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   <news:title>決定境界のトポロジカルデータ解析（Topological Data Analysis of Decision Boundaries with Application to Model Selection）</news:title>
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   <news:title>ヤンヤン地下研究所での高感度アルファ粒子検出器の初期性能（Initial performance of the high sensitivity alpha particle detector at the Yangyang underground laboratory）</news:title>
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    <news:language>ja</news:language>
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   <news:title>並列計算で使える機械数の限界（How Many Machines Can We Use in Parallel Computing for Kernel Ridge Regression?）</news:title>
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    <news:language>ja</news:language>
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   <news:title>Polynomially Coded Regressionによるストラグラー対策と分散学習の効率化（Polynomially Coded Regression: Optimal Straggler Mitigation via Data Encoding）</news:title>
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    <news:language>ja</news:language>
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   <news:title>フォトニックニューラルネットワークの現場学習法（Training of photonic neural networks through in situ backpropagation）</news:title>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>ニューラルネットワークの自動検証—安全性を担保するための現状と展望（Automated Verification of Neural Networks: Advances, Challenges and Perspectives）</news:title>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>フェルミオン-ボソン相互作用系のデジタル量子計算（Digital quantum computation of fermion-boson interacting systems）</news:title>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>予測のための確率的推論をメタ学習する枠組み（Meta-Learning Probabilistic Inference for Prediction）</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>ロバストな遠隔教師あり学習による関係抽出の強化（Robust Distant Supervision Relation Extraction via Deep Reinforcement Learning）</news:title>
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   <news:genres>Blog</news:genres>
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  <lastmod>2026-05-09T04:42:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>DSGANに学ぶ遠隔監督(Relation Extraction)のノイズ対処（DSGAN: Generative Adversarial Training for Distant Supervision Relation Extraction）</news:title>
   <news:publication_date>2026-05-09T04:42:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-05-09T03:51:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>マルチタスクDPPを活用した推薦の本質（Multi-Task Determinantal Point Processes for Recommendation）</news:title>
   <news:publication_date>2026-05-09T03:51:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>Fairness GANによる公平なデータ生成（Fairness GAN）</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>時系列からの構造学習と誤検出制御 (Structure Learning from Time Series with False Discovery Control)</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>テキスト結合ネットワークの拡張：Diffusion Mapsによる埋め込み（Diffusion Maps for Textual Network Embedding）</news:title>
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   <news:genres>Blog</news:genres>
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  <lastmod>2026-05-09T03:48:34Z</lastmod>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>列生成によるブール決定ルール学習（Boolean Decision Rules via Column Generation）</news:title>
   <news:publication_date>2026-05-09T03:48:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-05-09T03:47:54Z</lastmod>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>VIPERSによる銀河分類の再定義（The VIMOS Public Extragalactic Redshift Survey: The complexity of galaxy populations revealed with unsupervised machine-learning algorithms）</news:title>
   <news:publication_date>2026-05-09T03:47:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/688091</loc>
  <lastmod>2026-05-09T03:47:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>局所H0測定における宇宙分散の影響（The impact of the cosmic variance on H0 on cosmological analyses）</news:title>
   <news:publication_date>2026-05-09T03:47:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-05-09T02:56:12Z</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>共通メンバーシップ攻撃（Co-Membership Attacks）に関する解説</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>カルシウムイメージングから学ぶ脳活動の動力学：結合van der PolとLSTMのハイブリッド（Learning Brain Dynamics from Calcium Imaging with Coupled van der Pol and LSTM）</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>低ランク行列回帰における特異部分空間の信頼領域（Confidence Region of Singular Subspaces for Low-rank Matrix Regression）</news:title>
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   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/688083</loc>
  <lastmod>2026-05-09T02:54:26Z</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>反事実的公平性の下での因果モデルの統合（Pooling of Causal Models under Counterfactual Fairness via Causal Judgement Aggregation）</news:title>
   <news:publication_date>2026-05-09T02:54:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-05-09T02:54:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>学力分布を用いた教育格差の可視化（Measure of gap and inequalities in basic education students proficiencies）</news:title>
   <news:publication_date>2026-05-09T02:54:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-05-09T02:54:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>高速ニューラル機械翻訳の実装手法（Fast Neural Machine Translation Implementation）</news:title>
   <news:publication_date>2026-05-09T02:54:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-05-09T02:53:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>行動から読み解く「内部モデル」の推定法（Inverse Rational Control: Inferring What You Think from How You Forage）</news:title>
   <news:publication_date>2026-05-09T02:53:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/688075</loc>
  <lastmod>2026-05-09T02:01:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>z≈3の双AGN系の宿主銀河を捉える — HAWK-I+GRAALによる観測の重要性（A cosmic dance at z ∼3: Detecting the host galaxies of the dual AGN system LBQS 0302−0019 and Jil with HAWK-I+GRAAL）</news:title>
   <news:publication_date>2026-05-09T02:01:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/688073</loc>
  <lastmod>2026-05-09T02:01:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>H I自己吸収（HISA）温度の較正とリーゲル–クラッチャー雲の温度測定（Calibrating the HISA temperature: Measuring the temperature of the Riegel–Crutcher cloud）</news:title>
   <news:publication_date>2026-05-09T02:01:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
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 <url>
  <loc>https://aibr.jp/archives/688071</loc>
  <lastmod>2026-05-09T02:00:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>銀河の温暖中性中間層のHI 21cm吸収による検出 (Detection of the Galactic Warm Neutral Medium in HI 21cm absorption)</news:title>
   <news:publication_date>2026-05-09T02:00:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/688069</loc>
  <lastmod>2026-05-09T02:00:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ステレオ拡大：マルチプレーン画像を用いたビュー合成学習（Stereo Magnification: Learning view synthesis using multiplane images）</news:title>
   <news:publication_date>2026-05-09T02:00:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/688067</loc>
  <lastmod>2026-05-09T01:59:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>単純語埋め込みモデルの再評価（Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms）</news:title>
   <news:publication_date>2026-05-09T01:59:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/688065</loc>
  <lastmod>2026-05-09T01:59:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>Competitive Collaborationによる無監督での深度・カメラ動作・オプティカルフロー・動き分割の同時学習（Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation）</news:title>
   <news:publication_date>2026-05-09T01:59:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/688063</loc>
  <lastmod>2026-05-09T01:59:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>レッドシフト広帯吸収線クエーサーの機械学習による発見（Redshifted broad absorption line quasars found via machine-learned spectral similarity）</news:title>
   <news:publication_date>2026-05-09T01:59:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/688061</loc>
  <lastmod>2026-05-09T01:07:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>メタグラディエント強化学習の要点（Meta-Gradient Reinforcement Learning）</news:title>
   <news:publication_date>2026-05-09T01:07:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688059</loc>
  <lastmod>2026-05-09T01:07:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>基礎fMRIから自閉症治療反応を予測する手法（Prediction of Autism Treatment Response from Baseline fMRI using Random Forests and Tree Bagging）</news:title>
   <news:publication_date>2026-05-09T01:07:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688057</loc>
  <lastmod>2026-05-09T01:07:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Implicit Autoencoders（Implicit Autoencoders）</news:title>
   <news:publication_date>2026-05-09T01:07:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688055</loc>
  <lastmod>2026-05-09T01:06:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>光浸漬（Light Soaking）でJSCが低下する原因：EV+0.98 eVトラップの役割（Role of EV+0.98 eV trap in light soaking-induced short circuit current instability in CIGS solar cells）</news:title>
   <news:publication_date>2026-05-09T01:06:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688053</loc>
  <lastmod>2026-05-09T01:06:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>レイヤー単位のニューロン共有によるマルチタスク圧縮（Multi-Task Zipping via Layer-wise Neuron Sharing）</news:title>
   <news:publication_date>2026-05-09T01:06:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688051</loc>
  <lastmod>2026-05-09T01:06:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層性を活かす注意機構の再定義：Hyperbolic Attention Networks（Hyperbolic Attention Networks）</news:title>
   <news:publication_date>2026-05-09T01:06:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688049</loc>
  <lastmod>2026-05-09T01:05:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>バンディット問題におけるブートストラップの新知見（New Insights into Bootstrapping for Bandits）</news:title>
   <news:publication_date>2026-05-09T01:05:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688047</loc>
  <lastmod>2026-05-09T00:14:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層ニューラルネットワークにおけるエントロピーと相互情報量の定量化（Entropy and mutual information in models of deep neural networks）</news:title>
   <news:publication_date>2026-05-09T00:14:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688045</loc>
  <lastmod>2026-05-09T00:03:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>技術サポート文書から手順を抽出する方法（Mining Procedures from Technical Support Documents）</news:title>
   <news:publication_date>2026-05-09T00:03:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688043</loc>
  <lastmod>2026-05-09T00:03:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多タスク・コックス過程における効率的推論（Efficient Inference in Multi-task Cox Process Models）</news:title>
   <news:publication_date>2026-05-09T00:03:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688041</loc>
  <lastmod>2026-05-09T00:02:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Local SGDによる分散学習の通信最適化（Local SGD Converges Fast and Communicates Little）</news:title>
   <news:publication_date>2026-05-09T00:02:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688039</loc>
  <lastmod>2026-05-09T00:02:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチ時間分解能とマルチレベル特徴を組み合わせた環境音分類（Environmental Sound Classification Based on Multi-temporal Resolution Convolutional Neural Network Combining with Multi-level Features）</news:title>
   <news:publication_date>2026-05-09T00:02:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688037</loc>
  <lastmod>2026-05-09T00:02:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>精密なスパイク時刻を用いた学習：Liquid State Machineの新しいデコーディングアルゴリズム（Learning with precise spike times: A new decoding algorithm for liquid state machines）</news:title>
   <news:publication_date>2026-05-09T00:02:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688035</loc>
  <lastmod>2026-05-09T00:01:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>地理的Hidden Markov Treeによる洪水範囲推定（Geographical Hidden Markov Tree for Flood Extent Mapping）</news:title>
   <news:publication_date>2026-05-09T00:01:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688033</loc>
  <lastmod>2026-05-08T23:10:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>MobiFace：モバイル環境における顔追跡のための大規模データセット（MobiFace: A Novel Dataset for Mobile Face Tracking in the Wild）</news:title>
   <news:publication_date>2026-05-08T23:10:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688031</loc>
  <lastmod>2026-05-08T23:10:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>同字異形文字（Homoglyph）攻撃の検出に関するSiameseニューラルネットワークの提案（Detecting Homoglyph Attacks with a Siamese Neural Network）</news:title>
   <news:publication_date>2026-05-08T23:10:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688029</loc>
  <lastmod>2026-05-08T23:09:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>継続学習評価の再設計がもたらす本質的な変化（Towards Robust Evaluations of Continual Learning）</news:title>
   <news:publication_date>2026-05-08T23:09:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688027</loc>
  <lastmod>2026-05-08T23:08:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Fenchel-Young損失による分類器学習の新枠組み（Learning Classifiers with Fenchel-Young Losses: Generalized Entropies, Margins, and Algorithms）</news:title>
   <news:publication_date>2026-05-08T23:08:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688025</loc>
  <lastmod>2026-05-08T23:08:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>クロスドメイン分離のための画像間翻訳（Image-to-image translation for cross-domain disentanglement）</news:title>
   <news:publication_date>2026-05-08T23:08:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688023</loc>
  <lastmod>2026-05-08T23:08:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マージン付きデータからの凸多面体学習（Learning convex polyhedra with margin*）</news:title>
   <news:publication_date>2026-05-08T23:08:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688021</loc>
  <lastmod>2026-05-08T23:08:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所近似による動的システムの効率的符号化（Efficient Encoding of Dynamical Systems through Local Approximations）</news:title>
   <news:publication_date>2026-05-08T23:08:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688019</loc>
  <lastmod>2026-05-08T22:16:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GANによる深層ニューラルネットワークのパラメータ同時自動最適化（Autonomously and Simultaneously Refining Deep Neural Network Parameters by Generative Adversarial Networks）</news:title>
   <news:publication_date>2026-05-08T22:16:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688017</loc>
  <lastmod>2026-05-08T22:16:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>視覚的関係事実学習によるVQA改善（R-VQA: Learning Visual Relation Facts with Semantic Attention for Visual Question Answering）</news:title>
   <news:publication_date>2026-05-08T22:16:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688015</loc>
  <lastmod>2026-05-08T22:16:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>データ拡張と学習を同時最適化する手法（Jointly Optimize Data Augmentation and Network Training: Adversarial Data Augmentation in Human Pose Estimation）</news:title>
   <news:publication_date>2026-05-08T22:16:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688013</loc>
  <lastmod>2026-05-08T22:15:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>全結合再構成層を持つ深層残差ネットワークによる単一画像超解像（Deep Residual Networks with a Fully Connected Reconstruction Layer for Single Image Super-Resolution）</news:title>
   <news:publication_date>2026-05-08T22:15:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688011</loc>
  <lastmod>2026-05-08T22:14:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Interventionによる因果モデルの学習と検証（Learning and Testing Causal Models with Interventions）</news:title>
   <news:publication_date>2026-05-08T22:14:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688009</loc>
  <lastmod>2026-05-08T22:14:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>メタラーニングとエピソディックリコールが切り開く再発タスクの学習（Meta-Learning with Episodic Recall）</news:title>
   <news:publication_date>2026-05-08T22:14:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688007</loc>
  <lastmod>2026-05-08T22:14:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>適応的辞書学習によるGPR地雷分類の高速化と実地適応（Dictionary Learning for Adaptive GPR Landmine Classification）</news:title>
   <news:publication_date>2026-05-08T22:14:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688005</loc>
  <lastmod>2026-05-08T21:22:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>IDEASによるセキュリティ分析の再設計（Forming IDEAS: Interactive Data Exploration &amp;amp; Analysis System – Configurable Visual Analytics for Cyber Security Analysts）</news:title>
   <news:publication_date>2026-05-08T21:22:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688003</loc>
  <lastmod>2026-05-08T21:21:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>LF-Netによる局所特徴学習（LF-Net: Learning Local Features from Images）</news:title>
   <news:publication_date>2026-05-08T21:21:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/688001</loc>
  <lastmod>2026-05-08T21:21:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>電磁脳信号のための多変量畳み込みスパースコーディング（Multivariate Convolutional Sparse Coding for Electromagnetic Brain Signals）</news:title>
