ可視化の視覚文法 (Net2Vis – A Visual Grammar for Automatically Generating Publication-Tailored CNN Architecture Visualizations)</news:title>
   <news:publication_date>2026-08-05T20:56:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719927</loc>
  <lastmod>2026-08-05T20:55:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>エネルギーを学習に再投資するブロックチェーン（Energy-recycling Blockchain with Proof-of-Deep-Learning）</news:title>
   <news:publication_date>2026-08-05T20:55:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719925</loc>
  <lastmod>2026-08-05T20:55:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>サイクル式確率的勾配MCMCによるベイズ深層学習の再活性化（Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning）</news:title>
   <news:publication_date>2026-08-05T20:55:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719923</loc>
  <lastmod>2026-08-05T20:54:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>特徴量ランキングで再構築する動的ネットワーク（Reconstructing dynamical networks via feature ranking）</news:title>
   <news:publication_date>2026-08-05T20:54:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719921</loc>
  <lastmod>2026-08-05T20:54:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時間周波数特徴の敵対的生成（Adversarial Generation of Time-Frequency Features）</news:title>
   <news:publication_date>2026-08-05T20:54:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719919</loc>
  <lastmod>2026-08-05T20:54:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層ハッシュとエントロピー正則化によるプロダクト量子化ネットワーク（DEEP HASHING USING ENTROPY REGULARISED PRODUCT QUANTISATION NETWORK）</news:title>
   <news:publication_date>2026-08-05T20:54:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719917</loc>
  <lastmod>2026-08-05T20:54:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>次元削減に基づく深層学習を用いた電磁ナノ構造設計（Deep learning approach based on dimensionality reduction for designing electromagnetic nanostructures）</news:title>
   <news:publication_date>2026-08-05T20:54:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719915</loc>
  <lastmod>2026-08-05T20:03:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Curvelet領域におけるロバスト統計に基づく参照なし画像品質評価（Robust statistics and no-reference image quality assessment in Curvelet domain）</news:title>
   <news:publication_date>2026-08-05T20:03:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719913</loc>
  <lastmod>2026-08-05T20:03:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所的相互作用による大域的協調（Global Collaboration through Local Interaction in Competitive Learning）</news:title>
   <news:publication_date>2026-08-05T20:03:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719911</loc>
  <lastmod>2026-08-05T20:02:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散近似近傍探索による大規模Mean Shiftクラスタリングの効率化（A Distributed and Approximated Nearest Neighbors Algorithm for an Efficient Large Scale Mean Shift Clustering）</news:title>
   <news:publication_date>2026-08-05T20:02:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719909</loc>
  <lastmod>2026-08-05T20:01:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>再構成可能な集積導波路メッシュによるフォトニック信号処理と応用（Reconfigurable integrated waveguide meshes for photonic signal processing and emerging applications）</news:title>
   <news:publication_date>2026-08-05T20:01:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719907</loc>
  <lastmod>2026-08-05T20:01:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>セマンティック階層的事前知識を用いた物体内在表現の分解（Semantic Hierarchical Priors for Intrinsic Image Decomposition）</news:title>
   <news:publication_date>2026-08-05T20:01:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719905</loc>
  <lastmod>2026-08-05T20:01:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>乳がん良悪性予測：データマイニングによる医療応用（Prediction of Malignant &amp;amp; Benign Breast Cancer: A Data Mining Approach in Healthcare Applications）</news:title>
   <news:publication_date>2026-08-05T20:01:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719903</loc>
  <lastmod>2026-08-05T20:00:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>有限時間影響システムと群衆の知恵効果（FINITE-TIME INFLUENCE SYSTEMS AND THE WISDOM OF CROWD EFFECT）</news:title>
   <news:publication_date>2026-08-05T20:00:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719901</loc>
  <lastmod>2026-08-05T19:08:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>BRASSによる原子データ品質評価と星スペクトル検証（The Belgian repository of fundamental atomic data and stellar spectra (BRASS) II. Quality assessment of atomic data for unblended lines in FGK stars）</news:title>
   <news:publication_date>2026-08-05T19:08:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719899</loc>
  <lastmod>2026-08-05T19:08:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>半教師あり・タスク駆動型データ拡張による医用画像セグメンテーションの進化（Semi-Supervised and Task-Driven Data Augmentation）</news:title>
   <news:publication_date>2026-08-05T19:08:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719897</loc>
  <lastmod>2026-08-05T19:07:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>人の感情で人権侵害を見抜く（GET-AID: Global Emotional Traits for Abuse Identification）</news:title>
   <news:publication_date>2026-08-05T19:07:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719895</loc>
  <lastmod>2026-08-05T19:06:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>サンプルベースのオンラインモード推定法（An Online Sample Based Method for Mode Estimation using ODE Analysis of Stochastic Approximation Algorithms）</news:title>
   <news:publication_date>2026-08-05T19:06:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719893</loc>
  <lastmod>2026-08-05T19:06:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Drynx: 分散データ上のプライバシー保護型統計・機械学習システム（Drynx: Decentralized, Secure, Verifiable System for Statistical Queries and Machine Learning on Distributed Datasets）</news:title>
   <news:publication_date>2026-08-05T19:06:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719891</loc>
  <lastmod>2026-08-05T19:05:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>不確実性の構造を利用した効率的なマトロイド半セミバンディット（Exploiting Structure of Uncertainty for Efficient Matroid Semi-Bandits）</news:title>
   <news:publication_date>2026-08-05T19:05:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719889</loc>
  <lastmod>2026-08-05T18:14:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ポルノ画像認識のための加重複数インスタンス学習（Pornographic Image Recognition via Weighted Multiple Instance Learning）</news:title>
   <news:publication_date>2026-08-05T18:14:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719887</loc>
  <lastmod>2026-08-05T18:14:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>潜在空間を使った強化学習によるステアリング予測（Latent Space Reinforcement Learning for Steering Angle Prediction）</news:title>
   <news:publication_date>2026-08-05T18:14:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719885</loc>
  <lastmod>2026-08-05T18:14:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ランタイム検証のコア概念と実務応用（Runtime Verification Beyond Monitoring）</news:title>
   <news:publication_date>2026-08-05T18:14:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719883</loc>
  <lastmod>2026-08-05T18:13:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Path Capsule Networks（PATH CAPSULE NETWORKS）</news:title>
   <news:publication_date>2026-08-05T18:13:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719881</loc>
