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   <news:title>非漸近的かつ鋭い下側尾部確率の下界（On the Non-asymptotic and Sharp Lower Tail Bounds of Random Variables）</news:title>
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   <news:title>ハードディスクの残存寿命予測における特徴正規化とLSTM応用の仕組み（Mechanisms for Integrated Feature Normalization and Remaining Useful Life Estimation Using LSTMs Applied to Hard-Disks）</news:title>
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   <news:title>AT-TPCの軌跡分類における機械学習手法（Machine Learning Methods for Track Classification in the AT-TPC）</news:title>
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   <news:title>3D表面上のスペクトル変換ネットワークによる非剛体形状解析（Learning Spectral Transform Network on 3D Surface for Non-rigid Shape Analysis）</news:title>
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   <news:title>教示を通じて逆強化学習エージェントを育てる—特徴とデモンストレーションで教える方法（Teaching Inverse Reinforcement Learners via Features and Demonstrations）</news:title>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>CNNを用いた株式市場予測の実践的枠組み（CNNPred: CNN-based stock market prediction using several data sources）</news:title>
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   <news:genres>Blog</news:genres>
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  <lastmod>2026-06-26T00:32:02Z</lastmod>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>動的指数族モデルの学習―因果・側方依存性を持つニューロモルフィック計算のために (Training Dynamic Exponential Family Models with Causal and Lateral Dependencies for Generalized Neuromorphic Computing)</news:title>
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   <news:genres>Blog</news:genres>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>情報ボトルネックによる非2進LDPC復号（Decoding of Non-Binary LDPC Codes Using the Information Bottleneck Method）</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>ラベルノイズ除去による単調性分類の改善（Label Noise Filtering Techniques to Improve Monotonic Classification）</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>高解像度常微分方程式による加速現象の理解（Understanding the Acceleration Phenomenon via High-Resolution Differential Equations）</news:title>
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   <news:genres>Blog</news:genres>
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  <lastmod>2026-06-25T23:39:09Z</lastmod>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>動的平均拡散とランダム座標更新（DYNAMIC AVERAGE DIFFUSION WITH RANDOMIZED COORDINATE UPDATES）</news:title>
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   <news:genres>Blog</news:genres>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>ディープラーニングが変えたアナログ→デジタル変換の世界（Analog-to-digital Conversion Revolutionized by Deep Learning）</news:title>
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   <news:genres>Blog</news:genres>
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  <lastmod>2026-06-25T23:38:43Z</lastmod>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>組み込み機器における深層学習モデル圧縮の適用判断（To Compress, or Not to Compress: Characterizing Deep Learning Model Compression for Embedded Inference）</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>Instagram上のフーカー（ウォーターパイプ）画像の自動識別（Automated identification of hookahs (waterpipes) on Instagram: an application in feature extraction using Convolutional Neural Network and Support Vector Machine classification）</news:title>
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   <news:genres>Blog</news:genres>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>多チャンネルポリソムノグラフィーからの睡眠覚醒検出（Sleep Arousal Detection from Polysomnography using the Scattering Transform and Recurrent 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>異種多数コア向け3D NoC設計を学習で自動化する研究（Learning-based Application-Agnostic 3D NoC Design for Heterogeneous Manycore Systems）</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>連続領域のDPPからの多項式時間MCMC法（A Polynomial Time MCMC Method for Sampling from Continuous DPPs）</news:title>
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   <news:genres>Blog</news:genres>
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  <lastmod>2026-06-25T22:45:47Z</lastmod>
  <news:news>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>単語ペア埋め込みによる跨文推論の改善（pair2vec: Compositional Word-Pair Embeddings for Cross-Sentence Inference）</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>光学/近赤外アフターグロウにおける縁が明るいジェットの証拠（Evidence for a Bright-Edged Jet in the Optical/NIR Afterglow of GRB 160625B）</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>供給網におけるサービス障害予測のデータモデル (Data models for service failure prediction in supply-chain networks)</news:title>
   <news:publication_date>2026-06-25T22:45:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-06-25T22:44:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>層ごとの最適ビット幅でモデルを圧縮する新手法（Differentiable Fine-grained Quantization for Deep Neural Network Compression）</news:title>
   <news:publication_date>2026-06-25T22:44:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-06-25T21:53:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>ペアワイズ選好集約を効率化するHybrid-MST（Hybrid-MST: A Hybrid Active Sampling Strategy for Pairwise Preference Aggregation）</news:title>
   <news:publication_date>2026-06-25T21:53:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-06-25T21:53:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>学習が脳活動と結合性を異なる形で再編成する（Learning differentially reorganizes brain activity and connectivity）</news:title>
   <news:publication_date>2026-06-25T21:53:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-06-25T21:52:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>データ駆動タイトフレームによるクライオEM画像のノイズ除去とコンフォメーショナル分類（Data-Driven Tight Frame for Cryo-EM Image Denoising and Conformational Classification）</news:title>
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   <news:genres>Blog</news:genres>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>Interactive Reinforcement Learningエージェントの自己説明手法（Autonomous Self-Explanation of Behavior for Interactive Reinforcement Learning Agents）</news:title>
   <news:publication_date>2026-06-25T21:52:13Z</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>機械学習における公平性の最前線（The Frontiers of Fairness in Machine Learning）</news:title>
   <news:publication_date>2026-06-25T21:52:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-06-25T21:51:38Z</lastmod>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>アウトラインを用いた階層的テキスト生成（Hierarchical Text Generation using an Outline）</news:title>
   <news:publication_date>2026-06-25T21:51:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-06-25T21:51:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複合ラベルを用いたニューラル形態素タグ付け（Modeling Composite Labels for Neural Morphological Tagging）</news:title>
   <news:publication_date>2026-06-25T21:51:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704848</loc>
  <lastmod>2026-06-25T21:00:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ランダムフォレストで記述するレイノルズ応力テンソルのデータ駆動モデリング（Data-Driven Modelling of the Reynolds Stress Tensor using Random Forests with Invariance）</news:title>
   <news:publication_date>2026-06-25T21:00:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704846</loc>
  <lastmod>2026-06-25T21:00:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多様なデータからの集合学習による現場対応型エンティティ分類（Collective Learning From Diverse Datasets for Entity Typing in the Wild）</news:title>
   <news:publication_date>2026-06-25T21:00:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704844</loc>
  <lastmod>2026-06-25T20:59:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GANに基づく顔画像のセマンティック修復の改良技術（Improved Techniques for GAN based Facial Inpainting）</news:title>
   <news:publication_date>2026-06-25T20:59:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704842</loc>
  <lastmod>2026-06-25T20:58:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動き推定と補償で駆動するニューラルネットワークによる映像補間と強調（MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Interpolation and Enhancement）</news:title>
   <news:publication_date>2026-06-25T20:58:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704840</loc>
  <lastmod>2026-06-25T20:58:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ボルツマンマシンの自由エネルギーの厳密論点（Free energies of Boltzmann Machines: self-averaging, annealed and replica symmetric approximations in the thermodynamic limit）</news:title>
   <news:publication_date>2026-06-25T20:58:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704838</loc>
  <lastmod>2026-06-25T20:58:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>地球内部の超イオン性水素の発見と意義（Superionic hydrogen in Earth’s deep interior）</news:title>
   <news:publication_date>2026-06-25T20:58:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704836</loc>
  <lastmod>2026-06-25T20:58:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>属性対応協調フィルタリングの俯瞰と分類（Atribute-aware Collaborative Filtering: Survey and Classification）</news:title>
   <news:publication_date>2026-06-25T20:58:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704834</loc>
  <lastmod>2026-06-25T20:06:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>BCR-Netによる非標準ウェーブレット形式に基づくニューラルネットワーク（BCR-Net: a neural network based on the nonstandard wavelet form）</news:title>
   <news:publication_date>2026-06-25T20:06:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704832</loc>
  <lastmod>2026-06-25T20:06:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分布ロバスト最適化による均一性能学習（Learning Models with Uniform Performance via Distributionally Robust Optimization）</news:title>
   <news:publication_date>2026-06-25T20:06:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704830</loc>
  <lastmod>2026-06-25T20:06:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>OCTデータで学習した深層学習による眼底写真からの緑内障定量評価（FROM MACHINE TO MACHINE: AN OCT-TRAINED DEEP LEARNING ALGORITHM FOR OBJECTIVE QUANTIFICATION OF GLAUCOMATOUS DAMAGE IN FUNDUS PHOTOGRAPHS）</news:title>
   <news:publication_date>2026-06-25T20:06:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704828</loc>
  <lastmod>2026-06-25T20:05:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>探索負担の定量化とフリーライディングの不公平性（Quantifying the Burden of Exploration and the Unfairness of Free Riding）</news:title>
   <news:publication_date>2026-06-25T20:05:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704826</loc>
  <lastmod>2026-06-25T20:05:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>MMLSparkが変えた大規模機械学習の実務導入像（MMLSpark: Unifying Machine Learning Ecosystems at Massive Scales）</news:title>
   <news:publication_date>2026-06-25T20:05:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704824</loc>
  <lastmod>2026-06-25T20:05:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時間的近接性が導く属性類似性（Temporal Proximity induces Attributes Similarity）</news:title>
   <news:publication_date>2026-06-25T20:05:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704822</loc>
  <lastmod>2026-06-25T20:04:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>連続値ドメインにおけるGaussianネットワークの新しいスコアリング基準（Renormalized Normalized Maximum Likelihood and Three-Part Code Criteria For Learning Gaussian Networks）</news:title>
   <news:publication_date>2026-06-25T20:04:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704820</loc>
  <lastmod>2026-06-25T19:13:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Twitterにおけるトルコ語の固有表現認識の半教師あり埋め込み手法（Named Entity Recognition on Twitter for Turkish using Semi-supervised Learning with Word Embeddings）</news:title>
   <news:publication_date>2026-06-25T19:13:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704818</loc>
  <lastmod>2026-06-25T19:13:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>予測と制御のためのコップマン固有関数の最適構築（Optimal construction of Koopman eigenfunctions for prediction and control）</news:title>
   <news:publication_date>2026-06-25T19:13:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704816</loc>
  <lastmod>2026-06-25T19:12:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>素材認識に基づく3D局所記述子学習（Learning Material-Aware Local Descriptors for 3D Shapes）</news:title>
   <news:publication_date>2026-06-25T19:12:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704814</loc>
  <lastmod>2026-06-25T19:12:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ロジスティック回帰の条件数解析が示す実務的示唆（Condition Number Analysis of Logistic Regression, and its Implications for Standard First-Order Solution Methods）</news:title>
   <news:publication_date>2026-06-25T19:12:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704812</loc>
  <lastmod>2026-06-25T19:12:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Ocean Tensor Packageの要点と経営視点での意味（The Ocean Tensor Package）</news:title>
   <news:publication_date>2026-06-25T19:12:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704810</loc>
  <lastmod>2026-06-25T19:11:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SL2MFによるがんにおける合成致死性の予測（SL2MF: Predicting Synthetic Lethality in Human Cancers via Logistic Matrix Factorization）</news:title>
   <news:publication_date>2026-06-25T19:11:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704808</loc>
  <lastmod>2026-06-25T19:11:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>6自由度惑星着陸のための深層強化学習（Deep Reinforcement Learning for Six Degree-of-Freedom Planetary Powered Descent and Landing）</news:title>
   <news:publication_date>2026-06-25T19:11:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704806</loc>
  <lastmod>2026-06-25T18:20:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>対話から人物像を学習する注意型メモリネットワーク（Learning Personas from Dialogue with Attentive Memory Networks）</news:title>
   <news:publication_date>2026-06-25T18:20:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704804</loc>
  <lastmod>2026-06-25T18:20:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>モデル不確実性推定による安全な強化学習（Safe Reinforcement Learning with Model Uncertainty Estimates）</news:title>
   <news:publication_date>2026-06-25T18:20:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704802</loc>
  <lastmod>2026-06-25T18:19:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ゲシュタルト理論から読み解く深層畳み込みネットワーク（Understanding Deep Convolutional Networks through Gestalt Theory）</news:title>
   <news:publication_date>2026-06-25T18:19:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704800</loc>
  <lastmod>2026-06-25T18:18:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチタスク学習を活用した公平な分類の実現（Taking Advantage of Multitask Learning for Fair Classification）</news:title>
   <news:publication_date>2026-06-25T18:18:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704798</loc>
  <lastmod>2026-06-25T18:18:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分子最適化を深層強化学習で行う（Optimization of Molecules via Deep Reinforcement Learning）</news:title>
   <news:publication_date>2026-06-25T18:18:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704796</loc>
  <lastmod>2026-06-25T18:18:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>日常活動の音声認識を大規模埋め込みで学習する（Audio-Based Activities of Daily Living (ADL) Recognition with Large-Scale Acoustic Embeddings from Online Videos）</news:title>
   <news:publication_date>2026-06-25T18:18:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704794</loc>
  <lastmod>2026-06-25T18:18:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ケーブルニュースにおける比喩的暴力の分類のためのニューラルネットワーク（A neural network to classify metaphorical violence on cable news）</news:title>
   <news:publication_date>2026-06-25T18:18:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704792</loc>
  <lastmod>2026-06-25T17:26:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>社会的影響を内発的動機付けとするマルチエージェント深層強化学習（Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning）</news:title>
   <news:publication_date>2026-06-25T17:26:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704790</loc>
  <lastmod>2026-06-25T17:26:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ボランティアコンピューティングにおけるエネルギー浪費削減に機械学習を使う（Using Machine Learning to reduce the energy wasted in Volunteer Computing Environments）</news:title>
   <news:publication_date>2026-06-25T17:26:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704788</loc>
