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   <news:title>Iris R-CNNによる非協調環境下の虹彩分割（Iris R-CNN: Accurate Iris Segmentation in Non-cooperative Environment）</news:title>
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   <news:title>任意ショット学習のための特徴生成フレームワーク（f-VAEGAN-D2: A Feature Generating Framework for Any-Shot Learning）</news:title>
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   <news:title>異種表現で言語と知識をつなぐ手法（Connecting Language and Knowledge with Heterogeneous Representations for Neural Relation Extraction）</news:title>
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   <news:title>残差非局所注意ネットワークによる画像復元（Residual Non-Local Attention Networks for Image Restoration）</news:title>
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   <news:title>査読理解のための論証マイニング（Argument Mining for Understanding Peer Reviews）</news:title>
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   <news:genres>Blog</news:genres>
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   <news:title>磁気共鳻画像再構成のための変換学習（Transform Learning for Magnetic Resonance Image Reconstruction: From Model-based Learning to Building Neural Networks）</news:title>
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   <news:genres>Blog</news:genres>
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    <news:language>ja</news:language>
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   <news:title>成長次元における最適線形識別器（OPTIMAL LINEAR DISCRIMINATORS FOR THE DISCRETE CHOICE MODEL IN GROWING DIMENSIONS）</news:title>
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   <news:title>真のバッチ弟子学習と深層サクセッサーフィーチャー（Truly Batch Apprenticeship Learning with Deep Successor Features）</news:title>
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   <news:genres>Blog</news:genres>
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   <news:title>確率ジャンプ線形系の安全な学習ベース制御（Safe Learning-Based Control of Stochastic Jump Linear Systems: a Distributionally Robust Approach）</news:title>
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   <news:title>多クラス組織病理画像分類のための畳み込みニューラルネットワーク（Convolutional Neural Networks for Multi-class Histopathology Image Classification）</news:title>
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   <news:genres>Blog</news:genres>
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   <news:title>Lasso Weighted k-meansが示す高次元クラスタリングの新基準（A Strongly Consistent Sparse k-means Clustering with Direct l1 Penalization on Variable Weights）</news:title>
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   <news:genres>Blog</news:genres>
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   <news:genres>Blog</news:genres>
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   <news:title>深層ニューラルネットワークの堅牢性の形式化（A Formalization of Robustness for Deep Neural Networks）</news:title>
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   <news:genres>Blog</news:genres>
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   <news:title>k-means初期化の一般化（Generalization of k-means Related Algorithms）</news:title>
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   <news:genres>Blog</news:genres>
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   <news:genres>Blog</news:genres>
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    <news:language>ja</news:language>
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   <news:title>持続的気象パターン予測のための混合エキスパートモデル（A mixture of experts model for predicting persistent weather patterns）</news:title>
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   <news:genres>Blog</news:genres>
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   <news:title>識別的部分グラフ学習によるネットワーク状態予測（Discriminative Subgraph Learning via Sparse Self-Representation）</news:title>
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   <news:genres>Blog</news:genres>
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    <news:name>AI Benchmark Research</news:name>
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   <news:title>SNR適応型ID-OCTAによる血管可視化の精度向上（SNR-adaptive ID-OCTA）</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>高速アップリンク割当てとNOMAへのFederated Learning応用（Fast Uplink Grant for NOMA: a Federated Learning based Approach）</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>深層生成モデルによる近似クエリ処理（Approximate Query Processing for Data Exploration using Deep Generative Models）</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>ピクセル認識型関数混合ネットワークによる分光超解像（Pixel-aware Deep Function-mixture Network for Spectral Super-Resolution）</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>多属性選択率推定における深層学習の応用（Multi-Attribute Selectivity Estimation Using Deep Learning）</news:title>
   <news:publication_date>2026-08-21T16:12:19Z</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>多段階圧縮によるニューラルネットワークの効率化（MUSCO: Multi-Stage Compression of neural networks）</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>木星の大赤斑の深さを探る（Determining the depth of Jupiter’s Great Red Spot with Juno: a Slepian approach）</news:title>
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   <news:genres>Blog</news:genres>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>クラスタ整列を教師で促す手法（Cluster Alignment with a Teacher for Unsupervised Domain Adaptation）</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>高変形ソフト粒子の圧縮挙動とジャミング後の力学（Soft grain compression: beyond the jamming point）</news:title>
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   <news:genres>Blog</news:genres>
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  <lastmod>2026-08-21T15:19:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>深海面における非相互作用重力波の理論的展開（Non-interacting gravity waves on the surface of a deep fluid）</news:title>
   <news:publication_date>2026-08-21T15:19:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-08-21T15:19:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>話者抽出ニューラルネットワークの最適化（Optimization of Speaker Extraction Neural Network with Magnitude and Temporal Spectrum Approximation Loss）</news:title>
   <news:publication_date>2026-08-21T15:19:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725705</loc>
  <lastmod>2026-08-21T15:19:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>効率的な製品埋め込みに基づく深層レコメンダーエンジン（Deep recommender engine based on efficient product embeddings neural pipeline）</news:title>
   <news:publication_date>2026-08-21T15:19:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725703</loc>
  <lastmod>2026-08-21T15:17:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>潜在空間量子化を用いた変分推論による敵対的耐性（Variational Inference with Latent Space Quantization for Adversarial Resilience）</news:title>
   <news:publication_date>2026-08-21T15:17:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725701</loc>
  <lastmod>2026-08-21T15:17:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>HAXMLNetが示す極端多ラベル分類の新潮流（HAXMLNet: Hierarchical Attention Network for Extreme Multi-Label Text Classification）</news:title>
   <news:publication_date>2026-08-21T15:17:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725699</loc>
  <lastmod>2026-08-21T15:17:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>臨床テキストにおける最短依存経路ベースのLSTMによる関係抽出（Relation extraction between the clinical entities based on the shortest dependency path based LSTM）</news:title>
   <news:publication_date>2026-08-21T15:17:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725697</loc>
  <lastmod>2026-08-21T15:17:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>要約生成を特徴量化してフェイクニュース検出を改善する手法（Neural Abstractive Text Summarization and Fake News Detection）</news:title>
   <news:publication_date>2026-08-21T15:17:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725695</loc>
  <lastmod>2026-08-21T14:24:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>RGB画像を入力とするマップレスロボット航法のサンプル効率化（Using RGB Image as Visual Input for Mapless Robot Navigation）</news:title>
   <news:publication_date>2026-08-21T14:24:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725693</loc>
  <lastmod>2026-08-21T14:24:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ウェアラブルセンサの弱ラベルデータから活動を見つける注意機構CNN（Attention-based Convolutional Neural Network for Weakly Labeled Human Activities Recognition with Wearable Sensors）</news:title>
   <news:publication_date>2026-08-21T14:24:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725691</loc>
  <lastmod>2026-08-21T14:24:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>有限仮説クラス下のオンライン学習における改善された誤り境界（Algorithms and Improved bounds for online learning under finite hypothesis class）</news:title>
   <news:publication_date>2026-08-21T14:24:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725689</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>KPTransferによるキーポイント部分集合間ドメイン転移の実用的意義（KPTransfer: improved performance and faster convergence from keypoint subset-wise domain transfer in human pose estimation）</news:title>
   <news:publication_date>2026-08-21T14:22:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725687</loc>
  <lastmod>2026-08-21T14:22:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SRGAN: トレーニングデータが結果を決める（SRGAN: Training Dataset Matters）</news:title>
   <news:publication_date>2026-08-21T14:22:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725685</loc>
  <lastmod>2026-08-21T14:22:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複素値PolSARデータの効率的利用—マルチタスク深層学習フレームワーク（Efficiently utilizing complex-valued PolSAR image data via a multi-task deep learning framework）</news:title>
   <news:publication_date>2026-08-21T14:22:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725683</loc>
  <lastmod>2026-08-21T14:21:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散機械学習ジョブのオーケストレーションを実現するTonY（TonY: An Orchestrator for Distributed Machine Learning Jobs）</news:title>
   <news:publication_date>2026-08-21T14:21:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725680</loc>
  <lastmod>2026-08-21T13:30:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>sharpDARTS：より高速で高精度なDifferentiable Architecture Search（sharpDARTS: Faster and More Accurate Differentiable Architecture Search）</news:title>
   <news:publication_date>2026-08-21T13:30:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725678</loc>
  <lastmod>2026-08-21T13:29:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散再帰型オートエンコーダによる拡張可能な画像圧縮（DRASIC: Distributed Recurrent Autoencoder for Scalable Image Compression）</news:title>
   <news:publication_date>2026-08-21T13:29:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725676</loc>
  <lastmod>2026-08-21T13:29:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>変換に応答する表現の教師なし学習（AVT: Unsupervised Learning of Transformation-Equivariant Representations by Autoencoding Variational Transformations）</news:title>
   <news:publication_date>2026-08-21T13:29:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725674</loc>
  <lastmod>2026-08-21T13:28:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>実環境向けの長距離ニューラル航行ポリシー（Long Range Neural Navigation Policies for the Real World）</news:title>
   <news:publication_date>2026-08-21T13:28:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725672</loc>
  <lastmod>2026-08-21T13:28:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時相論理に導かれた安全強化学習と制御バリア関数（Temporal Logic Guided Safe Reinforcement Learning Using Control Barrier Functions）</news:title>
   <news:publication_date>2026-08-21T13:28:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725670</loc>
  <lastmod>2026-08-21T13:28:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>肺結節検出のためのエンドツーエンド統合フレームワーク（AN END-TO-END FRAMEWORK FOR INTEGRATED PULMONARY NODULE DETECTION AND FALSE POSITIVE REDUCTION）</news:title>
   <news:publication_date>2026-08-21T13:28:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725668</loc>
  <lastmod>2026-08-21T13:28:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>肺葉自動分割における深層学習の実用化可能性（AUTOMATIC PULMONARY LOBE SEGMENTATION USING DEEP LEARNING）</news:title>
   <news:publication_date>2026-08-21T13:28:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725666</loc>
  <lastmod>2026-08-21T12:36:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>StartNet：ストリーミング映像における行動開始検出の実時間化（StartNet: Online Detection of Action Start in Untrimmed Videos）</news:title>
   <news:publication_date>2026-08-21T12:36:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725664</loc>
  <lastmod>2026-08-21T12:36:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>差分プライバシー学習器に対するデータ汚染攻撃と防御（Data Poisoning against Differentially-Private Learners: Attacks and Defenses）</news:title>
   <news:publication_date>2026-08-21T12:36:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725662</loc>
  <lastmod>2026-08-21T12:36:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オンライン最適化による学習と制御の新枠組み（Online Optimisation for Online Learning and Control – From No-Regret to Generalised Error Convergence）</news:title>
   <news:publication_date>2026-08-21T12:36:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725660</loc>
  <lastmod>2026-08-21T12:35:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>協働型言語学習SNSにおける書記能力評価の提案（Toward the Evaluation of Written Proficiency on a Collaborative Social Network for Learning Languages: Yask）</news:title>
   <news:publication_date>2026-08-21T12:35:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725658</loc>
  <lastmod>2026-08-21T12:35:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>能力に基づくカリキュラム学習で変わる機械翻訳の学習効率（Competence-based Curriculum Learning for Neural Machine Translation）</news:title>
   <news:publication_date>2026-08-21T12:35:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725656</loc>
  <lastmod>2026-08-21T12:35:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>中性子星とブラックホールの時間領域研究：X線によるコンパクト天体の同定（Time Domain Studies of Neutron Star and Black Hole Populations: X-ray Identification of Compact Object Types）</news:title>
   <news:publication_date>2026-08-21T12:35:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725654</loc>
  <lastmod>2026-08-21T12:34:48Z</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>2026-08-21T12:34:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725652</loc>
  <lastmod>2026-08-21T11:43:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>HouseExpo：学習ベースの移動ロボット研究を加速する大規模2D室内レイアウトデータセット（HouseExpo: A Large-scale 2D Indoor Layout Dataset for Learning-based Algorithms on Mobile Robots）</news:title>
   <news:publication_date>2026-08-21T11:43:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725650</loc>
  <lastmod>2026-08-21T11:43:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層学習による時間位相アンラッピング（Temporal phase unwrapping using deep learning）</news:title>
   <news:publication_date>2026-08-21T11:43:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725648</loc>
  <lastmod>2026-08-21T11:42:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スケーラブルな差分プライバシーと認証付き堅牢性を両立する手法（Scalable Differential Privacy with Certified Robustness in Adversarial Learning）</news:title>
   <news:publication_date>2026-08-21T11:42:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725646</loc>
  <lastmod>2026-08-21T11:41:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>網膜OCT画像の疾患情報を残す意味的ノイズ除去（Semantic denoising autoencoders for retinal optical coherence tomography）</news:title>
   <news:publication_date>2026-08-21T11:41:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725644</loc>
  <lastmod>2026-08-21T11:41:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>知識に基づく応答生成の深化（Knowledge-Grounded Response Generation with Deep Attentional Latent-Variable Model）</news:title>
   <news:publication_date>2026-08-21T11:41:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725642</loc>
  <lastmod>2026-08-21T11:41:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>フィードバックネットワークによる画像超解像の新展開（Feedback Network for Image Super-Resolution）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725640</loc>
  <lastmod>2026-08-21T11:40:31Z</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/725638</loc>
  <lastmod>2026-08-21T10:49:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Coin.AIによる有用作業の証明スキーム（Coin.AI: A Proof-of-Useful-Work Scheme for Blockchain-Based Distributed Deep Learning）</news:title>
   <news:publication_date>2026-08-21T10:49:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-21T10:49:17Z</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>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-21T10:47:54Z</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>二値分類における半準パラメトリックな不確かさ境界（Semi-Parametric Uncertainty Bounds for Binary Classification）</news:title>
   <news:publication_date>2026-08-21T10:47:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725626</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>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Auto-ReID: 人物再識別向け部分認識ConvNetを自動探索する手法（Auto-ReID: Searching for a Part-Aware ConvNet for Person Re-Identification）</news:title>
   <news:publication_date>2026-08-21T09:55:08Z</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>
   </news:publication>
   <news:title>進展的DNN圧縮：ADMMを用いた超高率の重み剪定と量子化の実現（Progressive DNN Compression: A Key to Achieve Ultra-High Weight Pruning and Quantization Rates using ADMM）</news:title>
   <news:publication_date>2026-08-21T09:54:57Z</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>高速な水中画像強調による視覚認知の改善（Fast Underwater Image Enhancement for Improved Visual Perception）</news:title>
   <news:publication_date>2026-08-21T09:54:40Z</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>自律操作のためのシーン理解と深層学習（Scene Understanding for Autonomous Manipulation with Deep Learning）</news:title>
   <news:publication_date>2026-08-21T09:53:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725616</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>写真写実的スタイル転送を一変させた波形レット補正（Photorealistic Style Transfer via Wavelet Transforms）</news:title>
   <news:publication_date>2026-08-21T09:53:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725614</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>TTRに基づく報酬で学ぶ強化学習（TTR-Based Reward for Reinforcement Learning with Implicit Model Priors）</news:title>