   <news:publication_date>2026-05-08T21:21:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687999</loc>
  <lastmod>2026-05-08T21:20:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Regularized Nonlinear Acceleration の要点と実務への示唆（Online Regularized Nonlinear Acceleration）</news:title>
   <news:publication_date>2026-05-08T21:20:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687997</loc>
  <lastmod>2026-05-08T21:20:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>異文化が出会うとき――クロスカルチュラル・ナレッジスペースの設計（When Cultures Meet: Modelling Cross-Cultural Knowledge Spaces）</news:title>
   <news:publication_date>2026-05-08T21:20:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687995</loc>
  <lastmod>2026-05-08T21:19:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>結合マルチリンガル空間での平行データのフィルタリングと抽出（Filtering and Mining Parallel Data in a Joint Multilingual Space）</news:title>
   <news:publication_date>2026-05-08T21:19:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687993</loc>
  <lastmod>2026-05-08T21:19:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>不確実性を考慮した注意機構による信頼性向上（Uncertainty-Aware Attention for Reliable Interpretation and Prediction）</news:title>
   <news:publication_date>2026-05-08T21:19:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687991</loc>
  <lastmod>2026-05-08T20:27:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>無線共存管理における強化学習ベースの資源配分（Resource Allocation for a Wireless Coexistence Management System Based on Reinforcement Learning）</news:title>
   <news:publication_date>2026-05-08T20:27:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687989</loc>
  <lastmod>2026-05-08T20:27:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習中の干渉と前進の一歩（One step back, two steps forward: interference and learning in recurrent neural networks）</news:title>
   <news:publication_date>2026-05-08T20:27:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687987</loc>
  <lastmod>2026-05-08T20:27:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SOSELETOによる転移学習とノイズラベル対策の統一的アプローチ（SOSELETO: A Unified Approach to Transfer Learning and Training with Noisy Labels）</news:title>
   <news:publication_date>2026-05-08T20:27:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687985</loc>
  <lastmod>2026-05-08T20:26:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>N次元ベクトルニューロンの汎用バックプロパゲーション（Backpropagation with N-D Vector-Valued Neurons Using Arbitrary Bilinear Products）</news:title>
   <news:publication_date>2026-05-08T20:26:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687983</loc>
  <lastmod>2026-05-08T20:26:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>入門プログラミングにおける問題類似度の測定（Measuring Item Similarity in Introductory Programming: Python and Robot Programming Case Studies）</news:title>
   <news:publication_date>2026-05-08T20:26:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687981</loc>
  <lastmod>2026-05-08T20:25:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Residual Networksを「変形の流れ」として読む — ResNetと微分同相写像の関係（Residual Networks as Geodesic Flows of Diffeomorphisms）</news:title>
   <news:publication_date>2026-05-08T20:25:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687979</loc>
  <lastmod>2026-05-08T20:25:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>連続行動空間におけるAlpha Zeroの拡張（A0C: Alpha Zero in Continuous Action Space）</news:title>
   <news:publication_date>2026-05-08T20:25:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687977</loc>
  <lastmod>2026-05-08T19:34:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>偶然の深宇宙観測領域バイアスが示す注意点（Accidental deep field bias in CMB T and SNe z correlation）</news:title>
   <news:publication_date>2026-05-08T19:34:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687975</loc>
  <lastmod>2026-05-08T19:33:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>酸化物界面における絶縁状態の渦励起（Vortex excitations in the Insulating State of an Oxide Interface）</news:title>
   <news:publication_date>2026-05-08T19:33:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687973</loc>
  <lastmod>2026-05-08T19:32:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Primal-Dual Wasserstein GANの要点と事業活用の示唆（Primal-Dual Wasserstein GAN）</news:title>
   <news:publication_date>2026-05-08T19:32:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687971</loc>
  <lastmod>2026-05-08T19:32:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模論文・技術文書を機械的に読み解く仕組み（Corpus Conversion Service: A Machine Learning Platform to Ingest Documents at Scale）</news:title>
   <news:publication_date>2026-05-08T19:32:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687969</loc>
  <lastmod>2026-05-08T19:32:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>System APIに基づくAndroidランサムウェア検出の有効性（On the Effectiveness of System API-Related Information for Android Ransomware Detection）</news:title>
   <news:publication_date>2026-05-08T19:32:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687967</loc>
  <lastmod>2026-05-08T19:32:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>関連学習における可解釈性と合成性を高める共同訓練型オートエンコーダ（Interpretable and Compositional Relation Learning by Joint Training with an Autoencoder）</news:title>
   <news:publication_date>2026-05-08T19:32:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687965</loc>
  <lastmod>2026-05-08T19:31:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>潜在因子とその結合性を同時に学習する統一確率モデル（A Unified Probabilistic Model for Learning Latent Factors and Their Connectivities from High-Dimensional Data）</news:title>
   <news:publication_date>2026-05-08T19:31:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687963</loc>
  <lastmod>2026-05-08T18:39:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自動化されたECEIデータ前処理：機械学習による異常信号の自動識別（An Automatic Data Cleaning Procedure for Electron Cyclotron Emission Imaging on EAST Tokamak Using Machine Learning Algorithm）</news:title>
   <news:publication_date>2026-05-08T18:39:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687961</loc>
  <lastmod>2026-05-08T18:39:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>最適輸送を用いた過剰パラメータ化モデルの勾配降下法の大域収束（On the Global Convergence of Gradient Descent for Over-parameterized Models using Optimal Transport）</news:title>
   <news:publication_date>2026-05-08T18:39:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687959</loc>
  <lastmod>2026-05-08T18:39:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>AVIDによる視覚的異常検知の敵対的学習（Adversarial Visual Irregularity Detection）</news:title>
   <news:publication_date>2026-05-08T18:39:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687957</loc>
  <lastmod>2026-05-08T18:37:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Laplacian Networks：ニューラルネットワークにおけるクラス境界の平滑化を制御する正則化（Laplacian Networks: Bounding Indicator Function Smoothness for Neural Networks）</news:title>
   <news:publication_date>2026-05-08T18:37:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687955</loc>
  <lastmod>2026-05-08T18:37:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スタッキングによる分子原子化エネルギー予測の精度改善（Stacked Generalization Approach to Improve Prediction of Molecular Atomization Energies）</news:title>
   <news:publication_date>2026-05-08T18:37:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687953</loc>
  <lastmod>2026-05-08T18:36:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>潜在変数を扱う安定化仕様探索法（Stable specification search in structural equation model with latent variables）</news:title>
   <news:publication_date>2026-05-08T18:36:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687951</loc>
  <lastmod>2026-05-08T18:35:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>衛星画像に対する高速多段階物体検出の手法（You Only Look Twice: Rapid Multi-Scale Object Detection In Satellite Imagery）</news:title>
   <news:publication_date>2026-05-08T18:35:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687949</loc>
  <lastmod>2026-05-08T17:43:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフ中の単一コミュニティ探索（Searching for a Single Community in a Graph）</news:title>
   <news:publication_date>2026-05-08T17:43:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687947</loc>
  <lastmod>2026-05-08T17:43:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>KNOB-SynC: 非パラメトリック重複度に基づくシンシティアルクラスタリング（Kernel-estimated Nonparametric Overlap-Based Syncytial Clustering）</news:title>
   <news:publication_date>2026-05-08T17:43:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687945</loc>
  <lastmod>2026-05-08T17:42:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>データ拡張ポリシーの自動探索（AutoAugment: Learning Augmentation Strategies from Data）</news:title>
   <news:publication_date>2026-05-08T17:42:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687943</loc>
  <lastmod>2026-05-08T17:42:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>VisemeNet: 音声駆動のアニメーター向けスピーチアニメーション（VisemeNet: Audio-Driven Animator-Centric Speech Animation）</news:title>
   <news:publication_date>2026-05-08T17:42:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687941</loc>
  <lastmod>2026-05-08T17:41:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動的アドバイザベースアンサンブル（Dynamic Advisor-Based Ensemble (dynABE): Case study in stock trend prediction of critical metal companies）</news:title>
   <news:publication_date>2026-05-08T17:41:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687939</loc>
  <lastmod>2026-05-08T17:41:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散クラスタリングと外れ値検出の実務的アルゴリズム（A Practical Algorithm for Distributed Clustering and Outlier Detection）</news:title>
   <news:publication_date>2026-05-08T17:41:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687937</loc>
  <lastmod>2026-05-08T17:41:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>潜在空間探索と敵対的損失によるクロスドメイン画像生成（Cross Domain Image Generation through Latent Space Exploration with Adversarial Loss）</news:title>
   <news:publication_date>2026-05-08T17:41:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687935</loc>
  <lastmod>2026-05-08T16:49:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>なぜジョニーはパスワードを安全に保存できないのか（Why Johnny Can’t Store Passwords Securely? A Usability Evaluation of Bouncycastle Password Hashing）</news:title>
   <news:publication_date>2026-05-08T16:49:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687933</loc>
  <lastmod>2026-05-08T16:49:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチレベル深層カスケード木によるCVR予測（Multi-Level Deep Cascade Trees for Conversion Rate Prediction in Recommendation System）</news:title>
   <news:publication_date>2026-05-08T16:49:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687931</loc>
  <lastmod>2026-05-08T16:49:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>連続型非単調サブモジュラ最適化の最適アルゴリズム（Optimal Algorithms for Continuous Non-monotone Submodular and DR-Submodular Maximization）</news:title>
   <news:publication_date>2026-05-08T16:49:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687929</loc>
  <lastmod>2026-05-08T16:48:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>リソース制約下のIoTで大規模DNNを動かすローカル量子化（Deploy Large-Scale Deep Neural Networks in Resource Constrained IoT Devices with Local Quantization Region）</news:title>
   <news:publication_date>2026-05-08T16:48:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687927</loc>
  <lastmod>2026-05-08T16:48:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>VisualBackPropを用いた学習における特権情報の活用（VisualBackProp for learning using privileged information with CNNs）</news:title>
   <news:publication_date>2026-05-08T16:48:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687925</loc>
  <lastmod>2026-05-08T16:48:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>構造制約付き階層クラスタリング（Hierarchical Clustering with Structural Constraints）</news:title>
   <news:publication_date>2026-05-08T16:48:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687923</loc>
  <lastmod>2026-05-08T16:47:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非凸学習における非同期SGDの収束を制御する手法（Taming Convergence for Asynchronous Stochastic Gradient Descent with Unbounded Delay in Non-Convex Learning）</news:title>
   <news:publication_date>2026-05-08T16:47:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687921</loc>
  <lastmod>2026-05-08T15:56:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>エントリーワイズℓpノルム低ランク近似の実用的アルゴリズム（Simple and practical algorithms for ℓp-norm low-rank approximation）</news:title>
   <news:publication_date>2026-05-08T15:56:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687919</loc>
  <lastmod>2026-05-08T15:56:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>詳細な部品分割のための複雑な関係を組み込んだ深層構造予測モデル（Complex Relations in a Deep Structured Prediction Model for Fine Image Segmentation）</news:title>
   <news:publication_date>2026-05-08T15:56:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687917</loc>
  <lastmod>2026-05-08T15:55:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Deep Reinforcement Learningを用いたSeq2Seqモデルの強化（Deep Reinforcement Learning for Sequence-to-Sequence Models）</news:title>
   <news:publication_date>2026-05-08T15:55:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687915</loc>
  <lastmod>2026-05-08T15:54:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模グラフとゼロノイズ極限に関する半教師付き学習の理論的限界（LARGE DATA AND ZERO NOISE LIMITS OF GRAPH-BASED SEMI-SUPERVISED LEARNING ALGORITHMS）</news:title>
   <news:publication_date>2026-05-08T15:54:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687913</loc>
  <lastmod>2026-05-08T15:54:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>特徴に基づく生成モデルを用いたDyna計画（Dyna Planning using a Feature Based Generative Model）</news:title>
   <news:publication_date>2026-05-08T15:54:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687911</loc>
  <lastmod>2026-05-08T15:54:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>不変表現と敵対的学習を超える情報理論的アプローチ（Invariant Representations without Adversarial Training）</news:title>
   <news:publication_date>2026-05-08T15:54:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687909</loc>
  <lastmod>2026-05-08T15:54:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>慎重な深層学習（Cautious Deep Learning）</news:title>
   <news:publication_date>2026-05-08T15:54:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687907</loc>
  <lastmod>2026-05-08T15:02:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>二段階部分空間トラストリージョンによる深層ニューラルネットの訓練（A Two-Stage Subspace Trust Region Approach for Deep Neural Network Training）</news:title>
   <news:publication_date>2026-05-08T15:02:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687905</loc>
  <lastmod>2026-05-08T14:56:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>LSTMに内在する暗黙の言語モデルがOCRを変える（Implicit Language Model in LSTM for OCR）</news:title>
   <news:publication_date>2026-05-08T14:56:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687903</loc>
  <lastmod>2026-05-08T14:56:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>適応型確率的勾配ランジュバン力学（Adaptive Stochastic Gradient Langevin Dynamics）</news:title>
   <news:publication_date>2026-05-08T14:56:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687901</loc>
  <lastmod>2026-05-08T14:55:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>状態空間モデルのスケーラブルなベイズ学習と変分推論＋SMCサンプラー（Scalable Bayesian Learning for State Space Models using Variational Inference with SMC Samplers）</news:title>
   <news:publication_date>2026-05-08T14:55:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687899</loc>
  <lastmod>2026-05-08T14:54:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>補間とCNNを組み合わせたハイブリッド超解像法（A hybrid approach of interpolations and CNN to obtain super-resolution）</news:title>
   <news:publication_date>2026-05-08T14:54:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687897</loc>
  <lastmod>2026-05-08T14:54:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層能動学習による異常検知の転換（Deep Active Learning for Anomaly Detection）</news:title>
   <news:publication_date>2026-05-08T14:54:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687895</loc>
  <lastmod>2026-05-08T14:54:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>タスク依存適応メトリクスによるFew-Shot学習の前進（TADAM: Task dependent adaptive metric for improved few-shot learning）</news:title>
   <news:publication_date>2026-05-08T14:54:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687893</loc>
  <lastmod>2026-05-08T14:03:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>電子の経路を生成するモデル（A Generative Model for Electron Paths）</news:title>
   <news:publication_date>2026-05-08T14:03:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687891</loc>
  <lastmod>2026-05-08T14:02:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>日常動作「注ぐ」動作の予測と学習（Pouring Sequence Prediction using Recurrent Neural Network）</news:title>
   <news:publication_date>2026-05-08T14:02:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687889</loc>
  <lastmod>2026-05-08T14:02:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ロバスト適応制御に関する後悔境界の新展開（Regret Bounds for Robust Adaptive Control of the Linear Quadratic Regulator）</news:title>
   <news:publication_date>2026-05-08T14:02:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687887</loc>
  <lastmod>2026-05-08T14:01:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>顔画像の任意属性を選択的に匿名化する手法（Anonymizing k-Facial Attributes via Adversarial Perturbations）</news:title>