  <lastmod>2026-06-25T17:26:06Z</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活性化に対するサブセットスキャニング（Subset Scanning Over Neural Network Activations）</news:title>
   <news:publication_date>2026-06-25T17:26:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704786</loc>
  <lastmod>2026-06-25T17:25:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CLEVERの拡張：ニューラルネットワーク堅牢性評価アルゴリズムの拡張（ON EXTENSIONS OF CLEVER: A NEURAL NETWORK ROBUSTNESS EVALUATION ALGORITHM）</news:title>
   <news:publication_date>2026-06-25T17:25:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704784</loc>
  <lastmod>2026-06-25T17:25:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>OGLEサーベイで発見された高赤方偏移クエasar二件（Discovery of two quasars at z = 5 from the OGLE Survey）</news:title>
   <news:publication_date>2026-06-25T17:25:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704782</loc>
  <lastmod>2026-06-25T17:25:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ランキング評価における母集団・経験的PR曲線の扱い（Population and Empirical PR Curves to Assess Ranking Algorithms）</news:title>
   <news:publication_date>2026-06-25T17:25:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704780</loc>
  <lastmod>2026-06-25T17:25:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高速で頑健な複数ColorChecker検出法（Fast and Robust Multiple ColorChecker Detection using Deep Convolutional Neural Networks）</news:title>
   <news:publication_date>2026-06-25T17:25:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704778</loc>
  <lastmod>2026-06-25T16:33:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>部分的非同期システム向けモデル並列近接確率的勾配法（A Model Parallel Proximal Stochastic Gradient Algorithm for Partially Asynchronous Systems）</news:title>
   <news:publication_date>2026-06-25T16:33:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704776</loc>
  <lastmod>2026-06-25T16:33:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューラルネットワークにおけるバイアス・バリアンスの再考（A Modern Take on the Bias-Variance Tradeoff in Neural Networks）</news:title>
   <news:publication_date>2026-06-25T16:33:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704774</loc>
  <lastmod>2026-06-25T16:32:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単一参照拡張による夜間航空画像の都市検出（Detecting cities in aerial night-time images by learning structural invariants using single reference augmentation）</news:title>
   <news:publication_date>2026-06-25T16:32:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704772</loc>
  <lastmod>2026-06-25T16:32:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>離れた区間から成る固有表現を学習する方法（Learning to Recognize Discontiguous Entities）</news:title>
   <news:publication_date>2026-06-25T16:32:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704770</loc>
  <lastmod>2026-06-25T16:31:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>消費者の購買行動が示す概念組織（Conceptual Organization is Revealed by Consumer Activity Patterns）</news:title>
   <news:publication_date>2026-06-25T16:31:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704768</loc>
  <lastmod>2026-06-25T16:31:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>積演算を活性化関数として活用する手法（Leveraging Product as an Activation Function in Deep Networks）</news:title>
   <news:publication_date>2026-06-25T16:31:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704766</loc>
  <lastmod>2026-06-25T16:31:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>限られた試行で自律的に歩行を獲得する腱駆動肢の学習（Autonomous Functional Locomotion in a Tendon-Driven Limb via Limited Experience）</news:title>
   <news:publication_date>2026-06-25T16:31:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704764</loc>
  <lastmod>2026-06-25T15:40:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>弱い専門家を増幅して強い学習者を監督する（Supervising strong learners by amplifying weak experts）</news:title>
   <news:publication_date>2026-06-25T15:40:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704762</loc>
  <lastmod>2026-06-25T15:39:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的データアソシエーションのための深層人物再識別（Deep Person Re-identification for Probabilistic Data Association in Multiple Pedestrian Tracking）</news:title>
   <news:publication_date>2026-06-25T15:39:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704760</loc>
  <lastmod>2026-06-25T15:39:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Lomax delegate racingによる生存分析の新展開（Nonparametric Bayesian Lomax delegate racing for survival analysis with competing risks）</news:title>
   <news:publication_date>2026-06-25T15:39:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704758</loc>
  <lastmod>2026-06-25T15:38:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>公正さは誰のためか—ナッシュ福祉積による再定義（Fairness for Whom? Critically Reframing Fairness with Nash Welfare Product）</news:title>
   <news:publication_date>2026-06-25T15:38:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704756</loc>
  <lastmod>2026-06-25T15:38:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>勾配ターゲット伝搬が示した学習則の一般化（Gradient Target Propagation）</news:title>
   <news:publication_date>2026-06-25T15:38:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704754</loc>
  <lastmod>2026-06-25T15:38:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散医療データベースにおけるフェデレーテッドラーニングの実用性（Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Data）</news:title>
   <news:publication_date>2026-06-25T15:38:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704752</loc>
  <lastmod>2026-06-25T15:38:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非線形積分微分作用素の回帰とニューラルネットワーク（Nonlinear integro–differential operator regression with neural networks）</news:title>
   <news:publication_date>2026-06-25T15:38:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704750</loc>
  <lastmod>2026-06-25T14:47:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ベイズ距離クラスタリング（Bayesian Distance Clustering）</news:title>
   <news:publication_date>2026-06-25T14:47:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704748</loc>
  <lastmod>2026-06-25T14:46:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ngVLAによる太陽系科学の可能性（Potential for Solar System Science with the ngVLA）</news:title>
   <news:publication_date>2026-06-25T14:46:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704746</loc>
  <lastmod>2026-06-25T14:46:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習でナノフォトニクス設計空間を全方位マッピングする（Mapping the global design space of nanophotonic components using machine learning pattern recognition）</news:title>
   <news:publication_date>2026-06-25T14:46:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704744</loc>
  <lastmod>2026-06-25T14:45:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>項目単位の個別プライバシーを実現する推薦技術（Probabilistic Matrix Factorization with Personalized Differential Privacy）</news:title>
   <news:publication_date>2026-06-25T14:45:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704742</loc>
  <lastmod>2026-06-25T14:45:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>低線量CT画像からの全死因死亡予測をめぐるハイブリッド深層ニューラルネットワーク（HYBRID DEEP NEURAL NETWORKS FOR ALL-CAUSE MORTALITY PREDICTION FROM LDCT IMAGES）</news:title>
   <news:publication_date>2026-06-25T14:45:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704740</loc>
  <lastmod>2026-06-25T14:45:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Transfer LearningとMulti-agent Learningの比較 — 高速道路交通における分散意思決定の検討（Transfer Learning versus Multi-agent Learning regarding Distributed Decision-Making in Highway Traffic）</news:title>
   <news:publication_date>2026-06-25T14:45:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704738</loc>
  <lastmod>2026-06-25T14:45:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>最小制約のReverse Monte Carloが示す原子構造の信頼性（Assessing the Reliability of Minimally Constrained Reverse Monte Carlo Simulations for Model Metallic Liquids）</news:title>
   <news:publication_date>2026-06-25T14:45:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704727</loc>
  <lastmod>2026-06-25T13:53:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高速セグメンテーションの改善—教師-生徒学習による高速化と精度向上（Improving Fast Segmentation With Teacher-student Learning）</news:title>
   <news:publication_date>2026-06-25T13:53:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704725</loc>
  <lastmod>2026-06-25T13:45:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>KM3NeT/ARCAによる点状ニュートリノ源感度の評価（Sensitivity of the KM3NeT/ARCA neutrino telescope to point-like neutrino sources）</news:title>
   <news:publication_date>2026-06-25T13:45:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704723</loc>
  <lastmod>2026-06-25T13:44:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>実世界ネットワークとモデル生成ネットワークの構造比較（Network Classification Based Structural Analysis of Real Networks and their Model-Generated Counterparts）</news:title>
   <news:publication_date>2026-06-25T13:44:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704721</loc>
  <lastmod>2026-06-25T13:43:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>畳み込み層の推論高速化と圧縮を両立する辞書化セントロイド法（CNN inference acceleration using dictionary of centroids）</news:title>
   <news:publication_date>2026-06-25T13:43:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704719</loc>
  <lastmod>2026-06-25T13:43:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>リアルタイムニューラル入力方式の実用化（REAL-TIME NEURAL-BASED INPUT METHOD）</news:title>
   <news:publication_date>2026-06-25T13:43:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704717</loc>
  <lastmod>2026-06-25T13:43:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ピアノとオーケストラのMIDIを結ぶデータベースと自動編曲への応用（A Database Linking Piano and Orchestral MIDI Scores with Application to Automatic Projective Orchestration）</news:title>
   <news:publication_date>2026-06-25T13:43:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704715</loc>
  <lastmod>2026-06-25T13:42:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>経験モーメントとクリストッフェル関数によるデータ解析（Data analysis from empirical moments and the Christoffel function）</news:title>
   <news:publication_date>2026-06-25T13:42:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704713</loc>
  <lastmod>2026-06-25T12:51:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模高解像度セマンティック変化検出のためのマルチタスク学習（Multitask Learning for Large-scale Semantic Change Detection）</news:title>
   <news:publication_date>2026-06-25T12:51:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704711</loc>
  <lastmod>2026-06-25T12:51:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多スペクトル衛星画像を用いた都市変化検出（URBAN CHANGE DETECTION FOR MULTISPECTRAL EARTH OBSERVATION USING CONVOLUTIONAL NEURAL NETWORKS）</news:title>
   <news:publication_date>2026-06-25T12:51:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704709</loc>
  <lastmod>2026-06-25T12:50:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>完全畳み込みサイアミーズネットワークによる変化検出（FULLY CONVOLUTIONAL SIAMESE NETWORKS FOR CHANGE DETECTION）</news:title>
   <news:publication_date>2026-06-25T12:50:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704707</loc>
  <lastmod>2026-06-25T12:49:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>構造生物学におけるシミュレーションと機械学習の出会い (Simulations meet Machine Learning in Structural Biology)</news:title>
   <news:publication_date>2026-06-25T12:49:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704705</loc>
  <lastmod>2026-06-25T12:49:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>銀河団質量推定における機械学習応用の実務的意義（An application of machine learning techniques to galaxy cluster mass estimation using the MACSIS simulations）</news:title>
   <news:publication_date>2026-06-25T12:49:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704703</loc>
  <lastmod>2026-06-25T12:49:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>依存構造を手がかりにした効率的な固有表現抽出（Efficient Dependency-Guided Named Entity Recognition）</news:title>
   <news:publication_date>2026-06-25T12:49:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704701</loc>
  <lastmod>2026-06-25T12:49:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>特権情報を用いた学習と敵対的識別モダリティ蒸留（Learning with Privileged Information via Adversarial Discriminative Modality Distillation）</news:title>
   <news:publication_date>2026-06-25T12:49:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704699</loc>
  <lastmod>2026-06-25T11:57:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CVABS: 共通ベクトルアプローチによる動体分割（CVABS: Moving Object Segmentation with Common Vector Approach for Videos）</news:title>
   <news:publication_date>2026-06-25T11:57:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704697</loc>
  <lastmod>2026-06-25T11:57:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ScratchDetによるスクラッチ学習の再評価（ScratchDet: Training Single-Shot Object Detectors from Scratch）</news:title>
   <news:publication_date>2026-06-25T11:57:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704695</loc>
  <lastmod>2026-06-25T11:57:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ディラック液体における電子相互作用の場の理論的研究（Field theoretic study of electron-electron interaction effects in Dirac liquids）</news:title>
   <news:publication_date>2026-06-25T11:57:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704693</loc>
  <lastmod>2026-06-25T11:56:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模グラフニューラルネットワークの効率的計算に向けて (Towards Efficient Large-Scale Graph Neural Network Computing)</news:title>
   <news:publication_date>2026-06-25T11:56:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704691</loc>
  <lastmod>2026-06-25T11:55:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>同時翻訳の新しい枠組みと待機戦略の提案（STACL: Simultaneous Translation with Implicit Anticipation and Controllable Latency using Prefix-to-Prefix Framework）</news:title>
   <news:publication_date>2026-06-25T11:55:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704689</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>成人サイズの3Dプリントオープンヒューマノイド（NimbRo-OP2X: Adult-sized Open-source 3D Printed Humanoid Robot）</news:title>
   <news:publication_date>2026-06-25T11:55:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704687</loc>
  <lastmod>2026-06-25T11:55:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>コーパス品質がニューラル機械翻訳に与える影響（Impact of Corpora Quality on Neural Machine Translation）</news:title>
   <news:publication_date>2026-06-25T11:55:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704685</loc>
  <lastmod>2026-06-25T11:04:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>PyDCIによる分布対応インデクシングの再実装と新規実験（REVISITING DISTRIBUTIONAL CORRESPONDENCE INDEXING: A PYTHON REIMPLEMENTATION AND NEW EXPERIMENTS）</news:title>
   <news:publication_date>2026-06-25T11:04:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704683</loc>
  <lastmod>2026-06-25T10:59:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高スケーラビリティで省エネな人工ニューロン（A highly scalable and energy-efficient artificial neuron using an Ovonic Threshold Switch）</news:title>
   <news:publication_date>2026-06-25T10:59:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704681</loc>
  <lastmod>2026-06-25T10:58:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>QBDTによるシステム的不確かさを組み込むブースティング決定木（QBDT, a new boosting decision tree method with systematical uncertainties into training for High Energy Physics）</news:title>
   <news:publication_date>2026-06-25T10:58:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704679</loc>
  <lastmod>2026-06-25T10:58:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>呼び出し駆動型ニューラル近似計算と多クラス判別器・複数近似器の組合せ（Invocation-driven Neural Approximate Computing with a Multiclass-Classifier and Multiple Approximators）</news:title>
   <news:publication_date>2026-06-25T10:58:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704677</loc>
  <lastmod>2026-06-25T10:58:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数グラフを同時に構築するためのPANDAm手法（AdaPtive Noisy Data Augmentation (PANDA) for Simultaneous Construction of Multiple Graph Models）</news:title>
   <news:publication_date>2026-06-25T10:58:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704675</loc>
  <lastmod>2026-06-25T10:58:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Generative Low-Shot Network Expansion（Generative Low-Shot Network Expansion）</news:title>