  <lastmod>2026-08-05T18:13:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>未来の人物行動と位置の予測（Peeking into the Future: Predicting Future Person Activities and Locations in Videos）</news:title>
   <news:publication_date>2026-08-05T18:13:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719879</loc>
  <lastmod>2026-08-05T18:13:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>HINTによる視覚と言語モデルの視覚的グラウンディング強化（Taking a HINT: Leveraging Explanations to Make Vision and Language Models More Grounded）</news:title>
   <news:publication_date>2026-08-05T18:13:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719877</loc>
  <lastmod>2026-08-05T18:12:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模多クラス分類の効率的な原始双対アルゴリズム（Efficient Primal-Dual Algorithms for Large-Scale Multiclass Classification）</news:title>
   <news:publication_date>2026-08-05T18:12:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719875</loc>
  <lastmod>2026-08-05T17:21:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>長期間摂動インパルス転送の最適近似を高速化する深層ニューラルネットワーク（Fast Approximation of Optimal Perturbed Long-Duration Impulsive Transfers via Deep Neural Networks）</news:title>
   <news:publication_date>2026-08-05T17:21:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719873</loc>
  <lastmod>2026-08-05T17:21:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>低精度データを活用してベイズ最適化を高速化する手法（Harnessing Low-Fidelity Data to Accelerate Bayesian Optimization via Posterior Regularization）</news:title>
   <news:publication_date>2026-08-05T17:21:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719871</loc>
  <lastmod>2026-08-05T17:20:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>低推力推進転送の高速評価（Fast Evaluation of Low-Thrust Transfers via Deep Neural Networks）</news:title>
   <news:publication_date>2026-08-05T17:20:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719869</loc>
  <lastmod>2026-08-05T17:20:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ノルム・サブガウス分布の集中不等式の短い解説（A Short Note on Concentration Inequalities for Random Vectors with SubGaussian Norm）</news:title>
   <news:publication_date>2026-08-05T17:20:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719867</loc>
  <lastmod>2026-08-05T17:20:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフラプラシアン正則化推定器の誤差解析 (Error Analysis on Graph Laplacian Regularized Estimator)</news:title>
   <news:publication_date>2026-08-05T17:20:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719865</loc>
  <lastmod>2026-08-05T17:19:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>アルゴリズム時代の差別と検出可能性（Discrimination in the Age of Algorithms）</news:title>
   <news:publication_date>2026-08-05T17:19:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719863</loc>
  <lastmod>2026-08-05T17:19:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>音楽機械学習のための対話的ウェブデモ最小テンプレート (A Minimal Template for Interactive Web-based Demonstrations of Musical Machine Learning)</news:title>
   <news:publication_date>2026-08-05T17:19:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719861</loc>
  <lastmod>2026-08-05T16:28:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多様体最適化を用いたガウス変分近似 (Manifold optimization Assisted Gaussian Variational Approximation)</news:title>
   <news:publication_date>2026-08-05T16:28:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719859</loc>
  <lastmod>2026-08-05T16:28:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動くものを何でも分割する方向性（Towards Segmenting Anything That Moves）</news:title>
   <news:publication_date>2026-08-05T16:28:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719857</loc>
  <lastmod>2026-08-05T16:27:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>切断ガウス混合変分オートエンコーダ（Truncated Gaussian-Mixture Variational AutoEncoder）</news:title>
   <news:publication_date>2026-08-05T16:27:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719855</loc>
  <lastmod>2026-08-05T16:26:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>シンプレクティック離散化による加速――高解像度常微分方程式の役割（Acceleration via Symplectic Discretization of High-Resolution Differential Equations）</news:title>
   <news:publication_date>2026-08-05T16:26:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719853</loc>
  <lastmod>2026-08-05T16:26:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>エッジ置換文法でグラフを生成する手法の要点（Edge Replacement Grammars : A Formal Language Approach for Generating Graphs）</news:title>
   <news:publication_date>2026-08-05T16:26:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719851</loc>
  <lastmod>2026-08-05T16:26:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチクラウド上のNFVにおける障害・性能管理と浅層／深層予測構造（Fault and Performance Management in Multi-Cloud Based NFV using Shallow and Deep Predictive Structures）</news:title>
   <news:publication_date>2026-08-05T16:26:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719849</loc>
  <lastmod>2026-08-05T16:26:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>シミュレーションを活用した一般化学習（Generalization through Simulation: Integrating Simulated and Real Data into Deep Reinforcement Learning for Vision-Based Autonomous Flight）</news:title>
   <news:publication_date>2026-08-05T16:26:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719847</loc>
  <lastmod>2026-08-05T15:33:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SCADAシステムテストベッドによるサイバーセキュリティ研究（SCADA System Testbed for Cybersecurity Research Using Machine Learning Approach）</news:title>
   <news:publication_date>2026-08-05T15:33:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719845</loc>
  <lastmod>2026-08-05T15:23:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>密度推定における最適近似係数の解明（The Optimal Approximation Factor in Density Estimation）</news:title>
   <news:publication_date>2026-08-05T15:23:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719843</loc>
  <lastmod>2026-08-05T15:23:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>電子回折におけるパラダイムシフト（Paradigm shift in electron-based crystallography via machine learning）</news:title>
   <news:publication_date>2026-08-05T15:23:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719841</loc>
  <lastmod>2026-08-05T15:22:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ノイズ付きラベルから学ぶ：注釈者混乱行列の正則化推定（Learning From Noisy Labels By Regularized Estimation Of Annotator Confusion）</news:title>
   <news:publication_date>2026-08-05T15:22:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719839</loc>
  <lastmod>2026-08-05T15:21:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次元空間における差分類似性の理論と応用（Differential Similarity in Higher Dimensional Spaces）</news:title>
   <news:publication_date>2026-08-05T15:21:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719837</loc>
  <lastmod>2026-08-05T15:21:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>弱い重力レンズ観測における畳み込みニューラルネットワークの有用性（Weak lensing cosmology with convolutional neural networks on noisy data）</news:title>
   <news:publication_date>2026-08-05T15:21:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719835</loc>
  <lastmod>2026-08-05T15:21:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ギリシャ語コーパスにおけるメタファー検出のためのニューラル埋め込み（Neural embeddings for metaphor detection in a corpus of Greek texts）</news:title>