   <news:publication_date>2026-05-08T14:01:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687885</loc>
  <lastmod>2026-05-08T14:01:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Predictive Local Smoothnessによる確率的勾配法の適応学習率（Predictive Local Smoothness for Stochastic Gradient Methods）</news:title>
   <news:publication_date>2026-05-08T14:01:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687883</loc>
  <lastmod>2026-05-08T14:01:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>敵対的勾配の正則化によるニューラルネットワークの堅牢化（Towards Robust Training of Neural Networks by Regularizing Adversarial Gradients）</news:title>
   <news:publication_date>2026-05-08T14:01:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687881</loc>
  <lastmod>2026-05-08T14:01:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>モダリティ間の合意に基づく半教師あり分類（Semi-supervised classification by reaching consensus among modalities）</news:title>
   <news:publication_date>2026-05-08T14:01:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687879</loc>
  <lastmod>2026-05-08T13:09:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非定常環境における文脈付きバンディット学習（Learning Contextual Bandits in a Non-stationary Environment）</news:title>
   <news:publication_date>2026-05-08T13:09:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687877</loc>
  <lastmod>2026-05-08T13:09:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マーク付き時点過程の深層強化学習（Deep Reinforcement Learning of Marked Temporal Point Processes）</news:title>
   <news:publication_date>2026-05-08T13:09:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687875</loc>
  <lastmod>2026-05-08T13:08:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>語彙含意の評価を変えた方向性ネットワーク（Scoring Lexical Entailment with a Supervised Directional Similarity Network）</news:title>
   <news:publication_date>2026-05-08T13:08:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687873</loc>
  <lastmod>2026-05-08T13:07:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DeepToF によるオフ・ザ・シェルフToFカメラのリアルタイムMPI補正（DeepToF: Off-the-Shelf Real-Time Correction of Multipath Interference in Time-of-Flight Imaging）</news:title>
   <news:publication_date>2026-05-08T13:07:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687871</loc>
  <lastmod>2026-05-08T13:07:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層学習による通信アルゴリズムの自動発見（Communication Algorithms via Deep Learning）</news:title>
   <news:publication_date>2026-05-08T13:07:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687869</loc>
  <lastmod>2026-05-08T13:07:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>変分法で学ぶグローバーの量子探索アルゴリズム（Variationally Learning Grover’s Quantum Search Algorithm）</news:title>
   <news:publication_date>2026-05-08T13:07:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687867</loc>
  <lastmod>2026-05-08T13:07:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>作業記憶ネットワーク：メモリネットワークに関係推論モジュールを付与する試み（Working Memory Networks: Augmenting Memory Networks with a Relational Reasoning Module）</news:title>
   <news:publication_date>2026-05-08T13:07:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687865</loc>
  <lastmod>2026-05-08T12:14:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>入力空間と重み空間のスムージングによる半教師あり学習の改良（Input and Weight Space Smoothing for Semi-supervised Learning）</news:title>
   <news:publication_date>2026-05-08T12:14:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687863</loc>
  <lastmod>2026-05-08T12:14:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SNIPERによる効率的なマルチスケール学習（SNIPER: Efficient Multi-Scale Training）</news:title>
   <news:publication_date>2026-05-08T12:14:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687861</loc>
  <lastmod>2026-05-08T12:13:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>最小超球面エネルギーに向けた学習（Learning towards Minimum Hyperspherical Energy）</news:title>
   <news:publication_date>2026-05-08T12:13:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687859</loc>
  <lastmod>2026-05-08T12:12:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文脈依存拘束問題をリアルタイムに解く学習手法（Learning to Optimize Contextually Constrained Problems）</news:title>
   <news:publication_date>2026-05-08T12:12:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687857</loc>
  <lastmod>2026-05-08T12:12:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>リザバーコンピューティングを用いたデータからの流体変数の機械学習推定 (Machine-learning inference of fluid variables from data using reservoir computing)</news:title>
   <news:publication_date>2026-05-08T12:12:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687855</loc>
  <lastmod>2026-05-08T12:12:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>部分観測マルコフ決定過程におけるデータ効率的モデル学習のための変分推論（Variational Inference for Data-Efﬁcient Model Learning in POMDPs）</news:title>
   <news:publication_date>2026-05-08T12:12:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687853</loc>
  <lastmod>2026-05-08T12:11:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>エミュレータネットワークによる尤度なし推論の実務的意義（Likelihood-free inference with emulator networks）</news:title>
   <news:publication_date>2026-05-08T12:11:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687851</loc>
  <lastmod>2026-05-08T11:20:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ヘテロジニアスなチームに対する強化学習とPALO境界（Reinforcement Learning for Heterogeneous Teams with PALO Bounds）</news:title>
   <news:publication_date>2026-05-08T11:20:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687849</loc>
  <lastmod>2026-05-08T11:19:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>物体認識から学ぶ照明推定（Learning Illuminant Estimation from Object Recognition）</news:title>
   <news:publication_date>2026-05-08T11:19:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687847</loc>
  <lastmod>2026-05-08T11:19:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散環境下における協調オンライン学習（Collective Online Learning via Decentralized Gaussian Processes in Massive Multi-Agent Systems）</news:title>
   <news:publication_date>2026-05-08T11:19:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687845</loc>
  <lastmod>2026-05-08T11:18:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>同心円状リザバー（Concentric ESN: Assessing the Effect of Modularity in Cycle Reservoirs）</news:title>
   <news:publication_date>2026-05-08T11:18:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687843</loc>
  <lastmod>2026-05-08T11:18:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時間方向の情報流を改善するHighway State Gating（Highway State Gating for Recurrent Highway Networks: improving information flow through time）</news:title>
   <news:publication_date>2026-05-08T11:18:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687841</loc>
  <lastmod>2026-05-08T11:18:28Z</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（Partial Monitoring）における敵対的ゲームの完全分類（Cleaning up the neighborhood: A full classification for adversarial partial monitoring）</news:title>
   <news:publication_date>2026-05-08T11:18:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687839</loc>
  <lastmod>2026-05-08T11:18:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>光通信における深層ニューラルネットワークの応用（On the use of deep neural networks in optical communications）</news:title>
   <news:publication_date>2026-05-08T11:18:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687837</loc>
  <lastmod>2026-05-08T10:26:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Cramer-Wold AutoEncoder（Cramer-Wold AutoEncoder）</news:title>
   <news:publication_date>2026-05-08T10:26:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687835</loc>
  <lastmod>2026-05-08T10:26:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>肝臓病変の軽量化セグメンテーション手法（Segmentation of Liver Lesions with Reduced Complexity Deep Models）</news:title>
   <news:publication_date>2026-05-08T10:26:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687833</loc>
  <lastmod>2026-05-08T10:26:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>事象の地平線近傍における内在的構造の検出（Detection of Intrinsic Source Structure at ∼3 Schwarzschild Radii with Millimeter-VLBI Observations of Sagittarius A*）</news:title>
   <news:publication_date>2026-05-08T10:26:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687831</loc>
  <lastmod>2026-05-08T10:26:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>乗法重み法による共同PAC学習の厳密境界（Tight Bounds for Collaborative PAC Learning via Multiplicative Weights）</news:title>
   <news:publication_date>2026-05-08T10:26:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687829</loc>
  <lastmod>2026-05-08T10:25:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非対称木に強いモンテカルロ木探索の改良（Monte Carlo Tree Search for Asymmetric Trees）</news:title>
   <news:publication_date>2026-05-08T10:25:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687827</loc>
  <lastmod>2026-05-08T10:25:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>潜在変数を含む構造化予測モデルの学習とガウス摂動（Learning latent variable structured prediction models with Gaussian perturbations）</news:title>
   <news:publication_date>2026-05-08T10:25:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687825</loc>
  <lastmod>2026-05-08T10:25:04Z</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訓練の統一フレームワーク（A Unified Framework for Training Neural Networks）</news:title>
   <news:publication_date>2026-05-08T10:25:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687823</loc>
  <lastmod>2026-05-08T09:34:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>交互的ランダム化ブロック座標降下法が示す最小滑らかさ独立の可能性（Alternating Randomized Block Coordinate Descent）</news:title>
   <news:publication_date>2026-05-08T09:34:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687821</loc>
  <lastmod>2026-05-08T09:33:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ドロップアウトの境界を押し広げる（Pushing the Bounds of Dropout）</news:title>
   <news:publication_date>2026-05-08T09:33:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687819</loc>
  <lastmod>2026-05-08T09:33:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数顔画像からの被写体単位属性推定（Attributes in Multiple Facial Images）</news:title>
   <news:publication_date>2026-05-08T09:33:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687817</loc>
  <lastmod>2026-05-08T09:33:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>MNIST上での最初の敵対的ロバストニューラルネットワークに向けて（TOWARDS THE FIRST ADVERSARIALLY ROBUST NEURAL NETWORK MODEL ON MNIST）</news:title>
   <news:publication_date>2026-05-08T09:33:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687815</loc>
  <lastmod>2026-05-08T09:32:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ロボットを使ったモバイル学習プラットフォームの導入が変えるプログラミング教育（Introducing an innovative robot-based mobile platform for programming learning）</news:title>
   <news:publication_date>2026-05-08T09:32:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687813</loc>
  <lastmod>2026-05-08T09:32:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ASRを特徴抽出器として用いた音声感情認識—転移学習の実践的示唆（ASR-based Features for Emotion Recognition: A Transfer Learning Approach）</news:title>
   <news:publication_date>2026-05-08T09:32:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687811</loc>
  <lastmod>2026-05-08T09:31:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CT密度から深層学習で推定する吸収線量（Deep Learning Estimation of Absorbed Dose for Nuclear Medicine Diagnostics）</news:title>
   <news:publication_date>2026-05-08T09:31:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687809</loc>
  <lastmod>2026-05-08T08:41:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>無意識表象が明かす意識の仕組み（Why the Brain Knows More than We Do: Non-Conscious Representations and Their Role in the Construction of Conscious Experience）</news:title>
   <news:publication_date>2026-05-08T08:41:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687807</loc>
  <lastmod>2026-05-08T08:40:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>欠損特徴量と欠損ラベルを伴うマルチラベル分類の行列共同補完（Matrix Co-completion for Multi-label Classification with Missing Features and Labels）</news:title>
   <news:publication_date>2026-05-08T08:40:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687805</loc>
  <lastmod>2026-05-08T08:40:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スパース性を活かした高速後悔率（Fast-Rate）保証の実効的なオンラインアルゴリズム（EFFICIENT ONLINE ALGORITHMS FOR FAST-RATE REGRET BOUNDS UNDER SPARSITY）</news:title>
   <news:publication_date>2026-05-08T08:40:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687803</loc>
  <lastmod>2026-05-08T08:40:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>光電容積脈波からの血圧トレンドと夜間ディップ推定（Estimating blood pressure trends and the nocturnal dip from photoplethysmography）</news:title>
   <news:publication_date>2026-05-08T08:40:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687801</loc>
  <lastmod>2026-05-08T08:39:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的リーマン部分多様体学習とWGPLVM（Probabilistic Riemannian submanifold learning with wrapped Gaussian process latent variable models）</news:title>
   <news:publication_date>2026-05-08T08:39:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687799</loc>
  <lastmod>2026-05-08T08:39:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>RDF埋め込みによるオントロジー整合変化の分類（RDF2Vec-based Classification of Ontology Alignment Changes）</news:title>
   <news:publication_date>2026-05-08T08:39:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687797</loc>
  <lastmod>2026-05-08T08:39:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Gene Ontologyの注釈進化に基づくネガティブ例選定の分析（Analysis of Novel Annotations in the Gene Ontology for Boosting the Selection of Negative Examples）</news:title>
   <news:publication_date>2026-05-08T08:39:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687795</loc>
  <lastmod>2026-05-08T07:48:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>構造化データの最適輸送とグラフ応用（Optimal Transport for structured data with application on graphs）</news:title>
   <news:publication_date>2026-05-08T07:48:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687793</loc>
  <lastmod>2026-05-08T07:48:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ハイパーボリックニューラルネットワークの本質と実務応用（Hyperbolic Neural Networks）</news:title>
   <news:publication_date>2026-05-08T07:48:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687791</loc>
  <lastmod>2026-05-08T07:47:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>トウモロコシのハプロイド種子識別を高精度化する波長選択型LSTM-CNN（Maize Haploid Identification via LSTM-CNN and Hyperspectral Imaging Technology）</news:title>
   <news:publication_date>2026-05-08T07:47:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687789</loc>
  <lastmod>2026-05-08T07:47:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所パッチの周波数分布を推定して行う画像復元（Image Restoration by Estimating Frequency Distribution of Local Patches）</news:title>
   <news:publication_date>2026-05-08T07:47:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687787</loc>
  <lastmod>2026-05-08T07:47:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Excitation Dropoutによるニューラルネットの可塑性促進（Excitation Dropout: Encouraging Plasticity in Deep Neural Networks）</news:title>
   <news:publication_date>2026-05-08T07:47:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687785</loc>
  <lastmod>2026-05-08T07:46:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューラルネットワークによるアンサンブル天気予報の事後処理（Neural networks for post-processing ensemble weather forecasts）</news:title>
   <news:publication_date>2026-05-08T07:46:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687783</loc>
  <lastmod>2026-05-08T07:46:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>制約付きグラフ変分オートエンコーダによる分子設計（Constrained Graph Variational Autoencoders for Molecule Design）</news:title>
   <news:publication_date>2026-05-08T07:46:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687781</loc>
  <lastmod>2026-05-08T06:55:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>表現バランスによるMDPモデルでオフポリシー評価を改善する（Representation Balancing MDPs for Off-Policy Policy Evaluation）</news:title>
   <news:publication_date>2026-05-08T06:55:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687779</loc>
  <lastmod>2026-05-08T06:45:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単純な探索がサンプル効率的である条件（When Simple Exploration is Sample Efficient: Identifying Sufficient Conditions for Random Exploration to Yield PAC RL Algorithms）</news:title>
   <news:publication_date>2026-05-08T06:45:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687777</loc>
  <lastmod>2026-05-08T06:45:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>海馬-内嗅皮質系における構造的知識の一般化 (Generalisation of structural knowledge in the hippocampal-entorhinal system)</news:title>
   <news:publication_date>2026-05-08T06:45:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687775</loc>
  <lastmod>2026-05-08T06:44:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Phocas: 分散学習における次元不変のByzantine耐性を実現する新しい集約法（Phocas: dimensional Byzantine-resilient stochastic gradient descent）</news:title>
   <news:publication_date>2026-05-08T06:44:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687773</loc>
  <lastmod>2026-05-08T06:44:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>顕著領域に注目したオーロラ画像検索の効率化（Saliency Deep Embedding for Aurora Image Search）</news:title>
   <news:publication_date>2026-05-08T06:44:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
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 <url>