   <news:publication_date>2026-06-25T10:58:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704673</loc>
  <lastmod>2026-06-25T10:57:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>サリエンシー誘導型深層ネットワークによる弱教師あり画像セグメンテーション（Saliency Guided Deep Network for Weakly-Supervised Image Segmentation）</news:title>
   <news:publication_date>2026-06-25T10:57:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704671</loc>
  <lastmod>2026-06-25T10:07:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オンライン信頼性・パフォーマンスデータを用いた悪質ウェブドメイン識別（Malicious Web Domain Identification using Online Credibility and Performance Data by Considering the Class Imbalance Issue）</news:title>
   <news:publication_date>2026-06-25T10:07:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704669</loc>
  <lastmod>2026-06-25T10:06:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>超ピクセル単位の雲検出と階層融合CNN（Super-pixel cloud detection using Hierarchical Fusion CNN）</news:title>
   <news:publication_date>2026-06-25T10:06:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704667</loc>
  <lastmod>2026-06-25T10:06:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ngVLAによる宇宙機テレメトリ受信の可能性（Spacecraft Telecommunications）</news:title>
   <news:publication_date>2026-06-25T10:06:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704665</loc>
  <lastmod>2026-06-25T10:06:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習済みMLPにおける交換可能性とカーネル不変性（Exchangeability and Kernel Invariance in Trained MLPs）</news:title>
   <news:publication_date>2026-06-25T10:06:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704663</loc>
  <lastmod>2026-06-25T10:05:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>トーンマップされたHDR画像の品質評価（QUALITY ASSESSMENT FOR TONE-MAPPED HDR IMAGES USING MULTI-SCALE AND MULTI-LAYER INFORMATION）</news:title>
   <news:publication_date>2026-06-25T10:05:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704661</loc>
  <lastmod>2026-06-25T10:05:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Multi-Domain Pose Networkによるマルチ人物姿勢推定と追跡の改善（Multi-Domain Pose Network for Multi-Person Pose Estimation and Tracking）</news:title>
   <news:publication_date>2026-06-25T10:05:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704659</loc>
  <lastmod>2026-06-25T10:05:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>積み重ねオートエンコーダーによるオンライン状態監視の自動特徴抽出法（A STACKED AUTOENCODER NEURAL NETWORK BASED AUTOMATED FEATURE EXTRACTION METHOD FOR ANOMALY DETECTION IN ON-LINE CONDITION MONITORING）</news:title>
   <news:publication_date>2026-06-25T10:05:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704657</loc>
  <lastmod>2026-06-25T09:14:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層構造を使った転移特徴と射影学習によるゼロショット学習の革新（Transferrable Feature and Projection Learning with Class Hierarchy for Zero-Shot Learning）</news:title>
   <news:publication_date>2026-06-25T09:14:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704655</loc>
  <lastmod>2026-06-25T09:14:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>セマンティック特徴合成と競合学習によるゼロ/フューショット学習（Zero and Few Shot Learning with Semantic Feature Synthesis and Competitive Learning）</news:title>
   <news:publication_date>2026-06-25T09:14:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704653</loc>
  <lastmod>2026-06-25T09:14:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>微粒度オブジェクトの3D姿勢注釈改善（Improving Annotation for 3D Pose Dataset of Fine-Grained Object Categories）</news:title>
   <news:publication_date>2026-06-25T09:14:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704651</loc>
  <lastmod>2026-06-25T09:13:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ヘテロスケダスティックPCAのためのアルゴリズムと最適性（Heteroskedastic PCA: Algorithm, Optimality, and Applications）</news:title>
   <news:publication_date>2026-06-25T09:13:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704649</loc>
  <lastmod>2026-06-25T09:12:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>シーケンスト置換サンプリングによる学習の探索性向上（SEQUENCED-REPLACEMENT SAMPLING FOR DEEP LEARNING）</news:title>
   <news:publication_date>2026-06-25T09:12:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704647</loc>
  <lastmod>2026-06-25T09:12:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ドメイン不変射影学習によるゼロショット認識の要点（Domain-Invariant Projection Learning for Zero-Shot Recognition）</news:title>
   <news:publication_date>2026-06-25T09:12:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704645</loc>
  <lastmod>2026-06-25T09:12:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多層トランスフォーム学習が画像ノイズ処理を変える（LEARNING MULTI-LAYER TRANSFORM MODELS）</news:title>
   <news:publication_date>2026-06-25T09:12:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704643</loc>
  <lastmod>2026-06-25T08:21:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高解像度組織クリアリングデータにおける位置合わせ手法の比較（A Comparative Analysis of Registration Tools: Traditional vs Deep Learning Approach on High Resolution Tissue Cleared Data）</news:title>
   <news:publication_date>2026-06-25T08:21:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704641</loc>
  <lastmod>2026-06-25T08:21:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DeepXSによる粒子生成断面積の高速近似（DeepXS: Fast approximation of MSSM electroweak cross sections at NLO）</news:title>
   <news:publication_date>2026-06-25T08:21:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704639</loc>
  <lastmod>2026-06-25T08:21:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>通信頻度を動的に変えて学習時間と誤差を両立する手法（Adaptive Communication Strategies to Achieve the Best Error-Runtime Trade-off in Local-Update SGD）</news:title>
   <news:publication_date>2026-06-25T08:21:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704637</loc>
  <lastmod>2026-06-25T08:20:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>眼底写真でOCT由来の糖尿病性黄斑浮腫グレードを予測する深層学習（Predicting optical coherence tomography-derived diabetic macular edema grades from fundus photographs using deep learning）</news:title>
   <news:publication_date>2026-06-25T08:20:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704635</loc>
  <lastmod>2026-06-25T08:20:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>教師なし異常データ空間の仕様化（Unsupervised Anomalous Data Space Specification）</news:title>
   <news:publication_date>2026-06-25T08:20:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704633</loc>
  <lastmod>2026-06-25T08:20:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ソースコードにおけるオープンボキャブラリ学習とグラフ構造キャッシュ（Open Vocabulary Learning on Source Code with a Graph–Structured Cache）</news:title>
   <news:publication_date>2026-06-25T08:20:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704631</loc>
  <lastmod>2026-06-25T08:19:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>バイアフィン分類器のパラメータ冗長性削減（Reduction of Parameter Redundancy in Biaffine Classifiers with Symmetric and Circulant Weight Matrices）</news:title>
   <news:publication_date>2026-06-25T08:19:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704629</loc>
  <lastmod>2026-06-25T07:28:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>サンプル無しで入力文法を学ぶ方法（Sample-Free Learning of Input Grammars for Comprehensive Software Fuzzing）</news:title>
   <news:publication_date>2026-06-25T07:28:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/704627</loc>
  <lastmod>2026-06-25T07:27:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルウェア検出における敵対的事例の探究 (Exploring Adversarial Examples in Malware Detection)</news:title>
   <news:publication_date>2026-06-25T07:27:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704625</loc>
  <lastmod>2026-06-25T07:20:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層学習を含む自律システムの合成検証（Compositional Verification for Autonomous Systems with Deep Learning Components）</news:title>
   <news:publication_date>2026-06-25T07:20:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704623</loc>
  <lastmod>2026-06-25T07:19:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的プリマル・デュアルQ学習の実務的解説（Stochastic Primal-Dual Q-Learning）</news:title>
   <news:publication_date>2026-06-25T07:19:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704621</loc>
  <lastmod>2026-06-25T07:19:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CURE-OR: 現実と非現実をまたぐ物体認識の堅牢性検証データセット（CURE-OR: Challenging Unreal and Real Environments for Object Recognition）</news:title>
   <news:publication_date>2026-06-25T07:19:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704619</loc>
  <lastmod>2026-06-25T07:17:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>処方行動の社会的学習が抗生物質使用の集団最適を促す（Social learning of prescribing behavior can promote population optimum of antibiotic use）</news:title>
   <news:publication_date>2026-06-25T07:17:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704617</loc>
  <lastmod>2026-06-25T07:17:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>糖尿病性網膜症スクリーニングにおける深層学習と人間グレーダーの比較（Deep Learning vs. Human Graders for Classifying Severity Levels of Diabetic Retinopathy in a Real-World Nationwide Screening Program）</news:title>
   <news:publication_date>2026-06-25T07:17:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704615</loc>
  <lastmod>2026-06-25T06:26:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Optimal TransportとMMDをつなぐSinkhorn Divergences（Interpolating between Optimal Transport and MMD using Sinkhorn Divergences）</news:title>
   <news:publication_date>2026-06-25T06:26:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704613</loc>
  <lastmod>2026-06-25T06:25:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>無限因子型有限状態機械によるブラインド多元ユーザチャネル推定（Infinite Factorial Finite State Machine for Blind Multiuser Channel Estimation）</news:title>
   <news:publication_date>2026-06-25T06:25:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704611</loc>
  <lastmod>2026-06-25T06:25:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>BabyAI：人間をループに含めた基礎言語学習のサンプル効率化（BABYAI: A Platform to Study the Sample Efficiency of Grounded Language Learning）</news:title>
   <news:publication_date>2026-06-25T06:25:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704609</loc>
  <lastmod>2026-06-25T06:24:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>チャネル注目を連鎖させたMRI復元ネットワークの要点解説（MRI RECONSTRUCTION VIA CASCADED CHANNEL-WISE ATTENTION NETWORK）</news:title>
   <news:publication_date>2026-06-25T06:24:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704607</loc>
  <lastmod>2026-06-25T06:24:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>メモリ制約下の分位回帰（Quantile Regression Under Memory Constraint）</news:title>
   <news:publication_date>2026-06-25T06:24:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704605</loc>
  <lastmod>2026-06-25T06:24:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>実践における公平なケーキ分配（Fair Cake-Cutting in Practice）</news:title>
   <news:publication_date>2026-06-25T06:24:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704603</loc>
  <lastmod>2026-06-25T06:24:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次元予測モデリングにおける群変数の影響除去（Removing the influence of a group variable in high-dimensional predictive modelling）</news:title>
   <news:publication_date>2026-06-25T06:24:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704601</loc>
  <lastmod>2026-06-25T05:33:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習による熱力学と特徴抽出（Thermodynamics and Feature Extraction by Machine Learning）</news:title>
   <news:publication_date>2026-06-25T05:33:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704599</loc>
  <lastmod>2026-06-25T05:32:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>検索スニペットの“読む場所”をモデル化する研究（Micro-Browsing Models for Search Snippets）</news:title>
   <news:publication_date>2026-06-25T05:32:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704597</loc>
  <lastmod>2026-06-25T05:32:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>空力解析における深層学習の実用性と限界（Deep Learning Methods for Reynolds-Averaged Navier-Stokes Simulations of Airfoil Flows）</news:title>
   <news:publication_date>2026-06-25T05:32:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704595</loc>
  <lastmod>2026-06-25T05:32:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>宇宙再電離史における21cm信号への機械学習応用（Machine Learning Applied to the Reionization History of the Universe in the 21 cm Signal）</news:title>
   <news:publication_date>2026-06-25T05:32:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704593</loc>
  <lastmod>2026-06-25T05:31:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動画トレーラーの時間配列を読む推薦モデル（Convolutional Collaborative Filter Network for Video Based Recommendation Systems）</news:title>
   <news:publication_date>2026-06-25T05:31:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704591</loc>
  <lastmod>2026-06-25T05:31:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>4D-STEMデータを可視化する曼荼羅的学習（Manifold Learning of Four-dimensional Scanning Transmission Electron Microscopy）</news:title>
   <news:publication_date>2026-06-25T05:31:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704589</loc>
  <lastmod>2026-06-25T05:31:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>アンプチュードロイドの最深カット解析（Cutting Deep Into The Amplituhedron）</news:title>
   <news:publication_date>2026-06-25T05:31:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704587</loc>
  <lastmod>2026-06-25T04:39:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>修正ボルツマン法による熱QCD媒質の深いLPM領域における分裂頂点のモデル化（A modified-Boltzmann approach for modeling the hot QCD medium-induced splitting vertices in the deep LPM region）</news:title>
   <news:publication_date>2026-06-25T04:39:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704585</loc>
  <lastmod>2026-06-25T04:39:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>重要状態を見せて信頼を適切に構築する方法（Establishing Appropriate Trust via Critical States）</news:title>
   <news:publication_date>2026-06-25T04:39:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704583</loc>
  <lastmod>2026-06-25T04:38:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>勾配合意を最適化目的に据えたメタラーニング（Gradient Agreement as an Optimization Objective for Meta-Learning）</news:title>
   <news:publication_date>2026-06-25T04:38:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704581</loc>
  <lastmod>2026-06-25T04:38:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>行列ランク検定の非適応サンプル最適化（Non-adaptive Sample-Optimal Testing of Matrix Rank）</news:title>
   <news:publication_date>2026-06-25T04:38:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704579</loc>
  <lastmod>2026-06-25T04:38:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>古典的バンディットアルゴリズムを構造化バンディットへ翻訳する統一手法（A Unified Approach to Translate Classical Bandit Algorithms to the Structured Bandit Setting）</news:title>
   <news:publication_date>2026-06-25T04:38:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704577</loc>
  <lastmod>2026-06-25T04:38:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高レベル意味情報を利用した参照なし画像品質評価（Exploiting High-Level Semantics for No-Reference Image Quality Assessment of Realistic Blur Images）</news:title>
   <news:publication_date>2026-06-25T04:38:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704575</loc>
  <lastmod>2026-06-25T04:38:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>過去からのオンライン調整による高速深層強化学習（Fast deep reinforcement learning using online adjustments from the past）</news:title>
   <news:publication_date>2026-06-25T04:38:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704573</loc>
  <lastmod>2026-06-25T03:46:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>レーダーにおける深層逆センサーモデリング（Probably Unknown: Deep Inverse Sensor Modelling In Radar）</news:title>
   <news:publication_date>2026-06-25T03:46:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704571</loc>
  <lastmod>2026-06-25T03:46:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>TensorFlow上での秘密保持機械学習（Private Machine Learning in TensorFlow using Secure Computation）</news:title>
   <news:publication_date>2026-06-25T03:46:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704569</loc>