   <news:publication_date>2026-08-21T09:53:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725612</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>2026-08-21T09:52:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725610</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>2026-08-21T09:01:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Residual Pyramid Learningによるシングルショット分割の実務的示唆（Residual Pyramid Learning for Single-Shot Semantic Segmentation）</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>ラベルシフト下の正則化学習がもたらす実務的価値（Regularized Learning for Domain Adaptation under Label Shifts）</news:title>
   <news:publication_date>2026-08-21T09:00:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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   <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>シンボリック回帰を用いた強化学習の価値関数構築（Symbolic Regression Methods for Reinforcement Learning）</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>
  </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>CUR分解とその摂動解析が示す低ランク行列近似の安定性（CUR Decompositions, Approximations, and Perturbations）</news:title>
   <news:publication_date>2026-08-21T08:07:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-21T08:06:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>分布系状態推定のための物理認識ニューラルネットワーク（Physics-Aware Neural Networks for Distribution System State Estimation）</news:title>
   <news:publication_date>2026-08-21T08:06:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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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>微分可能プログラミングによるテンソルネットワーク最適化（Differentiable Programming Tensor Networks）</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>
  </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:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725576</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>Fog Roboticsによるロボット学習と表面片付けの実用化（A Fog Robotics Approach to Deep Robot Learning: Application to Object Recognition and Grasp Planning in Surface Decluttering）</news:title>
   <news:publication_date>2026-08-21T07:12:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725574</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>オンザフライ学習で得られる機械学習力場によるハイブリッドペロブスカイトの相転移（Phase transitions of hybrid perovskites simulated by machine-learning force fields trained on-the-fly with Bayesian inference）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news: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>
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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>Monte Carlo Neural Fictitious Self-Playによる不完全情報ゲームの近似ナッシュ均衡獲得（Monte Carlo Neural Fictitious Self-Play）</news:title>
   <news:publication_date>2026-08-21T06:19:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725562</loc>
  <lastmod>2026-08-21T06:18:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非マルコフ確率過程に対する機械学習によるメモリカーネルの閉じ込み (Machine learning memory kernels as closure for non-Markovian stochastic processes)</news:title>
   <news:publication_date>2026-08-21T06:18:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725560</loc>
  <lastmod>2026-08-21T06:17:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>網膜疾患の同時自動検出を目指した深層学習システムの評価 (Evaluation of a deep learning system for the joint automated detection of diabetic retinopathy and age-related macular degeneration)</news:title>
   <news:publication_date>2026-08-21T06:17:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725558</loc>
  <lastmod>2026-08-21T06:17:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>構造化文書における表理解（Table understanding in structured documents）</news:title>
   <news:publication_date>2026-08-21T06:17:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725556</loc>
  <lastmod>2026-08-21T06:17:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習手法で宇宙線反陽子フラックスにおけるダークマター信号を探る（Investigating the dark matter signal in the cosmic ray antiproton flux with the machine learning method）</news:title>
   <news:publication_date>2026-08-21T06:17:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725554</loc>
  <lastmod>2026-08-21T05:24:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>反復的な強化学習による二足歩行スキル設計（Iterative Reinforcement Learning Based Design of Dynamic Locomotion Skills for Cassie）</news:title>
   <news:publication_date>2026-08-21T05:24:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725552</loc>
  <lastmod>2026-08-21T05:24:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>類似した形態特徴を持つ細胞の識別におけるゴーストサイトメトリーの応用 (Use of Ghost Cytometry to Differentiate Cells with Similar Gross Morphologic Characteristics)</news:title>
   <news:publication_date>2026-08-21T05:24:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725550</loc>
  <lastmod>2026-08-21T05:24:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>電力市場におけるリスク最小化の機械学習アプローチ（A Machine Learning approach to Risk Minimisation in Electricity Markets with Coregionalized Sparse Gaussian Processes）</news:title>
   <news:publication_date>2026-08-21T05:24:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725548</loc>
  <lastmod>2026-08-21T05:23:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>磁化ダイナミクスを学習する（Learning magnetization dynamics）</news:title>
   <news:publication_date>2026-08-21T05:23:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725546</loc>
  <lastmod>2026-08-21T05:23:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多体系のエンタングルメント深度を装置非依存に評価する手法の最適化（Optimization of device-independent witnesses of entanglement depth from two-body correlators）</news:title>
   <news:publication_date>2026-08-21T05:23:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725544</loc>
  <lastmod>2026-08-21T05:23:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>完全確率的制御の感度と安全性（Sensitivity and safety of fully probabilistic control）</news:title>
   <news:publication_date>2026-08-21T05:23:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725542</loc>
  <lastmod>2026-08-21T05:22:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>公平性の目に見えない力（The invisible power of fairness. How machine learning shapes democracy）</news:title>
   <news:publication_date>2026-08-21T05:22:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725540</loc>
  <lastmod>2026-08-21T04:31:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>OODIDAによる分散データ分析の迅速なプロトタイピング（Facilitating Rapid Prototyping in the Distributed Data Analytics Platform OODIDA via Active-Code Replacement）</news:title>
   <news:publication_date>2026-08-21T04:31:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725538</loc>
  <lastmod>2026-08-21T04:31:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>小売の多次元売上に対する最適結合予測（Optimal Combination Forecasts on Retail Multi-Dimensional Sales Data）</news:title>
   <news:publication_date>2026-08-21T04:31:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725536</loc>
  <lastmod>2026-08-21T04:30:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Aggregated Deep Local Featuresによるリモートセンシング画像検索の合理化（Aggregated Deep Local Features for Remote Sensing Image Retrieval）</news:title>
   <news:publication_date>2026-08-21T04:30:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725534</loc>
  <lastmod>2026-08-21T04:29:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>固体・真空界面におけるイオン液体の構造と拡散挙動に関する分子動力学シミュレーションの知見（Insights from Molecular Dynamics Simulations on Structural Organization and Diffusive Dynamics of an Ionic Liquid at Solid and Vacuum Interfaces）</news:title>
   <news:publication_date>2026-08-21T04:29:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725532</loc>
  <lastmod>2026-08-21T04:29:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>薄膜金属の結晶核の構造と形態（Structure and Morphology of Crystalline Nuclei arising in a Crystallizing Liquid Metallic Film）</news:title>
   <news:publication_date>2026-08-21T04:29:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725530</loc>
  <lastmod>2026-08-21T04:29:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>透過壁環境での前面レーダー画像の歪みを軽減するノイズ除去オートエンコーダ（Mitigation of Through-Wall Distortions of Frontal Radar Images using Denoising Autoencoders）</news:title>
   <news:publication_date>2026-08-21T04:29:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725528</loc>
  <lastmod>2026-08-21T04:28:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>心臓画像解析における分離表現学習（Disentangled representation learning in cardiac image analysis）</news:title>
   <news:publication_date>2026-08-21T04:28:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725526</loc>
  <lastmod>2026-08-21T03:37:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>テキストクラスタリングのエンドツーエンドニューラルネットワークフレームワーク (An end-to-end Neural Network Framework for Text Clustering)</news:title>
   <news:publication_date>2026-08-21T03:37:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725524</loc>
  <lastmod>2026-08-21T03:37:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>バッチベイズ最適化のための獲得関数サンプリング（Sampling Acquisition Functions for Batch Bayesian Optimization）</news:title>
   <news:publication_date>2026-08-21T03:37:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725522</loc>
  <lastmod>2026-08-21T03:36:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>金属スクラップ選別にAIを持ち込む（ARTIFICIAL INTELLIGENCE-BASED PROCESS FOR METAL SCRAP SORTING）</news:title>
   <news:publication_date>2026-08-21T03:36:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725520</loc>
  <lastmod>2026-08-21T03:35:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>遅延シナプス可塑性で学ぶ（Learning with Delayed Synaptic Plasticity）</news:title>
   <news:publication_date>2026-08-21T03:35:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725518</loc>
  <lastmod>2026-08-21T03:35:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>群レベルfMRI解析のための制約付きICA‑EMDモデル (A constrained ICA-EMD Model for Group Level fMRI Analysis)</news:title>
   <news:publication_date>2026-08-21T03:35:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725516</loc>
  <lastmod>2026-08-21T03:35:21Z</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た高速ベイズ的不確実性推定と低減（Fast Bayesian Uncertainty Estimation and Reduction of Batch Normalized Single Image Super-Resolution Network）</news:title>
   <news:publication_date>2026-08-21T03:35:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725514</loc>
  <lastmod>2026-08-21T03:34:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューラルベクトル自己回帰による予測・因果・インパルス応答の統合的解析（Forecasting, Causality, and Impulse Response with Neural Vector Autoregressions）</news:title>
   <news:publication_date>2026-08-21T03:34:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725512</loc>
  <lastmod>2026-08-21T02:43:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>化学組成のベイズ最適化とRFe12型磁石への応用（Bayesian optimization of chemical composition: a comprehensive framework and its application to RFe12-type magnet compounds）</news:title>
   <news:publication_date>2026-08-21T02:43:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725510</loc>
  <lastmod>2026-08-21T02:43:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>勾配のみのラインサーチ（Gradient-only line searches: An Alternative to Probabilistic Line Searches）</news:title>
   <news:publication_date>2026-08-21T02:43:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725508</loc>
  <lastmod>2026-08-21T02:42:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチモーダル確率的予測による相互行動の解釈可能なモデル（Multi-modal Probabilistic Prediction of Interactive Behavior via an Interpretable Model）</news:title>
   <news:publication_date>2026-08-21T02:42:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725506</loc>
  <lastmod>2026-08-21T02:42:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>暗黙の正則化による高次元線形回帰（High-Dimensional Linear Regression via Implicit Regularization）</news:title>
   <news:publication_date>2026-08-21T02:42:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725504</loc>
  <lastmod>2026-08-21T02:42:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層フィクティシャス・プレイによる確率微分ゲームの解法（Deep Fictitious Play for Stochastic Differential Games）</news:title>
   <news:publication_date>2026-08-21T02:42:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725502</loc>
  <lastmod>2026-08-21T02:41:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>シーケンス分解によるマクロアクション強化学習（Macro Action Reinforcement Learning with Sequence Disentanglement using Variational Autoencoder）</news:title>
   <news:publication_date>2026-08-21T02:41:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725500</loc>
  <lastmod>2026-08-21T02:41:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多段階目標抽象による深層階層強化学習推薦（Deep Hierarchical Reinforcement Learning Based Recommendations via Multi-goals Abstraction）</news:title>
   <news:publication_date>2026-08-21T02:41:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725498</loc>
  <lastmod>2026-08-21T01:50:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単一画像からの3D顔再構成を2D画像で補助する学習法（3D Face Reconstruction from A Single Image Assisted by 2D Face Images in the Wild）</news:title>
   <news:publication_date>2026-08-21T01:50:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725496</loc>
  <lastmod>2026-08-21T01:50:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>小さなミニライゾトロンデータセットを乗り越える転移学習（Overcoming Small Minirhizotron Datasets Using Transfer Learning）</news:title>
   <news:publication_date>2026-08-21T01:50:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725494</loc>
  <lastmod>2026-08-21T01:49:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>二分空間分割フォレスト（Binary Space Partitioning Forests）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <lastmod>2026-08-21T01:49:23Z</lastmod>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Binary Space Partitioning-Tree Process（The Binary Space Partitioning-Tree Process）</news:title>
   <news:publication_date>2026-08-21T01:49:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725490</loc>
  <lastmod>2026-08-21T01:49:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>強化学習で解釈可能な決定木を学ぶ最適化手法（Optimization Methods for Interpretable Differentiable Decision Trees in Reinforcement Learning）</news:title>
   <news:publication_date>2026-08-21T01:49:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725488</loc>
  <lastmod>2026-08-21T01:49:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>MNMFを用いた教師なし音声強調とMVDRビームフォーミング（Unsupervised Speech Enhancement Based on Multichannel NMF-Informed Beamforming）</news:title>
   <news:publication_date>2026-08-21T01:49:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725486</loc>
  <lastmod>2026-08-21T01:48:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多モーダル画像の教師なし変形登録を離散表現で解く（Unsupervised Deformable Registration for Multi-Modal Images via Disentangled Representations）</news:title>
   <news:publication_date>2026-08-21T01:48:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725484</loc>
  <lastmod>2026-08-21T00:56:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>光コヒーレンストモグラフィ（OCT）画像のスペックル低減のためのResNetベース汎用手法 (A RESNET-BASED UNIVERSAL METHOD FOR SPECKLE REDUCTION IN OPTICAL COHERENCE TOMOGRAPHY IMAGES)</news:title>
   <news:publication_date>2026-08-21T00:56:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725482</loc>
  <lastmod>2026-08-21T00:56:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>けいれんと非けいれんの分類に対する新規IndRNNアプローチ (A Novel Independent RNN Approach to Classification of Seizures against Non-seizures)</news:title>
   <news:publication_date>2026-08-21T00:56:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725480</loc>
  <lastmod>2026-08-21T00:55:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層多天体分光観測がLSSTのダークエネルギー研究を強化する（Deep Multi-object Spectroscopy to Enhance Dark Energy Science from LSST）</news:title>
   <news:publication_date>2026-08-21T00:55:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725478</loc>
  <lastmod>2026-08-21T00:55:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>広域多天体分光観測がLSSTの暗黒エネルギー研究にもたらすもの（Wide-field Multi-object Spectroscopy to Enhance Dark Energy Science from LSST）</news:title>
   <news:publication_date>2026-08-21T00:55:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725476</loc>
  <lastmod>2026-08-21T00:55:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単一天体の撮像と分光がLSSTの暗黒エネルギー研究を強化する（Single-object Imaging and Spectroscopy to Enhance Dark Energy Science from LSST）</news:title>
   <news:publication_date>2026-08-21T00:55:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725474</loc>
  <lastmod>2026-08-21T00:54:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散電子医療記録を用いた患者クラスタリングはフェデレーテッド機械学習の効率を改善する（Patient Clustering Improves Efficiency of Federated Machine Learning to predict mortality and hospital stay time using distributed Electronic Medical Records）</news:title>
   <news:publication_date>2026-08-21T00:54:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725472</loc>
  <lastmod>2026-08-21T00:54:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散環境における加重ワンショット・リッジ回帰の実務的意義（WONDER: Weighted one-shot distributed ridge regression in high dimensions）</news:title>
   <news:publication_date>2026-08-21T00:54:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725470</loc>
  <lastmod>2026-08-21T00:02:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DQNに基づくモデル内探索による効率的学習（DQN with Model-Based Exploration: Efficient Learning on Environments with Sparse Rewards）</news:title>
   <news:publication_date>2026-08-21T00:02:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725468</loc>
  <lastmod>2026-08-21T00:01:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>生成対抗学習による最適な構造化CNN剪定（Towards Optimal Structured CNN Pruning via Generative Adversarial Learning）</news:title>
   <news:publication_date>2026-08-21T00:01:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725466</loc>
  <lastmod>2026-08-21T00:00:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>テンソルデータ向け分離辞書の混合学習（Learning Mixtures of Separable Dictionaries for Tensor Data: Analysis and Algorithms）</news:title>