  <loc>https://aibr.jp/archives/687771</loc>
  <lastmod>2026-05-08T06:44:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文脈ベクトルのアモタイゼーションによる注意機構の改良（Amortized Context Vector Inference for Sequence-to-Sequence Networks）</news:title>
   <news:publication_date>2026-05-08T06:44:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687769</loc>
  <lastmod>2026-05-08T06:43:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>偏極深部散乱における重フレーバー生成のNLO QCD修正（Next-to-Leading Order QCD Corrections to Inclusive Heavy-Flavor Production in Polarized Deep-Inelastic Scattering）</news:title>
   <news:publication_date>2026-05-08T06:43:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687767</loc>
  <lastmod>2026-05-08T05:52:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>コンテキスト対応アプリのためのオントロジー駆動推論フレームワーク（An Ontology-Based Reasoning Framework for Context-Aware Applications）</news:title>
   <news:publication_date>2026-05-08T05:52:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687765</loc>
  <lastmod>2026-05-08T05:52:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>属性駆動型アクティブラーニングによるアイテムのコールドスタート問題への対処（Addressing the Item Cold-start Problem by Attribute-driven Active Learning）</news:title>
   <news:publication_date>2026-05-08T05:52:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687763</loc>
  <lastmod>2026-05-08T05:52:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>イラスト画像分類のための転移学習（Transfer Learning for Illustration Classification）</news:title>
   <news:publication_date>2026-05-08T05:52:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687761</loc>
  <lastmod>2026-05-08T05:51:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動的占有格子地図上での深層物体追跡（Deep Object Tracking on Dynamic Occupancy Grid Maps Using RNNs）</news:title>
   <news:publication_date>2026-05-08T05:51:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687759</loc>
  <lastmod>2026-05-08T05:51:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>刺激と神経状態の一対一写像—記憶と分類（One-to-one mapping between stimulus and neural state: Memory and classification）</news:title>
   <news:publication_date>2026-05-08T05:51:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687757</loc>
  <lastmod>2026-05-08T05:51:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>音楽活動が生徒の技能に与える影響を推定する二重機械学習アプローチ（A Double Machine Learning Approach to Estimate the Effects of Musical Practice on Student’s Skills）</news:title>
   <news:publication_date>2026-05-08T05:51:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687755</loc>
  <lastmod>2026-05-08T05:50:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>対称性を保つ機械学習原子間ポテンシャルの実装（End-to-end Symmetry Preserving Inter-atomic Potential Energy Model for Finite and Extended Systems）</news:title>
   <news:publication_date>2026-05-08T05:50:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687753</loc>
  <lastmod>2026-05-08T04:59:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自己注意を用いたメッセージ関連応答生成（Self-Attention-Based Message-Relevant Response Generation for Neural Conversation Model）</news:title>
   <news:publication_date>2026-05-08T04:59:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687751</loc>
  <lastmod>2026-05-08T04:59:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数暗号通貨市場における戦略的マイニングのゲーム化と報酬設計（Game of Coins）</news:title>
   <news:publication_date>2026-05-08T04:59:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687749</loc>
  <lastmod>2026-05-08T04:59:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>RGBと熱画像の追跡ベンチマークと基準手法（RGB-T Object Tracking: Benchmark and Baseline）</news:title>
   <news:publication_date>2026-05-08T04:59:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687747</loc>
  <lastmod>2026-05-08T04:57:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深度順位で3次元姿勢を解く：DRPose3D（DRPose3D: Depth Ranking in 3D Human Pose Estimation）</news:title>
   <news:publication_date>2026-05-08T04:57:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687745</loc>
  <lastmod>2026-05-08T04:57:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>優れたImageNetモデルは他タスクでも強いのか（Do Better ImageNet Models Transfer Better?）</news:title>
   <news:publication_date>2026-05-08T04:57:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687743</loc>
  <lastmod>2026-05-08T04:57:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Particle Filter Networksによる視覚的自己位置推定の学習（Particle Filter Networks with Application to Visual Localization）</news:title>
   <news:publication_date>2026-05-08T04:57:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687741</loc>
  <lastmod>2026-05-08T04:57:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>思考する顕微鏡へ――光学顕微鏡と画像再構成におけるDeep Learningの到来（Toward a Thinking Microscope: Deep Learning in Optical Microscopy and Image Reconstruction）</news:title>
   <news:publication_date>2026-05-08T04:57:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687739</loc>
  <lastmod>2026-05-08T04:05:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>意味的ネットワーク解釈（Semantic Network Interpretation）</news:title>
   <news:publication_date>2026-05-08T04:05:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687737</loc>
  <lastmod>2026-05-08T04:04:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>強化学習における盲点の発見（Discovering Blind Spots in Reinforcement Learning）</news:title>
   <news:publication_date>2026-05-08T04:04:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687735</loc>
  <lastmod>2026-05-08T04:04:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動的ニューラルネットワークによる辞書学習（Dictionary Learning by Dynamical Neural Networks）</news:title>
   <news:publication_date>2026-05-08T04:04:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687733</loc>
  <lastmod>2026-05-08T04:03:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>乳房腫瘍の可視化と診断説明を目指すICADx（ICADx: Interpretable computer aided diagnosis of breast masses）</news:title>
   <news:publication_date>2026-05-08T04:03:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687731</loc>
  <lastmod>2026-05-08T04:03:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>加重確率的ブロックモデルにおけるハイパーグラフ・スペクトルクラスタリング（Hypergraph Spectral Clustering in the Weighted Stochastic Block Model）</news:title>
   <news:publication_date>2026-05-08T04:03:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687729</loc>
  <lastmod>2026-05-08T04:03:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GANを用いた半教師あり学習とマニフォールド正則化（SEMI-SUPERVISED LEARNING WITH GANS: REVISITING MANIFOLD REGULARIZATION）</news:title>
   <news:publication_date>2026-05-08T04:03:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687727</loc>
  <lastmod>2026-05-08T04:02:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ブランド単位のランキングシステムとAttention-GRUの応用（A Brand-level Ranking System with the Customized Attention-GRU Model）</news:title>
   <news:publication_date>2026-05-08T04:02:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687725</loc>
  <lastmod>2026-05-08T03:11:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>強相関フェルミ超流体における臨界渦放出の実験的解明（Critical Vortex Shedding in a Strongly Interacting Fermionic Superfluid）</news:title>
   <news:publication_date>2026-05-08T03:11:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687723</loc>
  <lastmod>2026-05-08T03:10:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Stack Overflow由来の自然言語とコードの整列ペア抽出法（Learning to Mine Aligned Code and Natural Language Pairs from Stack Overflow）</news:title>
   <news:publication_date>2026-05-08T03:10:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687721</loc>
  <lastmod>2026-05-08T03:10:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>同時強化学習におけるスケーラブルな協調探索（Scalable Coordinated Exploration in Concurrent Reinforcement Learning）</news:title>
   <news:publication_date>2026-05-08T03:10:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687719</loc>
  <lastmod>2026-05-08T03:09:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>AutoPruner: エンドツーエンドで学習できるフィルタプルーニング手法（AutoPruner: An End-to-End Trainable Filter Pruning Method for Efficient Deep Model Inference）</news:title>
   <news:publication_date>2026-05-08T03:09:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687717</loc>
  <lastmod>2026-05-08T03:09:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GPGPU上でのDNN学習高速化のための近似ランダムドロップアウト（Approximate Random Dropout for DNN training acceleration in GPGPU）</news:title>
   <news:publication_date>2026-05-08T03:09:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687715</loc>
  <lastmod>2026-05-08T03:09:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>産業用ビンピッキングの学習的手法（Learning Based Industrial Bin-picking Trained with Approximate Physics Simulator）</news:title>
   <news:publication_date>2026-05-08T03:09:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687713</loc>
  <lastmod>2026-05-08T03:09:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模建物抽出におけるCNNの実用化とその意義（Building Extraction at Scale using Convolutional Neural Network: Mapping of the United States）</news:title>
   <news:publication_date>2026-05-08T03:09:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687711</loc>
  <lastmod>2026-05-08T02:17:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模ニューロモルフィック・スパイキングアレイ処理器の探求（Large-Scale Neuromorphic Spiking Array Processors: A quest to mimic the brain）</news:title>
   <news:publication_date>2026-05-08T02:17:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687709</loc>
  <lastmod>2026-05-08T02:16:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-05-08T02:16:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687707</loc>
  <lastmod>2026-05-08T02:16:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>近似Newtonベースの確率的勾配のみを用いた統計的推定（Approximate Newton-based statistical inference using only stochastic gradients）</news:title>
   <news:publication_date>2026-05-08T02:16:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687705</loc>
  <lastmod>2026-05-08T02:15:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>状況認識を用いたミリ波ビーム予測（MmWave Beam Prediction with Situational Awareness: A Machine Learning Approach）</news:title>
   <news:publication_date>2026-05-08T02:15:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687703</loc>
  <lastmod>2026-05-08T02:15:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分布を意識したアクティブラーニング（Distribution Aware Active Learning）</news:title>
   <news:publication_date>2026-05-08T02:15:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687701</loc>
  <lastmod>2026-05-08T02:15:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文字特徴を動的に統合して中国語の意図分類を改善する（Enhancing Chinese Intent Classification by Dynamically Integrating Character Features into Word Embeddings with Ensemble Techniques）</news:title>
   <news:publication_date>2026-05-08T02:15:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687699</loc>
  <lastmod>2026-05-08T02:15:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>アモータイズド推論の正則化（Amortized Inference Regularization）</news:title>
   <news:publication_date>2026-05-08T02:15:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687697</loc>
  <lastmod>2026-05-08T01:23:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>AffinityNetによる少量ラベル学習の実用性と病型推定への応用（AffinityNet: Semi-supervised Few-shot Learning for Disease Type Prediction）</news:title>
   <news:publication_date>2026-05-08T01:23:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687695</loc>
  <lastmod>2026-05-08T01:23:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GPUメモリ使用量を削減するコンパイラEcho（Echo: Compiler-based GPU Memory Footprint Reduction for LSTM RNN Training）</news:title>
   <news:publication_date>2026-05-08T01:23:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687693</loc>
  <lastmod>2026-05-08T01:22:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>教室における教師の認知—視線と一人称動画で解き明かす授業観察（Teachers’ Perception in the Classroom）</news:title>
   <news:publication_date>2026-05-08T01:22:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687691</loc>
  <lastmod>2026-05-08T01:22:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチタスク最大因果エントロピー逆強化学習（Multi-task Maximum Causal Entropy Inverse Reinforcement Learning）</news:title>
   <news:publication_date>2026-05-08T01:22:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687689</loc>
  <lastmod>2026-05-08T01:22:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>新卒ソフトウェア人材は現場で通用するか（Are Computer Science and Engineering Graduates Ready for the Software Industry?）</news:title>
   <news:publication_date>2026-05-08T01:22:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687687</loc>
  <lastmod>2026-05-08T01:21:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Afﬁnity Network Fusionによるがん患者クラスタリングと半教師あり学習（Affinity Network Fusion and Semi-supervised Learning for Cancer Patient Clustering）</news:title>
   <news:publication_date>2026-05-08T01:21:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687685</loc>
  <lastmod>2026-05-08T01:21:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ステップサイズが深層学習にもたらす本質的影響（Step Size Matters in Deep Learning）</news:title>
   <news:publication_date>2026-05-08T01:21:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687683</loc>
  <lastmod>2026-05-08T00:30:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ARiA：Richardの曲線を活用して活性化関数の非単調性を制御する手法（ARiA: Utilizing Richard’s Curve for Controlling the Non-monotonicity of the Activation Function in Deep Neural Nets）</news:title>
   <news:publication_date>2026-05-08T00:30:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687681</loc>
  <lastmod>2026-05-08T00:30:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>対立的ラベリング学習による弱教師あり分類の堅牢化（Adversarial Label Learning）</news:title>
   <news:publication_date>2026-05-08T00:30:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687679</loc>
  <lastmod>2026-05-08T00:30:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ハイパーグラフ一致による教師なしドメイン適応（UNSUPERVISED DOMAIN ADAPTATION USING REGULARIZED HYPER-GRAPH MATCHING）</news:title>
   <news:publication_date>2026-05-08T00:30:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687677</loc>
  <lastmod>2026-05-08T00:29:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的勾配下でのアンダーダンパード・ランジュバン法の離散化と評価（Langevin Markov Chain Monte Carlo with stochastic gradients）</news:title>
   <news:publication_date>2026-05-08T00:29:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687675</loc>
  <lastmod>2026-05-08T00:29:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>反事実平均埋め込み（Counterfactual Mean Embeddings）</news:title>
   <news:publication_date>2026-05-08T00:29:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687673</loc>
  <lastmod>2026-05-08T00:29:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層生成モデルによる最適ノイズ除去（Rate-Optimal Denoising with Deep Neural Networks）</news:title>
   <news:publication_date>2026-05-08T00:29:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687671</loc>
  <lastmod>2026-05-08T00:28:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>医療画像翻訳における分布一致損失が特徴を</news:title>
   <news:publication_date>2026-05-08T00:28:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687669</loc>
  <lastmod>2026-05-07T23:37:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>量子スロー・フィーチャー解析によるMNIST分類（Classification of MNIST dataset with Quantum Slow Feature Analysis）</news:title>
   <news:publication_date>2026-05-07T23:37:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687667</loc>
  <lastmod>2026-05-07T23:37:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>クラスタリングの本質はモデル選択にあり（Clustering - What Both Theoreticians and Practitioners are Doing Wrong）</news:title>
   <news:publication_date>2026-05-07T23:37:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687665</loc>
  <lastmod>2026-05-07T23:36:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>敵対的損失による非パラメトリック密度推定（Nonparametric Density Estimation with Adversarial Losses）</news:title>
   <news:publication_date>2026-05-07T23:36:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687663</loc>
  <lastmod>2026-05-07T23:36:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>無限タスク学習の枠組みと応用（Infinite Task Learning in RKHSs）</news:title>
   <news:publication_date>2026-05-07T23:36:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687661</loc>
  <lastmod>2026-05-07T23:36:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ヒトが注目する場所を学ぶ注意機構の学習（LEARNING WHAT AND WHERE TO ATTEND）</news:title>
   <news:publication_date>2026-05-07T23:36:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687659</loc>
  <lastmod>2026-05-07T23:35:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層生成ネットワークによる地震波速度逆算の高速化（Rapid seismic domain transfer: Seismic velocity inversion and modeling using deep generative neural networks）</news:title>
   <news:publication_date>2026-05-07T23:35:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687657</loc>
  <lastmod>2026-05-07T23:34:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>惑星の居住可能性分類における機械学習の洞察（Habitability Classification of Exoplanets: A Machine Learning Insight）</news:title>
   <news:publication_date>2026-05-07T23:34:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687655</loc>
  <lastmod>2026-05-07T22:43:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>変形部品ネットワークによる姿勢不変特徴学習（Deformable Part Networks）</news:title>