  <lastmod>2026-06-25T03:46:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>双線形適応一般化ベクトル近似メッセージパッシング（Bilinear Adaptive Generalized Vector Approximate Message Passing）</news:title>
   <news:publication_date>2026-06-25T03:46:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704567</loc>
  <lastmod>2026-06-25T03:45:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>知識伝達敵対的ネットワークによる教師−生徒学習の再定義（Knowledge Transfer Adversarial Network）</news:title>
   <news:publication_date>2026-06-25T03:45:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704565</loc>
  <lastmod>2026-06-25T03:45:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>勾配降下法の一次・二次変種を統一的に理解する（FIRST-ORDER AND SECOND-ORDER VARIANTS OF THE GRADIENT DESCENT IN A UNIFIED FRAMEWORK）</news:title>
   <news:publication_date>2026-06-25T03:45:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704563</loc>
  <lastmod>2026-06-25T03:45:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>空撮画像におけるサリエンスバイアス損失（Salience Biased Loss for Object Detection in Aerial Images）</news:title>
   <news:publication_date>2026-06-25T03:45:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704561</loc>
  <lastmod>2026-06-25T03:44:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自律走行EVによる配車最適化と近似動的計画法（Approximate Dynamic Programming for Planning a Ride-Sharing System using Autonomous Fleets of Electric Vehicles）</news:title>
   <news:publication_date>2026-06-25T03:44:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704559</loc>
  <lastmod>2026-06-25T02:53:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DeepLens: 単一画像から生成する浅い被写界深度（DeepLens: Shallow Depth Of Field From A Single Image）</news:title>
   <news:publication_date>2026-06-25T02:53:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704557</loc>
  <lastmod>2026-06-25T02:53:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層モデルの変分ベイズにおける良好な初期化（Good Initializations of Variational Bayes for Deep Models）</news:title>
   <news:publication_date>2026-06-25T02:53:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704555</loc>
  <lastmod>2026-06-25T02:53:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ノイズあり回折パターンからの辞書学習位相回復（Dictionary Learning Phase Retrieval from Noisy Diffraction Patterns）</news:title>
   <news:publication_date>2026-06-25T02:53:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704553</loc>
  <lastmod>2026-06-25T02:52:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>矮小銀河の星形成履歴を復元する新手法（A new method to derive star formation histories in dwarf galaxies）</news:title>
   <news:publication_date>2026-06-25T02:52:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704551</loc>
  <lastmod>2026-06-25T02:52:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ヘリウム濃厚な準矮星O型星の可視・紫外スペクトルに対する定量的NLTE解析（A quantitative NLTE analysis of visual and ultraviolet spectra of four helium-rich subdwarf O stars）</news:title>
   <news:publication_date>2026-06-25T02:52:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704549</loc>
  <lastmod>2026-06-25T02:52:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>平均後悔率を最小化する集合の探索（Finding Average Regret Ratio Minimizing Set in Database）</news:title>
   <news:publication_date>2026-06-25T02:52:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704547</loc>
  <lastmod>2026-06-25T02:51:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ディスコースの文体装飾を学ぶ（Discourse Embellishment Using a Deep Encoder-Decoder Network）</news:title>
   <news:publication_date>2026-06-25T02:51:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704545</loc>
  <lastmod>2026-06-25T02:00:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>不完全な通信の数学理論とエネルギー効率性（A mathematical theory of imperfect communication: Energy efficiency considerations in multi-level coding）</news:title>
   <news:publication_date>2026-06-25T02:00:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704543</loc>
  <lastmod>2026-06-25T01:59:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層ReLUネットワークの適応性と次元の呪い回避（ADAPTIVITY OF DEEP RELU NETWORK FOR LEARNING IN BESOV AND MIXED SMOOTH BESOV SPACES）</news:title>
   <news:publication_date>2026-06-25T01:59:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704541</loc>
  <lastmod>2026-06-25T01:59:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スマホ撮影画像における光学フォント認識と本人証明書偽造検出への応用（Optical Font Recognition in Smartphone-Captured Images, and its Applicability for ID Forgery Detection）</news:title>
   <news:publication_date>2026-06-25T01:59:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704539</loc>
  <lastmod>2026-06-25T01:58:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>変分ノイズ対比推定の実務的意義（Variational Noise-Contrastive Estimation）</news:title>
   <news:publication_date>2026-06-25T01:58:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704537</loc>
  <lastmod>2026-06-25T01:58:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単一イオン量子ビットの高速・高忠実読み出しを機械学習で実現する（Fast and High-Fidelity Readout of Single Trapped-Ion Qubit via Machine Learning Methods）</news:title>
   <news:publication_date>2026-06-25T01:58:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704535</loc>
  <lastmod>2026-06-25T01:58:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>粒子衝突データの「雑音」除去が変わる——グラフニューラルネットワークによるパイルアップ緩和（Pileup mitigation at the Large Hadron Collider with Graph Neural Networks）</news:title>
   <news:publication_date>2026-06-25T01:58:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704533</loc>
  <lastmod>2026-06-25T01:58:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>浅潜から学ぶ深層学習：水中物体検出のためのソナー画像生成と訓練 (Deep Learning from Shallow Dives: Sonar Image Generation and Training for Underwater Object Detection)</news:title>
   <news:publication_date>2026-06-25T01:58:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704531</loc>
  <lastmod>2026-06-25T01:06:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>カテゴリ別把持のための形状空間レジストレーションを通じた姿勢シナジー学習（Learning Postural Synergies for Categorical Grasping through Shape Space Registration）</news:title>
   <news:publication_date>2026-06-25T01:06:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704529</loc>
  <lastmod>2026-06-25T01:05:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>LeukoNet: DCTベースのCNNによるB-ALL細胞分類（LEUKONET: DCT-BASED CNN ARCHITECTURE FOR THE CLASSIFICATION OF NORMAL VERSUS LEUKEMIC BLASTS IN B-ALL CANCER）</news:title>
   <news:publication_date>2026-06-25T01:05:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704527</loc>
  <lastmod>2026-06-25T01:05:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>観測誤差が因果探索に与える影響と上界推定による補正手法（An Upper Bound for Random Measurement Error in Causal Discovery）</news:title>
   <news:publication_date>2026-06-25T01:05:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704525</loc>
  <lastmod>2026-06-25T01:04:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層的マルチラベル分類における決定戦略と局所適合率（HIERLPR: DECISION MAKING IN HIERARCHICAL MULTI-LABEL CLASSIFICATION WITH LOCAL PRECISION RATES）</news:title>
   <news:publication_date>2026-06-25T01:04:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704523</loc>
  <lastmod>2026-06-25T01:04:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>S-Net: JPEG圧縮アーティファクト削減のためのスケーラブルCNN（S-Net: A Scalable Convolutional Neural Network for JPEG Compression Artifact Reduction）</news:title>
   <news:publication_date>2026-06-25T01:04:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704521</loc>
  <lastmod>2026-06-25T01:04:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>教師なしニューラル文簡易化の実践（Unsupervised Neural Text Simplification）</news:title>
   <news:publication_date>2026-06-25T01:04:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704519</loc>
  <lastmod>2026-06-25T01:04:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>畳み込みニューラルネットワークによる乱流混合燃焼モデル化（Modelling turbulent premixed flames using convolutional neural networks）</news:title>
   <news:publication_date>2026-06-25T01:04:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704517</loc>
  <lastmod>2026-06-25T00:13:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ランダム化ベンチマーキングからのスピンキュービットノイズ分光（Spin-qubit noise spectroscopy from randomized benchmarking by supervised learning）</news:title>
   <news:publication_date>2026-06-25T00:13:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704515</loc>
  <lastmod>2026-06-25T00:11:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スマートシティにおけるリアルタイム高精細大気質センシングネットワーク（Real-Time Fine-Grained Air Quality Sensing Networks in Smart City: Design, Implementation and Optimization）</news:title>
   <news:publication_date>2026-06-25T00:11:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704513</loc>
  <lastmod>2026-06-25T00:09:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>入力変数の影響を情報理論で解く：エントロピック変数射影による機械学習説明（Explaining machine learning models using entropic variable projection）</news:title>
   <news:publication_date>2026-06-25T00:09:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704511</loc>
  <lastmod>2026-06-25T00:08:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>合成画像から実世界の視線推定へ橋を架ける手法（Unsupervised Domain Adaptation for Learning Eye Gaze from a Million Synthetic Images: An Adversarial Approach）</news:title>
   <news:publication_date>2026-06-25T00:08:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704509</loc>
  <lastmod>2026-06-25T00:07:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次元におけるロバストスパース縮約ランク回帰（Robust Sparse Reduced Rank Regression in High Dimensions）</news:title>
   <news:publication_date>2026-06-25T00:07:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704507</loc>
  <lastmod>2026-06-25T00:07:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>クラス均衡自己学習によるセマンティックセグメンテーションのドメイン適応（Domain Adaptation for Semantic Segmentation via Class-Balanced Self-Training）</news:title>
   <news:publication_date>2026-06-25T00:07:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704505</loc>
  <lastmod>2026-06-25T00:07:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深海面波におけるソリトンと極端事象の共存（Coexistence of solitons and extreme events in deep water surface waves）</news:title>
   <news:publication_date>2026-06-25T00:07:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704503</loc>
  <lastmod>2026-06-24T23:15:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>暗号化トラフィック分類における深層学習の概観（Deep Learning for Encrypted Traffic Classification: An Overview）</news:title>
   <news:publication_date>2026-06-24T23:15:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704501</loc>
  <lastmod>2026-06-24T23:15:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>WikiHow: 大規模テキスト要約データセットの重要性（WikiHow: A Large Scale Text Summarization Dataset）</news:title>
   <news:publication_date>2026-06-24T23:15:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704499</loc>
  <lastmod>2026-06-24T23:14:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>部分観測環境におけるポリシー勾配：近似と収束（Policy Gradient in Partially Observable Environments: Approximation and Convergence）</news:title>
   <news:publication_date>2026-06-24T23:14:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704497</loc>
  <lastmod>2026-06-24T23:14:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>テスト時拡張を用いた畳み込みニューラルネットワークによる自動脳腫瘍セグメンテーション (Automatic Brain Tumor Segmentation using Convolutional Neural Networks with Test-Time Augmentation)</news:title>
   <news:publication_date>2026-06-24T23:14:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704495</loc>
  <lastmod>2026-06-24T23:13:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>幾何光学極限におけるレンズ化重力波の分類を機械学習で行う（Classifying Lensed Gravitational Waves in the Geometrical Optics Limit with Machine Learning）</news:title>
   <news:publication_date>2026-06-24T23:13:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704493</loc>
  <lastmod>2026-06-24T23:13:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>光ナノ構造の最適設計を強化学習で探る（Finding the best design parameters for optical nanostructures using reinforcement learning）</news:title>
   <news:publication_date>2026-06-24T23:13:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704491</loc>
  <lastmod>2026-06-24T23:13:37Z</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可視化と解釈（Analyzing and Interpreting Convolutional Neural Networks in NLP）</news:title>
   <news:publication_date>2026-06-24T23:13:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704489</loc>
  <lastmod>2026-06-24T22:22:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>通信・ネットワークにおける深層強化学習の応用（Applications of Deep Reinforcement Learning in Communications and Networking: A Survey）</news:title>
   <news:publication_date>2026-06-24T22:22:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704487</loc>
  <lastmod>2026-06-24T22:22:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自己組織化テンソル構造によるマルチビュークラスタリング（A Self-Organizing Tensor Architecture for Multi-View Clustering）</news:title>
   <news:publication_date>2026-06-24T22:22:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704485</loc>
  <lastmod>2026-06-24T22:21:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>物理教育における計算導入を促す要因の特定（Identifying features predictive of faculty integrating computation into physics courses）</news:title>
   <news:publication_date>2026-06-24T22:21:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704483</loc>
  <lastmod>2026-06-24T22:20:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>EdgeSpeechNets：エッジ端末向け高効率音声認識ネットワーク（EdgeSpeechNets: Highly Efficient Deep Neural Networks for Speech Recognition on the Edge）</news:title>
   <news:publication_date>2026-06-24T22:20:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704481</loc>
  <lastmod>2026-06-24T22:20:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチバンド銀河形態分類の転移学習（Multiband galaxy morphologies for CLASH: a convolutional neural network transferred from CANDELS）</news:title>
   <news:publication_date>2026-06-24T22:20:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704479</loc>
  <lastmod>2026-06-24T22:20:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次元シンプレックス学習によるアンミキシング問題の再考（On Statistical Learning of Simplices: Unmixing Problem Revisited）</news:title>
   <news:publication_date>2026-06-24T22:20:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704477</loc>
  <lastmod>2026-06-24T22:20:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ノイズの多いデータの分散kクラスタリング（Distributed k-Clustering for Data with Heavy Noise）</news:title>
   <news:publication_date>2026-06-24T22:20:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704475</loc>
  <lastmod>2026-06-24T21:28:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>核反応におけるクォーク・グルーオンの硬散乱とスケーリング（Hard Breakup and Spin in QCD）</news:title>
   <news:publication_date>2026-06-24T21:28:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704473</loc>
  <lastmod>2026-06-24T21:28:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>心音記録の正常/異常分類をめぐる実務的視点（Classification of Normal/Abnormal Heart Sound Recordings based on Multi-Domain Features and Back Propagation Neural Network）</news:title>
   <news:publication_date>2026-06-24T21:28:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704471</loc>
  <lastmod>2026-06-24T21:27:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Wasserstein変換（The Wasserstein Transform）</news:title>
   <news:publication_date>2026-06-24T21:27:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704469</loc>
  <lastmod>2026-06-24T21:27:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>小さなReLUネットワークは強力な記憶装置である（Small ReLU networks are powerful memorizers: a tight analysis of memorization capacity）</news:title>
   <news:publication_date>2026-06-24T21:27:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704467</loc>
  <lastmod>2026-06-24T21:27:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>周期性に着目した並列時系列予測アルゴリズム（A Periodicity-based Parallel Time Series Prediction Algorithm in Cloud Computing Environments）</news:title>
   <news:publication_date>2026-06-24T21:27:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704465</loc>