   <news:publication_date>2026-08-05T15:21:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719833</loc>
  <lastmod>2026-08-05T14:29:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>個人の文体を捉える単語埋め込み（Word embeddings for idiolect identification）</news:title>
   <news:publication_date>2026-08-05T14:29:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719831</loc>
  <lastmod>2026-08-05T14:29:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>強化学習エージェントの最適選択のためのバンディット枠組み（A Bandit Framework for Optimal Selection of Reinforcement Learning Agents）</news:title>
   <news:publication_date>2026-08-05T14:29:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719829</loc>
  <lastmod>2026-08-05T14:28:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>究極の深宇宙光通信容量への接近（Approaching the ultimate capacity limit in deep-space optical communication）</news:title>
   <news:publication_date>2026-08-05T14:28:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719827</loc>
  <lastmod>2026-08-05T14:28:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>低障壁磁石を用いた効率的なハードウェア型バイナリ確率ニューロン設計（Low Barrier Magnet Design for Efficient Hardware Binary Stochastic Neurons）</news:title>
   <news:publication_date>2026-08-05T14:28:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719825</loc>
  <lastmod>2026-08-05T14:27:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数グラフィカルモデルの同時推定に対するベイズ的アプローチ (A Bayesian Approach to Joint Estimation of Multiple Graphical Models)</news:title>
   <news:publication_date>2026-08-05T14:27:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719823</loc>
  <lastmod>2026-08-05T14:27:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>コンテキストを考慮した視覚的互換性予測（Context-Aware Visual Compatibility Prediction）</news:title>
   <news:publication_date>2026-08-05T14:27:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719821</loc>
  <lastmod>2026-08-05T14:27:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>反復的最小トリム二乗法による混合線形回帰の頑健化（Iterative Least Trimmed Squares for Mixed Linear Regression）</news:title>
   <news:publication_date>2026-08-05T14:27:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719815</loc>
  <lastmod>2026-08-05T13:35:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>主成分分析を用いた特徴選択によるマルウェア検出の機械学習（Machine Learning With Feature Selection Using Principal Component Analysis for Malware Detection: A Case Study）</news:title>
   <news:publication_date>2026-08-05T13:35:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719813</loc>
  <lastmod>2026-08-05T13:33:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>inf‑projectionによるKurdyka‑Łojasiewicz指数の保存性（Kurdyka‑Lojasiewicz exponent via inf‑projection）</news:title>
   <news:publication_date>2026-08-05T13:33:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719811</loc>
  <lastmod>2026-08-05T13:24:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単一隠れ層ニューラルネットワークによる連続関数の近似アルゴリズム（An Algorithm for Approximating Continuous Functions on Compact Subsets with a Neural Network with one Hidden Layer）</news:title>
   <news:publication_date>2026-08-05T13:24:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719809</loc>
  <lastmod>2026-08-05T13:23:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>(q,p)-Wasserstein GANsにおける基底距離の比較 ((q,p)-Wasserstein GANs: Comparing Ground Metrics for Wasserstein GANs)</news:title>
   <news:publication_date>2026-08-05T13:23:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719807</loc>
  <lastmod>2026-08-05T13:22:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>浅い三重ストリーム三次元CNNによる微表情認識（Shallow Triple Stream Three-dimensional CNN for Micro-expression Recognition）</news:title>
   <news:publication_date>2026-08-05T13:22:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719805</loc>
  <lastmod>2026-08-05T13:22:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>NICA/MPD ECalの空間分解能改善（Improving the spatial resolution of NICA/MPD ECAL with new reconstruction methods）</news:title>
   <news:publication_date>2026-08-05T13:22:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719803</loc>
  <lastmod>2026-08-05T13:22:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>共役方策による多様な探索手法（Diverse Exploration via Conjugate Policies for Policy Gradient Methods）</news:title>
   <news:publication_date>2026-08-05T13:22:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719793</loc>
  <lastmod>2026-08-05T12:30:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>PAUカメラによる高精度フォトメトリック測光とその意義（The Physics of the Accelerating Universe Camera）</news:title>
   <news:publication_date>2026-08-05T12:30:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719791</loc>
  <lastmod>2026-08-05T12:30:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>トランジェント検出における深層学習 (Deep learning detection of transients)</news:title>
   <news:publication_date>2026-08-05T12:30:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719789</loc>
  <lastmod>2026-08-05T12:30:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ソーシャルメディアにおけるフェイクニュース検出の幾何学的ディープラーニング（Fake News Detection on Social Media using Geometric Deep Learning）</news:title>
   <news:publication_date>2026-08-05T12:30:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719787</loc>
  <lastmod>2026-08-05T12:29:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>上気道消化管領域における病変自動分類への示唆（Towards Automatic Lesion Classification in the Upper Aerodigestive Tract Using OCT and Deep Transfer Learning Methods）</news:title>
   <news:publication_date>2026-08-05T12:29:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719785</loc>
  <lastmod>2026-08-05T12:29:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ハイブリッドフォレスト：概念ドリフトに強いデータストリーム解析手法（Hybrid Forest: A Concept Drift Aware Data Stream Mining Algorithm）</news:title>
   <news:publication_date>2026-08-05T12:29:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719783</loc>
  <lastmod>2026-08-05T12:28:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ELKI: 大規模オープンソースデータ解析ライブラリの現状と示唆（ELKI: A large open-source library for data analysis）</news:title>
   <news:publication_date>2026-08-05T12:28:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719781</loc>
  <lastmod>2026-08-05T12:28:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>敵対的学習による分離型3D顔形状モデルの提案（A Decoupled 3D Facial Shape Model by Adversarial Training）</news:title>
   <news:publication_date>2026-08-05T12:28:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719779</loc>
  <lastmod>2026-08-05T11:37:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>統一視覚認識モデルによる自動運転の多機能化（NeurAll: Towards a Unified Visual Perception Model for Automated Driving）</news:title>
   <news:publication_date>2026-08-05T11:37:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719777</loc>
  <lastmod>2026-08-05T11:37:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>回帰ファジィモデルによるソフトウェア工数見積りの実践知（Software Development Effort Estimation Using Regression Fuzzy Models）</news:title>
   <news:publication_date>2026-08-05T11:37:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719775</loc>
  <lastmod>2026-08-05T11:37:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>脆弱な道路利用者検出の最前線と課題（Vulnerable road user detection: state-of-the-art and open challenges）</news:title>
   <news:publication_date>2026-08-05T11:37:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719773</loc>