   <news:publication_date>2026-05-07T22:43:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687653</loc>
  <lastmod>2026-05-07T22:43:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>脳画像の複数ビューを統合するGraphニューラル法でPD判別精度が飛躍的に向上（Multi-View Graph Convolutional Network and Its Applications on Neuroimage Analysis for Parkinson’s Disease）</news:title>
   <news:publication_date>2026-05-07T22:43:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687651</loc>
  <lastmod>2026-05-07T22:43:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>資源に敏感なマルチ解像度人物再識別（Resource Aware Person Re-identification across Multiple Resolutions）</news:title>
   <news:publication_date>2026-05-07T22:43:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687649</loc>
  <lastmod>2026-05-07T22:42:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>通信量を劇的に削る分散学習の工夫（Sparse Binary Compression: Towards Distributed Deep Learning with minimal Communication）</news:title>
   <news:publication_date>2026-05-07T22:42:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687647</loc>
  <lastmod>2026-05-07T22:41:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>畳み込み特徴マップに基づく交通検出・分類向け深層ネットワーク（A Convolutional Feature Map based Deep Network targeted towards Traffic Detection and Classification）</news:title>
   <news:publication_date>2026-05-07T22:41:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687645</loc>
  <lastmod>2026-05-07T22:41:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>地球内に蓄積する相互作用型ダークマターの分布と制約（Dark Matter that Interacts with Baryons: Density Distribution within the Earth and New Constraints on the Interaction Cross-section）</news:title>
   <news:publication_date>2026-05-07T22:41:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687643</loc>
  <lastmod>2026-05-07T22:41:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>平均場理論が示す活性化関数の本質（Mean Field Theory of Activation Functions in Deep Neural Networks）</news:title>
   <news:publication_date>2026-05-07T22:41:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687641</loc>
  <lastmod>2026-05-07T21:49:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>半教師あり学習の利得と限界（On semi-supervised learning）</news:title>
   <news:publication_date>2026-05-07T21:49:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687639</loc>
  <lastmod>2026-05-07T21:49:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>偽ニュース検出のための深層拡散ニューラルネットワーク（FAKEDETECTOR: Effective Fake News Detection with Deep Diffusive Neural Network）</news:title>
   <news:publication_date>2026-05-07T21:49:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687637</loc>
  <lastmod>2026-05-07T21:48:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>HST/COS近傍の銀河赤方偏移サーベイの意義（A Galaxy Redshift Survey Near HST/COS AGN Sight Lines）</news:title>
   <news:publication_date>2026-05-07T21:48:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687635</loc>
  <lastmod>2026-05-07T21:47:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>EgoCoderによるインテリジェントなプログラム合成（EgoCoder: Intelligent Program Synthesis with Hierarchical Sequential Neural Network Model）</news:title>
   <news:publication_date>2026-05-07T21:47:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687633</loc>
  <lastmod>2026-05-07T21:47:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューラルネットの線形領域を数える熱帯幾何学的アプローチ（A Tropical Approach to Neural Networks with Piecewise Linear Activations）</news:title>
   <news:publication_date>2026-05-07T21:47:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687631</loc>
  <lastmod>2026-05-07T21:47:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CascadeCNN: 量子化の性能限界を押し上げる手法（CascadeCNN: Pushing the performance limits of quantisation）</news:title>
   <news:publication_date>2026-05-07T21:47:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687629</loc>
  <lastmod>2026-05-07T21:46:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>構造化勾配正則化による敵対的耐性の強化（Adversarially Robust Training through Structured Gradient Regularization）</news:title>
   <news:publication_date>2026-05-07T21:46:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687627</loc>
  <lastmod>2026-05-07T20:55:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>バスケット補完のためのWord2Vecの敵対的訓練（Adversarial Training of Word2Vec for Basket Completion）</news:title>
   <news:publication_date>2026-05-07T20:55:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687625</loc>
  <lastmod>2026-05-07T20:53:50Z</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を用いた効率的確率的勾配降下法（Efficient Stochastic Gradient Descent for Learning with Distributionally Robust Optimization）</news:title>
   <news:publication_date>2026-05-07T20:53:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687623</loc>
  <lastmod>2026-05-07T20:53:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>データから幅と深さを自動で決める省力的ベイズ深層ネットワーク（Parsimonious Bayesian deep networks）</news:title>
   <news:publication_date>2026-05-07T20:53:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687621</loc>
  <lastmod>2026-05-07T20:51:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ランキングにおける露出格差を減らす学習法（Reducing Disparate Exposure in Ranking: A Learning To Rank Approach）</news:title>
   <news:publication_date>2026-05-07T20:51:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687619</loc>
  <lastmod>2026-05-07T20:51:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>食連星におけるセファイドから何が分かるか（Cepheids in Eclipsing Binaries. What and How We Can Learn About Them）</news:title>
   <news:publication_date>2026-05-07T20:51:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687617</loc>
  <lastmod>2026-05-07T20:51:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自己教師ありマルチビュー人物同定と応用（Self-supervised Multi-view Person Association and Its Applications）</news:title>
   <news:publication_date>2026-05-07T20:51:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687615</loc>
  <lastmod>2026-05-07T20:50:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>海水中40K崩壊を用いたANTARES光学モジュール効率の長期モニタリング（Long-term monitoring of the ANTARES optical module efficiencies using 40K decays in sea water）</news:title>
   <news:publication_date>2026-05-07T20:50:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687613</loc>
  <lastmod>2026-05-07T19:59:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オートエンコーディング変分ベイズにおける情報制約（Information Constraints on Auto-Encoding Variational Bayes）</news:title>
   <news:publication_date>2026-05-07T19:59:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687611</loc>
  <lastmod>2026-05-07T19:59:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>たった1個のニューロン追加で消える悪い局所最適解（Adding One Neuron Can Eliminate All Bad Local Minima）</news:title>
   <news:publication_date>2026-05-07T19:59:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687609</loc>
  <lastmod>2026-05-07T19:58:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>空間相関を明示的に扱う構造化ベイズGP-LVM（Structured Bayesian Gaussian process latent variable model）</news:title>
   <news:publication_date>2026-05-07T19:58:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687607</loc>
  <lastmod>2026-05-07T19:57:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単語レベル整合を伴う階層型注意戦略によるマルチモーダル感情解析（Multimodal Affective Analysis Using Hierarchical Attention Strategy with Word-Level Alignment）</news:title>
   <news:publication_date>2026-05-07T19:57:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687605</loc>
  <lastmod>2026-05-07T19:57:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所化された複数カーネル学習を高速に解くLMKL-Net（LMKL-Net: A Fast Localized Multiple Kernel Learning Solver via Deep Neural Networks）</news:title>
   <news:publication_date>2026-05-07T19:57:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687603</loc>
  <lastmod>2026-05-07T19:56:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>堅牢な条件付き生成敵対ネットワーク（ROBUST CONDITIONAL GENERATIVE ADVERSARIAL NETWORKS）</news:title>
   <news:publication_date>2026-05-07T19:56:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687601</loc>
  <lastmod>2026-05-07T19:56:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>COCO-CNによる多言語画像注釈と検索の基盤（COCO-CN for Cross-Lingual Image Tagging, Captioning and Retrieval）</news:title>
   <news:publication_date>2026-05-07T19:56:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687599</loc>
  <lastmod>2026-05-07T19:04:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>普遍的識別量子ニューラルネットワーク（Universal discriminative quantum neural networks）</news:title>
   <news:publication_date>2026-05-07T19:04:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687597</loc>
  <lastmod>2026-05-07T19:04:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数統計量を動的に選ぶ近似ベイズ計算の実務的意義（Multi-Statistic Approximate Bayesian Computation with Multi-Armed Bandits）</news:title>
   <news:publication_date>2026-05-07T19:04:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687595</loc>
  <lastmod>2026-05-07T19:03:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>補助変数を用いた非線形独立成分分析と一般化コントラスト学習（Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learning）</news:title>
   <news:publication_date>2026-05-07T19:03:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687593</loc>
  <lastmod>2026-05-07T19:01:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>タンパク質配列の確率文脈自由文法推定と接触マップ制約（Estimating probabilistic context-free grammars for proteins using contact map constraints）</news:title>
   <news:publication_date>2026-05-07T19:01:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687591</loc>
  <lastmod>2026-05-07T19:01:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>リプレイ攻撃に対するCNNベースのエンドツーエンド検出の検証（A Study On Convolutional Neural Network Based End-To-End Replay Anti-Spoofing）</news:title>
   <news:publication_date>2026-05-07T19:01:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687589</loc>
  <lastmod>2026-05-07T19:00:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>組み込み機器における深層学習推論：固定小数点とポジット（Deep Learning Inference on Embedded Devices: Fixed-Point vs Posit）</news:title>
   <news:publication_date>2026-05-07T19:00:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687587</loc>
  <lastmod>2026-05-07T19:00:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>コスト意識のあるカスケード型バンディット（Cost-aware Cascading Bandits）</news:title>
   <news:publication_date>2026-05-07T19:00:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687585</loc>
  <lastmod>2026-05-07T18:08:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所最適とベイズ最適の最適な切り替え――BLOSSOMが示した効率的探索と停止基準 (Optimization, fast and slow: optimally switching between local and Bayesian optimization)</news:title>
   <news:publication_date>2026-05-07T18:08:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687583</loc>
  <lastmod>2026-05-07T18:08:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ヒート拡散による深層特徴集約と画像再ランキング（Deep Feature Aggregation and Image Re-ranking with Heat Diffusion for Image Retrieval）</news:title>
   <news:publication_date>2026-05-07T18:08:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687581</loc>
  <lastmod>2026-05-07T18:07:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>選択バイアスに強い方策改善（Confounding-Robust Policy Improvement）</news:title>
   <news:publication_date>2026-05-07T18:07:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687579</loc>
  <lastmod>2026-05-07T18:07:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ロボット操作のためのフレームワーク（A Framework for Robot Manipulation: Skill Formalism, Meta Learning and Adaptive Control）</news:title>
   <news:publication_date>2026-05-07T18:07:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687577</loc>
  <lastmod>2026-05-07T18:06:46Z</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コアセットに関する研究（On Coresets for Logistic Regression）</news:title>
   <news:publication_date>2026-05-07T18:06:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687575</loc>
  <lastmod>2026-05-07T18:06:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>活性化関数のボトルネックを破る適応的パラメータ化（Breaking the Activation Function Bottleneck through Adaptive Parameterization）</news:title>
   <news:publication_date>2026-05-07T18:06:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687573</loc>
  <lastmod>2026-05-07T18:06:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>インタラクティブ学習に信頼をもたらす説明の枠組み（Why Should I Trust Interactive Learners? Explaining Interactive Queries of Classifiers to Users）</news:title>
   <news:publication_date>2026-05-07T18:06:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687571</loc>
  <lastmod>2026-05-07T17:14:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチルーム環境における予測可能性と遅い特徴解析を用いた全域ナビゲーション（Global Navigation Using Predictable and Slow Feature Analysis in Multiroom Environments, Path Planning and Other Control Tasks）</news:title>
   <news:publication_date>2026-05-07T17:14:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687569</loc>
  <lastmod>2026-05-07T17:13:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>2Dレーザーレンジファインダーを用いたパレット検出データセット（A 2D laser rangefinder scans dataset of standard EUR pallets）</news:title>
   <news:publication_date>2026-05-07T17:13:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687567</loc>
  <lastmod>2026-05-07T17:12:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多様な世界で最良を選ぶ：オンライン教師あり学習のロバストなモデル選択（Best of many worlds: Robust model selection for online supervised learning）</news:title>
   <news:publication_date>2026-05-07T17:12:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687565</loc>
  <lastmod>2026-05-07T17:12:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ハッシュトリックの完全理解（Fully Understanding the Hashing Trick）</news:title>
   <news:publication_date>2026-05-07T17:12:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687563</loc>
  <lastmod>2026-05-07T17:12:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチタスクグラフ上の学習 — Part I: 安定性解析 (Learning over Multitask Graphs – Part I: Stability Analysis)</news:title>
   <news:publication_date>2026-05-07T17:12:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687561</loc>
  <lastmod>2026-05-07T17:11:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>音楽源分離におけるスタックド・アワーグラスネットワーク（Music Source Separation Using Stacked Hourglass Networks）</news:title>
   <news:publication_date>2026-05-07T17:11:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687559</loc>
  <lastmod>2026-05-07T17:10:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチタスクグラフ上の学習 — 性能解析（Learning over Multitask Graphs – Part II: Performance Analysis）</news:title>
   <news:publication_date>2026-05-07T17:10:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687557</loc>
  <lastmod>2026-05-07T16:19:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>パラメータ—関数マップの単純さバイアスが深層学習の一般化を説明する（DEEP LEARNING GENERALIZES BECAUSE THE PARAMETER-FUNCTION MAP IS BIASED TOWARDS SIMPLE FUNCTIONS）</news:title>
   <news:publication_date>2026-05-07T16:19:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687555</loc>
  <lastmod>2026-05-07T16:17:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>次元が与えられたネットワークにおける加速的ゴシップ法（ACCELERATED GOSSIP IN NETWORKS OF GIVEN DIMENSION USING JACOBI POLYNOMIAL ITERATIONS）</news:title>
   <news:publication_date>2026-05-07T16:17:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687553</loc>
  <lastmod>2026-05-07T16:16:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>部分集合最小化のための安全要素スクリーニング（Safe Element Screening for Submodular Function Minimization）</news:title>
   <news:publication_date>2026-05-07T16:16:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687551</loc>
  <lastmod>2026-05-07T16:14:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>組み込み型分析データベース MonetDBLite の実装と意義（MonetDBLite: An Embedded Analytical Database）</news:title>
   <news:publication_date>2026-05-07T16:14:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687549</loc>
  <lastmod>2026-05-07T16:13:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>生成的音色空間：知覚メトリクスで正則化した変分オートエンコーダ（GENERATIVE TIMBRE SPACES: REGULARIZING VARIATIONAL AUTO-ENCODERS WITH PERCEPTUAL METRICS）</news:title>
   <news:publication_date>2026-05-07T16:13:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687547</loc>
  <lastmod>2026-05-07T16:13:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>画質をピクセル単位で「見る」技術が変える評価の現場（Blind Predicting Similar Quality Map for Image Quality Assessment）</news:title>
   <news:publication_date>2026-05-07T16:13:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687545</loc>
  <lastmod>2026-05-07T16:12:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>暗黙的再パラメータ化勾配（Implicit Reparameterization Gradients）</news:title>
   <news:publication_date>2026-05-07T16:12:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687543</loc>
  <lastmod>2026-05-07T15:20:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフを使ったプログラム生成モデル（GENERATIVE CODE MODELING WITH GRAPHS）</news:title>
   <news:publication_date>2026-05-07T15:20:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687541</loc>
  <lastmod>2026-05-07T15:20:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>知識ベースの全畳み込みネットワークと肺CT画像セグメンテーションへの応用（Knowledge-based Fully Convolutional Network and Its Application in Segmentation of Lung CT Images）</news:title>
   <news:publication_date>2026-05-07T15:20:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687539</loc>