   <news:publication_date>2026-08-21T00:00:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725464</loc>
  <lastmod>2026-08-21T00:00:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>HARDIの高速かつ高精度な再構成を実現する1Dエンコーダ・デコーダ畳み込みネットワーク（FAST AND ACCURATE RECONSTRUCTION OF HARDI USING A 1D ENCODER-DECODER CONVOLUTIONAL NETWORK）</news:title>
   <news:publication_date>2026-08-21T00:00:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725462</loc>
  <lastmod>2026-08-21T00:00:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ワルファリン臨床用量アルゴリズムの適用範囲判定（A Computer-Aided System for Determining the Application Range of a Warfarin Clinical Dosing Algorithm Using Support Vector Machines with a Polynomial Kernel Function）</news:title>
   <news:publication_date>2026-08-21T00:00:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725460</loc>
  <lastmod>2026-08-20T23:59:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数の生物医学データベースからアソシエーションルールとオントロジーを用いてメタデータ推奨を生成する方法（Using association rule mining and ontologies to generate metadata recommendations from multiple biomedical databases）</news:title>
   <news:publication_date>2026-08-20T23:59:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725458</loc>
  <lastmod>2026-08-20T23:59:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>カメラ貼付ステッカーによる物理的攻撃（Adversarial camera stickers: A physical camera-based attack on deep learning systems）</news:title>
   <news:publication_date>2026-08-20T23:59:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725456</loc>
  <lastmod>2026-08-20T23:08:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>潜在空間でU-Netを模倣する自己符号化器による骨盤骨セグメンテーション向上（Imitating U-Net Enhanced Autoencoders in Latent Space for Improved Pelvic Bone Segmentation in MRI）</news:title>
   <news:publication_date>2026-08-20T23:08:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725454</loc>
  <lastmod>2026-08-20T23:08:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>宇宙論と初期宇宙（Cosmology and the Early Universe）</news:title>
   <news:publication_date>2026-08-20T23:08:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725452</loc>
  <lastmod>2026-08-20T23:08:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散オフポリシーActor‑Criticと方策コンセンサス（Distributed off-Policy Actor-Critic Reinforcement Learning with Policy Consensus）</news:title>
   <news:publication_date>2026-08-20T23:08:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725450</loc>
  <lastmod>2026-08-20T23:07:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非線形フィルタリング総覧（The Hitchhiker’s Guide to Nonlinear Filtering）</news:title>
   <news:publication_date>2026-08-20T23:07:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725448</loc>
  <lastmod>2026-08-20T23:07:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>連続時間領域で学習可能な時系列整列手法：Trainable Time Warping（Trainable Time Warping: Aligning Time-Series in the Continuous-Time Domain）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725446</loc>
  <lastmod>2026-08-20T23:07:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ロボット指示の効率的自然言語理解のためのコンパクト表現の推定 (Inferring Compact Representations for Efficient Natural Language Understanding of Robot Instructions)</news:title>
   <news:publication_date>2026-08-20T23:07:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725444</loc>
  <lastmod>2026-08-20T23:06:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ULMFitとバックトランスレーションによる少量データのテキスト分類（Low Resource Text Classification with ULMFit and Backtranslation）</news:title>
   <news:publication_date>2026-08-20T23:06:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725442</loc>
  <lastmod>2026-08-20T22:15:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチ系列MRIを用いた脳腫瘍検出と分類のDeep Radiomics（Deep Radiomics for Brain Tumor Detection and Classification from Multi-Sequence MRI）</news:title>
   <news:publication_date>2026-08-20T22:15:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725440</loc>
  <lastmod>2026-08-20T22:15:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチドメイン敵対学習が変える現場導入の常識（Multi-Domain Adversarial Learning）</news:title>
   <news:publication_date>2026-08-20T22:15:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725438</loc>
  <lastmod>2026-08-20T22:14:29Z</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>2026-08-20T22:14:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725436</loc>
  <lastmod>2026-08-20T22:14:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Top-1誤差に注目した較正済み不確実性推定（Calibrated Top-1 Uncertainty estimates for classification by score based models）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725434</loc>
  <lastmod>2026-08-20T22:13:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>形状理解のためのSkelNetOnチャレンジ（SkelNetOn 2019: Dataset and Challenge on Deep Learning for Geometric Shape Understanding）</news:title>
   <news:publication_date>2026-08-20T22:13:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725432</loc>
  <lastmod>2026-08-20T22:13:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>細胞同定のための確率的アトラス（A probabilistic atlas for cell identification）</news:title>
   <news:publication_date>2026-08-20T22:13:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/725430</loc>
  <lastmod>2026-08-20T22:12:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高閾値活性化を用いた深層ネットワークの最下層復元（Recovering the Lowest Layer of Deep Networks with High Threshold Activations）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-20T21:21:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>サイクロステーショナリ過程のネットワークにおける正確なトポロジー学習（Exact Topology Learning in a Network of Cyclostationary Processes）</news:title>
   <news:publication_date>2026-08-20T21:21:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725426</loc>
  <lastmod>2026-08-20T21:21:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Penobscotデータセット：地震データで機械学習を育てる（Penobscot Dataset: Fostering Machine Learning Development for Seismic Interpretation）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>システム全体を視野に入れた動的評価フレームワーク（A simulation based dynamic evaluation framework for system-wide algorithmic fairness）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>異種混合マルチタスク学習のためのマルチネットワーク自動構築に向けて（Towards automatic construction of multi-network models for heterogeneous multi-task learning）</news:title>
   <news:publication_date>2026-08-20T21:19:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725420</loc>
  <lastmod>2026-08-20T21:19:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>クエーサーのマイクロレンズ光度曲線解析に深層学習を用いる研究（Quasar microlensing light curve analysis using deep machine learning）</news:title>
   <news:publication_date>2026-08-20T21:19:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725418</loc>
  <lastmod>2026-08-20T21:19:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>画像のマルチラベル分類API比較とセマンティック評価の重要性（Comparison of State-of-the-Art Deep Learning APIs for Image Multi-Label Classification using Semantic Metrics）</news:title>
   <news:publication_date>2026-08-20T21:19:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725416</loc>
  <lastmod>2026-08-20T21:19:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Sparse2Denseによる単眼SLAMの密な再構築（Sparse2Dense: From direct sparse odometry to dense 3D reconstruction）</news:title>
   <news:publication_date>2026-08-20T21:19:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725414</loc>
  <lastmod>2026-08-20T20:27:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ALMAスペクトロスコピー調査が示す分子ガスの宇宙進化（The ALMA Spectroscopic Survey in the Hubble Ultra Deep Field: Evolution of the molecular gas in CO-selected galaxies）</news:title>
   <news:publication_date>2026-08-20T20:27:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725412</loc>
  <lastmod>2026-08-20T20:18:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>HUDFにおけるALMA分光調査が示した銀河ガスの実像（The ALMA Spectroscopic Survey in the HUDF: Nature and physical properties of gas-mass selected galaxies using MUSE spectroscopy）</news:title>
   <news:publication_date>2026-08-20T20:18:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725410</loc>
  <lastmod>2026-08-20T20:17:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>HUDFにおけるALMA分光サーベイ：銀河のCO輝度関数と宇宙史を通じた分子ガス量の変遷（THE ALMA SPECTROSCOPIC SURVEY IN THE HUDF: CO LUMINOSITY FUNCTIONS AND THE MOLECULAR GAS CONTENT OF GALAXIES THROUGH COSMIC HISTORY）</news:title>
   <news:publication_date>2026-08-20T20:17:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725408</loc>
  <lastmod>2026-08-20T20:16:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ノイズを「害さない」補間の理論 — 高次元線形回帰における無害な補間（Harmless interpolation of noisy data in regression）</news:title>
   <news:publication_date>2026-08-20T20:16:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725406</loc>
  <lastmod>2026-08-20T20:15:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>HUDFにおけるALMA分光調査：銀河の分子ガス量とモデルとの乖離（The ALMA Spectroscopic Survey in the HUDF: the molecular gas content of galaxies and tensions with IllustrisTNG and the Santa Cruz SAM）</news:title>
   <news:publication_date>2026-08-20T20:15:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725404</loc>
  <lastmod>2026-08-20T20:15:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>HUDFにおけるALMA分光観測：CO輝線と3mm連続体源（The ALMA Spectroscopic Survey in the HUDF: CO emission lines and 3 mm continuum sources）</news:title>
   <news:publication_date>2026-08-20T20:15:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725402</loc>
  <lastmod>2026-08-20T20:15:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>回転するブラックホールによる潮汐破壊の影響（Tidal disruptions by rotating black holes: effects of spin and impact parameter）</news:title>
   <news:publication_date>2026-08-20T20:15:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725400</loc>
  <lastmod>2026-08-20T19:22:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>摂動履歴探索による確率的線形バンディット（Perturbed-History Exploration in Stochastic Linear Bandits）</news:title>
   <news:publication_date>2026-08-20T19:22:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725398</loc>
  <lastmod>2026-08-20T19:22:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>衝撃を受けたHMXにおけるメソスケールのエネルギー局在化のモデル化（Modeling meso-scale energy localization in shocked HMX, Part II: training machine-learned surrogate models for void shape and void-void interaction effects）</news:title>
   <news:publication_date>2026-08-20T19:22:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725396</loc>
  <lastmod>2026-08-20T19:22:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非線形ガウス近似メッセージ伝播の近似手法（ON APPROXIMATE NONLINEAR GAUSSIAN MESSAGE PASSING ON FACTOR GRAPHS）</news:title>
   <news:publication_date>2026-08-20T19:22:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725394</loc>
  <lastmod>2026-08-20T19:21:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>孤立波（ローグウェーブ）事象の持続時間解析（Lifetimes of rogue wave events in direct numerical simulations of deep-water irregular sea waves）</news:title>
   <news:publication_date>2026-08-20T19:21:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725392</loc>
  <lastmod>2026-08-20T19:21:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的システム同定の有限サンプル解析（Finite Sample Analysis of Stochastic System Identification）</news:title>
   <news:publication_date>2026-08-20T19:21:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725390</loc>
  <lastmod>2026-08-20T19:21:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深度カメラとサンプルエントロピーによる歩行パターン解析（Exploratory studies of human gait changes using depth cameras and sample entropy）</news:title>
   <news:publication_date>2026-08-20T19:21:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725388</loc>
  <lastmod>2026-08-20T19:20:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチタスク学習におけるタスク類似性学習の原理的アプローチ（A Principled Approach for Learning Task Similarity in Multitask Learning）</news:title>
   <news:publication_date>2026-08-20T19:20:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725386</loc>
  <lastmod>2026-08-20T18:29:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>走査プローブの状態認識を自動化するニューラルネットワーク群（Scanning Probe State Recognition With Multi-Class Neural Network Ensembles）</news:title>
   <news:publication_date>2026-08-20T18:29:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725384</loc>
  <lastmod>2026-08-20T18:28:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分子グラフの階層的潜在表現：グループを明示するティア方式（Tiered Latent Representations and Latent Spaces for Molecular Graphs）</news:title>
   <news:publication_date>2026-08-20T18:28:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725382</loc>
  <lastmod>2026-08-20T18:28:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単眼動画からの衝突までの時間予測（Forecasting Time-to-Collision from Monocular Video: Feasibility, Dataset, and Challenges）</news:title>
   <news:publication_date>2026-08-20T18:28:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725380</loc>
  <lastmod>2026-08-20T18:27:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Stripe 82深層画像による銀河のバルジ・ディスク分解カタログ（Bulge plus disc and Sérsic decomposition catalogues for 16,908 galaxies in the SDSS Stripe 82 co-adds）</news:title>
   <news:publication_date>2026-08-20T18:27:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725378</loc>
  <lastmod>2026-08-20T18:27:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>個別の快適温度を少ない問合せで学ぶ（Learning Personalized Thermal Preferences via Bayesian Active Learning with Unimodality Constraints）</news:title>
   <news:publication_date>2026-08-20T18:27:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725376</loc>
  <lastmod>2026-08-20T18:27:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>予算制約下のエッジサービス配置と需要推定（Budget-constrained Edge Service Provisioning with Demand Estimation via Bandit Learning）</news:title>
   <news:publication_date>2026-08-20T18:27:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725374</loc>
  <lastmod>2026-08-20T18:27:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>狭い隙間をニューラルネットワークで飛行させる—エンドツーエンド計画と制御のアプローチ（Flying through a narrow gap using neural network: an end-to-end planning and control approach）</news:title>
   <news:publication_date>2026-08-20T18:27:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725372</loc>
  <lastmod>2026-08-20T17:34:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>再電離期に迫る――LAGERが示したz≈7のライマンα銀河の意味（LYMAN ALPHA GALAXIES IN THE EPOCH OF REIONIZATION）</news:title>
   <news:publication_date>2026-08-20T17:34:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725370</loc>
  <lastmod>2026-08-20T17:34:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>等変性を持つエンティティ関係ネットワーク（Equivariant Entity-Relationship Networks）</news:title>
   <news:publication_date>2026-08-20T17:34:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725368</loc>
  <lastmod>2026-08-20T17:34:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>個別処方型サポートベクターマシンによる再入院抑止（Prescriptive Cluster-Dependent Support Vector Machines with an Application to Reducing Hospital Readmissions）</news:title>
   <news:publication_date>2026-08-20T17:34:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725366</loc>
  <lastmod>2026-08-20T17:33:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>潜在単体位相モデル：多視点クラスタリングと不確実性の可視化（Latent Simplex Position Model: High Dimensional Multi-view Clustering with Uncertainty Quantification）</news:title>
   <news:publication_date>2026-08-20T17:33:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725364</loc>
  <lastmod>2026-08-20T17:33:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>データが乏しくても深層学習を使うための生成モデル（Generative Models For Deep Learning with Very Scarce Data）</news:title>
   <news:publication_date>2026-08-20T17:33:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725362</loc>
  <lastmod>2026-08-20T17:33:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>屋内廊下環境における単眼カメラを用いたUAV位置推定（Localization of Unmanned Aerial Vehicles in Corridor Environments using Deep Learning）</news:title>
   <news:publication_date>2026-08-20T17:33:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725360</loc>
  <lastmod>2026-08-20T17:32:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>サブグラフネットワークによる構造特徴空間の拡張（Subgraph Networks with Application to Structural Feature Space Expansion）</news:title>
   <news:publication_date>2026-08-20T17:32:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725358</loc>
  <lastmod>2026-08-20T16:41:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ココエルシビティ、平滑性とバイアスが変える分散低減確率的勾配法（Cocoercivity, Smoothness and Bias in Variance-Reduced Stochastic Gradient Methods）</news:title>
   <news:publication_date>2026-08-20T16:41:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725356</loc>
  <lastmod>2026-08-20T16:41:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非共有結合に対する量子機械学習補正が密度汎関数計算を変える（Non-covalent quantum machine learning corrections to density functionals）</news:title>
   <news:publication_date>2026-08-20T16:41:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725354</loc>
  <lastmod>2026-08-20T16:40:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>HYDRAによる応力最小化型ハイパーボリック埋め込み（HYDRA: A METHOD FOR STRAIN-MINIMIZING HYPERBOLIC EMBEDDING OF NETWORK- AND DISTANCE-BASED DATA）</news:title>
   <news:publication_date>2026-08-20T16:40:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725352</loc>
  <lastmod>2026-08-20T16:40:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多目的時系列解析による薬物応答モデル化（Multi-Task Time Series Analysis applied to Drug Response Modelling）</news:title>
   <news:publication_date>2026-08-20T16:40:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725350</loc>
  <lastmod>2026-08-20T16:39:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>斜め落下する液滴の深い液槽への衝突（Oblique droplet impact onto a deep liquid pool）</news:title>
   <news:publication_date>2026-08-20T16:39:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725348</loc>
  <lastmod>2026-08-20T16:39:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>市販サラダの微生物汚染評価のための統一スペクトル解析ワークフロー（A unified spectra analysis workflow for the assessment of microbial contamination of ready-to-eat green salads）</news:title>