  <lastmod>2026-08-05T11:36:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大腸がんの予後予測を切り拓く組織画像解析（Colorectal Cancer Outcome Prediction from H&amp;amp;E Whole Slide Images using Machine Learning and Automatically Inferred Phenotype Profiles）</news:title>
   <news:publication_date>2026-08-05T11:36:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719771</loc>
  <lastmod>2026-08-05T11:36:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層ニューラルネットワークによる到来角推定の性能優位性（PERFORMANCE ADVANTAGES OF DEEP NEURAL NETWORKS FOR ANGLE OF ARRIVAL ESTIMATION）</news:title>
   <news:publication_date>2026-08-05T11:36:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719769</loc>
  <lastmod>2026-08-05T11:36:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>広告取引所でのエージェント最適応答学習（Learning Best Response Strategies for Agents in Ad Exchanges）</news:title>
   <news:publication_date>2026-08-05T11:36:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719767</loc>
  <lastmod>2026-08-05T11:36:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>前眼部OCTに基づく隅角閉塞検出の多階層深層ネットワーク（Angle-Closure Detection in Anterior Segment OCT based on Multi-Level Deep Network）</news:title>
   <news:publication_date>2026-08-05T11:36:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719765</loc>
  <lastmod>2026-08-05T10:44:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>TASK2VECによるタスク埋め込みとメタラーニング（TASK2VEC: Task Embedding for Meta-Learning）</news:title>
   <news:publication_date>2026-08-05T10:44:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719763</loc>
  <lastmod>2026-08-05T10:44:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Twitter共有データからのフェイクニュース検出（Identifying Fake News from Twitter Sharing Data: A Large-Scale Study）</news:title>
   <news:publication_date>2026-08-05T10:44:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719761</loc>
  <lastmod>2026-08-05T10:43:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>NIR-VIS異スペクトル顔補完の実用的意義（Cross-spectral Face Completion for NIR-VIS Heterogeneous Face Recognition）</news:title>
   <news:publication_date>2026-08-05T10:43:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719759</loc>
  <lastmod>2026-08-05T10:42:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>小児の活動量計データに基づくADHD分類（Classifying attention deficit hyperactivity disorder in children with non-linearities in actigraphy）</news:title>
   <news:publication_date>2026-08-05T10:42:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719757</loc>
  <lastmod>2026-08-05T10:42:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>一般化された空間ポアソン点過程の散乱統計 (Scattering Statistics of Generalized Spatial Poisson Point Processes)</news:title>
   <news:publication_date>2026-08-05T10:42:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719755</loc>
  <lastmod>2026-08-05T10:42:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多ラベル変数の特徴選択における依存性最大化（Feature Selection for Multi-Labeled Variables via Dependency Maximization）</news:title>
   <news:publication_date>2026-08-05T10:42:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719753</loc>
  <lastmod>2026-08-05T10:42:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>IoTマルウェアのエンドポイント解析が示す攻撃の構図（Analyzing Endpoints in the Internet of Things）</news:title>
   <news:publication_date>2026-08-05T10:42:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719751</loc>
  <lastmod>2026-08-05T09:50:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ガウス仮定を外したVAEの実装と意義（Biadversarial Variational Autoencoder）</news:title>
   <news:publication_date>2026-08-05T09:50:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719749</loc>
  <lastmod>2026-08-05T09:49:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スケーラブルな公平クラスタリング（Scalable Fair Clustering）</news:title>
   <news:publication_date>2026-08-05T09:49:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719747</loc>
  <lastmod>2026-08-05T09:49:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層学習と顔認識の現状（Deep learning and face recognition: the state of the art）</news:title>
   <news:publication_date>2026-08-05T09:49:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719745</loc>
  <lastmod>2026-08-05T09:48:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Besov空間下におけるGANの非パラメトリック密度推定と収束（Nonparametric Density Estimation and Convergence of GANs under Besov IPM Losses）</news:title>
   <news:publication_date>2026-08-05T09:48:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719743</loc>
  <lastmod>2026-08-05T09:48:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数ドメイン翻訳のための非結合オートエンコーダ学習（Multi-Domain Translation by Learning Uncoupled Autoencoders）</news:title>
   <news:publication_date>2026-08-05T09:48:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719741</loc>
  <lastmod>2026-08-05T09:48:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>顔のミクロ表情の検出と認識を時間差分特徴と記憶モジュールで強化する手法（FACIAL MICRO-EXPRESSION SPOTTING AND RECOGNITION USING TIME CONTRASTED FEATURE WITH VISUAL MEMORY）</news:title>
   <news:publication_date>2026-08-05T09:48:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719739</loc>
  <lastmod>2026-08-05T09:48:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>逆投影表現とカテゴリ貢献率による頑健な腫瘍分類（Inverse Projection Representation and Category Contribution Rate for Robust Tumor Recognition）</news:title>
   <news:publication_date>2026-08-05T09:48:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719737</loc>
  <lastmod>2026-08-05T08:55:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習モデルの局所的解釈可能性の評価（Assessing the Local Interpretability of Machine Learning Models）</news:title>
   <news:publication_date>2026-08-05T08:55:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719735</loc>
  <lastmod>2026-08-05T08:53:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多言語ニューラル機械翻訳における語彙表現の分離化（Multilingual Neural Machine Translation with Soft Decoupled Encoding）</news:title>
   <news:publication_date>2026-08-05T08:53:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719733</loc>
  <lastmod>2026-08-05T08:53:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ストリーミングモデルにおける線形予測の空間下限（Space lower bounds for linear prediction in the streaming model）</news:title>
   <news:publication_date>2026-08-05T08:53:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719731</loc>
  <lastmod>2026-08-05T08:52:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ルーメン境界検出における不確定性クラスタリングの実用性（Lumen boundary detection using neutrosophic c-means in IVOCT images）</news:title>
   <news:publication_date>2026-08-05T08:52:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719729</loc>
  <lastmod>2026-08-05T08:52:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>未分類スペクトルからM型星を掘り起こすハッシュ学習（Recognition of M-type stars in the unclassified spectra of LAMOST DR5 using a hash learning method）</news:title>
   <news:publication_date>2026-08-05T08:52:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719727</loc>
  <lastmod>2026-08-05T08:52:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>アルゴリズム展開による深層画像デブラーリング（AN ALGORITHM UNROLLING APPROACH TO DEEP IMAGE DEBLURRING）</news:title>
   <news:publication_date>2026-08-05T08:52:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719725</loc>