  <lastmod>2026-05-07T15:19:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散非同期勾配降下法におけるエネルギー整合（Gradient Energy Matching for Distributed Asynchronous Gradient Descent）</news:title>
   <news:publication_date>2026-05-07T15:19:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687537</loc>
  <lastmod>2026-05-07T15:18:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>再編成による潜在凸テンソル分解の完全回復（Beyond Unfolding: Exact Recovery of Latent Convex Tensor Decomposition under Reshuffling）</news:title>
   <news:publication_date>2026-05-07T15:18:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687535</loc>
  <lastmod>2026-05-07T15:18:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>テンソルリングにおけるランク最小化：スケーラブルなテンソル分解と補完の新パラダイム (Rank Minimization on Tensor Ring: A New Paradigm in Scalable Tensor Decomposition and Completion)</news:title>
   <news:publication_date>2026-05-07T15:18:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687533</loc>
  <lastmod>2026-05-07T15:17:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ヘッセ行列を用いたメタ学習による深層ニューラルネット訓練（Meta-Learning with Hessian-Free Approach in Deep Neural Nets Training）</news:title>
   <news:publication_date>2026-05-07T15:17:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687531</loc>
  <lastmod>2026-05-07T15:16:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>集約出力に対する変分学習とガウス過程（Variational Learning on Aggregate Outputs with Gaussian Processes）</news:title>
   <news:publication_date>2026-05-07T15:16:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687529</loc>
  <lastmod>2026-05-07T14:25:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>反復的視覚認識を組み合わせた学習型ランダム化ビンピッキング（Experiments on Learning Based Industrial Bin-picking with Iterative Visual Recognition）</news:title>
   <news:publication_date>2026-05-07T14:25:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687527</loc>
  <lastmod>2026-05-07T14:24:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>画像ベースのローカリゼーションにおけるシーン座標と対応学習（Scene Coordinate and Correspondence Learning for Image-Based Localization）</news:title>
   <news:publication_date>2026-05-07T14:24:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687525</loc>
  <lastmod>2026-05-07T14:23:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深部組織の広視野イメージングを可能にするフーリエ共役アダプティブ光学（Fourier image plane conjugate adaptive optics）</news:title>
   <news:publication_date>2026-05-07T14:23:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687523</loc>
  <lastmod>2026-05-07T14:22:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>雑音部分空間の代数構造を用いた未知個数の信号源の同時検出と定位（Joint Detection and Localization of an Unknown Number of Sources Using Algebraic Structure of the Noise Subspace）</news:title>
   <news:publication_date>2026-05-07T14:22:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687521</loc>
  <lastmod>2026-05-07T14:22:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>人間生成文字列からの正規表現のベイズ推論 (Bayesian Inference of Regular Expressions from Human-Generated Example Strings)</news:title>
   <news:publication_date>2026-05-07T14:22:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687519</loc>
  <lastmod>2026-05-07T14:22:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層ニューラルネットワークの不確かさ評価に関する勘所（Classification Uncertainty of Deep Neural Networks Based on Gradient Information）</news:title>
   <news:publication_date>2026-05-07T14:22:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687517</loc>
  <lastmod>2026-05-07T14:20:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>RPCは有害か：RDMA上での高速分散深層学習（RPC Considered Harmful: Fast Distributed Deep Learning on RDMA）</news:title>
   <news:publication_date>2026-05-07T14:20:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687515</loc>
  <lastmod>2026-05-07T13:28:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>レビュー本文から評価を推定する方法（Estimating the Rating of Reviewers Based on the Text）</news:title>
   <news:publication_date>2026-05-07T13:28:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687513</loc>
  <lastmod>2026-05-07T13:28:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自動スケール選択によるセマンティックセグメンテーションの改良（Autofocus Layer for Semantic Segmentation）</news:title>
   <news:publication_date>2026-05-07T13:28:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687511</loc>
  <lastmod>2026-05-07T13:28:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ウェブ画像で畳み込みネットワークを訓練する（Training Convolutional Networks with Web Images）</news:title>
   <news:publication_date>2026-05-07T13:28:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687509</loc>
  <lastmod>2026-05-07T13:26:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>状態デノイズ型リカレントニューラルネットワーク（State‑Denoised Recurrent Neural Networks）</news:title>
   <news:publication_date>2026-05-07T13:26:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687507</loc>
  <lastmod>2026-05-07T13:26:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>適応深層埋め込み：kショット誘導的転移学習の手法の統合 (Adapted Deep Embeddings: A Synthesis of Methods for k-Shot Inductive Transfer Learning)</news:title>
   <news:publication_date>2026-05-07T13:26:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687505</loc>
  <lastmod>2026-05-07T13:26:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>シネマティックレンダリングを用いた深層学習の微調整（Deep Learning with Cinematic Rendering: Fine-Tuning Deep Neural Networks Using Photorealistic Medical Images）</news:title>
   <news:publication_date>2026-05-07T13:26:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687503</loc>
  <lastmod>2026-05-07T13:26:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Wassersteinを用いた逆最適制御の学習（Learning to Optimize via Wasserstein Deep Inverse Optimal Control）</news:title>
   <news:publication_date>2026-05-07T13:26:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687501</loc>
  <lastmod>2026-05-07T12:33:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>小規模ダイナモの光球面における磁場増幅（SMALL-SCALE DYNAMO SIMULATIONS: MAGNETIC FIELD AMPLIFICATION IN EXPLODING GRANULES AND THE ROLE OF DEEP AND SHALLOW RECIRCULATION）</news:title>
   <news:publication_date>2026-05-07T12:33:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687499</loc>
  <lastmod>2026-05-07T12:33:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Wasserstein自然勾配による統計多様体の最適輸送的アプローチ（OPTIMAL TRANSPORT NATURAL GRADIENT FOR STATISTICAL MANIFOLDS WITH CONTINUOUS SAMPLE SPACE）</news:title>
   <news:publication_date>2026-05-07T12:33:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687497</loc>
  <lastmod>2026-05-07T12:32:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>画像キャプション生成と視覚質問応答の共同学習（Joint Image Captioning and Question Answering）</news:title>
   <news:publication_date>2026-05-07T12:32:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687495</loc>
  <lastmod>2026-05-07T12:32:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Markov Clustering Network によるシーン文字検出の新展開（Learning Markov Clustering Networks for Scene Text Detection）</news:title>
   <news:publication_date>2026-05-07T12:32:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687493</loc>
  <lastmod>2026-05-07T12:32:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>誘電体粒子の逆問題を解く進化戦略（Evolutionary strategy for inverse charge measurements of dielectric particles）</news:title>
   <news:publication_date>2026-05-07T12:32:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687491</loc>
  <lastmod>2026-05-07T12:31:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>映像から自動で年齢層を推定する効率化手法（Speeding-up Age Estimation in Intelligent Demographics System via Network Optimization）</news:title>
   <news:publication_date>2026-05-07T12:31:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687489</loc>
  <lastmod>2026-05-07T12:31:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>協調PAC学習の改善アルゴリズム（Improved Algorithms for Collaborative PAC Learning）</news:title>
   <news:publication_date>2026-05-07T12:31:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687487</loc>
  <lastmod>2026-05-07T11:40:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文の埋め込みを学習する再帰ネットワークの比較（Learning sentence embeddings using Recursive Networks）</news:title>
   <news:publication_date>2026-05-07T11:40:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/687485</loc>
  <lastmod>2026-05-07T11:40:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ディープラーニングのブラックボックスを開く（Opening the black box of deep learning）</news:title>
   <news:publication_date>2026-05-07T11:40:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687483</loc>
  <lastmod>2026-05-07T11:39:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次元で解析可能なGANのモデル（A Solvable High-Dimensional Model of GAN）</news:title>
   <news:publication_date>2026-05-07T11:39:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687481</loc>
  <lastmod>2026-05-07T11:38:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>特殊相対性理論の視覚化と教育的価値（Visualizing Special Relativity）</news:title>
   <news:publication_date>2026-05-07T11:38:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687479</loc>
  <lastmod>2026-05-07T11:38:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ロジスティック過程の消滅時間（EXTINCTION TIME OF THE LOGISTIC PROCESS）</news:title>
   <news:publication_date>2026-05-07T11:38:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687477</loc>
  <lastmod>2026-05-07T11:38:09Z</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訓練におけるパラメータ空間の削減（REDUCING PARAMETER SPACE FOR NEURAL NETWORK TRAINING）</news:title>
   <news:publication_date>2026-05-07T11:38:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687475</loc>
  <lastmod>2026-05-07T11:37:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>最近傍に基づく密度汎関数推定と逆ラプラス変換（Nearest Neighbor Density Functional Estimation from Inverse Laplace Transform）</news:title>
   <news:publication_date>2026-05-07T11:37:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687473</loc>
  <lastmod>2026-05-07T10:45:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>最大因果ツァリスエントロピーによる模倣学習（Maximum Causal Tsallis Entropy Imitation Learning）</news:title>
   <news:publication_date>2026-05-07T10:45:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687471</loc>
  <lastmod>2026-05-07T10:44:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ベアメタルクラウドにおけるデータ集約ワークロードの記憶装置とメモリ特性（Storage and Memory Characterization of Data Intensive Workloads for Bare Metal Cloud）</news:title>
   <news:publication_date>2026-05-07T10:44:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687469</loc>
  <lastmod>2026-05-07T10:44:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スケーラブルな集中型深層マルチエージェント強化学習（Scalable Centralized Deep Multi-Agent Reinforcement Learning via Policy Gradients）</news:title>
   <news:publication_date>2026-05-07T10:44:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687467</loc>
  <lastmod>2026-05-07T10:42:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Guided Feature Transformation（Guided Feature Transformation (GFT): A Neural Language Grounding Module for Embodied Agents）</news:title>
   <news:publication_date>2026-05-07T10:42:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687465</loc>
  <lastmod>2026-05-07T10:42:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>持続図の平均とクラスタを最適輸送で大規模計算（Large Scale computation of Means and Clusters for Persistence Diagrams using Optimal Transport）</news:title>
   <news:publication_date>2026-05-07T10:42:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687463</loc>
  <lastmod>2026-05-07T10:42:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ADGRAPH: グラフベースの広告・トラッカーブロッキング（ADGRAPH: A Graph-Based Approach to Ad and Tracker Blocking）</news:title>
   <news:publication_date>2026-05-07T10:42:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687461</loc>
  <lastmod>2026-05-07T10:42:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>検証可能な強化学習の方針抽出（Verifiable Reinforcement Learning via Policy Extraction）</news:title>
   <news:publication_date>2026-05-07T10:42:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687459</loc>
  <lastmod>2026-05-07T09:49:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>二次モーメント符号化による頑健な勾配降下法（Robust Gradient Descent via Moment Encoding with LDPC Codes）</news:title>
   <news:publication_date>2026-05-07T09:49:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687457</loc>
  <lastmod>2026-05-07T09:49:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>忘れやすい学習者への複数概念の教授法（Teaching Multiple Concepts to a Forgetful Learner）</news:title>
   <news:publication_date>2026-05-07T09:49:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687455</loc>
  <lastmod>2026-05-07T09:49:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>バンディットに基づくモンテカルロ最適化による近傍探索（Bandit-Based Monte Carlo Optimization for Nearest Neighbors）</news:title>
   <news:publication_date>2026-05-07T09:49:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687453</loc>
  <lastmod>2026-05-07T09:48:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>専門家デモで学ぶ安全な方策学習（Learning safe policies with expert guidance）</news:title>
   <news:publication_date>2026-05-07T09:48:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687451</loc>
  <lastmod>2026-05-07T09:48:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>AgileNet：軽量辞書ベースのFew-shot学習（AgileNet: Lightweight Dictionary-based Few-shot Learning）</news:title>
   <news:publication_date>2026-05-07T09:48:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687449</loc>
  <lastmod>2026-05-07T09:48:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自己注意を用いた敵対的生成ネットワーク（Self-Attention Generative Adversarial Networks）</news:title>
   <news:publication_date>2026-05-07T09:48:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687447</loc>
  <lastmod>2026-05-07T09:47:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>水平ゲーテッド再帰ユニットによる長距離空間依存の学習（Learning long-range spatial dependencies with horizontal gated recurrent units）</news:title>
   <news:publication_date>2026-05-07T09:47:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687445</loc>
  <lastmod>2026-05-07T08:56:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ハードウェア志向のニューラルネットワーク訓練：AxTrainが示した近似実行への新しい道（AxTrain: Hardware-Oriented Neural Network Training for Approximate Inference）</news:title>
   <news:publication_date>2026-05-07T08:56:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687443</loc>
  <lastmod>2026-05-07T08:55:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>geomstatsによる機械学習向けリーマン幾何の実装基盤（geomstats: a Python Package for Riemannian Geometry in Machine Learning）</news:title>
   <news:publication_date>2026-05-07T08:55:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687441</loc>
  <lastmod>2026-05-07T08:55:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層エネルギー推定ネットワーク（Deep Energy Estimator Networks）</news:title>
   <news:publication_date>2026-05-07T08:55:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687439</loc>
  <lastmod>2026-05-07T08:54:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Deep CNNの共同スパース化による汎用圧縮とデュアルドメイン展開（Compression of Deep Convolutional Neural Networks under Joint Sparsity Constraints）</news:title>
   <news:publication_date>2026-05-07T08:54:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687437</loc>
  <lastmod>2026-05-07T08:54:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>データ効率の高い階層型強化学習（Data-Efficient Hierarchical Reinforcement Learning）</news:title>
   <news:publication_date>2026-05-07T08:54:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687435</loc>
  <lastmod>2026-05-07T08:53:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模プラズマモデリングにおける逆問題の不安定性（Inverse Problem Instabilities in Large-Scale Plasma Modelling）</news:title>
   <news:publication_date>2026-05-07T08:53:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687433</loc>
  <lastmod>2026-05-07T08:53:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>医用画像報告のためのハイブリッド検索・生成強化エージェント（Hybrid Retrieval-Generation Reinforced Agent for Medical Image Report Generation）</news:title>
   <news:publication_date>2026-05-07T08:53:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687431</loc>
  <lastmod>2026-05-07T08:01:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>計量集中と大規模ランダム行列（Concentration of Measure and Large Random Matrices）</news:title>
   <news:publication_date>2026-05-07T08:01:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687429</loc>
  <lastmod>2026-05-07T08:00:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>関数空間でネットワークを測り正則化する手法（MEASURING AND REGULARIZING NETWORKS IN FUNCTION SPACE）</news:title>
   <news:publication_date>2026-05-07T08:00:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687427</loc>
  <lastmod>2026-05-07T08:00:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高性能計算のための高位合成コード変換（Transformations of High-Level Synthesis Codes for High-Performance Computing）</news:title>
   <news:publication_date>2026-05-07T08:00:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687425</loc>
  <lastmod>2026-05-07T07:59:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Halo: 意味構造を意識した表現学習による越境情報抽出の改善（Halo: Learning Semantics-Aware Representations for Cross-Lingual Information Extraction）</news:title>
   <news:publication_date>2026-05-07T07:59:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687423</loc>