   <news:publication_date>2026-08-20T16:39:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725346</loc>
  <lastmod>2026-08-20T15:48:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>短期予測とマルチカメラ融合によるセマンティックグリッド（Short-Term Prediction and Multi-Camera Fusion on Semantic Grids）</news:title>
   <news:publication_date>2026-08-20T15:48:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725344</loc>
  <lastmod>2026-08-20T15:48:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>限られたデータで機械的聴覚を改善する方法（Improving Machine Hearing on Limited Data Sets）</news:title>
   <news:publication_date>2026-08-20T15:48:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725342</loc>
  <lastmod>2026-08-20T15:48:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>対話応答選択における階層的情報学習（Learning Multi-Level Information for Dialogue Response Selection）</news:title>
   <news:publication_date>2026-08-20T15:48:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725340</loc>
  <lastmod>2026-08-20T15:47:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>埋め込みと規則を反復学習する知識グラフ推論（Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning）</news:title>
   <news:publication_date>2026-08-20T15:47:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725338</loc>
  <lastmod>2026-08-20T15:47:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>後期M型矮星の均質サンプル化が示す地平（A homogeneous sample of 34 000 M7−M9.5 dwarfs brighter than J = 17.5）</news:title>
   <news:publication_date>2026-08-20T15:47:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725336</loc>
  <lastmod>2026-08-20T15:47:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>特徴でMCTSをバイアスする一般ゲームへの適用（Biasing MCTS with Features for General Games）</news:title>
   <news:publication_date>2026-08-20T15:47:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725334</loc>
  <lastmod>2026-08-20T15:46:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>屋外画像における広告挿入候補スペースのデータセット（The CASE Dataset of Candidate Spaces for Advert Implantation）</news:title>
   <news:publication_date>2026-08-20T15:46:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725332</loc>
  <lastmod>2026-08-20T14:54:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>拒否者再統合による与信評価の実務課題（R´eint´egration des refus´es en Credit Scoring）</news:title>
   <news:publication_date>2026-08-20T14:54:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725330</loc>
  <lastmod>2026-08-20T14:54:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>バッチ単位の最適輸送損失による3D形状認識の高速化と精度改善（Learning with Batch-wise Optimal Transport Loss for 3D Shape Recognition）</news:title>
   <news:publication_date>2026-08-20T14:54:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725328</loc>
  <lastmod>2026-08-20T14:53:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>PPGnetによるデバイス非依存心拍数推定（PPGnet: Deep Network for Device Independent Heart Rate Estimation from Photoplethysmogram）</news:title>
   <news:publication_date>2026-08-20T14:53:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725326</loc>
  <lastmod>2026-08-20T14:53:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>情報の「写し」と「変換」を分解する（Decomposing information into copying versus transformation）</news:title>
   <news:publication_date>2026-08-20T14:53:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725324</loc>
  <lastmod>2026-08-20T14:53:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時間的グラフにおけるノード埋め込み（Node Embedding over Temporal Graphs）</news:title>
   <news:publication_date>2026-08-20T14:53:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725322</loc>
  <lastmod>2026-08-20T14:53:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>風力タービンの稼働状態分類モデルの移植性（Transferability of Operational Status Classification Models Among Different Wind Turbine Types）</news:title>
   <news:publication_date>2026-08-20T14:53:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725320</loc>
  <lastmod>2026-08-20T14:52:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Deep Q-Learningにおける発散の特徴づけ（Towards Characterizing Divergence in Deep Q-Learning）</news:title>
   <news:publication_date>2026-08-20T14:52:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725318</loc>
  <lastmod>2026-08-20T14:01:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>個別化多層テンソル学習と画像解析への応用（Individualized Multilayer Tensor Learning with An Application in Imaging Analysis）</news:title>
   <news:publication_date>2026-08-20T14:01:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725316</loc>
  <lastmod>2026-08-20T14:01:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DNNベース音源強調のための完全再構成フィルタバンクのデータ駆動設計 (DATA-DRIVEN DESIGN OF PERFECT RECONSTRUCTION FILTERBANK FOR DNN-BASED SOUND SOURCE ENHANCEMENT)</news:title>
   <news:publication_date>2026-08-20T14:01:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725314</loc>
  <lastmod>2026-08-20T14:00:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>臨床データにおける発作検出と分類の畳み込みニューラルネットワーク（Convolutional neural network for detection and classification of seizures in clinical data）</news:title>
   <news:publication_date>2026-08-20T14:00:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725312</loc>
  <lastmod>2026-08-20T14:00:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ソート演算子の連続緩和による確率的最適化（STOCHASTIC OPTIMIZATION OF SORTING NETWORKS VIA CONTINUOUS RELAXATIONS）</news:title>
   <news:publication_date>2026-08-20T14:00:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725310</loc>
  <lastmod>2026-08-20T13:59:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>脳接続性とCNNによる統合的分類アプローチ（Classification of EEG-Based Brain Connectivity Networks in Schizophrenia Using a Multi-Domain Connectome Convolutional Neural Network）</news:title>
   <news:publication_date>2026-08-20T13:59:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725308</loc>
  <lastmod>2026-08-20T13:59:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>OverSketched Newtonによるサーバレス最適化の実務的意義（OverSketched Newton: Fast Convex Optimization for Serverless Systems）</news:title>
   <news:publication_date>2026-08-20T13:59:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725306</loc>
  <lastmod>2026-08-20T13:58:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>衛星画像時系列の分離表現学習（Learning Disentangled Representations of Satellite Image Time Series）</news:title>
   <news:publication_date>2026-08-20T13:58:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725304</loc>
  <lastmod>2026-08-20T13:07:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単一入力出力層スパイキング分類器と時変重みモデルの効率化（Efficient single input-output layer spiking neural classifier with time-varying weight model）</news:title>
   <news:publication_date>2026-08-20T13:07:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725302</loc>
  <lastmod>2026-08-20T13:07:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ジオメトリ認識に基づく弱教師あり学習による3D人体姿勢推定（Weakly-Supervised Discovery of Geometry-Aware Representation for 3D Human Pose Estimation）</news:title>
   <news:publication_date>2026-08-20T13:07:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725300</loc>
  <lastmod>2026-08-20T13:06:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>超低消費電力の遠隔心電図（ECG）モニタリングシステム（Ultra Low-Power System for Remote ECG Monitoring）</news:title>
   <news:publication_date>2026-08-20T13:06:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725298</loc>
  <lastmod>2026-08-20T13:06:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>運転シーンにおける時間的ダイナミクス情報の価値（Value of Temporal Dynamics Information in Driving Scene Segmentation）</news:title>
   <news:publication_date>2026-08-20T13:06:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725296</loc>
  <lastmod>2026-08-20T13:05:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>半正則三角メッシュ上の畳み込みニューラルネットワークと脳画像への応用（Convolutional Neural Network on Semi-Regular Triangulated Meshes and its Application to Brain Image Data）</news:title>
   <news:publication_date>2026-08-20T13:05:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725294</loc>
  <lastmod>2026-08-20T13:05:43Z</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>2026-08-20T13:05:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725292</loc>
  <lastmod>2026-08-20T13:05:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層的ディリクレ過程に対する正確なスライスサンプラー（Exact slice sampler for Hierarchical Dirichlet Processes）</news:title>
   <news:publication_date>2026-08-20T13:05:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725289</loc>
  <lastmod>2026-08-20T12:13:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多目的粒子群最適化で進化させる深層ニューラルネット（Evolving Deep Neural Networks by Multi-objective Particle Swarm Optimization for Image Classification）</news:title>
   <news:publication_date>2026-08-20T12:13:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725287</loc>
  <lastmod>2026-08-20T12:13:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Dual Residual Networksによる画像復元の新展開（Dual Residual Networks Leveraging the Potential of Paired Operations for Image Restoration）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725285</loc>
  <lastmod>2026-08-20T12:12:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>アフィン変換と非パラメトリックを同時に扱う3D画像登録のネットワーク（Networks for Joint Affine and Non-parametric Image Registration）</news:title>
   <news:publication_date>2026-08-20T12:12:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725283</loc>
  <lastmod>2026-08-20T12:11:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>入力点を中心に最大のℓp球をはめる方法（Provable Certificates for Adversarial Examples: Fitting a Ball in the Union of Polytopes）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューラルネットの解釈をフリップポイントで考える（Interpreting Neural Networks Using Flip Points）</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>
   </news:publication>
   <news:title>安全性重視のエンドツーエンド強化学習—制御バリア関数を用いた安全保証（End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks）</news:title>
   <news:publication_date>2026-08-20T12:11:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725277</loc>
  <lastmod>2026-08-20T12:11:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>信頼できる機械学習と自動化をつなぐ枠組み（A Unified Analytical Framework for Trustable Machine Learning and Automation Running with Blockchain）</news:title>
   <news:publication_date>2026-08-20T12:11:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725275</loc>
  <lastmod>2026-08-20T11:19:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>民族歌のモチーフの分散ベクトル表現（Distributed Vector Representations of Folksong Motifs）</news:title>
   <news:publication_date>2026-08-20T11:19:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725273</loc>
  <lastmod>2026-08-20T11:19:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GBMを加速する考え方と実装の要点（Accelerating Gradient Boosting Machines）</news:title>
   <news:publication_date>2026-08-20T11:19:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725271</loc>
  <lastmod>2026-08-20T11:19:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Byzantine耐性分散線形回帰（Byzantine Fault Tolerant Distributed Linear Regression）</news:title>
   <news:publication_date>2026-08-20T11:19:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725269</loc>
  <lastmod>2026-08-20T11:18:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>セグメンテーション品質評価の堅牢化（Robust Image Segmentation Quality Assessment）</news:title>
   <news:publication_date>2026-08-20T11:18:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725267</loc>
  <lastmod>2026-08-20T11:18:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>制約付きバッチ方策学習（Batch Policy Learning under Constraints）</news:title>
   <news:publication_date>2026-08-20T11:18:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725265</loc>
  <lastmod>2026-08-20T11:18:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>直接知覚におけるアフォーダンス学習が自動運転を変える（Affordance Learning In Direct Perception for Autonomous Driving）</news:title>
   <news:publication_date>2026-08-20T11:18:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725263</loc>
  <lastmod>2026-08-20T11:17:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ノイジー加速べき乗法による固有値問題の効率化（Noisy Accelerated Power Method for Eigenproblems with Applications）</news:title>
   <news:publication_date>2026-08-20T11:17:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725261</loc>
  <lastmod>2026-08-20T10:25:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>LaserNet：レンジ画像で効率的に不確かさを扱う3D物体検出（LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving）</news:title>
   <news:publication_date>2026-08-20T10:25:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725259</loc>
  <lastmod>2026-08-20T10:25:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>量子オートエンコーダによる損失なし量子データ圧縮の実現（Realization of a quantum autoencoder for lossless compression of quantum data）</news:title>
   <news:publication_date>2026-08-20T10:25:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725257</loc>
  <lastmod>2026-08-20T10:25:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所経験を活かしたグローバル動作計画（Using Local Experiences for Global Motion Planning）</news:title>
   <news:publication_date>2026-08-20T10:25:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725255</loc>
  <lastmod>2026-08-20T10:24:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ハイブリッド空間における効率的な内積近似（Efficient Inner Product Approximation in Hybrid Spaces）</news:title>
   <news:publication_date>2026-08-20T10:24:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725253</loc>
  <lastmod>2026-08-20T10:24:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Polyakの学習率を用いた確率的勾配降下法（Stochastic Gradient Descent with Polyak’s Learning Rate）</news:title>
   <news:publication_date>2026-08-20T10:24:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725251</loc>
  <lastmod>2026-08-20T10:24:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>量子回路のパラメータ化とキュービット品質の時間変動への対処（Addressing Temporal Variations in Qubit Quality Metrics for Parameterized Quantum Circuits）</news:title>
   <news:publication_date>2026-08-20T10:24:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725249</loc>
  <lastmod>2026-08-20T10:23:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>エネルギー基底モデルによる暗黙的生成とモデリング（Implicit Generation and Modeling with Energy-Based Models）</news:title>
   <news:publication_date>2026-08-20T10:23:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725247</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>写真から自在に鉛筆画を作る技術の要点（Im2Pencil: Controllable Pencil Illustration from Photographs）</news:title>
   <news:publication_date>2026-08-20T09:32:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725245</loc>
  <lastmod>2026-08-20T09:32:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オンライン継続学習のための勾配ベースサンプル選択（Gradient based sample selection for online continual learning）</news:title>
   <news:publication_date>2026-08-20T09:32:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725243</loc>
  <lastmod>2026-08-20T09:32:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>TATi—熱力学解析ツールキット（TATi-Thermodynamic Analytics ToolkIt: TensorFlow-based software for posterior sampling in machine learning applications）</news:title>
   <news:publication_date>2026-08-20T09:32:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725241</loc>
  <lastmod>2026-08-20T09:30:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>磁気選択的増強で学習が速い分子スピンバルブのシナプス（Fast learning synapses with molecular spin valves via selective magnetic potentiation）</news:title>
   <news:publication_date>2026-08-20T09:30:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725239</loc>
  <lastmod>2026-08-20T09:30:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>統計的部分多様体に対する近似情報検定（Approximate Information Tests on Statistical Submanifolds）</news:title>
   <news:publication_date>2026-08-20T09:30:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725237</loc>
  <lastmod>2026-08-20T09:30:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Column2Vecによるデータベーススキーマの分散表現（Column2Vec: Structural Understanding via Distributed Representations of Database Schemas）</news:title>
   <news:publication_date>2026-08-20T09:30:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725235</loc>
  <lastmod>2026-08-20T09:29:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的最適化におけるより良いモデルの重要性（The importance of better models in stochastic optimization）</news:title>
   <news:publication_date>2026-08-20T09:29:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725233</loc>
  <lastmod>2026-08-20T08:38:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-20T08:38:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725231</loc>
  <lastmod>2026-08-20T08:38:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>敵対的運転に対する単一ステップオプション（Single-step Options for Adversary Driving）</news:title>
   <news:publication_date>2026-08-20T08:38:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725229</loc>
  <lastmod>2026-08-20T08:37:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所特徴のバッグモデルでCNNを近似するとImageNetで驚くほど高精度である（Approximating CNNs with Bag-of-Local-Features Models Works Surprisingly Well on ImageNet）</news:title>
   <news:publication_date>2026-08-20T08:37:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725227</loc>
  <lastmod>2026-08-20T08:36:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DC-SPP-YOLOによる物体検出の改良（DC-SPP-YOLO: Dense Connection and Spatial Pyramid Pooling Based YOLO for Object Detection）</news:title>
   <news:publication_date>2026-08-20T08:36:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725225</loc>
  <lastmod>2026-08-20T08:36:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Wikipediaをグラフで扱うためのデータセット整備（A graph-structured dataset for Wikipedia research）</news:title>
   <news:publication_date>2026-08-20T08:36:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725223</loc>
  <lastmod>2026-08-20T08:36:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>EHAAS: エネルギーハーベスタをセンサーに使う場認識（EHAAS: Energy Harvesters As A Sensor for Place Recognition on Wearables）</news:title>
   <news:publication_date>2026-08-20T08:36:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725221</loc>