  <lastmod>2026-08-05T08:51:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層アルゴリズム・アンローリングによるブラインド画像復元（Deep Algorithm Unrolling for Blind Image Deblurring）</news:title>
   <news:publication_date>2026-08-05T08:51:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719723</loc>
  <lastmod>2026-08-05T08:00:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文脈で初期状態を学習するRNN（Contextual Recurrent Neural Networks）</news:title>
   <news:publication_date>2026-08-05T08:00:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719721</loc>
  <lastmod>2026-08-05T08:00:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>合成データ生成と差分プライバシーの接点（Synthetic Data Generators – Sequential and Private）</news:title>
   <news:publication_date>2026-08-05T08:00:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719719</loc>
  <lastmod>2026-08-05T07:53:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ケイ酸塩ガラスのための機械学習フォースフィールド（Machine Learning Forcefield for Silicate Glasses）</news:title>
   <news:publication_date>2026-08-05T07:53:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719717</loc>
  <lastmod>2026-08-05T07:52:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数回答を持つ純探索問題のサンプル複雑性（Pure Exploration with Multiple Correct Answers）</news:title>
   <news:publication_date>2026-08-05T07:52:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719715</loc>
  <lastmod>2026-08-05T07:52:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Omniglotチャレンジ：3年の進捗報告（The Omniglot challenge: a 3-year progress report）</news:title>
   <news:publication_date>2026-08-05T07:52:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719713</loc>
  <lastmod>2026-08-05T07:50:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>超高速リアルタイム顔ランドマーク検出と形状フィッティング（SUPER-REALTIME FACIAL LANDMARK DETECTION AND SHAPE FITTING BY DEEP REGRESSION OF SHAPE MODEL PARAMETERS）</news:title>
   <news:publication_date>2026-08-05T07:50:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719711</loc>
  <lastmod>2026-08-05T07:50:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層型マルチタスク深層ニューラルネットワークによるエンドツーエンド運転（Hierarchical Multi-task Deep Neural Network Architecture for End-to-End Driving）</news:title>
   <news:publication_date>2026-08-05T07:50:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719709</loc>
  <lastmod>2026-08-05T06:58:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>野外画像からの3D手形状とポーズ（3D Hand Shape and Pose from Images in the Wild）</news:title>
   <news:publication_date>2026-08-05T06:58:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719707</loc>
  <lastmod>2026-08-05T06:49:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>構造的近傍に基づく距離尺度学習（Distance metric learning based on structural neighborhoods for dimensionality reduction and classification performance improvement）</news:title>
   <news:publication_date>2026-08-05T06:49:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719705</loc>
  <lastmod>2026-08-05T06:49:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Venn GANによる複数分布の共通点と差異の発見（Venn GAN: Discovering Commonalities and Particularities of Multiple Distributions）</news:title>
   <news:publication_date>2026-08-05T06:49:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719703</loc>
  <lastmod>2026-08-05T06:48:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自動運転にGANを応用する可能性と現実（Yes, we GAN: Applying Adversarial Techniques for Autonomous Driving）</news:title>
   <news:publication_date>2026-08-05T06:48:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719701</loc>
  <lastmod>2026-08-05T06:47:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>低域通過フィルタをベイズ推論として捉える（Low‑Pass Filtering as Bayesian Inference）</news:title>
   <news:publication_date>2026-08-05T06:47:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719699</loc>
  <lastmod>2026-08-05T06:47:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>パラメータ化ネットワークによる力学系の外挿シミュレーション（Simulating extrapolated dynamics with parameterization networks）</news:title>
   <news:publication_date>2026-08-05T06:47:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719697</loc>
  <lastmod>2026-08-05T06:47:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>3次元化学空間での生理活性分子クラスタリング（Clustering Bioactive Molecules in 3D Chemical Space with Unsupervised Deep Learning）</news:title>
   <news:publication_date>2026-08-05T06:47:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719695</loc>
  <lastmod>2026-08-05T05:55:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>代理モデルと表現型距離で効率化するニューラル進化 (Improving NeuroEvolution Efficiency by Surrogate Model-based Optimization with Phenotypic Distance Kernels)</news:title>
   <news:publication_date>2026-08-05T05:55:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719693</loc>
  <lastmod>2026-08-05T05:54:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>データ駆動型車両軌道予測の実務的意義（Data-Driven Vehicle Trajectory Forecasting）</news:title>
   <news:publication_date>2026-08-05T05:54:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719691</loc>
  <lastmod>2026-08-05T05:54:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>下水ポンプ場におけるデータ駆動型予測エネルギー最適化（Data-driven Predictive Energy Optimization in a Wastewater Pumping Station）</news:title>
   <news:publication_date>2026-08-05T05:54:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-05T05:53:41Z</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/719685</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>2026-08-05T05:53:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719683</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>2026-08-05T05:53:05Z</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>2026-08-05T05:01:48Z</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>2026-08-05T05:01:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719677</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>領域ベースのアンサンブル学習ネットワークによる細粒度分類（Region based Ensemble Learning Network for Fine-grained Classification）</news:title>
   <news:publication_date>2026-08-05T05:01:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719675</loc>
  <lastmod>2026-08-05T05:00:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>医療データに学ぶ患者類似度の測定（Measuring Patient Similarities via a Deep Architecture with Medical Concept Embedding）</news:title>
   <news:publication_date>2026-08-05T05:00:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719673</loc>
  <lastmod>2026-08-05T05:00:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>最小記憶でSDPを解く近似相補性の手法（An Optimal-Storage Approach to Semidefinite Programming Using Approximate Complementarity）</news:title>
   <news:publication_date>2026-08-05T05:00:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719671</loc>
  <lastmod>2026-08-05T05:00:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>皮膚病変解析とメラノーマ検出に関する挑戦（Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC))</news:title>
   <news:publication_date>2026-08-05T05:00:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719669</loc>
  <lastmod>2026-08-05T05:00:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動的系の潜在表現――二つ持つほうが優れている理由（Latent Representations of Dynamical Systems: When Two is Better Than One）</news:title>