  <lastmod>2026-05-07T07:59:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数処置に対する観測データからの因果推論と潜在交絡（Multiple Causal Inference with Latent Confounding）</news:title>
   <news:publication_date>2026-05-07T07:59:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687421</loc>
  <lastmod>2026-05-07T07:59:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>有効次元に基づくスケッチ最適化の理論と実践（Optimal Sketching Bounds for Exp-concave Stochastic Minimization）</news:title>
   <news:publication_date>2026-05-07T07:59:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687419</loc>
  <lastmod>2026-05-07T07:58:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>水晶なしクロックでの通信（Communication with Crystal-Free Radios）</news:title>
   <news:publication_date>2026-05-07T07:58:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687417</loc>
  <lastmod>2026-05-07T07:06:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>初期化と活性化関数の選択が深層ニューラルネットワークに与える影響（ON THE SELECTION OF INITIALIZATION AND ACTIVATION FUNCTION FOR DEEP NEURAL NETWORKS）</news:title>
   <news:publication_date>2026-05-07T07:06:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687415</loc>
  <lastmod>2026-05-07T07:06:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>部分観測領域における何を伝えるべきかを学ぶ（Learning What Information to Give in Partially Observed Domains）</news:title>
   <news:publication_date>2026-05-07T07:06:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687413</loc>
  <lastmod>2026-05-07T07:05:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>実数値学習器のサンプル圧縮（Sample Compression for Real-Valued Learners）</news:title>
   <news:publication_date>2026-05-07T07:05:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687411</loc>
  <lastmod>2026-05-07T07:05:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分類器に依存しないサリエンシーマップ抽出（Classifier-Agnostic Saliency Map Extraction）</news:title>
   <news:publication_date>2026-05-07T07:05:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687409</loc>
  <lastmod>2026-05-07T07:05:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文脈依存トークン符号化に基づくメタBiLSTMを用いた形態統語タグ付け（Morphosyntactic Tagging with a Meta-BiLSTM Model over Context Sensitive Token Encodings）</news:title>
   <news:publication_date>2026-05-07T07:05:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687407</loc>
  <lastmod>2026-05-07T07:05:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非同期確率的勾配降下の確率修正方程式（Stochastic modified equations for the asynchronous stochastic gradient descent）</news:title>
   <news:publication_date>2026-05-07T07:05:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687405</loc>
  <lastmod>2026-05-07T07:04:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>システム神経科学における教師あり機械学習の役割（The Roles of Supervised Machine Learning in Systems Neuroscience）</news:title>
   <news:publication_date>2026-05-07T07:04:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687403</loc>
  <lastmod>2026-05-07T06:12:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>信頼度推定の偏りを減らす手法（BIAS-REDUCED UNCERTAINTY ESTIMATION FOR DEEP NEURAL CLASSIFIERS）</news:title>
   <news:publication_date>2026-05-07T06:12:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687401</loc>
  <lastmod>2026-05-07T06:12:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多項式時間における構造化予測のための最大事後確率摂動モデルの学習 (Learning Maximum-A-Posteriori Perturbation Models for Structured Prediction in Polynomial Time)</news:title>
   <news:publication_date>2026-05-07T06:12:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687399</loc>
  <lastmod>2026-05-07T06:12:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Maskingによるノイズ付きラベル処理の新視点 (Masking: A New Perspective of Noisy Supervision)</news:title>
   <news:publication_date>2026-05-07T06:12:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687397</loc>
  <lastmod>2026-05-07T06:11:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>シンプルなキャッシュモデルによる画像認識の精度向上（A Simple Cache Model for Image Recognition）</news:title>
   <news:publication_date>2026-05-07T06:11:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687395</loc>
  <lastmod>2026-05-07T06:11:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>党派情報を使った投票予測モデルの強化（Party Matters: Enhancing Legislative Embeddings with Author Attributes for Vote Prediction）</news:title>
   <news:publication_date>2026-05-07T06:11:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687393</loc>
  <lastmod>2026-05-07T06:10:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>サイド情報を用いた制約付きスパース部分空間クラスタリングの強化（Constrained Sparse Subspace Clustering with Side-Information）</news:title>
   <news:publication_date>2026-05-07T06:10:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687391</loc>
  <lastmod>2026-05-07T06:10:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層型強化学習によるトピック一貫性のある視覚ストーリー生成（Hierarchically Structured Reinforcement Learning for Topically Coherent Visual Story Generation）</news:title>
   <news:publication_date>2026-05-07T06:10:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687389</loc>
  <lastmod>2026-05-07T05:18:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>言語モデルの数値処理能力（Numeracy for Language Models: Evaluating and Improving their Ability to Predict Numbers）</news:title>
   <news:publication_date>2026-05-07T05:18:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687387</loc>
  <lastmod>2026-05-07T05:11:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自然画像における物体の数え上げ学習（Learning To Count Objects In Natural Images For Visual Question Answering）</news:title>
   <news:publication_date>2026-05-07T05:11:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687385</loc>
  <lastmod>2026-05-07T05:11:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>テンソルプログラムの最適化を学習で自動化する（Learning to Optimize Tensor Programs）</news:title>
   <news:publication_date>2026-05-07T05:11:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687383</loc>
  <lastmod>2026-05-07T05:10:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CaptionBotとDrawingBotのターボ学習（Turbo Learning for CaptionBot and DrawingBot）</news:title>
   <news:publication_date>2026-05-07T05:10:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687381</loc>
  <lastmod>2026-05-07T05:09:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ソーシャルメディア検索のための多視点関連性マッチングと階層的ConvNet（Multi-Perspective Relevance Matching with Hierarchical ConvNets for Social Media Search）</news:title>
   <news:publication_date>2026-05-07T05:09:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687379</loc>
  <lastmod>2026-05-07T05:09:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>光学・赤外全天サーベイにおける機械学習による銀河外天体同定（Machine-learning identification of extragalactic objects in the optical-infrared all-sky surveys）</news:title>
   <news:publication_date>2026-05-07T05:09:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687377</loc>
  <lastmod>2026-05-07T05:09:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>VideoCapsuleNetによる行動検出の単純化（VideoCapsuleNet: A Simplified Network for Action Detection）</news:title>
   <news:publication_date>2026-05-07T05:09:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687375</loc>
  <lastmod>2026-05-07T04:16:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Dominant Setsによるスピーカクラスタリングの実務的解説（Speaker Clustering Using Dominant Sets）</news:title>
   <news:publication_date>2026-05-07T04:16:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687373</loc>
  <lastmod>2026-05-07T04:16:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>微分可能な閉形式ソルバーを用いたメタ学習（Meta-Learning with Differentiable Closed-Form Solvers）</news:title>
   <news:publication_date>2026-05-07T04:16:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687371</loc>
  <lastmod>2026-05-07T04:16:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>サンプル圧縮スキームを用いた非仮定学習の新たな下界（A New Lower Bound for Agnostic Learning with Sample Compression Schemes）</news:title>
   <news:publication_date>2026-05-07T04:16:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687369</loc>
  <lastmod>2026-05-07T04:14:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的勾配降下法における適応ステップサイズの収束性（On the Convergence of Stochastic Gradient Descent with Adaptive Stepsizes）</news:title>
   <news:publication_date>2026-05-07T04:14:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687367</loc>
  <lastmod>2026-05-07T04:14:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>過剰な情報と学習の罠（Overabundant Information and Learning Traps）</news:title>
   <news:publication_date>2026-05-07T04:14:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687365</loc>
  <lastmod>2026-05-07T04:14:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>データのためのマーケットプレイス：アルゴリズム的解決策 (A Marketplace for Data: An Algorithmic Solution)</news:title>
   <news:publication_date>2026-05-07T04:14:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687363</loc>
  <lastmod>2026-05-07T04:14:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>頑健な確率オペレータ群による強化学習の改善（A Family of Robust Stochastic Operators for Reinforcement Learning）</news:title>
   <news:publication_date>2026-05-07T04:14:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687361</loc>
  <lastmod>2026-05-07T03:22:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所領域に着目する注意機構で細粒度ゼロショット学習を改善する（Stacked Semantic-Guided Attention Model for Fine-Grained Zero-Shot Learning）</news:title>
   <news:publication_date>2026-05-07T03:22:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687359</loc>
  <lastmod>2026-05-07T03:21:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>地平線・空線検出の自動化比較（Comparison of Semantic Segmentation Approaches for Horizon/Sky Line Detection）</news:title>
   <news:publication_date>2026-05-07T03:21:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687357</loc>
  <lastmod>2026-05-07T03:21:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非定常1次元信号解析のためのカーネル最適化スキーム PiPs（PiPs: a Kernel-based Optimization Scheme for Analyzing Non-Stationary 1D Signals）</news:title>
   <news:publication_date>2026-05-07T03:21:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687355</loc>
  <lastmod>2026-05-07T03:20:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自己段階学習を凹共役理論で読み解く（Understanding Self-Paced Learning under Concave Conjugacy Theory）</news:title>
   <news:publication_date>2026-05-07T03:20:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687353</loc>
  <lastmod>2026-05-07T03:20:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>小さな一歩、大きな飛躍：ディープラーニングのための最小ニュートンソルバー（Small steps and giant leaps: Minimal Newton solvers for Deep Learning）</news:title>
   <news:publication_date>2026-05-07T03:20:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687351</loc>
  <lastmod>2026-05-07T03:19:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>敵対的検閲付きオートエンコーダからの不変表現（Invariant Representations from Adversarially Censored Autoencoders）</news:title>
   <news:publication_date>2026-05-07T03:19:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687349</loc>
  <lastmod>2026-05-07T03:19:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>混合ノイズ除去における変分手法とCNN正則化（Variational based Mixed Noise Removal with CNN）</news:title>
   <news:publication_date>2026-05-07T03:19:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687347</loc>
  <lastmod>2026-05-07T02:27:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>参照表現生成の統合的ニューラル手法（NeuralREG: An end-to-end approach to referring expression generation）</news:title>
   <news:publication_date>2026-05-07T02:27:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687345</loc>
  <lastmod>2026-05-07T02:27:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフカプセル畳み込みニューラルネットワーク（Graph Capsule Convolutional Neural Networks）</news:title>
   <news:publication_date>2026-05-07T02:27:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687343</loc>
  <lastmod>2026-05-07T02:26:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>近似テンソル演算による高速ニューラルネットワーク学習（Faster Neural Network Training with Approximate Tensor Operations）</news:title>
   <news:publication_date>2026-05-07T02:26:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687341</loc>
  <lastmod>2026-05-07T02:26:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>One Monadを使った証明の枠組みを読み解く（One Monad to Prove Them All）</news:title>
   <news:publication_date>2026-05-07T02:26:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687339</loc>
  <lastmod>2026-05-07T02:26:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SAS環境におけるSuper Learnerの導入と実装（Super learning in the SAS system）</news:title>
   <news:publication_date>2026-05-07T02:26:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687337</loc>
  <lastmod>2026-05-07T02:25:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>NEWMAによるスケーラブルなモデルフリーオンライン変化点検出（NEWMA: a new method for scalable model-free online change-point detection）</news:title>
   <news:publication_date>2026-05-07T02:25:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687335</loc>
  <lastmod>2026-05-07T02:25:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>カーネル化されたMDPにおけるオンライン学習の実践的意味（Online Learning in Kernelized Markov Decision Processes）</news:title>
   <news:publication_date>2026-05-07T02:25:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687333</loc>
  <lastmod>2026-05-07T01:34:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>イタリア天文アーカイブの公開基盤：モジュール化と分散（Italian center for Astronomical Archives publishing solution: modular and distributed）</news:title>
   <news:publication_date>2026-05-07T01:34:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687331</loc>
  <lastmod>2026-05-07T01:33:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>楕円混合モデル学習の普遍的枠組み（A universal framework for learning the elliptical mixture model）</news:title>
   <news:publication_date>2026-05-07T01:33:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687329</loc>
  <lastmod>2026-05-07T01:32:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>後ろを見ない学習法――EnKFに基づくバックプロパゲーション不要のニューラルネットワーク訓練（Never look back - A modified EnKF method and its application to the training of neural networks without back propagation）</news:title>
   <news:publication_date>2026-05-07T01:32:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687327</loc>
  <lastmod>2026-05-07T01:32:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DiDA: ドメイン適応のための分離合成（DiDA: Disentangled Synthesis for Domain Adaptation）</news:title>
   <news:publication_date>2026-05-07T01:32:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687325</loc>
  <lastmod>2026-05-07T01:32:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>行き先をどう考えているのか：行動から内部力学信念を推定する手法（Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior）</news:title>
   <news:publication_date>2026-05-07T01:32:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687323</loc>
  <lastmod>2026-05-07T01:32:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DifNet: 拡散によるセマンティックセグメンテーションの実践（DifNet: Semantic Segmentation by Diffusion Networks）</news:title>
   <news:publication_date>2026-05-07T01:32:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687321</loc>
  <lastmod>2026-05-07T01:31:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複雑な物体形態の視覚空間学習（VISUAL SPATIAL LEARNING OF COMPLEX OBJECT MORPHOLOGIES THROUGH INTERACTION WITH VIRTUAL AND REAL-WORLD DATA）</news:title>
   <news:publication_date>2026-05-07T01:31:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687319</loc>
  <lastmod>2026-05-07T00:39:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>アニメ風ポートレイトのスタイル空間探索（Anime Style Space Exploration Using Metric Learning and Generative Adversarial Networks）</news:title>
   <news:publication_date>2026-05-07T00:39:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687317</loc>
  <lastmod>2026-05-07T00:38:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>双方向学習によるニューラルネットワークの堅牢化（Bidirectional Learning for Robust Neural Networks）</news:title>
   <news:publication_date>2026-05-07T00:38:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687315</loc>
  <lastmod>2026-05-07T00:38:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>中間層に入れる「敵対的ノイズ層」が示すCNNの正則化効果（Adversarial Noise Layer: Regularize Neural Network By Adding Noise）</news:title>
   <news:publication_date>2026-05-07T00:38:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687313</loc>
  <lastmod>2026-05-07T00:37:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>原始惑星系円盤の光蒸発と金属量依存性（RADIATION HYDRODYNAMICS SIMULATIONS OF PHOTOEVAPORATION OF PROTOPLANETARY DISKS II: METALLICITY DEPENDENCE OF UV AND X-RAY PHOTOEVAPORATION）</news:title>
   <news:publication_date>2026-05-07T00:37:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687311</loc>
  <lastmod>2026-05-07T00:37:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>テンソルを用いたサブモード座標アルゴリズムによる株価予測（A Tensor-Based Sub-Mode Coordinate Algorithm for Stock Prediction）</news:title>
   <news:publication_date>2026-05-07T00:37:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687309</loc>
  <lastmod>2026-05-07T00:36:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>生の音声（raw speech）からの敵対的学習によるドメイン不変音声認識（ADVERSARIAL LEARNING OF RAW SPEECH FEATURES FOR DOMAIN INVARIANT SPEECH RECOGNITION）</news:title>
   <news:publication_date>2026-05-07T00:36:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687307</loc>