  <lastmod>2026-08-20T08:35:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次元データを扱う文脈付きバンディットの高速化と次元削減の実践（Contextual Bandits with Random Projection）</news:title>
   <news:publication_date>2026-08-20T08:35:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725219</loc>
  <lastmod>2026-08-20T07:44:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>広帯域JVLAデータによる高ダイナミックレンジ電波画像生成手法（A Procedure for Making High Dynamic-Range Radio Images）</news:title>
   <news:publication_date>2026-08-20T07:44:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725217</loc>
  <lastmod>2026-08-20T07:43:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ロゼット植物における葉のセグメンテーションとカウントのためのデータ拡張（Data Augmentation for Leaf Segmentation and Counting Tasks in Rosette Plants）</news:title>
   <news:publication_date>2026-08-20T07:43:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725215</loc>
  <lastmod>2026-08-20T07:42:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ブラックホール誕生の電磁ウィンドウ（Electromagnetic Window into the Dawn of Black Holes）</news:title>
   <news:publication_date>2026-08-20T07:42:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725213</loc>
  <lastmod>2026-08-20T07:41:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>OCGANによるワン・クラス異常検知の再定義（OCGAN: One-class Novelty Detection Using GANs with Constrained Latent Representations）</news:title>
   <news:publication_date>2026-08-20T07:41:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725211</loc>
  <lastmod>2026-08-20T07:41:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>修正なしランジュヴァンアルゴリズムの高速収束（Rapid Convergence of the Unadjusted Langevin Algorithm: Isoperimetry Suffices）</news:title>
   <news:publication_date>2026-08-20T07:41:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-20T07:41:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高抗力の恒星間天体と銀河の動的ストリーム（High-Drag Interstellar Objects And Galactic Dynamical Streams）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725207</loc>
  <lastmod>2026-08-20T07:40:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動的ミニバッチ再サンプリングがもたらす損失関数のノイズと最適化への影響（Traversing the noise of dynamic mini-batch sub-sampled loss functions: A visual guide）</news:title>
   <news:publication_date>2026-08-20T07:40:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725205</loc>
  <lastmod>2026-08-20T06:49:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>圧縮センシングCT再構成のための畳み込みスパースコーディング (Convolutional Sparse Coding for Compressed Sensing CT Reconstruction)</news:title>
   <news:publication_date>2026-08-20T06:49:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725203</loc>
  <lastmod>2026-08-20T06:49:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習型畳み込み変換による点群ジオメトリの損失圧縮（LEARNING CONVOLUTIONAL TRANSFORMS FOR LOSSY POINT CLOUD GEOMETRY COMPRESSION）</news:title>
   <news:publication_date>2026-08-20T06:49:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725201</loc>
  <lastmod>2026-08-20T06:48:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単一ハドロン生成におけるビームヘリシティ非対称性（Beam-helicity asymmetries for single-hadron production in semi-inclusive deep-inelastic scattering from unpolarized hydrogen and deuterium targets）</news:title>
   <news:publication_date>2026-08-20T06:48:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725199</loc>
  <lastmod>2026-08-20T06:48:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>表面欠陥検出のためのセグメンテーションベース深層学習（Segmentation-Based Deep-Learning Approach for Surface-Defect Detection）</news:title>
   <news:publication_date>2026-08-20T06:48:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725197</loc>
  <lastmod>2026-08-20T06:48:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>やさしく触れるロボット学習 — Curiosityで学ぶ衝撃最小化（Learning Gentle Object Manipulation with Curiosity-Driven Deep Reinforcement Learning）</news:title>
   <news:publication_date>2026-08-20T06:48:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725195</loc>
  <lastmod>2026-08-20T06:47:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>強化学習で熱力学経路を最適化する（Optimizing thermodynamic trajectories using evolutionary and gradient-based reinforcement learning）</news:title>
   <news:publication_date>2026-08-20T06:47:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725193</loc>
  <lastmod>2026-08-20T05:55:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単一画像から集合画像へ──弱教師あり学習による高精度3D顔再構成（Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set）</news:title>
   <news:publication_date>2026-08-20T05:55:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725191</loc>
  <lastmod>2026-08-20T05:55:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>トポロジーに基づく代表データセットで学習資源を削減する（Topology-based Representative Datasets to Reduce Neural Network Training Resources）</news:title>
   <news:publication_date>2026-08-20T05:55:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725189</loc>
  <lastmod>2026-08-20T05:54:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単眼（モノキュラー）視差推定におけるドメイン変換と曖昧性学習による新規ネットワーク（A NOVEL MONOCULAR DISPARITY ESTIMATION NETWORK WITH DOMAIN TRANSFORMATION AND AMBIGUITY LEARNING）</news:title>
   <news:publication_date>2026-08-20T05:54:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725187</loc>
  <lastmod>2026-08-20T05:54:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高精度なメンタルストレスの早期検出（Early Detection of Mental Stress Using Advanced Neuroimaging and Artificial Intelligence）</news:title>
   <news:publication_date>2026-08-20T05:54:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725185</loc>
  <lastmod>2026-08-20T05:54:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ラベル順位のための選好ルール発掘（Preference rules for label ranking: Mining patterns in multi-target relations）</news:title>
   <news:publication_date>2026-08-20T05:54:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/725183</loc>
  <lastmod>2026-08-20T05:54:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SpiNNaker 2上の報酬ベース構造可塑性の効率化（Efficient Reward-Based Structural Plasticity on a SpiNNaker 2 Prototype）</news:title>
   <news:publication_date>2026-08-20T05:54:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725181</loc>
  <lastmod>2026-08-20T05:53:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>実数階（実次数）全方位トータルバリエーションと最適構造の学習（REAL ORDER (AN)-ISOTROPIC TOTAL VARIATION IN IMAGE PROCESSING - PART II: LEARNING OF OPTIMAL STRUCTURES）</news:title>
   <news:publication_date>2026-08-20T05:53:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725179</loc>
  <lastmod>2026-08-20T05:02:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習を用いた超高速量子磁性の研究（Investigating ultrafast quantum magnetism with machine learning）</news:title>
   <news:publication_date>2026-08-20T05:02:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725177</loc>
  <lastmod>2026-08-20T05:02:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>三次元点群からの個体樹冠分割を実現する可変クラス・グラフカット法（Three-dimensional Segmentation of Trees Through a Flexible Multi-Class Graph Cut Algorithm (MCGC))</news:title>
   <news:publication_date>2026-08-20T05:02:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-20T05:01:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深い八元数ネットワーク（Deep Octonion Networks）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725173</loc>
  <lastmod>2026-08-20T05:00:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>構造的ジャンプLSTMによるニューラル速読（Neural Speed Reading with Structural-Jump-LSTM）</news:title>
   <news:publication_date>2026-08-20T05:00:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725171</loc>
  <lastmod>2026-08-20T05:00:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>視覚関係検出におけるクラス不均衡と背景フィルタリングの考察（On Class Imbalance and Background Filtering in Visual Relationship Detection）</news:title>
   <news:publication_date>2026-08-20T05:00:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725169</loc>
  <lastmod>2026-08-20T05:00:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スパイク列の依存性特徴の解析（A study of dependency features of spike trains through copulas）</news:title>
   <news:publication_date>2026-08-20T05:00:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725167</loc>
  <lastmod>2026-08-20T05:00:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>リアルタイム道路走行画像のための事前学習ImageNetアーキテクチャ擁護（In Defense of Pre-trained ImageNet Architectures for Real-time Semantic Segmentation of Road-driving Images）</news:title>
   <news:publication_date>2026-08-20T05:00:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725165</loc>
  <lastmod>2026-08-20T04:08:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>履歴の時間減衰を学習する注意機構と話者指標による音声言語理解（Decay-Function-Free Time-Aware Attention to Context and Speaker Indicator for Spoken Language Understanding）</news:title>
   <news:publication_date>2026-08-20T04:08:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725163</loc>
  <lastmod>2026-08-20T04:07:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>サイバーセキュリティ意識向上のためのゲーミフィケーション手法（Gamification Techniques for Raising Cyber Security Awareness）</news:title>
   <news:publication_date>2026-08-20T04:07:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725161</loc>
  <lastmod>2026-08-20T04:07:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ツイートの感情強度推定におけるExperts Model（Affect in Tweets Using Experts Model）</news:title>
   <news:publication_date>2026-08-20T04:07:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725159</loc>
  <lastmod>2026-08-20T04:07:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>条件付き深層畳み込み敵対的生成ネットワークによるナノフォトニクス設計（DESIGNING NANOPHOTONIC STRUCTURES USING CONDITIONAL-DEEP CONVOLUTIONAL GENERATIVE ADVERSARIAL NETWORKS）</news:title>
   <news:publication_date>2026-08-20T04:07:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725157</loc>
  <lastmod>2026-08-20T04:06:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>系統的レンジングによるFlyeye望遠鏡発見NEO（近地球天体）の追跡手法（A SYSTEMATIC RANGING TECHNIQUE FOR FOLLOW-UPS OF NEOS DETECTED WITH THE FLYEYE TELESCOPE）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725155</loc>
  <lastmod>2026-08-20T04:06:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>反例駆動によるPOMDP戦略改善（Counterexample-Guided Strategy Improvement for POMDPs Using Recurrent Neural Networks）</news:title>
   <news:publication_date>2026-08-20T04:06:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725153</loc>
  <lastmod>2026-08-20T04:06:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>電子カルテの階層表現で臨床予測を強化するアプローチ（Learning Hierarchical Representations of Electronic Health Records for Clinical Outcome Prediction）</news:title>
   <news:publication_date>2026-08-20T04:06:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725151</loc>
  <lastmod>2026-08-20T03:14:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>チェックすべき文の自動抽出を強化するニューラルランキング（Neural Check-Worthiness Ranking with Weak Supervision）</news:title>
   <news:publication_date>2026-08-20T03:14:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725149</loc>
  <lastmod>2026-08-20T03:14:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>連続する音楽トラックのスキップ予測を行うMulti-RNNアプローチ（Modelling Sequential Music Track Skips using a Multi-RNN Approach）</news:title>
   <news:publication_date>2026-08-20T03:14:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725147</loc>
  <lastmod>2026-08-20T03:14:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフ誤差のモデル化と堅牢なグラフ信号処理への道（Modelling Graph Errors: Towards Robust Graph Signal Processing）</news:title>
   <news:publication_date>2026-08-20T03:14:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725145</loc>
  <lastmod>2026-08-20T03:13:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>データサイエンスが技術的痕跡探索にもたらす可能性（The Promise of Data Science for the Technosignatures Field）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725143</loc>
  <lastmod>2026-08-20T03:13:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>制約付きクエリグラフと重み付き集合に対する適応的多数問題 (Adaptive Majority Problems for Restricted Query Graphs and for Weighted Sets)</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725141</loc>
  <lastmod>2026-08-20T03:13:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>偶数サイズカーネルと対称パディングによる畳み込みの改善（Convolution with even-sized kernels and symmetric padding）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <lastmod>2026-08-20T03:13:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>パーツベースによる形態学的演算の近似（Part-based approximations for morphological operators using asymmetric auto-encoders）</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>継続学習でモデルを自動拡張し圧縮する仕組み（Regularize, Expand and Compress: Multi-task based Lifelong Learning via NonExpansive AutoML）</news:title>
   <news:publication_date>2026-08-20T02:21:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725135</loc>
  <lastmod>2026-08-20T02:21:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習による動作生成の現状と意味（Machine Learning for Data-Driven Movement Generation: a Review of the State of the Art）</news:title>
   <news:publication_date>2026-08-20T02:21:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725133</loc>
  <lastmod>2026-08-20T02:20:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分子特性予測の不確実性定量化（Uncertainty quantification of molecular property prediction with Bayesian neural networks）</news:title>
   <news:publication_date>2026-08-20T02:20:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725131</loc>
  <lastmod>2026-08-20T02:19:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>生成的堅牢推論によるロボット認知の強化（GRIP: Generative Robust Inference and Perception for Semantic Robot Manipulation in Adversarial Environments）</news:title>
   <news:publication_date>2026-08-20T02:19:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725129</loc>
  <lastmod>2026-08-20T02:19:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Video Object Segmentationを使った視覚サーボと深度推定（Video Object Segmentation-based Visual Servo Control and Object Depth Estimation on a Mobile Robot）</news:title>
   <news:publication_date>2026-08-20T02:19:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725127</loc>
  <lastmod>2026-08-20T02:19:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>可変な最適測定角を伴う量子相関領域の検出可能性（On the possibility to detect quantum correlation regions with the variable optimal measurement angle）</news:title>
   <news:publication_date>2026-08-20T02:19:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725125</loc>
  <lastmod>2026-08-20T02:19:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模データにおける多ラベル特徴選択の分散化（Distributed Maximization of Submodular plus Diversity Functions for Multi-label Feature Selection on Huge Datasets）</news:title>
   <news:publication_date>2026-08-20T02:19:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725123</loc>
  <lastmod>2026-08-20T01:27:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>経験的レバレッジスコアに基づくランダム特徴サンプリングの実装と理論保証（On Sampling Random Features From Empirical Leverage Scores: Implementation and Theoretical Guarantees）</news:title>
   <news:publication_date>2026-08-20T01:27:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725121</loc>
  <lastmod>2026-08-20T01:27:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Deep K-近傍法の堅牢性に関する考察（On the Robustness of Deep K-Nearest Neighbors）</news:title>
   <news:publication_date>2026-08-20T01:27:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725119</loc>
  <lastmod>2026-08-20T01:27:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分布に基づくゲーム理論的解概念の学習枠組み（A Learning Framework for Distribution-Based Game-Theoretic Solution Concepts）</news:title>
   <news:publication_date>2026-08-20T01:27:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725117</loc>
  <lastmod>2026-08-20T01:26:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>乳がん検診における放射線科医の性能向上に寄与する深層ニューラルネットワーク（Deep Neural Networks Improve Radiologists’ Performance in Breast Cancer Screening）</news:title>
   <news:publication_date>2026-08-20T01:26:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725115</loc>
  <lastmod>2026-08-20T01:26:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>地下鉱山車両のビジョンのみを用いたリアルタイム高精度自己位置推定（LookUP: Vision-Only Real-Time Precise Underground Localisation for Autonomous Mining Vehicles）</news:title>
   <news:publication_date>2026-08-20T01:26:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725113</loc>
  <lastmod>2026-08-20T01:26:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非剛体3D形状検索におけるマルチビュー・メトリック学習（NON-RIGID 3D SHAPE RETRIEVAL BASED ON MULTI-VIEW METRIC LEARNING）</news:title>
   <news:publication_date>2026-08-20T01:26:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725111</loc>
  <lastmod>2026-08-20T01:25:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>未来を予測して言語から解釈可能な計画を作る—Prospectionによるロボット制御の読み方 (Prospection: Interpretable Plans From Language By Predicting the Future)</news:title>
   <news:publication_date>2026-08-20T01:25:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725109</loc>
  <lastmod>2026-08-20T00:34:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>意図分類とスロットラベリングの高速で正確な統合設計（Simple, Fast, Accurate Intent Classification and Slot Labeling for Goal-Oriented Dialogue Systems）</news:title>
   <news:publication_date>2026-08-20T00:34:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725107</loc>
  <lastmod>2026-08-20T00:34:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>限定ラベルと大量非ラベルで効くGANベースのスパム検出（GANs for Semi-Supervised Opinion Spam Detection）</news:title>
   <news:publication_date>2026-08-20T00:34:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725105</loc>
  <lastmod>2026-08-20T00:34:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スマートフォンでのコーヒー葉害虫・病害の検出と分類（A smartphone application to detection and classification of coffee leaf miner and coffee leaf rust）</news:title>
   <news:publication_date>2026-08-20T00:34:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725103</loc>
  <lastmod>2026-08-20T00:33:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的コンテキスト変数による効率的オフポリシーメタ強化学習 (Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables)</news:title>
   <news:publication_date>2026-08-20T00:33:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725101</loc>