   <news:publication_date>2026-08-05T05:00:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719667</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スパースビュー・マイクロCTのシノグラム補間と深層学習（Sinogram interpolation for sparse-view micro-CT with deep learning neural network）</news:title>
   <news:publication_date>2026-08-05T04:08:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719665</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>生成モデルによる画像分解と分類（Image Decomposition and Classification through a Generative Model）</news:title>
   <news:publication_date>2026-08-05T04:08:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719663</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的変分不等式に対する分散削減を備えた前進―後退―前進法（Forward-Backward-Forward Methods with Variance Reduction for Stochastic Variational Inequalities）</news:title>
   <news:publication_date>2026-08-05T04:07:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719661</loc>
  <lastmod>2026-08-05T04:07:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>メタカーブチャーで速く適応する学習法の本質（Meta-Curvature）</news:title>
   <news:publication_date>2026-08-05T04:07:48Z</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>2026-08-05T04:07:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719657</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>WarpFlowによるペタバイト空間時間データの探索（WarpFlow: Exploring Petabytes of Space-Time Data）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719655</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/719653</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>2026-08-05T03:16:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719651</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>ミニバッチ学習による指数族有限混合モデルの最尤推定（Mini-batch learning of exponential family finite mixture models）</news:title>
   <news:publication_date>2026-08-05T03:15:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719649</loc>
  <lastmod>2026-08-05T03:15:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>状態非依存可逆VAMPnetsによる遅い分子モードの非線形発見（Nonlinear Discovery of Slow Molecular Modes using State-Free Reversible VAMPnets）</news:title>
   <news:publication_date>2026-08-05T03:15:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719647</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>グラフ構造的スパース性を用いたベイズモデル選択（Bayesian Model Selection with Graph Structured Sparsity）</news:title>
   <news:publication_date>2026-08-05T03:14:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719645</loc>
  <lastmod>2026-08-05T03:14:38Z</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>2026-08-05T03:14:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719643</loc>
  <lastmod>2026-08-05T03:14:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>アーキテクチャ圧縮（Architecture Compression）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719641</loc>
  <lastmod>2026-08-05T03:14:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>アクティブエリアカバレッジと平衡状態からのデータ取得（Active Area Coverage from Equilibrium）</news:title>
   <news:publication_date>2026-08-05T03:14:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719639</loc>
  <lastmod>2026-08-05T02:22:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スケーラブルなホリスティック線形回帰（Scalable Holistic Linear Regression）</news:title>
   <news:publication_date>2026-08-05T02:22:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719637</loc>
  <lastmod>2026-08-05T02:15:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>主成分分析における次元数の自動選択と無情報度スコア（Automatic dimensionality selection for principal component analysis models with the ignorance score）</news:title>
   <news:publication_date>2026-08-05T02:15:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719635</loc>
  <lastmod>2026-08-05T02:15:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習可能な活性化関数の単純で効率的な構造（A simple and efficient architecture for trainable activation functions）</news:title>
   <news:publication_date>2026-08-05T02:15:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719633</loc>
  <lastmod>2026-08-05T02:14:05Z</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/719631</loc>
  <lastmod>2026-08-05T02:13:46Z</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>2026-08-05T02:13:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719629</loc>
  <lastmod>2026-08-05T02:13:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>データ駆動型ネットワークアラインメント（Data-driven network alignment）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719627</loc>
  <lastmod>2026-08-05T02:12:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>知識表現と認識的学習によるELオントロジー学習（Learning Ontologies with Epistemic Reasoning: The EL Case）</news:title>
   <news:publication_date>2026-08-05T02:12:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719625</loc>
  <lastmod>2026-08-05T01:21:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>FSNetによる畳み込みニューラルネットワークの圧縮（FSNet: Compression of Deep Convolutional Neural Networks by Filter Summary）</news:title>
   <news:publication_date>2026-08-05T01:21:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719623</loc>
  <lastmod>2026-08-05T01:21:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <lastmod>2026-08-05T01:20:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>構造化予測モデルのより滑らかな学習法（A Smoother Way to Train Structured Prediction Models）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <lastmod>2026-08-05T01:20:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 <url>
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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>
  </news:news>
 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>肺がん検出と診断のための3D確率的深層学習システム（A 3D Probabilistic Deep Learning System for Detection and Diagnosis of Lung Cancer Using Low-Dose CT Scans）</news:title>
   <news:publication_date>2026-08-05T01:19:53Z</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>2026-08-05T01:19:31Z</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:title>ロバストなストリーミング主成分分析（Robust Streaming PCA）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-05T00:16:38Z</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>
   <news:title>プレート化された因子グラフのためのテンソル変数消去（Tensor Variable Elimination for Plated Factor Graphs）</news:title>
   <news:publication_date>2026-08-05T00:16:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719601</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>2Dセルフアテンションによる話者ダイアリゼーション（SPEAKER DIARISATION USING 2D SELF-ATTENTIVE COMBINATION OF EMBEDDINGS）</news:title>
   <news:publication_date>2026-08-05T00:16:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719599</loc>
  <lastmod>2026-08-05T00:16:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ソフトウェア定義FPGAアクセラレータ設計によるモバイル向け深層学習の高速化（Software-Defined FPGA Accelerator Design for Mobile Deep Learning Applications）</news:title>
   <news:publication_date>2026-08-05T00:16:05Z</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>2026-08-04T23:24:08Z</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>
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   <news:publication_date>2026-08-04T23:23:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>行動列の編集距離に基づく新奇探索による深層強化学習ポリシー重みの探索（Novelty Search for Deep Reinforcement Learning Policy Network Weights by Action Sequence Edit Metric Distance）</news:title>