  <lastmod>2026-06-24T21:26:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模PPIネットワークにおけるマルチソース学習を用いた並列タンパク質コミュニティ検出（Parallel Protein Community Detection in Large-scale PPI Networks Based on Multi-source Learning）</news:title>
   <news:publication_date>2026-06-24T21:26:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704463</loc>
  <lastmod>2026-06-24T21:26:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチエージェント完全分散価値関数学習と線形収束の保証（Multi-Agent Fully Decentralized Value Function Learning with Linear Convergence Rates）</news:title>
   <news:publication_date>2026-06-24T21:26:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704461</loc>
  <lastmod>2026-06-24T20:35:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>生成モデルにおけるオブジェクト構成性の検証（INVESTIGATING OBJECT COMPOSITIONALITY IN GENERATIVE ADVERSARIAL NETWORKS）</news:title>
   <news:publication_date>2026-06-24T20:35:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704459</loc>
  <lastmod>2026-06-24T20:35:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ビッグデータマイニングとクラウドコンピューティングに基づく疾病診断・治療推奨システム（A Disease Diagnosis and Treatment Recommendation System Based on Big Data Mining and Cloud Computing）</news:title>
   <news:publication_date>2026-06-24T20:35:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704457</loc>
  <lastmod>2026-06-24T20:34:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>信頼できないネットワーク上の分散学習（Distributed Learning over Unreliable Networks）</news:title>
   <news:publication_date>2026-06-24T20:34:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704455</loc>
  <lastmod>2026-06-24T20:33:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>脳構造の体積的形状表現を学習する畳み込みオートエンコーダ法（A Convolutional Autoencoder Approach to Learn Volumetric Shape Representations for Brain Structures）</news:title>
   <news:publication_date>2026-06-24T20:33:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704453</loc>
  <lastmod>2026-06-24T20:33:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>PepCVAEによる抗菌ペプチド設計の半教師あり生成（PepCVAE: Semi-Supervised Targeted Design of Antimicrobial Peptide Molecules）</news:title>
   <news:publication_date>2026-06-24T20:33:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704451</loc>
  <lastmod>2026-06-24T20:33:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模畳み込みニューラルネットワークのための二層並列学習アーキテクチャ（A Bi-layered Parallel Training Architecture for Large-scale Convolutional Neural Networks）</news:title>
   <news:publication_date>2026-06-24T20:33:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704449</loc>
  <lastmod>2026-06-24T20:33:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>UCR時系列アーカイブの拡張と実務的示唆（The UCR Time Series Archive）</news:title>
   <news:publication_date>2026-06-24T20:33:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704447</loc>
  <lastmod>2026-06-24T19:42:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>人と教えることでロボットが学ぶ映像物体分割（Video Object Segmentation using Teacher-Student Adaptation in a Human Robot Interaction (HRI) Setting）</news:title>
   <news:publication_date>2026-06-24T19:42:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704445</loc>
  <lastmod>2026-06-24T19:41:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>mass-Peak Patch法による高速ダークマターハローカタログ生成（The mass-Peak Patch algorithm for fast generation of deep all-sky dark matter halo catalogues and its N-Body validation）</news:title>
   <news:publication_date>2026-06-24T19:41:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704443</loc>
  <lastmod>2026-06-24T19:41:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ゼロ次近接法による非凸非滑らか制約最適化の実務的示唆（A Proximal Zeroth-Order Algorithm for Nonconvex Nonsmooth Problems）</news:title>
   <news:publication_date>2026-06-24T19:41:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704441</loc>
  <lastmod>2026-06-24T19:40:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深い線形ネットワークの損失地形を代数幾何学の視点で見る（The Loss Surface Of Deep Linear Networks Viewed Through The Algebraic Geometry Lens）</news:title>
   <news:publication_date>2026-06-24T19:40:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704439</loc>
  <lastmod>2026-06-24T19:40:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>銀河団のX線質量推定に対する深層学習アプローチ（A Deep Learning Approach to Galaxy Cluster X-ray Masses）</news:title>
   <news:publication_date>2026-06-24T19:40:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704437</loc>
  <lastmod>2026-06-24T19:40:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>RIn-Close CVC2による数値データのビクラスタ列挙の効率化（RIn-Close CVC2: an even more efficient enumerative algorithm for biclustering of numerical datasets）</news:title>
   <news:publication_date>2026-06-24T19:40:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704435</loc>
  <lastmod>2026-06-24T19:40:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深共晶溶媒がリン脂質単分子層の構造に与える影響のベイズ的解析（Bayesian determination of the effect of a deep eutectic solvent on the structure of lipid monolayers）</news:title>
   <news:publication_date>2026-06-24T19:40:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704433</loc>
  <lastmod>2026-06-24T18:49:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>部分最小二乗による深層ネットワークの剪定（Pruning Deep Networks using Partial Least Squares）</news:title>
   <news:publication_date>2026-06-24T18:49:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704431</loc>
  <lastmod>2026-06-24T18:49:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>プライベートデータ商用化に対する逆張り契約設計（Adversarial Contract Design for Private Data Commercialization）</news:title>
   <news:publication_date>2026-06-24T18:49:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704429</loc>
  <lastmod>2026-06-24T18:48:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>注意機構の解析：ニューラル機械翻訳における語義曖昧性の検討（An Analysis of Attention Mechanisms: The Case of Word Sense Disambiguation in Neural Machine Translation）</news:title>
   <news:publication_date>2026-06-24T18:48:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704427</loc>
  <lastmod>2026-06-24T18:47:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>年齢不変顔認識のための直交深層特徴分解（Orthogonal Deep Features Decomposition for Age-Invariant Face Recognition）</news:title>
   <news:publication_date>2026-06-24T18:47:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704425</loc>
  <lastmod>2026-06-24T18:47:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ユニタリー不変な低ランク誘導ノルムの近接写像計算の効率化（Efficient Proximal Mapping Computation for Unitarily Invariant Low-Rank Inducing Norms）</news:title>
   <news:publication_date>2026-06-24T18:47:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704423</loc>
  <lastmod>2026-06-24T18:47:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ミルザハニの地震流に関する研究（Mirzakhani’s Work on Earthquake Flow）</news:title>
   <news:publication_date>2026-06-24T18:47:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704421</loc>
  <lastmod>2026-06-24T18:47:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>サブグラディエントの図的収束と弱凸最適化（Graphical Convergence of Subgradients in Nonconvex Optimization and Learning）</news:title>
   <news:publication_date>2026-06-24T18:47:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704419</loc>
  <lastmod>2026-06-24T17:55:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>経路ベースの非線形拡張率とラグランジュ不確実性の指標（Measures of Path-Based Nonlinear Expansion Rates and Lagrangian Uncertainty in Stochastic Flows）</news:title>
   <news:publication_date>2026-06-24T17:55:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704417</loc>
  <lastmod>2026-06-24T17:55:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>乳幼児の自由運動から学ぶ3D身体形状と追跡（Learning and Tracking the 3D Body Shape of Freely Moving Infants from RGB-D sequences）</news:title>
   <news:publication_date>2026-06-24T17:55:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704415</loc>
  <lastmod>2026-06-24T17:55:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>知識ベース補完の評価を問い直す（On Evaluating Embedding Models for Knowledge Base Completion）</news:title>
   <news:publication_date>2026-06-24T17:55:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704413</loc>
  <lastmod>2026-06-24T17:54:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ホログラフィックPomeronによる高エネルギー散乱の解析（DIS at small x and hadron-hadron scattering at high energies via the holographic Pomeron exchange）</news:title>
   <news:publication_date>2026-06-24T17:54:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704411</loc>
  <lastmod>2026-06-24T17:54:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ReLUネットワークの証明可能な堅牢性の向上（Provable Robustness of ReLU networks via Maximization of Linear Regions）</news:title>
   <news:publication_date>2026-06-24T17:54:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704409</loc>
  <lastmod>2026-06-24T17:54:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>胸部X線における骨抑制と肺領域分割はいつ病変分類を改善するか（WHEN DOES BONE SUPPRESSION AND LUNG FIELD SEGMENTATION IMPROVE CHEST X-RAY DISEASE CLASSIFICATION?）</news:title>
   <news:publication_date>2026-06-24T17:54:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704407</loc>
  <lastmod>2026-06-24T17:53:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフ編集距離とトリプレットネットワークを組み合わせたオフライン署名認証（Offline Signature Verification by Combining Graph Edit Distance and Triplet Networks）</news:title>
   <news:publication_date>2026-06-24T17:53:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704405</loc>
  <lastmod>2026-06-24T17:03:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>矮小銀河レオAにおける星団と若い恒星集団（Star clusters and young populations in the dwarf irregular galaxy Leo A）</news:title>
   <news:publication_date>2026-06-24T17:03:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704403</loc>
  <lastmod>2026-06-24T17:02:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>矮小銀河レオAの明るい赤色星が示すもの（Bright-red stars in the dwarf irregular galaxy Leo A）</news:title>
   <news:publication_date>2026-06-24T17:02:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704401</loc>
  <lastmod>2026-06-24T17:02:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層的モーメント法の実務的意義（Hierarchical Methods of Moments）</news:title>
   <news:publication_date>2026-06-24T17:02:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704399</loc>
  <lastmod>2026-06-24T17:01:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マゼラン系の三次元構造をOGLE変光星で解く（OGLE-ing the Magellanic System: Three-Dimensional Structure）</news:title>
   <news:publication_date>2026-06-24T17:01:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704397</loc>
  <lastmod>2026-06-24T17:01:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>地震の頻度―大きさ分布を一般化する非対称ラプラス混合モデル（Generalized Earthquake Frequency-Magnitude Distribution Described by Asymmetric Laplace Mixture Modelling）</news:title>
   <news:publication_date>2026-06-24T17:01:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704395</loc>
  <lastmod>2026-06-24T17:01:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>染色不変なCNNの学習戦略（STRATEGIES FOR TRAINING STAIN INVARIANT CNNS）</news:title>
   <news:publication_date>2026-06-24T17:01:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704393</loc>
  <lastmod>2026-06-24T17:01:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CADモデルのリバースエンジニアリング（Reverse engineering of CAD models via clustering and approximate implicitization）</news:title>
   <news:publication_date>2026-06-24T17:01:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704391</loc>
  <lastmod>2026-06-24T16:09:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>MRI取得差を吸収する表現の学習（LEARNING AN MR ACQUISITION-INVARIANT REPRESENTATION USING SIAMESE NEURAL NETWORKS）</news:title>
   <news:publication_date>2026-06-24T16:09:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704389</loc>
  <lastmod>2026-06-24T16:08:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>観測データのバイアスを是正する敵対的重み付け（Adversarial Balancing for Causal Inference）</news:title>
   <news:publication_date>2026-06-24T16:08:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704387</loc>
  <lastmod>2026-06-24T15:59:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>注視点のHMM推定誤差と必要サンプル数（EMHMM Simulation Study）</news:title>
   <news:publication_date>2026-06-24T15:59:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704385</loc>
  <lastmod>2026-06-24T15:59:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>肺気腫の範囲を定量化する学習：どのようなラベルが必要か（Learning to quantify emphysema extent: What labels do we need?）</news:title>
   <news:publication_date>2026-06-24T15:59:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704383</loc>
  <lastmod>2026-06-24T15:59:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>並列時系列ニューラル符号化ネットワーク（Parallel Temporal Neural Coding Network）</news:title>
   <news:publication_date>2026-06-24T15:59:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704381</loc>
  <lastmod>2026-06-24T15:58:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>表情認識における一貫性制約（Coherence Constraints in Facial Expression Recognition）</news:title>
   <news:publication_date>2026-06-24T15:58:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704379</loc>
  <lastmod>2026-06-24T15:58:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>温熱ネットワークのデータ駆動型同定（Data-driven identification of a thermal network in multi-zone building）</news:title>
   <news:publication_date>2026-06-24T15:58:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704377</loc>
  <lastmod>2026-06-24T15:06:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>部分的バイオメトリック特徴の認識（Recognizing Partial Biometric Patterns）</news:title>
   <news:publication_date>2026-06-24T15:06:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704375</loc>
  <lastmod>2026-06-24T15:05:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>鉄道事故の記述解析に深層学習を用いる意義（Analysis of Railway Accidents’ Narratives Using Deep Learning）</news:title>
   <news:publication_date>2026-06-24T15:05:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704373</loc>
  <lastmod>2026-06-24T15:05:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>格子上のハイブリッド物質波—マイクロ波ソリトン（Hybrid matter-wave - microwave solitons on the lattice）</news:title>
   <news:publication_date>2026-06-24T15:05:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704371</loc>
  <lastmod>2026-06-24T15:04:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>一般化ゼロショット学習のための合成空間学習（Learning the Compositional Spaces for Generalized Zero-shot Learning）</news:title>
   <news:publication_date>2026-06-24T15:04:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704369</loc>
  <lastmod>2026-06-24T15:04:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>コンテキスト付きバンディットにおける単純後悔最小化（Simple Regret Minimization for Contextual Bandits）</news:title>
   <news:publication_date>2026-06-24T15:04:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704367</loc>
  <lastmod>2026-06-24T15:04:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>セファイドとRRラエイの時系列解析が示す新たな物理制約（Time-series analyses of Cepheid and RR Lyrae variables）</news:title>
   <news:publication_date>2026-06-24T15:04:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704365</loc>
  <lastmod>2026-06-24T15:04:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>漸進的重みプルーニングによる深層ニューラルネットワークの高率圧縮（PROGRESSIVE WEIGHT PRUNING OF DEEP NEURAL NETWORKS USING ADMM）</news:title>
   <news:publication_date>2026-06-24T15:04:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704363</loc>
  <lastmod>2026-06-24T14:12:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>反復収束型機械学習におけるフォールトトレランス（Fault Tolerance in Iterative-Convergent Machine Learning）</news:title>
   <news:publication_date>2026-06-24T14:12:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704361</loc>
  <lastmod>2026-06-24T14:12:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>最適化オラクルを持つ非凸ゲーム学習の進展（Learning in Non-convex Games with an Optimization Oracle）</news:title>
   <news:publication_date>2026-06-24T14:12:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704359</loc>
  <lastmod>2026-06-24T14:11:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高エネルギーニュートリノと核反応の理解（High-energy neutrino-nucleus interactions）</news:title>