  <lastmod>2026-08-20T00:33:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>対話型医用画像分割における全畳み込みニューラルネットワーク（Interactive segmentation of medical images through fully convolutional neural networks）</news:title>
   <news:publication_date>2026-08-20T00:33:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725099</loc>
  <lastmod>2026-08-20T00:33:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>指示動画から学ぶタスク横断弱教師あり学習（Cross-task Weakly Supervised Learning from Instructional Videos）</news:title>
   <news:publication_date>2026-08-20T00:33:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725097</loc>
  <lastmod>2026-08-20T00:33:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>量子アニーリングによる極端クラスタリングへのアプローチ（A Quantum Annealing-Based Approach to Extreme Clustering）</news:title>
   <news:publication_date>2026-08-20T00:33:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725095</loc>
  <lastmod>2026-08-19T23:41:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>適応ハードスレッショルディングによる一貫したロバスト回帰（Adaptive Hard Thresholding for Near-optimal Consistent Robust Regression）</news:title>
   <news:publication_date>2026-08-19T23:41:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725093</loc>
  <lastmod>2026-08-19T23:41:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深い ugrizY 画像とDEEP2/3 分光観測によるフォトメトリック赤方偏移検証（Deep ugrizY Imaging and DEEP2/3 Spectroscopy: A Photometric Redshift Testbed for LSST and Public Release of Data from the DEEP3 Galaxy Redshift Survey）</news:title>
   <news:publication_date>2026-08-19T23:41:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725091</loc>
  <lastmod>2026-08-19T23:41:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マーケティング疲労下における逐次選択バンディット問題の動的学習（Dynamic Learning of Sequential Choice Bandit Problem under Marketing Fatigue）</news:title>
   <news:publication_date>2026-08-19T23:41:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725089</loc>
  <lastmod>2026-08-19T23:39:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深度映像における動作認識の実践的手法（3D Human Action Analysis and Recognition through GLAC descriptor on 2D Motion and Static Posture Images）</news:title>
   <news:publication_date>2026-08-19T23:39:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725087</loc>
  <lastmod>2026-08-19T23:39:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>近傍ボイドで見つかった赤い超拡散銀河とその距離測定（Discovery of a red ultra-diffuse galaxy in a nearby void based on its globular cluster luminosity function）</news:title>
   <news:publication_date>2026-08-19T23:39:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725085</loc>
  <lastmod>2026-08-19T23:39:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>James Webb Telescopeによる深宇宙撮像サーベイの最大化（Maximising the power of deep extragalactic imaging surveys with the James Webb Space Telescope）</news:title>
   <news:publication_date>2026-08-19T23:39:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725083</loc>
  <lastmod>2026-08-19T23:39:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>再電離期における高赤方偏移銀河のUVと[CII]分布の解像度解析（RESOLVED UV AND [CII] STRUCTURES OF LUMINOUS GALAXIES WITHIN THE EPOCH OF REIONISATION）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725081</loc>
  <lastmod>2026-08-19T22:47:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>AlphaZeroのハイパーパラメータ探索が示す実務的示唆（Hyper-Parameter Sweep on AlphaZero General）</news:title>
   <news:publication_date>2026-08-19T22:47:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725079</loc>
  <lastmod>2026-08-19T22:47:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>畳み込みニューラルネットワークのカーネルへの翻訳（Kernel-based Translations of Convolutional Networks）</news:title>
   <news:publication_date>2026-08-19T22:47:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725077</loc>
  <lastmod>2026-08-19T22:46:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>双方向再帰モデルによる攻撃的ツイート分類（Bidirectional Recurrent Models for Offensive Tweet Classification）</news:title>
   <news:publication_date>2026-08-19T22:46:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725075</loc>
  <lastmod>2026-08-19T22:46:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ライブラリ専門家の発見手法が変える採用と貢献者発掘（Identifying Experts in Software Libraries）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725073</loc>
  <lastmod>2026-08-19T22:46:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次元リッジレス最小二乗補間の驚き（Surprises in High-Dimensional Ridgeless Least Squares Interpolation）</news:title>
   <news:publication_date>2026-08-19T22:46:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725071</loc>
  <lastmod>2026-08-19T22:45:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>百万点のデータで「正確な」ガウス過程が回る時代へ（Exact Gaussian Processes on a Million Data Points）</news:title>
   <news:publication_date>2026-08-19T22:45:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725069</loc>
  <lastmod>2026-08-19T22:45:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オンライン非凸学習におけるFTPLの最適性（Online Non-Convex Learning: Following the Perturbed Leader is Optimal）</news:title>
   <news:publication_date>2026-08-19T22:45:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725067</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:publication_date>2026-08-19T21:53:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-19T21:53:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news: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>2026-08-19T21:53:01Z</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>3次元イジング模型に対する少数ショット機械学習（Few-Shot Machine Learning in 3D Ising Model）</news:title>
   <news:publication_date>2026-08-19T21:51:56Z</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>2026-08-19T21:51:44Z</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>学習可能な量子化閾値が切り開く実用的な8ビット推論（Trained Quantization Thresholds for Accurate and Efficient Fixed-Point Inference）</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:publication_date>2026-08-19T21:51:26Z</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>2026-08-19T20:59:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>イベント駆動パルス制御と必要時のモデル学習（Event-triggered Pulse Control with Model Learning (if Necessary))</news:title>
   <news:publication_date>2026-08-19T20:59:36Z</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>2026-08-19T20:58:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-19T20:58:32Z</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>2026-08-19T20:58:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725043</loc>
  <lastmod>2026-08-19T20:58:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>進化的手法によるデータ駆動の偏微分方程式発見（Data-driven PDE discovery with evolutionary approach）</news:title>
   <news:publication_date>2026-08-19T20:58:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725041</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>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </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>2026-08-19T20:04:24Z</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>
  </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>
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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>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725021</loc>
  <lastmod>2026-08-19T19:12:01Z</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>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-19T19:11:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>教師なしネットワーク表現学習の比較研究 (A Comparative Study for Unsupervised Network Representation Learning)</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-19T19:10:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-19T19:10:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>敵対的バンディットに関する一階境界・分散・ギャップ依存境界 (On First-Order Bounds, Variance and Gap-Dependent Bounds for Adversarial Bandits)</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-19T19:10:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>臨床試験のコホート選択に対するハイブリッド手法（Hybrid Approaches for Cohort Selection for Clinical Trials）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725011</loc>
  <lastmod>2026-08-19T18:19:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ロバスト平均推定の計算困難性（How Hard is Robust Mean Estimation?）</news:title>
   <news:publication_date>2026-08-19T18:19:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <lastmod>2026-08-19T18:18:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>イベント駆動型ビジョンによる姿勢不変物体認識（Pose-invariant object recognition for event-based vision with slow-ELM）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725007</loc>
  <lastmod>2026-08-19T18:18:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ドメイン横断知識転移による教師なし車両再識別（CROSS DOMAIN KNOWLEDGE TRANSFER FOR UNSUPERVISED VEHICLE RE-IDENTIFICATION）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-19T18:17:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>デモ不要で模倣学習を実現する方法（Hindsight Generative Adversarial Imitation Learning）</news:title>
   <news:publication_date>2026-08-19T18:17:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/725003</loc>
  <lastmod>2026-08-19T18:17:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>クラス増分学習とDeep Model Consolidation（Class-incremental Learning via Deep Model Consolidation）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-19T18:17:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>個人化ニューラル埋め込みによるテキスト対応協調フィルタリング（Personalized Neural Embeddings for Collaborative Filtering with Text）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <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/724993</loc>
  <lastmod>2026-08-19T17:13:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>KL-UCB+方策の理論的根拠と実務的示唆（A Note on KL-UCB+ Policy for the Stochastic Bandit）</news:title>
   <news:publication_date>2026-08-19T17:13:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724991</loc>
  <lastmod>2026-08-19T17:13:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>不完全なチャネル知識下での効率的なMIMO検出（Efficient MIMO Detection with Imperfect Channel Knowledge – A Deep Learning Approach）</news:title>
   <news:publication_date>2026-08-19T17:13:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724989</loc>
  <lastmod>2026-08-19T17:13:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非負表現に基づく識別的辞書学習による顔認識（Non-negative representation based discriminative dictionary learning for face recognition）</news:title>
   <news:publication_date>2026-08-19T17:13:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724987</loc>
  <lastmod>2026-08-19T17:12:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>低ランク識別的最小二乗回帰による画像分類の改良（Low-Rank Discriminative Least Squares Regression for Image Classification）</news:title>
   <news:publication_date>2026-08-19T17:12:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724985</loc>
  <lastmod>2026-08-19T17:12:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Fisher判別付き最小二乗回帰による画像分類の改良（Fisher Discriminative Least Squares Regression for Image Classification）</news:title>
   <news:publication_date>2026-08-19T17:12:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724983</loc>
  <lastmod>2026-08-19T16:21:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Compressed Sensingを臨床へつなぐデータ駆動学習の実装と示唆（Compressed Sensing: From Research to Clinical Practice with Data-Driven Learning）</news:title>
   <news:publication_date>2026-08-19T16:21:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724981</loc>
  <lastmod>2026-08-19T16:20:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>EEGアーティファクト除去のための機械学習：ベンチマークの確立（Machine Learning for removing EEG artifacts: Setting the benchmark）</news:title>
   <news:publication_date>2026-08-19T16:20:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724979</loc>
  <lastmod>2026-08-19T16:20:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多様性を重視する対話型推薦の設計（Diversity-Promoting Deep Reinforcement Learning for Interactive Recommendation）</news:title>
   <news:publication_date>2026-08-19T16:20:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724977</loc>
  <lastmod>2026-08-19T16:19:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マスク指導型スタイル転送ネットワークによる実画像の浄化（Mask-Guided Style Transfer Network for Purifying Real Images）</news:title>
   <news:publication_date>2026-08-19T16:19:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724975</loc>
  <lastmod>2026-08-19T16:19:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>主題に寄り添う文字表現の自動生成（Trick or Treat: Thematic Reinforcement for Artistic Typography）</news:title>
   <news:publication_date>2026-08-19T16:19:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724973</loc>
  <lastmod>2026-08-19T16:19:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>嗅覚の快不快をCNNで予測する試み（POP-CNN: Predicting Odor’s Pleasantness）</news:title>
   <news:publication_date>2026-08-19T16:19:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724971</loc>
  <lastmod>2026-08-19T16:19:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>半教師あり深層学習による異常脳波検出の実用性（A semi-supervised deep learning algorithm for abnormal EEG identification）</news:title>
   <news:publication_date>2026-08-19T16:19:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724969</loc>
  <lastmod>2026-08-19T15:26:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自己重み付けマルチビュー距離学習による相互相関最大化（SELF-WEIGHTED MULTIVIEW METRIC LEARNING BY MAXIMIZING THE CROSS CORRELATIONS）</news:title>
   <news:publication_date>2026-08-19T15:26:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724967</loc>
  <lastmod>2026-08-19T15:26:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>酸素殻燃焼の多次元シミュレーション――超新星直前の対流の実像（One-, Two-, and Three-dimensional Simulations of Oxygen Shell Burning Just Before the Core-Collapse of Massive Stars）</news:title>
   <news:publication_date>2026-08-19T15:26:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724965</loc>
  <lastmod>2026-08-19T15:26:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動的ハースト指数（Dynamic Hurst Exponent in Time Series）</news:title>
   <news:publication_date>2026-08-19T15:26:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724963</loc>
  <lastmod>2026-08-19T15:25:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>差分プライベート合意に基づく分散最適化（Differentially Private Consensus-Based Distributed Optimization）</news:title>
   <news:publication_date>2026-08-19T15:25:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724961</loc>
  <lastmod>2026-08-19T15:25:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>網膜血管分割のための動的深層ネットワーク（Dynamic Deep Networks for Retinal Vessel Segmentation）</news:title>
   <news:publication_date>2026-08-19T15:25:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724959</loc>
  <lastmod>2026-08-19T15:25:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>群衆密度推定におけるウェーブレット変換と機械学習の応用（Estimation of crowd density applying wavelet transform and machine learning）</news:title>
   <news:publication_date>2026-08-19T15:25:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724957</loc>
  <lastmod>2026-08-19T15:24:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>不規則領域における都市全域の群衆流予測（Predicting Citywide Crowd Flows in Irregular Regions Using Multi-View Graph Convolutional Networks）</news:title>
   <news:publication_date>2026-08-19T15:24:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724955</loc>
  <lastmod>2026-08-19T14:33:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>視覚刺激のカテゴリ復元にBRNNを適用する研究（Category decoding of visual stimuli from human brain activity using a bidirectional recurrent neural network）</news:title>
   <news:publication_date>2026-08-19T14:33:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724953</loc>
  <lastmod>2026-08-19T14:33:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ラベルノイズを内側から直す仕組み：PENCIL（Probabilistic End-to-end Noise Correction for Learning with Noisy Labels）</news:title>
   <news:publication_date>2026-08-19T14:33:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724951</loc>
  <lastmod>2026-08-19T14:33:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>クラウドソーシングと機械学習の協働が変えるデータラベリングの現場（Modeling with the Crowd: Optimizing the Human-Machine Partnership with Zooniverse）</news:title>
   <news:publication_date>2026-08-19T14:33:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724949</loc>
  <lastmod>2026-08-19T14:32:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>デコリレーションによる深層強化学習の表現学習改善（Deep Reinforcement Learning with Decorrelation）</news:title>
   <news:publication_date>2026-08-19T14:32:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724947</loc>
  <lastmod>2026-08-19T14:32:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>粒子加速器の最適化を飛躍的に高速化する機械学習手法（Machine Learning for Orders of Magnitude Speedup in Multi-Objective Optimization of Particle Accelerator Systems）</news:title>
   <news:publication_date>2026-08-19T14:32:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724945</loc>
  <lastmod>2026-08-19T14:32:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>感情可視化ジャーナルLemotif（Lemotif: An Affective Visual Journal Using Deep Neural Networks）</news:title>
   <news:publication_date>2026-08-19T14:32:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724943</loc>
  <lastmod>2026-08-19T14:32:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層的ルーティング混合専門家モデルの要点解説（Hierarchical Routing Mixture of Experts）</news:title>
   <news:publication_date>2026-08-19T14:32:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724941</loc>
  <lastmod>2026-08-19T13:40:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>銀河分類の機械学習解析（Galaxy classification: A machine learning analysis of GAMA catalogue data）</news:title>
   <news:publication_date>2026-08-19T13:40:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724939</loc>
  <lastmod>2026-08-19T13:39:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時間変動を柔軟に扱う対比較モデル（Pairwise Comparisons with Flexible Time-Dynamics）</news:title>
   <news:publication_date>2026-08-19T13:39:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724937</loc>
  <lastmod>2026-08-19T13:39:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>集合で学ぶ複数インスタンス回帰—リモートセンシングへの応用（Learning with Sets in Multiple Instance Regression Applied to Remote Sensing）</news:title>