   <news:publication_date>2026-08-04T23:23:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719589</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:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-04T23:23:03Z</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>
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   <news:title>線形関数近似を伴う分布型強化学習（Distributional reinforcement learning with linear function approximation）</news:title>
   <news:publication_date>2026-08-04T23:22:57Z</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:genres>Blog</news:genres>
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 </url>
 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-04T22:31:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news: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>2026-08-04T22:30:20Z</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: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>背景知識を用いたアイテム集合のランキング（Using Background Knowledge to Rank Itemsets）</news:title>
   <news:publication_date>2026-08-04T22:29:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-04T21:38:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>重い裾の分布に対するアフィン不変共分散推定（Affine Invariant Covariance Estimation for Heavy-Tailed Distributions）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-04T21:38:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スケルトンに基づくオンライン行動予測とスケール選択ネットワーク（Skeleton-Based Online Action Prediction Using Scale Selection Network）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層的批評家から学ぶ強化学習（REINFORCEMENT LEARNING FROM HIERARCHICAL CRITICS）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719561</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>Knowledge Graphの事実予測を進化させるテンソル分解（Knowledge Graph Fact Prediction via Knowledge-Enriched Tensor Factorization）</news:title>
   <news:publication_date>2026-08-04T21:36:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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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>サイズ非依存のニューラル転移学習によるRDDL計画（Size Independent Neural Transfer for RDDL Planning）</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>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自己共役性による正則化経験リスク最小化の高速収束（Beyond Least-Squares: Fast Rates for Regularized Empirical Risk Minimization through Self-Concordance）</news:title>
   <news:publication_date>2026-08-04T20:42:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719545</loc>
  <lastmod>2026-08-04T20:42:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>心拍だけで感情を推定する確率的枠組み（A Bayesian Deep Learning Framework for End-To-End Prediction of Emotion from Heartbeat）</news:title>
   <news:publication_date>2026-08-04T20:42:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719543</loc>
  <lastmod>2026-08-04T20:41:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>部分ラベル学習における自己誘導再学習（Partial Label Learning with Self-Guided Retraining）</news:title>
   <news:publication_date>2026-08-04T20:41:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719541</loc>
  <lastmod>2026-08-04T19:49:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>バンディット主成分分析が変える部分観測下の学習法（Bandit Principal Component Analysis）</news:title>
   <news:publication_date>2026-08-04T19:49:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719539</loc>
  <lastmod>2026-08-04T19:49:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>暗いガンマ線バーストGRB 140713Aの詳細な多波長解析（Detailed multi-wavelength modelling of the dark GRB 140713A and its host galaxy）</news:title>
   <news:publication_date>2026-08-04T19:49:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719537</loc>
  <lastmod>2026-08-04T19:48:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>構造和（structural sums）を用いたランダム複合材料の特徴ベクトル化と分類（Classifying and analysis of random composites using structural sums feature vector）</news:title>
   <news:publication_date>2026-08-04T19:48:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719535</loc>
  <lastmod>2026-08-04T19:48:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフ上の共分散・相関に基づく類似度測定（Covariance and Correlation Measures on a Graph in a Generalized Bag-of-Paths Formalism）</news:title>
   <news:publication_date>2026-08-04T19:48:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719533</loc>
  <lastmod>2026-08-04T19:47:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>初期重みのセキュリティ重要性（On the security relevance of weights in deep learning）</news:title>
   <news:publication_date>2026-08-04T19:47:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719531</loc>
  <lastmod>2026-08-04T19:47:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次モチーフ特徴に基づくリンク予測（Link Prediction via Higher-Order Motif Features）</news:title>
   <news:publication_date>2026-08-04T19:47:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719529</loc>
  <lastmod>2026-08-04T19:47:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>残差自己相関の分布と季節性ARMAモデルの診断（Distribution of residual autocorrelations for multiplicative seasonal ARMA models with uncorrelated but non-independent error terms）</news:title>
   <news:publication_date>2026-08-04T19:47:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719523</loc>
  <lastmod>2026-08-04T18:55:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ハイパーボリック空間上のラップド正規分布（A Wrapped Normal Distribution on Hyperbolic Space for Gradient-Based Learning）</news:title>
   <news:publication_date>2026-08-04T18:55:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719521</loc>
  <lastmod>2026-08-04T18:55:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>不完全な予測下での公正な意思決定（Fair Decisions Despite Imperfect Predictions）</news:title>
   <news:publication_date>2026-08-04T18:55:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719519</loc>
  <lastmod>2026-08-04T18:54:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>バイナライズド知識グラフ埋め込み（Binarized Knowledge Graph Embeddings）</news:title>
   <news:publication_date>2026-08-04T18:54:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719517</loc>
  <lastmod>2026-08-04T18:53:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>不完全なアルゴリズムに対処する人間中心ツール（Human-Centered Tools for Coping with Imperfect Algorithms During Medical Decision-Making）</news:title>
   <news:publication_date>2026-08-04T18:53:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719515</loc>
  <lastmod>2026-08-04T18:53:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフで学ぶ物理の差分表現──Differentiable Physics-informed Graph Networks（Differentiable Physics-informed Graph Networks）</news:title>
   <news:publication_date>2026-08-04T18:53:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719513</loc>
  <lastmod>2026-08-04T18:53:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>相関バンディットとオンラインでの平均二乗誤差最小化（Correlated bandits or: How to minimize mean-squared error online）</news:title>
   <news:publication_date>2026-08-04T18:53:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719511</loc>
  <lastmod>2026-08-04T18:53:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>遺伝的プログラミングでマニホールド学習はできるか（Can Genetic Programming Do Manifold Learning Too?）</news:title>
   <news:publication_date>2026-08-04T18:53:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719509</loc>
  <lastmod>2026-08-04T18:02:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>一画素攻撃の理解—伝播マップと局所性解析（Understanding the One-pixel Attack: Propagation Maps and Locality Analysis）</news:title>