   <news:publication_date>2026-06-24T14:11:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704357</loc>
  <lastmod>2026-06-24T14:10:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Deep Diabetologist（Deep Diabetologist: Learning to Prescribe Hypoglycemia Medications with Hierarchical Recurrent Neural Networks）</news:title>
   <news:publication_date>2026-06-24T14:10:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704355</loc>
  <lastmod>2026-06-24T14:10:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ランダム行列の小偏差不等式が示すもの（Small-Deviation Inequalities for Sums of Random Matrices）</news:title>
   <news:publication_date>2026-06-24T14:10:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704353</loc>
  <lastmod>2026-06-24T14:10:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>球面三角アルゴリズム：凸包メンバーシップ問合せの高速オラクル（Spherical Triangle Algorithm: A Fast Oracle for Convex Hull Membership Queries）</news:title>
   <news:publication_date>2026-06-24T14:10:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704351</loc>
  <lastmod>2026-06-24T14:10:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自律的深層学習：動的環境の継続学習アプローチ（Autonomous Deep Learning: Continual Learning Approach for Dynamic Environments）</news:title>
   <news:publication_date>2026-06-24T14:10:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704349</loc>
  <lastmod>2026-06-24T13:18:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフカーネルと能動学習による分子の原子化エネルギー予測（Prediction of Atomization Energy Using Graph Kernel and Active Learning）</news:title>
   <news:publication_date>2026-06-24T13:18:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704347</loc>
  <lastmod>2026-06-24T13:17:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文ベクトル空間の探究と自動要約への応用（Exploring Sentence Vector Spaces through Automatic Summarization）</news:title>
   <news:publication_date>2026-06-24T13:17:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704345</loc>
  <lastmod>2026-06-24T13:17:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>短い発話に強い話者認証のためのi-vectorマッピング（Deep neural network based i-vector mapping for speaker verification using short utterances）</news:title>
   <news:publication_date>2026-06-24T13:17:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704343</loc>
  <lastmod>2026-06-24T13:16:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SCALE-Simによるシストリック配列CNNアクセラレータ評価（SCALE-SIM: Systolic CNN Accelerator Simulator）</news:title>
   <news:publication_date>2026-06-24T13:16:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704341</loc>
  <lastmod>2026-06-24T13:16:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ReDMark: 残差拡散フレームワークによるウォーターマーキング（ReDMark: Framework for Residual Diffusion）</news:title>
   <news:publication_date>2026-06-24T13:16:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704339</loc>
  <lastmod>2026-06-24T13:16:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オンラインマルコフ復号の近似アルゴリズムと下限（Online Markov Decoding: Lower Bounds and Near-Optimal Approximation Algorithms）</news:title>
   <news:publication_date>2026-06-24T13:16:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704337</loc>
  <lastmod>2026-06-24T13:15:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層ニューラルマップ（Deep Neural Maps）</news:title>
   <news:publication_date>2026-06-24T13:15:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704335</loc>
  <lastmod>2026-06-24T12:24:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マイクロブログにおける自動イベント検出と増分学習（Automatic Event Detection in Microblogs using Incremental Machine Learning）</news:title>
   <news:publication_date>2026-06-24T12:24:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704333</loc>
  <lastmod>2026-06-24T12:23:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>エンハンサー関連転写因子結合の同定に向けた「signed iterative random forests（siRF）」の意義（Signed iterative random forests to identify enhancer-associated transcription factor binding）</news:title>
   <news:publication_date>2026-06-24T12:23:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704331</loc>
  <lastmod>2026-06-24T12:23:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>過剰パラメータ化モデルにおけるSGDの高速収束（Fast and Faster Convergence of SGD for Over-Parameterized Models）</news:title>
   <news:publication_date>2026-06-24T12:23:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704329</loc>
  <lastmod>2026-06-24T12:22:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ヒト皮質における集団的抑制と興奮（Ensemble Inhibition and Excitation in the Human Cortex: an Ising Model Analysis with Uncertainties）</news:title>
   <news:publication_date>2026-06-24T12:22:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704327</loc>
  <lastmod>2026-06-24T12:22:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>人間の反応速度で行う深層強化学習（At Human Speed: Deep Reinforcement Learning with Action Delay）</news:title>
   <news:publication_date>2026-06-24T12:22:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704325</loc>
  <lastmod>2026-06-24T12:22:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所的差分プライバシー下の最適推定に関する考察（Optimal locally private estimation under ℓp loss for 1 ≤ p ≤ 2）</news:title>
   <news:publication_date>2026-06-24T12:22:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704323</loc>
  <lastmod>2026-06-24T12:22:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>強化学習における状態の「重要度（クリティカリティ）」の概念（The Concept of Criticality in Reinforcement Learning）</news:title>
   <news:publication_date>2026-06-24T12:22:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704321</loc>
  <lastmod>2026-06-24T11:30:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Reduced-Gate Convolutional LSTM を用いた予測符号化による時空間予測（Reduced-Gate Convolutional LSTM Using Predictive Coding for Spatiotemporal Prediction）</news:title>
   <news:publication_date>2026-06-24T11:30:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704319</loc>
  <lastmod>2026-06-24T11:30:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>AutoGraph：グラフベースの性能を備えた命令型スタイルのコーディング（AutoGraph: Imperative-Style Coding with Graph-Based Performance）</news:title>
   <news:publication_date>2026-06-24T11:30:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704317</loc>
  <lastmod>2026-06-24T11:30:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>運動学と環境文脈を統合した逆強化学習によるオフロード車両軌跡予測（Integrating kinematics and environment context into deep inverse reinforcement learning for predicting off-road vehicle trajectories）</news:title>
   <news:publication_date>2026-06-24T11:30:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704315</loc>
  <lastmod>2026-06-24T11:29:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>段階的な少数ショット学習と注意アトラクターネットワーク（Incremental Few-Shot Learning with Attention Attractor Networks）</news:title>
   <news:publication_date>2026-06-24T11:29:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704313</loc>
  <lastmod>2026-06-24T11:29:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>空間一次差分による横断面の不可観測ヘテロ接収の扱い（Accounting for Unobservable Heterogeneity in Cross Section Using Spatial First Differences）</news:title>
   <news:publication_date>2026-06-24T11:29:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704311</loc>
  <lastmod>2026-06-24T11:29:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>映像と文章を階層的に結び付ける手法の要点（Cross-Modal and Hierarchical Modeling of Video and Text）</news:title>
   <news:publication_date>2026-06-24T11:29:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704309</loc>
  <lastmod>2026-06-24T11:28:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層的生成モデルによる制御可能な音声合成（Hierarchical Generative Modeling for Controllable Speech Synthesis）</news:title>
   <news:publication_date>2026-06-24T11:28:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704307</loc>
  <lastmod>2026-06-24T10:37:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>実務者視点の不均衡データ対策の実証的評価（An empirical evaluation of imbalanced data strategies from a practitioner&amp;#039;s point of view）</news:title>
   <news:publication_date>2026-06-24T10:37:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704305</loc>
  <lastmod>2026-06-24T10:37:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>フォールトトレラント量子計算のための強化学習デコーダ（Reinforcement Learning Decoders for Fault-Tolerant Quantum Computation）</news:title>
   <news:publication_date>2026-06-24T10:37:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704303</loc>
  <lastmod>2026-06-24T10:37:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>サブサンプリングを用いたラインサーチ法の第二次保証（A Subsampling Line-Search Method with Second-Order Results）</news:title>
   <news:publication_date>2026-06-24T10:37:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704301</loc>
  <lastmod>2026-06-24T10:36:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>光を使った物理的攻撃が示す脆弱性（Projecting Trouble: Light Based Adversarial Attacks on Deep Learning Classifiers）</news:title>
   <news:publication_date>2026-06-24T10:36:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704299</loc>
  <lastmod>2026-06-24T10:36:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>線形回帰モデルにおける差別的プロキシの探索（Hunting for Discriminatory Proxies in Linear Regression Models）</news:title>
   <news:publication_date>2026-06-24T10:36:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704297</loc>
  <lastmod>2026-06-24T10:36:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複合可能な行動条件予測器（Composable Action-Conditioned Predictors）</news:title>
   <news:publication_date>2026-06-24T10:36:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704295</loc>
  <lastmod>2026-06-24T10:36:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ガウス過程によるデータアソシエーション（Data Association with Gaussian Processes）</news:title>
   <news:publication_date>2026-06-24T10:36:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704293</loc>
  <lastmod>2026-06-24T09:45:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多体系SU(N)対称性をもつ超冷却フェルミン原子の有効相互作用（Effective multi-body SU(N)-symmetric interactions of ultracold fermionic atoms on a 3-D lattice）</news:title>
   <news:publication_date>2026-06-24T09:45:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704291</loc>
  <lastmod>2026-06-24T09:44:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>小さなデータで効くサブワード・セマンティックハッシング（Subword Semantic Hashing for Intent Classification on Small Datasets）</news:title>
   <news:publication_date>2026-06-24T09:44:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704289</loc>
  <lastmod>2026-06-24T09:44:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>メトロポリス・ヘイスティングス視点から見た変分推論と敵対的訓練（Metropolis-Hastings View on Variational Inference and Adversarial Training）</news:title>
   <news:publication_date>2026-06-24T09:44:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704287</loc>
  <lastmod>2026-06-24T09:43:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>過学習とは違う「記憶化」——過パラメータ化オートエンコーダの挙動を読み解く (Memorization in Overparameterized Autoencoders)</news:title>
   <news:publication_date>2026-06-24T09:43:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704285</loc>
  <lastmod>2026-06-24T09:43:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深宇宙電波連続波調査の新しいパラメータ空間（New Parameter Space for Deep Field Radio Continuum Surveys）</news:title>
   <news:publication_date>2026-06-24T09:43:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704283</loc>
  <lastmod>2026-06-24T09:42:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>共変量による影響を考慮した精度行列の共同非パラメトリック推定（Joint Nonparametric Precision Matrix Estimation with Confounding）</news:title>
   <news:publication_date>2026-06-24T09:42:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704281</loc>
  <lastmod>2026-06-24T09:42:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層学習と統計モデルによるデータ品質改善（Improving Data Quality Through Deep Learning and Statistical Models）</news:title>
   <news:publication_date>2026-06-24T09:42:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704279</loc>
  <lastmod>2026-06-24T08:50:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>KPZ方程式の裾野確率と一般初期データ（KPZ equation tails for general initial data）</news:title>
   <news:publication_date>2026-06-24T08:50:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704277</loc>
  <lastmod>2026-06-24T08:50:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次元可変インデックス係数モデルとSteinの恒等式（High-dimensional Varying Index Coefficient Models via Stein’s Identity）</news:title>
   <news:publication_date>2026-06-24T08:50:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704275</loc>
  <lastmod>2026-06-24T08:50:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CoNLL–SIGMORPHON 2018 共有タスク：普遍的形態素再帰生成の意義（The CoNLL–SIGMORPHON 2018 Shared Task: Universal Morphological Reinflection）</news:title>
   <news:publication_date>2026-06-24T08:50:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704273</loc>
  <lastmod>2026-06-24T08:49:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>増分学習による抽象的離散プランニング領域と連続的観測への写像（Incremental learning abstract discrete planning domains and mappings to continuous perceptions）</news:title>
   <news:publication_date>2026-06-24T08:49:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704271</loc>
  <lastmod>2026-06-24T08:49:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ビデオ顕著領域検出における深層非局所ニューラルネットワーク（Salient Object Detection in Video using Deep Non-Local Neural Networks）</news:title>
   <news:publication_date>2026-06-24T08:49:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704269</loc>
  <lastmod>2026-06-24T08:49:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大量の模倣学習用ロボット実演データを作る（Multiple Interactions Made Easy (MIME): Large Scale Demonstrations Data for Imitation）</news:title>
   <news:publication_date>2026-06-24T08:49:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704267</loc>
  <lastmod>2026-06-24T08:49:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ビザンチン耐性を備えた分散状態推定の有限時間保証（Finite-time Guarantees for Byzantine-Resilient Distributed State Estimation with Noisy Measurements）</news:title>
   <news:publication_date>2026-06-24T08:49:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704265</loc>
  <lastmod>2026-06-24T07:57:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>INFODENS: テキスト表現学習のためのオープンフレームワーク（INFODENS: An Open-source Framework for Learning Text Representations）</news:title>
   <news:publication_date>2026-06-24T07:57:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704263</loc>
  <lastmod>2026-06-24T07:57:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模出力空間における負例の確率的選別（Stochastic Negative Mining for Learning with Large Output Spaces）</news:title>
   <news:publication_date>2026-06-24T07:57:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704261</loc>
  <lastmod>2026-06-24T07:57:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>1次元と2次元の深層畳み込みニューラルネットワークによる心電図分類の比較（A Comparison of 1-D and 2-D Deep Convolutional Neural Networks in ECG Classification）</news:title>
   <news:publication_date>2026-06-24T07:57:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704259</loc>
  <lastmod>2026-06-24T07:56:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>半包絡的深部散乱における縦二重スピン非対称性（Longitudinal double-spin asymmetries in semi-inclusive deep-inelastic scattering）</news:title>
   <news:publication_date>2026-06-24T07:56:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704257</loc>
  <lastmod>2026-06-24T07:56:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文脈情報融合構造を備えた多段階フレームワークによる皮膚病変分割（A MULTI-STAGE FRAMEWORK WITH CONTEXT INFORMATION FUSION STRUCTURE FOR SKIN LESION SEGMENTATION）</news:title>
   <news:publication_date>2026-06-24T07:56:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704255</loc>
  <lastmod>2026-06-24T07:56:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>疎検出に適応するサブバンド適応フィルタの罰則付き学習（Study of Sparsity-Aware Subband Adaptive Filtering Algorithms with Adjustable Penalties）</news:title>
   <news:publication_date>2026-06-24T07:56:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704253</loc>