   <news:publication_date>2026-08-19T13:39:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724935</loc>
  <lastmod>2026-08-19T13:38:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>劣化ブロードキャストチャネルにおける深層学習（Deep Learning for the Degraded Broadcast Channel）</news:title>
   <news:publication_date>2026-08-19T13:38:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724933</loc>
  <lastmod>2026-08-19T13:38:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>軌跡分類のための異種部分列発見（Discovering Heterogeneous Subsequences for Trajectory Classification）</news:title>
   <news:publication_date>2026-08-19T13:38:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724931</loc>
  <lastmod>2026-08-19T13:38:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的な「人」の到達可能領域を予測する手法（Predicting Stochastic Human Forward Reachable Sets Based on Learned Human Behavior）</news:title>
   <news:publication_date>2026-08-19T13:38:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724929</loc>
  <lastmod>2026-08-19T13:38:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>合成深度画像の拡張学習によるシム2リアル方策転移（Learning to Augment Synthetic Images for Sim2Real Policy Transfer）</news:title>
   <news:publication_date>2026-08-19T13:38:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724927</loc>
  <lastmod>2026-08-19T12:46:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>視線の届かない場所での物体認識（Direct Object Recognition Without Line-of-Sight Using Optical Coherence）</news:title>
   <news:publication_date>2026-08-19T12:46:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724925</loc>
  <lastmod>2026-08-19T12:46:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>実用的な隠れ音声攻撃が示す脅威と備え（Practical Hidden Voice Attacks against Speech and Speaker Recognition Systems）</news:title>
   <news:publication_date>2026-08-19T12:46:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724923</loc>
  <lastmod>2026-08-19T12:46:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Real and Discreteを用いた深層混合モデルの新展開（A RAD approach to deep mixture models）</news:title>
   <news:publication_date>2026-08-19T12:46:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724921</loc>
  <lastmod>2026-08-19T12:45:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>部分的な閉じ込めが引き起こす超臨界流体の構造と動力学（Soft-wall induced structure and dynamics of partially confined supercritical fluids）</news:title>
   <news:publication_date>2026-08-19T12:45:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/724919</loc>
  <lastmod>2026-08-19T12:45:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-19T12:45:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724917</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>深層ファンダメンタル因子モデルの実務的意義（Deep Fundamental Factor Models）</news:title>
   <news:publication_date>2026-08-19T12:44:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724915</loc>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>Duet v2によるパッセージ再ランキングの改良（AN UPDATED DUET MODEL FOR PASSAGE RE-RANKING）</news:title>
   <news:publication_date>2026-08-19T12:44:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724913</loc>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>IllustrisTNGと深層学習で読み解く銀河形態の再現性（The Hubble Sequence at z ∼0 in the IllustrisTNG simulation with deep learning）</news:title>
   <news:publication_date>2026-08-19T11:52:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
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 <url>
  <loc>https://aibr.jp/archives/724911</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>GOODS領域における微弱電波源の本質（Nature of Faint Radio Sources in GOODS-North and GOODS-South Fields – I. Spectral Index and Radio-FIR Correlation）</news:title>
   <news:publication_date>2026-08-19T11:52:40Z</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>ブラックボックス分類器の多重差分公平性監査（Multi-Differential Fairness Auditor for Black Box Classifiers）</news:title>
   <news:publication_date>2026-08-19T11:52:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724907</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文字レベルCNNによるテキスト分類とエンコーディングの比較（Character-level Convolutional Networks for Text Classification）</news:title>
   <news:publication_date>2026-08-19T11:51:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724905</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>時間の循環一貫性から学ぶ対応関係（Learning Correspondence from the Cycle-consistency of Time）</news:title>
   <news:publication_date>2026-08-19T11:50:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>モデル不確実性とパラメータ不確実性を同時に扱うベイズニューラルネットワーク（Combining model and parameter uncertainty in Bayesian Neural Networks）</news:title>
   <news:publication_date>2026-08-19T11:50:46Z</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>知識グラフ畳み込みネットワークによる推薦システム（Knowledge Graph Convolutional Networks for Recommender Systems）</news:title>
   <news:publication_date>2026-08-19T11:50:34Z</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>弱い特徴量に関する二重降下モデル（Two models of double descent for weak features）</news:title>
   <news:publication_date>2026-08-19T10:58:59Z</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>ボコーダーを用いた歌声抽出法（A VOCODER BASED METHOD FOR SINGING VOICE EXTRACTION）</news:title>
   <news:publication_date>2026-08-19T10:58:48Z</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>2026-08-19T10:58:36Z</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>EV-IMO: 屋内高速物体のイベントカメラによる動き分割とデータセット（EV-IMO: Motion Segmentation Dataset and Learning Pipeline for Event Cameras）</news:title>
   <news:publication_date>2026-08-19T10:57:51Z</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>2026-08-19T10:57:37Z</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>2026-08-19T10:57:20Z</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>
  </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>2026-08-19T10:06:31Z</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>2026-08-19T10:06:19Z</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>2026-08-19T10:05:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-19T10:04:54Z</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>2026-08-19T10:04:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-19T10:04:38Z</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>2026-08-19T10:04:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724873</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>2026-08-19T10:04:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724871</loc>
  <lastmod>2026-08-19T09:12:28Z</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>2026-08-19T09:12:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724869</loc>
  <lastmod>2026-08-19T09:12:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-19T09:12:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724867</loc>
  <lastmod>2026-08-19T09:11:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オフラインとオンラインの深層学習による画像認識（Offline and Online Deep Learning for Image Recognition）</news:title>
   <news:publication_date>2026-08-19T09:11:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724865</loc>
  <lastmod>2026-08-19T09:10:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-19T09:10:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724863</loc>
  <lastmod>2026-08-19T09:10:49Z</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>2026-08-19T09:10:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-19T09:10:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-19T09:09:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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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>CRAFTによる音声プロソディ可視化の教育的転換（CRAFT: A Multifunction Online Platform for Speech Prosody Visualisation）</news:title>
   <news:publication_date>2026-08-19T08:17:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724847</loc>
  <lastmod>2026-08-19T08:17:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>手書き文字認識のためのシーケンス・トゥ・シーケンスモデル評価（Evaluating Sequence-to-Sequence Models for Handwritten Text Recognition）</news:title>
   <news:publication_date>2026-08-19T08:17:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724845</loc>
  <lastmod>2026-08-19T08:17:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スマートメーターで捉える認知症患者の生活行動（Detecting Activities of Daily Living and Routine Behaviours in Dementia Patients Living Alone Using Smart Meter Load Disaggregation）</news:title>
   <news:publication_date>2026-08-19T08:17:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724843</loc>
  <lastmod>2026-08-19T07:25:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Eコマース向けファッションアウトフィット生成（Fashion Outfit Generation for E-commerce）</news:title>
   <news:publication_date>2026-08-19T07:25:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724841</loc>
  <lastmod>2026-08-19T07:15:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチンゲール表現によるマルコフ連鎖の分散削減（Variance reduction for additive functional of Markov chains via martingale representations）</news:title>
   <news:publication_date>2026-08-19T07:15:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724839</loc>
  <lastmod>2026-08-19T07:15:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>未知光源下での自己校正フォトメトリーステレオ（Self-calibrating Deep Photometric Stereo Networks）</news:title>
   <news:publication_date>2026-08-19T07:15:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724837</loc>
  <lastmod>2026-08-19T07:14:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>印刷可能なグラフィカルコードの複製可能性と機械学習による解析（CLONABILITY OF ANTI-COUNTERFEITING PRINTABLE GRAPHICAL CODES: A MACHINE LEARNING APPROACH）</news:title>
   <news:publication_date>2026-08-19T07:14:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724835</loc>
  <lastmod>2026-08-19T07:13:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>数値モデルの不明部分の機械学習による表現（Representing ill-known parts of a numerical model using a machine learning approach）</news:title>
   <news:publication_date>2026-08-19T07:13:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724833</loc>
  <lastmod>2026-08-19T07:13:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>IvaNetによる物体検出とセグメンテーションの同時学習（IVANET: LEARNING TO JOINTLY DETECT AND SEGMENT OBJETS WITH THE HELP OF LOCAL TOP-DOWN MODULES）</news:title>
   <news:publication_date>2026-08-19T07:13:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724831</loc>
  <lastmod>2026-08-19T07:13:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Xホールを用いたF-RANのオンライン強化学習によるコンテンツ配信最適化（Online Reinforcement Learning of X-Haul Content Delivery Mode in Fog Radio Access Networks）</news:title>
   <news:publication_date>2026-08-19T07:13:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724829</loc>
  <lastmod>2026-08-19T06:21:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>集合データ学習と集約関数の選び方（On Deep Set Learning and the Choice of Aggregations）</news:title>
   <news:publication_date>2026-08-19T06:21:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724827</loc>
  <lastmod>2026-08-19T06:21:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>IMDBとTwitterデータに対する感情分析 — 機械学習とベクトル空間による手法 (Sentiment Analysis on IMDB Movie Comments and Twitter Data by Machine Learning and Vector Space Techniques)</news:title>
   <news:publication_date>2026-08-19T06:21:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724825</loc>
  <lastmod>2026-08-19T06:21:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多系列MRIを用いた深層学習による脳転移の自動検出とセグメンテーション（Deep Learning Enables Automatic Detection and Segmentation of Brain Metastases on Multi-Sequence MRI）</news:title>
   <news:publication_date>2026-08-19T06:21:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724823</loc>
  <lastmod>2026-08-19T06:21:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>左右循環制約と適応正則化による教師なし単眼深度推定（Bilateral Cyclic Constraint and Adaptive Regularization for Unsupervised Monocular Depth Prediction）</news:title>
   <news:publication_date>2026-08-19T06:21:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724821</loc>
  <lastmod>2026-08-19T06:20:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>MRIベースのアルツハイマー病分類におけるLRPによる説明性の向上（Layer-wise relevance propagation for explaining deep neural network decisions in MRI-based Alzheimer’s disease classification）</news:title>
   <news:publication_date>2026-08-19T06:20:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724819</loc>
  <lastmod>2026-08-19T06:20:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Deep Gaussian Processesを用いたマルチフィデリティモデリング（Deep Gaussian Processes for Multi-fidelity Modeling）</news:title>
   <news:publication_date>2026-08-19T06:20:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724817</loc>
  <lastmod>2026-08-19T06:20:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>低ランク近似による双曲線埋め込みの効率化（Low-rank approximations of hyperbolic embeddings）</news:title>
   <news:publication_date>2026-08-19T06:20:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724815</loc>
  <lastmod>2026-08-19T05:28:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>M²VAEによるマルチモーダル生成の論点整理（M²VAE – Derivation of a Multi-Modal Variational Autoencoder）</news:title>
   <news:publication_date>2026-08-19T05:28:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724813</loc>
  <lastmod>2026-08-19T05:28:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフ系列の自己回帰モデル（Autoregressive Models for Sequences of Graphs）</news:title>
   <news:publication_date>2026-08-19T05:28:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724811</loc>
  <lastmod>2026-08-19T05:28:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチユーザ分散型大規模MIMOのパイロット設計に深層学習を用いる手法（Deep Learning Based Pilot Design for Multi-user Distributed Massive MIMO Systems）</news:title>
   <news:publication_date>2026-08-19T05:28:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724809</loc>
  <lastmod>2026-08-19T05:27:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>空間適応正規化によるセマンティック画像合成（Semantic Image Synthesis with Spatially-Adaptive Normalization）</news:title>
   <news:publication_date>2026-08-19T05:27:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724807</loc>
  <lastmod>2026-08-19T05:27:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>外観ベースの視線推定における拡張畳み込みの活用（Appearance-Based Gaze Estimation Using Dilated-Convolutions）</news:title>
   <news:publication_date>2026-08-19T05:27:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724805</loc>
  <lastmod>2026-08-19T05:27:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>異種グラフに注目する表現学習の新潮流（Heterogeneous Graph Attention Network）</news:title>
   <news:publication_date>2026-08-19T05:27:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724803</loc>
  <lastmod>2026-08-19T05:27:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>パディングがLSTMとCNNの振る舞いに与える影響（Effects of Padding on LSTMs and CNNs）</news:title>
   <news:publication_date>2026-08-19T05:27:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724801</loc>
  <lastmod>2026-08-19T04:36:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>条件付き生成敵対ネットワークによる敵対的事例生成（Generating Adversarial Examples With Conditional Generative Adversarial Net）</news:title>
   <news:publication_date>2026-08-19T04:36:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724799</loc>
  <lastmod>2026-08-19T04:36:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>NGC 1052周辺で見つかった複数の恒星ストリームの検出（A tidal tale: detection of multiple stellar streams in the environment of NGC 1052）</news:title>
   <news:publication_date>2026-08-19T04:36:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724797</loc>
  <lastmod>2026-08-19T04:35:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層畳み込み構造を用いたPOI意味モデル（POI Semantic Model with a Deep Convolutional Structure）</news:title>
   <news:publication_date>2026-08-19T04:35:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724795</loc>
  <lastmod>2026-08-19T04:35:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>強連結ネットワーク上での勾配追跡を用いた分散確率的最適化（Distributed stochastic optimization with gradient tracking over strongly-connected networks）</news:title>
   <news:publication_date>2026-08-19T04:35:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724793</loc>
  <lastmod>2026-08-19T04:35:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>期待認識型プランニングの統一的枠組み（Expectation-Aware Planning）</news:title>
   <news:publication_date>2026-08-19T04:35:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724791</loc>
  <lastmod>2026-08-19T04:35:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>EEGによる感情認識と機械学習の実装と評価（Emotion Recognition with Machine Learning Using EEG Signals）</news:title>
   <news:publication_date>2026-08-19T04:35:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724789</loc>
  <lastmod>2026-08-19T04:34:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>概念ドリフト下のプロトタイプ分類器の挙動解析（Prototype-based classifiers in the presence of concept drift: A modelling framework）</news:title>
   <news:publication_date>2026-08-19T04:34:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724787</loc>
  <lastmod>2026-08-19T03:43:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散最適化におけるアニーリングによる大域解収束（Annealing for Distributed Global Optimization）</news:title>
   <news:publication_date>2026-08-19T03:43:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724785</loc>
  <lastmod>2026-08-19T03:43:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフ畳み込みによるラベルノイズクリーナ（Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly Detection）</news:title>
   <news:publication_date>2026-08-19T03:43:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724783</loc>
  <lastmod>2026-08-19T03:43:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>品質認識テンプレートマッチング（Quality-Aware Template Matching For Deep Learning）</news:title>
   <news:publication_date>2026-08-19T03:43:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724781</loc>
  <lastmod>2026-08-19T03:42:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>準確率的近似の収束速度最適性（Optimal Rate of Convergence for Quasi-Stochastic Approximation）</news:title>