   <news:publication_date>2026-08-04T18:02:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719507</loc>
  <lastmod>2026-08-04T17:53:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>EILearn：過去知識を利用した逐次学習手法（EILearn: Learning Incrementally Using Previous Knowledge Obtained From an Ensemble of Classifiers）</news:title>
   <news:publication_date>2026-08-04T17:53:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719505</loc>
  <lastmod>2026-08-04T17:52:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>磁化曲線とスピンギャップの推定に機械学習を使う（Machine learning as an improved estimator for magnetization curve and spin gap）</news:title>
   <news:publication_date>2026-08-04T17:52:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719503</loc>
  <lastmod>2026-08-04T17:51:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>極値損失による支持（サポート）の生成（Generating the support with extreme value losses）</news:title>
   <news:publication_date>2026-08-04T17:51:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719501</loc>
  <lastmod>2026-08-04T17:51:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>対象依存感情分類のためのマルチタスク学習（Multi-task Learning for Target-dependent Sentiment Classification）</news:title>
   <news:publication_date>2026-08-04T17:51:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719499</loc>
  <lastmod>2026-08-04T17:50:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>手書き数式認識の堅牢な符号化器–復号器学習枠組み（Robust Encoder-Decoder Learning Framework towards Offline Handwritten Mathematical Expression Recognition Based on Multi-Scale Deep Neural Network）</news:title>
   <news:publication_date>2026-08-04T17:50:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719497</loc>
  <lastmod>2026-08-04T17:50:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>最適輸送地図の不連続性とGANのモード崩壊の理論的接続（MODE COLLAPSE AND REGULARITY OF OPTIMAL TRANSPORTATION MAPS）</news:title>
   <news:publication_date>2026-08-04T17:50:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719495</loc>
  <lastmod>2026-08-04T16:59:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>特徴の統合と強化で速く正確に検出する単発物体検出器（A SINGLE-SHOT OBJECT DETECTOR WITH FEATURE AGGREGATION AND ENHANCEMENT）</news:title>
   <news:publication_date>2026-08-04T16:59:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719493</loc>
  <lastmod>2026-08-04T16:59:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>AdaScaleによるリアルタイム動画物体検出の高速化と精度向上（AdaScale: Towards Real-time Video Object Detection Using Adaptive Scaling）</news:title>
   <news:publication_date>2026-08-04T16:59:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719491</loc>
  <lastmod>2026-08-04T16:58:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ランダム化スムージングによる認証付き敵対的堅牢性（Certified Adversarial Robustness via Randomized Smoothing）</news:title>
   <news:publication_date>2026-08-04T16:58:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719489</loc>
  <lastmod>2026-08-04T16:57:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>モビリティ・オンデマンド導入時のモード切替行動の異質性を解く（Modeling Heterogeneity in Mode-Switching Behavior Under a Mobility-on-Demand Transit System: An Interpretable Machine Learning Approach）</news:title>
   <news:publication_date>2026-08-04T16:57:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719487</loc>
  <lastmod>2026-08-04T16:57:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>IoMTにおけるデータ有用性とプライバシーの両立（Achieving Data Utility-Privacy Tradeoff in Internet of Medical Things: A Machine Learning Approach）</news:title>
   <news:publication_date>2026-08-04T16:57:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719485</loc>
  <lastmod>2026-08-04T16:57:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Source TracesによるTemporal Difference学習の新視点（Source Traces for Temporal Difference Learning）</news:title>
   <news:publication_date>2026-08-04T16:57:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719483</loc>
  <lastmod>2026-08-04T16:57:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>割引率の再考：意思決定理論的アプローチ（Rethinking the Discount Factor in Reinforcement Learning: A Decision Theoretic Approach）</news:title>
   <news:publication_date>2026-08-04T16:57:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719481</loc>
  <lastmod>2026-08-04T16:05:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>通信制約下での分布学習に関する下限—フィッシャー情報を用いて（Lower Bounds for Learning Distributions under Communication Constraints via Fisher Information）</news:title>
   <news:publication_date>2026-08-04T16:05:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719479</loc>
  <lastmod>2026-08-04T16:04:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数近傍LBPを用いた土地利用分類（Land Use Classification Using Multi-neighborhood LBPs）</news:title>
   <news:publication_date>2026-08-04T16:04:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719477</loc>
  <lastmod>2026-08-04T16:03:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文脈を用いたオンライン偽発見率制御（Contextual Online False Discovery Rate Control）</news:title>
   <news:publication_date>2026-08-04T16:03:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719475</loc>
  <lastmod>2026-08-04T16:02:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習率減衰と重み減衰を複合的に扱う複雑度勾配降下法（Combining Learning Rate Decay and Weight Decay with Complexity Gradient Descent）</news:title>
   <news:publication_date>2026-08-04T16:02:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719473</loc>
  <lastmod>2026-08-04T16:02:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ロボット支援作業のための深層実行モニタ（Deep execution monitor for robot assistive tasks）</news:title>
   <news:publication_date>2026-08-04T16:02:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719471</loc>
  <lastmod>2026-08-04T16:02:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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  <news:news>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-04T16:01:55Z</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:publication_date>2026-08-04T15:09:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-08-04T15:09:41Z</lastmod>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-04T15:09:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/719463</loc>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-04T15:09:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/719461</loc>
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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:publication_date>2026-08-04T15:07:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/719459</loc>
  <lastmod>2026-08-04T15:07:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
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   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/719457</loc>
  <lastmod>2026-08-04T15:07:25Z</lastmod>
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    <news:language>ja</news:language>
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   <news:publication_date>2026-08-04T15:07:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/719455</loc>
  <lastmod>2026-08-04T15:07:13Z</lastmod>
  <news:news>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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