  <lastmod>2026-06-24T07:55:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Shallow-Deep Networksによる過考の可視化と対策（Shallow-Deep Networks: Understanding and Mitigating Network Overthinking）</news:title>
   <news:publication_date>2026-06-24T07:55:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704251</loc>
  <lastmod>2026-06-24T07:04:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自己誘導型で密なアノテーションを生成する弱教師ありセマンティックセグメンテーション（Generating Self-Guided Dense Annotations for Weakly Supervised Semantic Segmentation）</news:title>
   <news:publication_date>2026-06-24T07:04:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704249</loc>
  <lastmod>2026-06-24T07:04:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ハイパースフィア埋め込みの内向きスケーリング学習（LEARNING INWARD SCALED HYPERSPHERE EMBEDDING: EXPLORING PROJECTIONS IN HIGHER DIMENSIONS）</news:title>
   <news:publication_date>2026-06-24T07:04:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704247</loc>
  <lastmod>2026-06-24T07:03:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非平滑複合問題に対する効率的な貪欲座標降下（Efficient Greedy Coordinate Descent for Composite Problems）</news:title>
   <news:publication_date>2026-06-24T07:03:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704245</loc>
  <lastmod>2026-06-24T07:02:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SCPNetによる全体と部分の人物再識別の統合（SCPNet: Spatial-Channel Parallelism Network for Joint Holistic and Partial Person Re-Identification）</news:title>
   <news:publication_date>2026-06-24T07:02:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704243</loc>
  <lastmod>2026-06-24T07:02:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>集団に接近するロボットの社会的行動学習（Learning Socially Appropriate Robot Approaching Behavior Toward Groups using Deep Reinforcement Learning）</news:title>
   <news:publication_date>2026-06-24T07:02:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704241</loc>
  <lastmod>2026-06-24T07:02:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>特徴レベルの変動分解を可能にする共変量付きGPLVM（Decomposing feature-level variation with Covariate Gaussian Process Latent Variable Models）</news:title>
   <news:publication_date>2026-06-24T07:02:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704239</loc>
  <lastmod>2026-06-24T07:02:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>再帰動的ダイナミクスを要さない生物学的に尤もらしいオンライン主成分分析（Biologically Plausible Online Principal Component Analysis Without Recurrent Neural Dynamics）</news:title>
   <news:publication_date>2026-06-24T07:02:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704237</loc>
  <lastmod>2026-06-24T06:10:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルウェア・トリアージによるAPT活動の早期特定（Malware triage for early identification of Advanced Persistent Threat activities）</news:title>
   <news:publication_date>2026-06-24T06:10:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704235</loc>
  <lastmod>2026-06-24T06:10:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層的トリプレット損失による深層距離学習（Deep Metric Learning with Hierarchical Triplet Loss）</news:title>
   <news:publication_date>2026-06-24T06:10:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704233</loc>
  <lastmod>2026-06-24T06:10:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>畳み込みフィルタの構造を学ぶ事前分布：Deep Weight Prior（The Deep Weight Prior）</news:title>
   <news:publication_date>2026-06-24T06:10:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704231</loc>
  <lastmod>2026-06-24T06:09:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CNNによる前処理でWatershedベースの3D細胞セグメンテーションを最適化する手法（CNN-BASED PREPROCESSING TO OPTIMIZE WATERSHED-BASED CELL SEGMENTATION IN 3D CONFOCAL MICROSCOPY IMAGES）</news:title>
   <news:publication_date>2026-06-24T06:09:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704229</loc>
  <lastmod>2026-06-24T06:08:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>チャンネル注意機構と多レベル特徴融合による単一画像超解像（Channel Attention and Multi-level Features Fusion for Single Image Super-Resolution）</news:title>
   <news:publication_date>2026-06-24T06:08:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704227</loc>
  <lastmod>2026-06-24T06:08:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ハイパープロセスモデルによる回帰向けゼロショット学習（Hyper-Process Model: A Zero-Shot Learning algorithm for Regression Problems based on Shape Analysis）</news:title>
   <news:publication_date>2026-06-24T06:08:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704225</loc>
  <lastmod>2026-06-24T06:08:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>トピカル・ノード埋め込みでネットワーク表現を強化する手法（TNE: A Latent Model for Representation Learning on Networks）</news:title>
   <news:publication_date>2026-06-24T06:08:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704223</loc>
  <lastmod>2026-06-24T05:16:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>クリーンデータでのファインチューニングによる音声翻訳改善（Fine-tuning on Clean Data for End-to-End Speech Translation: FBK @ IWSLT 2018）</news:title>
   <news:publication_date>2026-06-24T05:16:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704221</loc>
  <lastmod>2026-06-24T05:16:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層的確率的主成分分析によるテクスチャ生成モデル（A Generative Model of Textures Using Hierarchical Probabilistic Principal Component Analysis）</news:title>
   <news:publication_date>2026-06-24T05:16:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704219</loc>
  <lastmod>2026-06-24T05:15:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習で『新しいペルシャ詩人』を生み出す試み（Creating a New Persian Poet Based on Machine Learning）</news:title>
   <news:publication_date>2026-06-24T05:15:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704217</loc>
  <lastmod>2026-06-24T05:15:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>法務文書向けマルチタスク深層学習の実務意義（Multi-Task Deep Learning for Legal Document Translation, Summarization and Multi-Label Classification）</news:title>
   <news:publication_date>2026-06-24T05:15:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704215</loc>
  <lastmod>2026-06-24T05:14:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>対敵的機械学習の安全性概観（SECURITY MATTERS: A SURVEY ON ADVERSARIAL MACHINE LEARNING）</news:title>
   <news:publication_date>2026-06-24T05:14:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704213</loc>
  <lastmod>2026-06-24T05:14:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>木にデータを語らせる潜在表現 — LORACs prior for VAEs (The LORACs prior for VAEs: Letting the Trees Speak for the Data)</news:title>
   <news:publication_date>2026-06-24T05:14:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704211</loc>
  <lastmod>2026-06-24T05:14:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数データセンターにまたがる協調深層学習（Collaborative Deep Learning Across Multiple Data Centers）</news:title>
   <news:publication_date>2026-06-24T05:14:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704209</loc>
  <lastmod>2026-06-24T04:23:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高速化されたランダム化SVDによる行列補完の高速化（Faster Matrix Completion Using Randomized SVD）</news:title>
   <news:publication_date>2026-06-24T04:23:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704207</loc>
  <lastmod>2026-06-24T04:23:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>レジリエントなIoTのロードマップ（A Roadmap Towards Resilient Internet of Things for Cyber-Physical Systems）</news:title>
   <news:publication_date>2026-06-24T04:23:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704205</loc>
  <lastmod>2026-06-24T04:22:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>音声変換のためのシーケンス・ツー・シーケンス音響モデリング（Sequence-to-Sequence Acoustic Modeling for Voice Conversion）</news:title>
   <news:publication_date>2026-06-24T04:22:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704203</loc>
  <lastmod>2026-06-24T04:22:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>有限標本解析におけるM推定量と自己整合性の応用（Finite-sample analysis of M-estimators using self-concordance）</news:title>
   <news:publication_date>2026-06-24T04:22:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704201</loc>
  <lastmod>2026-06-24T04:22:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>離散損失を扱う学習理論の鋭い解析（Sharp Analysis of Learning with Discrete Losses）</news:title>
   <news:publication_date>2026-06-24T04:22:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704199</loc>
  <lastmod>2026-06-24T04:21:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>導関数不要の最適化による単調DR-部分モジュラ連続関数の最大化（Maximizing Monotone DR-submodular Continuous Functions by Derivative-free Optimization）</news:title>
   <news:publication_date>2026-06-24T04:21:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704197</loc>
  <lastmod>2026-06-24T04:21:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>意味注意に基づく深層物体共分割（Semantic Aware Attention Based Deep Object Co-segmentation）</news:title>
   <news:publication_date>2026-06-24T04:21:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704195</loc>
  <lastmod>2026-06-24T03:30:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>静止特徴と動き特徴を制御して映像の行動認識を高める手法（Combined Static and Motion Features for Deep-Networks Based Activity Recognition in Videos）</news:title>
   <news:publication_date>2026-06-24T03:30:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704193</loc>
  <lastmod>2026-06-24T03:30:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>疎データ向け高速ランダム化PCA（Fast Randomized PCA for Sparse Data）</news:title>
   <news:publication_date>2026-06-24T03:30:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704191</loc>
  <lastmod>2026-06-24T03:30:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>異種ワイヤレスネットワークのためのキャリアセンス多重アクセスと深層強化学習（Carrier-Sense Multiple Access for Heterogeneous Wireless Networks Using Deep Reinforcement Learning）</news:title>
   <news:publication_date>2026-06-24T03:30:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704189</loc>
  <lastmod>2026-06-24T03:28:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニュートン・スキーム：運動法則を深層学習に組み込む手法（The Newton Scheme for Deep Learning）</news:title>
   <news:publication_date>2026-06-24T03:28:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704187</loc>
  <lastmod>2026-06-24T03:28:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動画理解のための柔軟な3D CNNアクセラレータ（Morph: Flexible Acceleration for 3D CNN-based Video Understanding）</news:title>
   <news:publication_date>2026-06-24T03:28:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704185</loc>
  <lastmod>2026-06-24T03:28:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>品質重視の無線ビデオ伝送における深層学習を用いた送信電力制御（Deep Learning Based Power Control for Quality-Driven Wireless Video Transmissions）</news:title>
   <news:publication_date>2026-06-24T03:28:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704183</loc>
  <lastmod>2026-06-24T03:28:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>共マニフォールド学習と欠損データの取り扱い（CO-MANIFOLD LEARNING WITH MISSING DATA）</news:title>
   <news:publication_date>2026-06-24T03:28:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704181</loc>
  <lastmod>2026-06-24T02:36:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>準ハイパーボリック・モーメントとQHAdamが変えた最適化の常識（QUASI-HYPERBOLIC MOMENTUM AND ADAM FOR DEEP LEARNING）</news:title>
   <news:publication_date>2026-06-24T02:36:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704179</loc>
  <lastmod>2026-06-24T02:36:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>近接メタ方策探索（Proximal Meta-Policy Search）</news:title>
   <news:publication_date>2026-06-24T02:36:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704177</loc>
  <lastmod>2026-06-24T02:35:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>対称入力下での二層ニューラルネットワーク学習（Learning Two-layer Neural Networks with Symmetric Inputs）</news:title>
   <news:publication_date>2026-06-24T02:35:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704175</loc>
  <lastmod>2026-06-24T02:35:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SINE: 大規模かつ不完全なネットワークで使える埋め込み技術（SINE: Scalable Incomplete Network Embedding）</news:title>
   <news:publication_date>2026-06-24T02:35:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704173</loc>
  <lastmod>2026-06-24T02:34:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>進化的確率的勾配降下法（Evolutionary Stochastic Gradient Descent）</news:title>
   <news:publication_date>2026-06-24T02:34:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704171</loc>
  <lastmod>2026-06-24T02:34:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Approximate Fisher Information Matrixで深層学習の訓練を可視化する（Approximate Fisher Information Matrix to Characterise the Training of Deep Neural Networks）</news:title>
   <news:publication_date>2026-06-24T02:34:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704169</loc>
  <lastmod>2026-06-24T02:34:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層学習による超解像MRI（Super-resolution MRI through Deep Learning）</news:title>
   <news:publication_date>2026-06-24T02:34:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704167</loc>
  <lastmod>2026-06-24T01:43:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DN-ResNet：効率的な深層残差ネットワークによる画像ノイズ除去（DN-ResNet: Efficient Deep Residual Network for Image Denoising）</news:title>
   <news:publication_date>2026-06-24T01:43:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704165</loc>
  <lastmod>2026-06-24T01:43:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>長期臨床記録の自動匿名化に関する総説（A survey of automatic de-identification of longitudinal clinical narratives）</news:title>
   <news:publication_date>2026-06-24T01:43:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704163</loc>
  <lastmod>2026-06-24T01:42:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>乱流の深層学習から物理情報学習への展開（From Deep to Physics-Informed Learning of Turbulence: Diagnostics）</news:title>
   <news:publication_date>2026-06-24T01:42:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704161</loc>
  <lastmod>2026-06-24T01:42:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>データ領域で公平な表現を見つける（Discovering Fair Representations in the Data Domain）</news:title>
   <news:publication_date>2026-06-24T01:42:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704159</loc>
  <lastmod>2026-06-24T01:42:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>最適回転座標系による適応型スパースグリッド最小二乗回帰（Optimally rotated coordinate systems for adaptive least-squares regression on sparse grids）</news:title>
   <news:publication_date>2026-06-24T01:42:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704157</loc>
  <lastmod>2026-06-24T01:41:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>同時フィルタリングとパラメータ推定のためのブロック座標降下近接法（A Block Coordinate Descent Proximal Method for Simultaneous Filtering and Parameter Estimation）</news:title>
   <news:publication_date>2026-06-24T01:41:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704155</loc>
  <lastmod>2026-06-24T01:41:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GANの出力を「選りすぐる」手法で質を高める（Discriminator Rejection Sampling, DRS）</news:title>
   <news:publication_date>2026-06-24T01:41:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704153</loc>
  <lastmod>2026-06-24T00:50:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ロボットアームの連続制御における深層強化学習の応用（Using Deep Reinforcement Learning for the Continuous Control of Robotic Arms）</news:title>
   <news:publication_date>2026-06-24T00:50:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704151</loc>
  <lastmod>2026-06-24T00:49:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>意味的一致性が多感覚統合と衝突解決に与える寄与の評価（Assessing the Contribution of Semantic Congruency to Multisensory Integration and Conflict Resolution）</news:title>
   <news:publication_date>2026-06-24T00:49:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704149</loc>
  <lastmod>2026-06-24T00:48:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>段階的強化学習による物体検出（MULTI-STAGE REINFORCEMENT LEARNING FOR OBJECT DETECTION）</news:title>
   <news:publication_date>2026-06-24T00:48:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704147</loc>
  <lastmod>2026-06-24T00:48:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>認知的誤謬の整理法（Bringing Order to the Cognitive Fallacy Zoo）</news:title>