   <news:publication_date>2026-08-19T03:42:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724779</loc>
  <lastmod>2026-08-19T03:41:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複雑なPolSAR画像のシーン分類に向けた自己段階学習（Complex Scene Classification of PolSAR Imagery Based on a Self-Paced Learning Approach）</news:title>
   <news:publication_date>2026-08-19T03:41:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724777</loc>
  <lastmod>2026-08-19T03:41:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-19T03:41:30Z</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/724773</loc>
  <lastmod>2026-08-19T02:50:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>可搬型加速度計と深層学習で現場の地面反力を推定する（Multidimensional ground reaction forces and moments from wearable sensor accelerations via deep learning）</news:title>
   <news:publication_date>2026-08-19T02:50:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724771</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>射影を用いた状態安定性から見るエピソディック学習の制御理論的解析（A Control Lyapunov Perspective on Episodic Learning via Projection to State Stability）</news:title>
   <news:publication_date>2026-08-19T02:49:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724769</loc>
  <lastmod>2026-08-19T02:49:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SNS上で共有されるワクチン情報の信頼性を自動で判定する手法の実用性（Automatically applying a credibility appraisal tool to track vaccine-related communications shared on social media）</news:title>
   <news:publication_date>2026-08-19T02:49:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724767</loc>
  <lastmod>2026-08-19T02:49:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習ベースの衣服アニメーションによるバーチャルトライオン（Learning-Based Animation of Clothing for Virtual Try-On）</news:title>
   <news:publication_date>2026-08-19T02:49:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724765</loc>
  <lastmod>2026-08-19T02:49:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模かつ密な部分相関ネットワークの計算と応用（On the Computation and Applications of Large Dense Partial Correlation Networks）</news:title>
   <news:publication_date>2026-08-19T02:49:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724763</loc>
  <lastmod>2026-08-19T02:49:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>AttoNetsによるエッジ向け超小型高効率ニューラルネットワーク（AttoNets: Compact and Efficient Deep Neural Networks for the Edge via Human-Machine Collaborative Design）</news:title>
   <news:publication_date>2026-08-19T02:49:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724761</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>モデル・フリーによるモデル調整（Model-Free Model Reconciliation）</news:title>
   <news:publication_date>2026-08-19T02:48:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724759</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>機械学習の“精度”は操作できる—がん予測研究の暗部（Machine Learning: A Dark Side of Cancer Computing）</news:title>
   <news:publication_date>2026-08-19T01:57:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724757</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>構文認識を変えるポインタネットワーク応用（Syntax-aware Representation Learning With Pointer Networks）</news:title>
   <news:publication_date>2026-08-19T01:57:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724755</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>欠損データに強いRNNを用いたアルツハイマー病進行モデル化（Training recurrent neural networks robust to incomplete data: application to Alzheimer’s disease progression modeling）</news:title>
   <news:publication_date>2026-08-19T01:57:01Z</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>人間と機械の相互作用を最小限の仮定で設計する（Modeling and Optimization of Human-Machine Interaction Processes via the Maximum Entropy Principle）</news:title>
   <news:publication_date>2026-08-19T01:56:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>JWSTによるトランジット系外惑星の分光特性化（Characterizing Transiting Exoplanets with JWST）</news:title>
   <news:publication_date>2026-08-19T01:56:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724749</loc>
  <lastmod>2026-08-19T01:56:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>脳に学ぶ高スパースニューラルネットワークの訓練アルゴリズム（A Brain-inspired Algorithm for Training Highly Sparse Neural Networks）</news:title>
   <news:publication_date>2026-08-19T01:56:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724747</loc>
  <lastmod>2026-08-19T01:55:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>画像復元のための近接分割ネットワーク（Proximal Splitting Networks for Image Restoration）</news:title>
   <news:publication_date>2026-08-19T01:55:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724745</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深いResNetの安定化と鋭いスケーリング係数τ (Stabilize Deep ResNet with A Sharp Scaling Factor τ)</news:title>
   <news:publication_date>2026-08-19T01:04:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724743</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>トピック誘導型変分オートエンコーダによる文章生成（Topic-Guided Variational Autoencoders for Text Generation）</news:title>
   <news:publication_date>2026-08-19T01:03:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724741</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>ウェブ抽出テーブルとパイプラインモデルによる質問応答 (Question Answering via Web Extracted Tables and Pipelined Models)</news:title>
   <news:publication_date>2026-08-19T01:03:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724739</loc>
  <lastmod>2026-08-19T01:02:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>部分人物再識別のための対ペア空間変換ネットワーク（STNReID: Deep Convolutional Networks with Pairwise Spatial Transformer Networks for Partial Person Re-identification）</news:title>
   <news:publication_date>2026-08-19T01:02:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724737</loc>
  <lastmod>2026-08-19T01:02:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時系列データベースで予測を直接実行する仕組み（tspDB: Time Series Predict DB）</news:title>
   <news:publication_date>2026-08-19T01:02:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724735</loc>
  <lastmod>2026-08-19T01:02:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>用量探索試験におけるマルチアームド・バンディット設計の応用（On Multi-Armed Bandit Designs for Dose-Finding Trials）</news:title>
   <news:publication_date>2026-08-19T01:02:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724733</loc>
  <lastmod>2026-08-19T01:02:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ゼロショット翻訳に欠けていた要素（The Missing Ingredient in Zero-Shot Neural Machine Translation）</news:title>
   <news:publication_date>2026-08-19T01:02:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724731</loc>
  <lastmod>2026-08-19T00:11:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>教師-生徒ネットワークを用いた深層特徴選択（Deep Feature Selection using a Teacher-Student Network）</news:title>
   <news:publication_date>2026-08-19T00:11:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724729</loc>
  <lastmod>2026-08-19T00:10:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>AdaGraphによる予測的・連続的ドメイン適応の統一（AdaGraph: Unifying Predictive and Continuous Domain Adaptation through Graphs）</news:title>
   <news:publication_date>2026-08-19T00:10:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724727</loc>
  <lastmod>2026-08-19T00:03:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層人物再識別の強力なベースラインとトレーニングの小技集 (Bag of Tricks and A Strong Baseline for Deep Person Re-identification)</news:title>
   <news:publication_date>2026-08-19T00:03:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724725</loc>
  <lastmod>2026-08-19T00:02:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>イベントベース映像に対する時空間フィルタの応用（Spatiotemporal Filtering for Event-Based Action Recognition）</news:title>
   <news:publication_date>2026-08-19T00:02:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724723</loc>
  <lastmod>2026-08-19T00:01:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>競合的かつ判別的再構成による異常検知の学習（Learning Competitive and Discriminative Reconstructions for Anomaly Detection）</news:title>
   <news:publication_date>2026-08-19T00:01:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724721</loc>
  <lastmod>2026-08-19T00:01:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時系列分類に対する敵対的攻撃の脆弱性（Adversarial Attacks on Deep Neural Networks for Time Series Classification）</news:title>
   <news:publication_date>2026-08-19T00:01:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724719</loc>
  <lastmod>2026-08-19T00:01:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散型同時摂動勾配降下法（DSPG: Decentralized Simultaneous Perturbations Gradient Descent Scheme）</news:title>
   <news:publication_date>2026-08-19T00:01:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724717</loc>
  <lastmod>2026-08-18T23:09:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>コンパイラ支援によるbig.LITTLEシステムの適応的プログラムスケジューリング（Compiler-assisted Adaptive Program Scheduling in big.LITTLE Systems）</news:title>
   <news:publication_date>2026-08-18T23:09:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724715</loc>
  <lastmod>2026-08-18T23:09:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>TurkScanner：マイクロタスクの時給を予測する（TurkScanner: Predicting the Hourly Wage of Microtasks）</news:title>
   <news:publication_date>2026-08-18T23:09:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724713</loc>
  <lastmod>2026-08-18T23:08:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>リスク認知型Top-k推薦の考え方（Risk Aware Ranking for Top-k Recommendation）</news:title>
   <news:publication_date>2026-08-18T23:08:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724711</loc>
  <lastmod>2026-08-18T23:08:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>全脳イメージからの神経細胞再構築が変えたもの（RECONSTRUCTING NEURONAL ANATOMY FROM WHOLE-BRAIN IMAGES）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724709</loc>
  <lastmod>2026-08-18T23:08:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>組織標本におけるヒストパソロジー画像の折りたたみ検出の深層特徴解析（Deep Features for Tissue-Fold Detection in Histopathology Images）</news:title>
   <news:publication_date>2026-08-18T23:08:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724707</loc>
  <lastmod>2026-08-18T23:08:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Zeno++：完全非同期環境下での堅牢なSGD（Zeno++: Robust Fully Asynchronous SGD）</news:title>
   <news:publication_date>2026-08-18T23:08:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </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: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: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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    <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:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-18T22:14:39Z</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:publication_date>2026-08-18T22:14:14Z</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>分割（パーティション）中心の分散アルゴリズムによる大規模グラフのオイラー回路探索（A Partition-centric Distributed Algorithm for Identifying Euler Circuits in Large Graphs）</news:title>
   <news:publication_date>2026-08-18T21:22:57Z</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>
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   <news:title>不均衡なマルチラベル分類と抽出型要約を同時学習で改善する手法（Imbalanced multi-label classification using multi-task learning with extractive summarization）</news:title>
   <news:publication_date>2026-08-18T21:22:42Z</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>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news: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>2026-08-18T20:28:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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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>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <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:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/724633</loc>
  <lastmod>2026-08-18T17:47:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>未知環境での視覚認識を手軽に強化する手法（Visual recognition in the wild by sampling deep similarity functions）</news:title>
   <news:publication_date>2026-08-18T17:47:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/724631</loc>
  <lastmod>2026-08-18T17:37:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ピクセル検出器データの超高速処理と機械学習フレームワーク（Ultrafast Processing of Pixel Detector Data with Machine Learning Frameworks）</news:title>
   <news:publication_date>2026-08-18T17:37:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/724629</loc>
  <lastmod>2026-08-18T17:37:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GAN生成画像の検出に共起行列を用いる方法（Detecting GAN generated Fake Images using Co-occurrence Matrices）</news:title>
   <news:publication_date>2026-08-18T17:37:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/724627</loc>
  <lastmod>2026-08-18T17:36:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>見えない角度を生成する単一画像からの視点依存画像生成（Generate What You Can&amp;#039;t See - a View-dependent Image Generation）</news:title>
   <news:publication_date>2026-08-18T17:36:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/724625</loc>
  <lastmod>2026-08-18T17:36:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>天文学におけるアストロインフォマティクスとアストロスタティスティクスの次の10年（The Next Decade of Astroinformatics and Astrostatistics）</news:title>
   <news:publication_date>2026-08-18T17:36:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724623</loc>
  <lastmod>2026-08-18T17:36:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>電極材料の電圧を機械学習で予測する（Machine Learning the Voltage of Electrode Materials in Metal-ion Batteries）</news:title>
   <news:publication_date>2026-08-18T17:36:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724621</loc>
  <lastmod>2026-08-18T17:36:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>相関したパターン集合からの一般化（Generalization from correlated sets of patterns in the perceptron）</news:title>
   <news:publication_date>2026-08-18T17:36:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724619</loc>
  <lastmod>2026-08-18T16:44:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>宇宙黎明期のブラックホール放射と21 cm全体信号への影響（The Radio Scream from Black Holes at Cosmic Dawn）</news:title>
   <news:publication_date>2026-08-18T16:44:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724617</loc>
  <lastmod>2026-08-18T16:44:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>イソペリメトリック損失を用いたゼロショット学習（Zero Shot Learning with the Isoperimetric Loss）</news:title>
   <news:publication_date>2026-08-18T16:44:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724615</loc>
  <lastmod>2026-08-18T16:43:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ヘテロジニアスセルラーネットワークにおけるオンラインアンテナ調整と深層強化学習（Online Antenna Tuning in Heterogeneous Cellular Networks with Deep Reinforcement Learning）</news:title>
   <news:publication_date>2026-08-18T16:43:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724613</loc>
  <lastmod>2026-08-18T16:43:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>壁越し姿勢推定のリアルタイム化──Many-to-Many Encoder/Decoderによる突破（Through-Wall Pose Imaging in Real-Time with a Many-to-Many Encoder/Decoder Paradigm）</news:title>
   <news:publication_date>2026-08-18T16:43:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724611</loc>
  <lastmod>2026-08-18T16:42:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スマートな深層コピー＆ペースト（SMART, DEEP COPY-PASTE）</news:title>
   <news:publication_date>2026-08-18T16:42:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724609</loc>
  <lastmod>2026-08-18T16:42:40Z</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検証のアルゴリズム（Algorithms for Verifying Deep Neural Networks）</news:title>
   <news:publication_date>2026-08-18T16:42:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724607</loc>
  <lastmod>2026-08-18T16:42:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GitLabログを活用したソフトウェア工学教育の学習分析手法（A Methodology for Using GitLab for Software Engineering Learning Analytics）</news:title>
   <news:publication_date>2026-08-18T16:42:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724605</loc>
  <lastmod>2026-08-18T15:51:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Atari-HEAD：人間の視線と操作を同時に記録した大規模データセット（Atari Human Eye-Tracking and Demonstration Dataset）</news:title>
   <news:publication_date>2026-08-18T15:51:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724603</loc>
  <lastmod>2026-08-18T15:50:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>米国国内線の到着遅延予測を現場に活かす（A Data Mining Approach to Flight Arrival Delay Prediction for American Airlines）</news:title>
   <news:publication_date>2026-08-18T15:50:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724601</loc>
  <lastmod>2026-08-18T15:50:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ReLUの死亡現象と初期化（Dying ReLU and Initialization: Theory and Numerical Examples）</news:title>
   <news:publication_date>2026-08-18T15:50:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724599</loc>
  <lastmod>2026-08-18T15:49:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>キャンパス行動から見る大学生の成績予測（Predicting Academic Performance for College Students: A Campus Behavior Perspective）</news:title>
   <news:publication_date>2026-08-18T15:49:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724597</loc>
  <lastmod>2026-08-18T15:49:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模状態空間における混合方策学習のサンプル効率（ON SAMPLE COMPLEXITY OF PROJECTION-FREE PRIMAL-DUAL METHODS FOR LEARNING MIXTURE POLICIES IN MARKOV DECISION PROCESSES）</news:title>
   <news:publication_date>2026-08-18T15:49:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724595</loc>
  <lastmod>2026-08-18T15:49:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模電力網の多段階故障警報システム（MULTI-STAGE FAULT WARNING FOR LARGE ELECTRIC GRIDS USING ANOMALY DETECTION AND MACHINE LEARNING）</news:title>
   <news:publication_date>2026-08-18T15:49:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724593</loc>
  <lastmod>2026-08-18T15:48:48Z</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量子状態による二次元フラストレートJ1-J2モデルの研究（Study of the Two-Dimensional Frustrated J1-J2 Model with Neural Network Quantum States）</news:title>
   <news:publication_date>2026-08-18T15:48:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
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