  <lastmod>2026-05-07T00:36:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフデータに対する敵対的攻撃の研究（Adversarial Attacks on Neural Networks for Graph Data）</news:title>
   <news:publication_date>2026-05-07T00:36:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687305</loc>
  <lastmod>2026-05-06T23:45:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ナビゲーション指示を理解するためのデータセットとモデルの提示（A new dataset and model for learning to understand navigational instructions）</news:title>
   <news:publication_date>2026-05-06T23:45:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687303</loc>
  <lastmod>2026-05-06T23:44:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>メモリ拡張ニューラルネットワーク向け省エネ推論アクセラレータ（Energy-Efﬁcient Inference Accelerator for Memory-Augmented Neural Networks on an FPGA）</news:title>
   <news:publication_date>2026-05-06T23:44:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687301</loc>
  <lastmod>2026-05-06T23:44:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多段階グリーディ方策のオンラインおよび近似強化学習における応用（Multiple-Step Greedy Policies in Online and Approximate Reinforcement Learning）</news:title>
   <news:publication_date>2026-05-06T23:44:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687299</loc>
  <lastmod>2026-05-06T23:43:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Transductive Boltzmann Machines（Transductive Boltzmann Machines）</news:title>
   <news:publication_date>2026-05-06T23:43:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687297</loc>
  <lastmod>2026-05-06T23:43:16Z</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量子化による低消費電力・高スループット推論（Quantizing Convolutional Neural Networks for Low-Power High-Throughput Inference Engines）</news:title>
   <news:publication_date>2026-05-06T23:43:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687295</loc>
  <lastmod>2026-05-06T23:43:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>最初の星と21-cmグローバル信号の関係（Stellar mass dependence of the 21-cm signal around the first star and its impact on the global signal）</news:title>
   <news:publication_date>2026-05-06T23:43:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687293</loc>
  <lastmod>2026-05-06T23:42:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>正規化されたクリストッフェル関数を用いたレバレッジスコアと密度の関係（Relating Leverage Scores and Density using Regularized Christoffel Functions）</news:title>
   <news:publication_date>2026-05-06T23:42:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687291</loc>
  <lastmod>2026-05-06T22:51:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ビリニア・アテンション・ネットワークの要点（Bilinear Attention Networks）</news:title>
   <news:publication_date>2026-05-06T22:51:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687289</loc>
  <lastmod>2026-05-06T22:51:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>粗から細への顕著物体検出（Coarse-to-Fine Salient Object Detection with Low-Rank Matrix Recovery）</news:title>
   <news:publication_date>2026-05-06T22:51:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687287</loc>
  <lastmod>2026-05-06T22:51:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Batch-Instance Normalizationによるスタイル不変性の獲得（Batch-Instance Normalization for Adaptively Style-Invariant Neural Networks）</news:title>
   <news:publication_date>2026-05-06T22:51:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687285</loc>
  <lastmod>2026-05-06T22:50:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散プリマル・デュアル時系列差分学習（Primal-Dual Distributed Temporal Difference Learning）</news:title>
   <news:publication_date>2026-05-06T22:50:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687283</loc>
  <lastmod>2026-05-06T22:50:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>観察から潜在ポリシーを模倣する手法（Imitating Latent Policies from Observation）</news:title>
   <news:publication_date>2026-05-06T22:50:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687281</loc>
  <lastmod>2026-05-06T22:49:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>MRPCによる飛行時間検出器技術の現状（Status of technology of MRPC time of flight system）</news:title>
   <news:publication_date>2026-05-06T22:49:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687279</loc>
  <lastmod>2026-05-06T22:49:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>進化に導かれた方策勾配—ERLが示す探索と勾配の融合（Evolution-Guided Policy Gradient in Reinforcement Learning）</news:title>
   <news:publication_date>2026-05-06T22:49:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687277</loc>
  <lastmod>2026-05-06T21:57:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>貪欲粒子最適化によるベイズ事後近似（Bayesian posterior approximation via greedy particle optimization）</news:title>
   <news:publication_date>2026-05-06T21:57:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687275</loc>
  <lastmod>2026-05-06T21:57:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文脈予測による教師なし深層背景推定と前景分割（Unsupervised Deep Context Prediction for Background Estimation and Foreground Segmentation）</news:title>
   <news:publication_date>2026-05-06T21:57:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687273</loc>
  <lastmod>2026-05-06T21:56:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Quickshift++：サンプルベースMean Shiftの理論的に優れた初期化 (Quickshift++: Provably Good Initializations for Sample-Based Mean Shift)</news:title>
   <news:publication_date>2026-05-06T21:56:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687271</loc>
  <lastmod>2026-05-06T21:56:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>生成モデルによる制約のない敵対的例の構築（Constructing Unrestricted Adversarial Examples with Generative Models）</news:title>
   <news:publication_date>2026-05-06T21:56:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687269</loc>
  <lastmod>2026-05-06T21:55:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>低解像度マルチスペクトル顔画像におけるクラス代表オートエンコーダによる性別分類（Class Representative Autoencoder for Low Resolution Multi-Spectral Gender Classification）</news:title>
   <news:publication_date>2026-05-06T21:55:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687267</loc>
  <lastmod>2026-05-06T21:55:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Sharp Minimaを平滑化して汎化性能を高める手法（SmoothOut: Smoothing Out Sharp Minima to Improve Generalization in Deep Learning）</news:title>
   <news:publication_date>2026-05-06T21:55:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687265</loc>
  <lastmod>2026-05-06T21:54:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>対流性嵐による停電予測（PREDICTING ELECTRICITY OUTAGES CAUSED BY CONVECTIVE STORMS）</news:title>
   <news:publication_date>2026-05-06T21:54:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687263</loc>
  <lastmod>2026-05-06T21:02:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>双方向依存木表現によるアスペクト抽出の改善（Improving Aspect Term Extraction with Bidirectional Dependency Tree Representation）</news:title>
   <news:publication_date>2026-05-06T21:02:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687261</loc>
  <lastmod>2026-05-06T21:01:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所化された複数カーネル学習による異常検知（Localized Multiple Kernel Learning for Anomaly Detection: One-class Classification）</news:title>
   <news:publication_date>2026-05-06T21:01:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687259</loc>
  <lastmod>2026-05-06T21:00:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ラックスケール・パラメータサーバによる分散DNN訓練の高速化（Parameter Hub: a Rack-Scale Parameter Server for Distributed Deep Neural Network Training）</news:title>
   <news:publication_date>2026-05-06T21:00:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687257</loc>
  <lastmod>2026-05-06T21:00:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動画から心拍と呼吸を測るDeepPhys（DeepPhys: Video-Based Physiological Measurement Using Convolutional Attention Networks）</news:title>
   <news:publication_date>2026-05-06T21:00:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687255</loc>
  <lastmod>2026-05-06T20:59:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CNNとRNNのサンプル効率はなぜ高いのか（sample-complexity of Estimating Convolutional and Recurrent Neural Networks）</news:title>
   <news:publication_date>2026-05-06T20:59:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687253</loc>
  <lastmod>2026-05-06T20:59:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>弱いメモリモデルの構築手法（Constructing a Weak Memory Model）</news:title>
   <news:publication_date>2026-05-06T20:59:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687251</loc>
  <lastmod>2026-05-06T20:59:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数埋め込みと多層比較による文センテンス表現（Sentence Modeling via Multiple Word Embeddings and Multi-level Comparison for Semantic Textual Similarity）</news:title>
   <news:publication_date>2026-05-06T20:59:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687249</loc>
  <lastmod>2026-05-06T20:07:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非凸トランケート損失での学習（Learning with Non-Convex Truncated Losses by SGD）</news:title>
   <news:publication_date>2026-05-06T20:07:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687247</loc>
  <lastmod>2026-05-06T20:06:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>3D点群のための球面畳み込みニューラルネットワーク（Spherical Convolutional Neural Network for 3D Point Clouds）</news:title>
   <news:publication_date>2026-05-06T20:06:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687245</loc>
  <lastmod>2026-05-06T20:06:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GSAE: 遺伝子セットノードを組み込んだオートエンコーダによる機能的ゲノミクス解析（GSAE: an autoencoder with embedded gene-set nodes for genomics functional characterization）</news:title>
   <news:publication_date>2026-05-06T20:06:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687243</loc>
  <lastmod>2026-05-06T20:05:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ハイブリッド・マクロ／マイクロ逆伝播による深層スパイキングニューラルネットワークの学習（Hybrid Macro/Micro Level Backpropagation for Training Deep Spiking Neural Networks）</news:title>
   <news:publication_date>2026-05-06T20:05:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687241</loc>
  <lastmod>2026-05-06T20:05:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オンライン逆強化学習の枠組みと手法（A Framework and Method for Online Inverse Reinforcement Learning）</news:title>
   <news:publication_date>2026-05-06T20:05:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687239</loc>
  <lastmod>2026-05-06T20:05:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Featurized Bidirectional GANによる敵対的防御の考え方（Featurized Bidirectional GAN: Adversarial Defense via Adversarially Learned Semantic Inference）</news:title>
   <news:publication_date>2026-05-06T20:05:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687237</loc>
  <lastmod>2026-05-06T20:04:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>周辺機器の振る舞いを学習する再帰型ニューラルネットワーク（Learning Device Models with Recurrent Neural Networks）</news:title>
   <news:publication_date>2026-05-06T20:04:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687235</loc>
  <lastmod>2026-05-06T19:13:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ユニバーサル音楽変換ネットワーク（A Universal Music Translation Network）</news:title>
   <news:publication_date>2026-05-06T19:13:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687233</loc>
  <lastmod>2026-05-06T19:13:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>補助データで高速化するベイズ最適化（Accelerated Bayesian Optimization through Weight-Prior Tuning）</news:title>
   <news:publication_date>2026-05-06T19:13:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687231</loc>
  <lastmod>2026-05-06T19:12:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>平行移動畳み込みが変えた点――多様体上で使える畳み込みの実装路（Parallel Transport Convolution: A New Tool for Convolutional Neural Networks on Manifolds）</news:title>
   <news:publication_date>2026-05-06T19:12:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687229</loc>
  <lastmod>2026-05-06T19:12:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>一般化された交差エントロピー損失によるノイズ耐性学習（Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels）</news:title>
   <news:publication_date>2026-05-06T19:12:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687227</loc>
  <lastmod>2026-05-06T19:11:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>投影不要アルゴリズムによる高次元推定の効率化（Projection-Free Algorithms in Statistical Estimation）</news:title>
   <news:publication_date>2026-05-06T19:11:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687225</loc>
  <lastmod>2026-05-06T19:11:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散環境向け投影不要アルゴリズムの通信効率化（Communication-Efficient Projection-Free Algorithm for Distributed Optimization）</news:title>
   <news:publication_date>2026-05-06T19:11:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687223</loc>
  <lastmod>2026-05-06T19:11:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大気中の流星による衝撃波の物理学（Physics of Meteor Generated Shock Waves in the Earth’s Atmosphere – A Review）</news:title>
   <news:publication_date>2026-05-06T19:11:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687221</loc>
  <lastmod>2026-05-06T18:19:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>木構造の確率推定を一般化する手法（Generalizing Tree Probability Estimation via Bayesian Networks）</news:title>
   <news:publication_date>2026-05-06T18:19:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687219</loc>
  <lastmod>2026-05-06T18:19:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スパース多課題回帰におけるワッサースタイン正則化（Wasserstein regularization for sparse multi-task regression）</news:title>
   <news:publication_date>2026-05-06T18:19:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687217</loc>
  <lastmod>2026-05-06T18:18:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>シミュレーション最適化のための雑音耐性構造化探索（Optimizing Simulations with Noise-Tolerant Structured Exploration）</news:title>
   <news:publication_date>2026-05-06T18:18:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687215</loc>
  <lastmod>2026-05-06T18:18:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ブラックボックス音声システムに対する標的型敵対的事例（Targeted Adversarial Examples for Black Box Audio Systems）</news:title>
   <news:publication_date>2026-05-06T18:18:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687213</loc>
  <lastmod>2026-05-06T18:18:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>協調型マルチエージェント強化学習における「教えるを学ぶ」枠組み（Learning to Teach in Cooperative Multiagent Reinforcement Learning）</news:title>
   <news:publication_date>2026-05-06T18:18:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687211</loc>
  <lastmod>2026-05-06T18:17:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>疑似逆行列学習（Pseudoinverse Learning）とVESTの要点整理（A VEST of the Pseudoinverse Learning Algorithm）</news:title>
   <news:publication_date>2026-05-06T18:17:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687209</loc>
  <lastmod>2026-05-06T18:17:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>低コストな畳み込みニューラルネットワークの設計（Low-Cost Parameterizations of Deep Convolutional Neural Networks）</news:title>
   <news:publication_date>2026-05-06T18:17:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687207</loc>
  <lastmod>2026-05-06T17:26:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層構造化自己注意による抽出型文書要約モデル（A Hierarchical Structured Self-Attentive Model for Extractive Document Summarization）</news:title>
   <news:publication_date>2026-05-06T17:26:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687205</loc>
  <lastmod>2026-05-06T17:20:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>夢で学ぶロボット制御：実世界で使える視覚運動ポリシーの獲得（Learning Real-World Robot Policies by Dreaming）</news:title>
   <news:publication_date>2026-05-06T17:20:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687203</loc>
  <lastmod>2026-05-06T17:19:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オンライン構造化ラプラス近似による忘却問題の克服（Online Structured Laplace Approximations For Overcoming Catastrophic Forgetting）</news:title>
   <news:publication_date>2026-05-06T17:19:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687201</loc>
  <lastmod>2026-05-06T17:18:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ピクセル離散化による防御の限界を探る（Towards Understanding Limitations of Pixel Discretization Against Adversarial Attacks）</news:title>
   <news:publication_date>2026-05-06T17:18:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687199</loc>
  <lastmod>2026-05-06T17:17:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>安全で効率的な方策改善法：Rerouted Behavior Improvement（CONSTRAINED POLICY IMPROVEMENT FOR SAFE AND EFFICIENT REINFORCEMENT LEARNING）</news:title>
   <news:publication_date>2026-05-06T17:17:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687197</loc>
  <lastmod>2026-05-06T17:16:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多層カーネルリッジ回帰によるワン・クラス分類の進化（Multi-layer Kernel Ridge Regression for One-class Classification）</news:title>
   <news:publication_date>2026-05-06T17:16:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687195</loc>
  <lastmod>2026-05-06T17:16:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
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   <news:title>構造的規則性に基づくネットワーク再構築と制御（Network Reconstruction and Controlling Based on Structural Regularity Analysis）</news:title>
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   <news:publication>
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   <news:publication>
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