   <news:publication_date>2026-06-24T00:48:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704145</loc>
  <lastmod>2026-06-24T00:48:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非線形拡散に基づく教師なし学習（Learning by Unsupervised Nonlinear Diffusion）</news:title>
   <news:publication_date>2026-06-24T00:48:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704143</loc>
  <lastmod>2026-06-24T00:48:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>病変領域に着目した超解像の実践（Lesion Focused Super-Resolution）</news:title>
   <news:publication_date>2026-06-24T00:48:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704141</loc>
  <lastmod>2026-06-24T00:47:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ブロックチェーン活動データに基づくデジタル資産市場予測（Predicting digital asset market based on blockchain activity data）</news:title>
   <news:publication_date>2026-06-24T00:47:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704139</loc>
  <lastmod>2026-06-23T23:56:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>カラムジェネレーションを用いた分類木の数理ヒューリスティック（Column generation based math-heuristic for classification trees）</news:title>
   <news:publication_date>2026-06-23T23:56:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704137</loc>
  <lastmod>2026-06-23T23:56:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>トレリスネットワークによる系列モデリングの再定義（Trellis Networks for Sequence Modeling）</news:title>
   <news:publication_date>2026-06-23T23:56:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704135</loc>
  <lastmod>2026-06-23T23:56:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高速に学び、遅く忘れる：反復作業を行う未知かつ変化する動力学を持つシステムの安全な予測学習制御（Learn Fast, Forget Slow: Safe Predictive Learning Control for Systems with Unknown and Changing Dynamics Performing Repetitive Tasks）</news:title>
   <news:publication_date>2026-06-23T23:56:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704133</loc>
  <lastmod>2026-06-23T23:55:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>テキスト要約のための深層転移強化学習（Deep Transfer Reinforcement Learning for Text Summarization）</news:title>
   <news:publication_date>2026-06-23T23:55:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704131</loc>
  <lastmod>2026-06-23T23:55:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>法務文書の固有表現リンクにおける転移学習の応用（Named-Entity Linking Using Deep Learning For Legal Documents: A Transfer Learning Approach）</news:title>
   <news:publication_date>2026-06-23T23:55:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704129</loc>
  <lastmod>2026-06-23T23:55:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ラピディティ・マス行列を使ったイベント分類の標準化（Machine learning using rapidity-mass matrices for event classification problems in HEP）</news:title>
   <news:publication_date>2026-06-23T23:55:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704127</loc>
  <lastmod>2026-06-23T23:54:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>地域と機会をつなぐngVLA（Reaching Communities and Creating New Opportunities with the ngVLA）</news:title>
   <news:publication_date>2026-06-23T23:54:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704125</loc>
  <lastmod>2026-06-23T23:04:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-06-23T23:04:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704123</loc>
  <lastmod>2026-06-23T23:04:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>モバイルデータサイエンスの実務応用（Mobile Data Science: Towards Understanding Data-Driven Intelligent Mobile Applications）</news:title>
   <news:publication_date>2026-06-23T23:04:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704121</loc>
  <lastmod>2026-06-23T23:03:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マグリブ方言アラビア語の母音付加（Diacritization of Maghrebi Arabic Sub-Dialects）</news:title>
   <news:publication_date>2026-06-23T23:03:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704119</loc>
  <lastmod>2026-06-23T23:03:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ngVLAによる超大質量ブラックホールの高精度質量測定（Precision Gas-dynamical Mass Measurement of Supermassive Black Holes with the ngVLA）</news:title>
   <news:publication_date>2026-06-23T23:03:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704117</loc>
  <lastmod>2026-06-23T23:02:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ToDoリストによるOWL推論器のヒューリスティック最適化（Optimizing Heuristics for Tableau-based OWL Reasoners）</news:title>
   <news:publication_date>2026-06-23T23:02:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704115</loc>
  <lastmod>2026-06-23T22:12:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>コヒーレントイメージングにおけるディープラーニングを用いた超解像（Deep learning-based super-resolution in coherent imaging systems）</news:title>
   <news:publication_date>2026-06-23T22:12:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704113</loc>
  <lastmod>2026-06-23T22:11:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>凍結ガウス近似を用いた深層学習による地震サブ構造検出（Deep Learning Seismic Substructure Detection Using the Frozen Gaussian Approximation）</news:title>
   <news:publication_date>2026-06-23T22:11:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704111</loc>
  <lastmod>2026-06-23T22:11:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>宇宙時代のフィードバックを分子ガスのアウトフローで描く（Characterizing Feedback Through Molecular Outflows Across Cosmic Time）</news:title>
   <news:publication_date>2026-06-23T22:11:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704109</loc>
  <lastmod>2026-06-23T22:11:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非線形オーディオエフェクトを終端まで学習するディープニューラルネットワーク（MODELING NONLINEAR AUDIO EFFECTS WITH END-TO-END DEEP NEURAL NETWORKS）</news:title>
   <news:publication_date>2026-06-23T22:11:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704107</loc>
  <lastmod>2026-06-23T22:11:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>近傍の低質量銀河における降着型超巨大ブラックホールの検出可能性（Accreting Supermassive Black Holes in Nearby Low-mass Galaxies）</news:title>
   <news:publication_date>2026-06-23T22:11:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704105</loc>
  <lastmod>2026-06-23T22:10:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>敵対的訓練によるニューラルネットの簡潔な説明（Concise Explanations of Neural Networks using Adversarial Training）</news:title>
   <news:publication_date>2026-06-23T22:10:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704103</loc>
  <lastmod>2026-06-23T22:10:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スティックブレイキング事前分布の感度評価（Evaluating Sensitivity to the Stick-Breaking Prior in Bayesian Nonparametrics）</news:title>
   <news:publication_date>2026-06-23T22:10:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704101</loc>
  <lastmod>2026-06-23T21:19:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Poincaré GloVeによる単語表現の再設計（POINCARÉ GLOVE: HYPERBOLIC WORD EMBEDDINGS）</news:title>
   <news:publication_date>2026-06-23T21:19:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704099</loc>
  <lastmod>2026-06-23T21:18:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>シーン事前知識を使った視覚意味的ナビゲーション（Visual Semantic Navigation Using Scene Priors）</news:title>
   <news:publication_date>2026-06-23T21:18:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704097</loc>
  <lastmod>2026-06-23T21:18:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層イミテーションモデルによる目標指向プランニングと制御（Deep Imitative Models for Flexible Inference, Planning, and Control）</news:title>
   <news:publication_date>2026-06-23T21:18:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704095</loc>
  <lastmod>2026-06-23T21:18:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>物体表面の視点依存輝度を神経網で再現する手法（Deep Surface Light Fields）</news:title>
   <news:publication_date>2026-06-23T21:18:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704093</loc>
  <lastmod>2026-06-23T21:18:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Successor Uncertaintiesによる探索と時間的差分学習の不確実性（Successor Uncertainties: Exploration and Uncertainty in Temporal Difference Learning）</news:title>
   <news:publication_date>2026-06-23T21:18:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704091</loc>
  <lastmod>2026-06-23T21:17:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>構造化されたコンテンツ保持による教師なしテキストスタイル変換（Structured Content Preservation for Unsupervised Text Style Transfer）</news:title>
   <news:publication_date>2026-06-23T21:17:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704089</loc>
  <lastmod>2026-06-23T21:17:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>因数分解された機械の自己確信と意思決定エージェント（Factorized Machine Self-Confidence for Decision-Making Agents）</news:title>
   <news:publication_date>2026-06-23T21:17:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704087</loc>
  <lastmod>2026-06-23T20:26:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>予測と補正で方策学習を加速する枠組み（Predictor-Corrector Policy Optimization）</news:title>
   <news:publication_date>2026-06-23T20:26:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704085</loc>
  <lastmod>2026-06-23T20:25:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>合成セグメンテーションで「ラベル不要」の医用画像解析をめざす（SynSeg-Net: Synthetic Segmentation Without Target Modality Ground Truth）</news:title>
   <news:publication_date>2026-06-23T20:25:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704083</loc>
  <lastmod>2026-06-23T20:24:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>光度観測による超新星分類を深層学習で改善する（Improved Photometric Classification of Supernovae using Deep Learning）</news:title>
   <news:publication_date>2026-06-23T20:24:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704081</loc>
  <lastmod>2026-06-23T20:24:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>エンコーダ・デコーダ特徴と識別器で拓くリモートセンシング画像マッチング（Unsupervised Deep Features for Remote Sensing Image Matching via Discriminator Network）</news:title>
   <news:publication_date>2026-06-23T20:24:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704079</loc>
  <lastmod>2026-06-23T20:24:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単一MR画像の超解像におけるチャネル分割ネットワーク（Channel Splitting Network for Single MR Image Super-Resolution）</news:title>
   <news:publication_date>2026-06-23T20:24:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704077</loc>
  <lastmod>2026-06-23T20:24:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>匿名化MRI画像からの顔再構成（REFACING: RECONSTRUCTING ANONYMIZED FACIAL FEATURES USING GANS）</news:title>
   <news:publication_date>2026-06-23T20:24:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704075</loc>
  <lastmod>2026-06-23T20:24:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ヘッジングアルゴリズムと反復行列ゲームの示唆（Hedging Algorithms and Repeated Matrix Games）</news:title>
   <news:publication_date>2026-06-23T20:24:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704073</loc>
  <lastmod>2026-06-23T19:33:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>電話の「止めどき」を自動で見つける研究（SilentPhone: Inferring User Unavailability based Opportune Moments to Minimize Call Interruptions）</news:title>
   <news:publication_date>2026-06-23T19:33:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704071</loc>
  <lastmod>2026-06-23T19:32:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>デジタル病理における組織染色の仮想化（Virtualization of Tissue Staining in Digital Pathology Using an Unsupervised Deep Learning Approach）</news:title>
   <news:publication_date>2026-06-23T19:32:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704069</loc>
  <lastmod>2026-06-23T19:32:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>データ中心の社会的文脈が個人化モバイルアプリに与える役割（Understanding the Role of Data-Centric Social Context in Personalized Mobile Applications）</news:title>
   <news:publication_date>2026-06-23T19:32:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704067</loc>
  <lastmod>2026-06-23T19:31:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Isingモデル近似による教師なしアンサンブル学習とフェノタイピング予測への応用（Unsupervised Ensemble Learning via Ising Model Approximation with Application to Phenotyping Prediction）</news:title>
   <news:publication_date>2026-06-23T19:31:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704065</loc>
  <lastmod>2026-06-23T19:31:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>障害に強い科学ワークフローの効率的スケジューリング（An Efficient Fault Tolerant Workflow Scheduling Approach using Replication Heuristics and Checkpointing in the Cloud）</news:title>
   <news:publication_date>2026-06-23T19:31:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704063</loc>
  <lastmod>2026-06-23T19:31:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Translocatome：細胞内タンパク質の「移動」を可視化するデータベース（Translocatome: a novel resource for the analysis of protein translocation between cellular organelles）</news:title>
   <news:publication_date>2026-06-23T19:31:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704061</loc>
  <lastmod>2026-06-23T19:30:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>二層ニューラルネットワークの母集団リスクに関する先験的評価（A Priori Estimates of the Population Risk for Two-Layer Neural Networks）</news:title>
   <news:publication_date>2026-06-23T19:30:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704059</loc>
  <lastmod>2026-06-23T18:39:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層強化学習の概要と実務的意義 (Deep Reinforcement Learning: An Overview)</news:title>
   <news:publication_date>2026-06-23T18:39:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704057</loc>
  <lastmod>2026-06-23T18:39:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>車両の細粒度分類におけるResNetと局所化、空間重み付きプーリングの有効性（Vehicle classification using ResNets, localisation and spatially-weighted pooling）</news:title>
   <news:publication_date>2026-06-23T18:39:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/704055</loc>
  <lastmod>2026-06-23T18:38:53Z</lastmod>
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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:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>帯域幅と後悔のトレードオフ（Regret vs. Bandwidth Trade-off for Recommendation Systems）</news:title>
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   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/704041</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>分布的転移によるハイパーパラメータ学習（Hyperparameter Learning via Distributional Transfer）</news:title>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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  <loc>https://aibr.jp/archives/704037</loc>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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  <loc>https://aibr.jp/archives/704035</loc>
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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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  <loc>https://aibr.jp/archives/704033</loc>
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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>2026-06-23T17:44:04Z</news:publication_date>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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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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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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  <loc>https://aibr.jp/archives/704023</loc>
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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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  <loc>https://aibr.jp/archives/704021</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 <url>
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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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  <loc>https://aibr.jp/archives/704017</loc>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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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: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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 <url>
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   <news:genres>Blog</news:genres>
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