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   <news:title>シンボリック表現のベクトル化を学習する方法（LEARNING AND ANALYZING VECTOR ENCODING OF SYMBOLIC REPRESENTATIONS）</news:title>
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   <news:title>サブモジュラハイパーグラフとp-ラプラシアンによるスペクトラルクラスタリング（Submodular Hypergraphs: p-Laplacians, Cheeger Inequalities and Spectral Clustering）</news:title>
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    <news:name>AI Benchmark Research</news:name>
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   <news:title>重み付きグラフに対する差分プライバシー下のクラスタリング（Graph-based Clustering under Differential Privacy）</news:title>
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   <news:title>Bi1−xSbx合金における磁場誘起Weyl半金属状態の拡張観測（Observation of Chiral character deep in the topological insulating regime in Bi1−xSbx）</news:title>
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   <news:title>分散ブロックチェーンを用いたIoT異常検知の協調フレームワーク（CIoTA: Collaborative IoT Anomaly Detection via Blockchain）</news:title>
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    <news:language>ja</news:language>
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   <news:title>eコマース需要予測を改善する深層学習モデルの実務解説（AR-MDN: Associative and Recurrent Mixture Density Networks for eRetail Demand Forecasting）</news:title>
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   <news:genres>Blog</news:genres>
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  <lastmod>2026-04-15T19:01:49Z</lastmod>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>コードリポジトリから学ぶクイックフィックス（Learning Quick Fixes from Code Repositories）</news:title>
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   <news:genres>Blog</news:genres>
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  <lastmod>2026-04-15T18:10:45Z</lastmod>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>大規模データから小規模データへ知識を移す方法：Deep Cross-media Knowledge Transfer（Deep Cross-media Knowledge Transfer）</news:title>
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   <news:genres>Blog</news:genres>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>深層学習の表現力と汎化性—深層ネットの理論的優位性の解明（Generalization and Expressivity for Deep Nets）</news:title>
   <news:publication_date>2026-04-15T18:10:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/679711</loc>
  <lastmod>2026-04-15T18:10:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Driving Scene Perception Network（Driving Scene Perception Network: Real-time Joint Detection, Depth Estimation and Semantic Segmentation）</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>分散ネットワークの衝撃（VARIANCE NETWORKS: WHEN EXPECTATION DOES NOT MEET YOUR EXPECTATIONS）</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>有限要素法によるMatérn確率場を生成するSPDEの解法（How to solve the stochastic partial differential equation that gives a Matérn random field using the finite element method）</news:title>
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   <news:genres>Blog</news:genres>
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  <lastmod>2026-04-15T18:09:05Z</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>音声認識：画像認識を利用したキーワードスポッティング（Speech Recognition: Key Word Spotting through Image Recognition）</news:title>
   <news:publication_date>2026-04-15T18:09:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-04-15T18:08:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>条件差分推定へのミニマックス代替損失法（A Minimax Surrogate Loss Approach to Conditional Difference Estimation）</news:title>
   <news:publication_date>2026-04-15T18:08:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/679701</loc>
  <lastmod>2026-04-15T17:17:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Newton: クロスバー加速の物理限界に迫る設計（Newton: Gravitating Towards the Physical Limits of Crossbar Acceleration）</news:title>
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   <news:genres>Blog</news:genres>
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  <lastmod>2026-04-15T17:16:00Z</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>破産予測モデルの識別能力に対する事象比率の影響（Influence of the Event Rate on Discrimination Abilities of Bankruptcy Prediction Models）</news:title>
   <news:publication_date>2026-04-15T17:16:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/679697</loc>
  <lastmod>2026-04-15T17:15:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>深層マルチタスクネットワークの進化的アーキテクチャ探索（Evolutionary Architecture Search For Deep Multitask Networks）</news:title>
   <news:publication_date>2026-04-15T17:15:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/679695</loc>
  <lastmod>2026-04-15T17:15:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>人混みに溶け込むロボットの航法学習（DeepMoTIon: Learning to Navigate Like Humans）</news:title>
   <news:publication_date>2026-04-15T17:15:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/679693</loc>
  <lastmod>2026-04-15T17:15:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>注意に基づくグラフニューラルネットワーク（Attention-based Graph Neural Network for Semi-supervised Learning）</news:title>
   <news:publication_date>2026-04-15T17:15:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/679691</loc>
  <lastmod>2026-04-15T17:14:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>知識ベースモデルと機械学習を組み合わせるハイブリッド予測（Hybrid Forecasting of Chaotic Processes: Using Machine Learning in Conjunction with a Knowledge-Based Model）</news:title>
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   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/679689</loc>
  <lastmod>2026-04-15T17:13:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>石英結晶共振器における電極配列と電気的散逸（Electrode configuration and electrical dissipation of mechanical energy in quartz crystal resonators）</news:title>
   <news:publication_date>2026-04-15T17:13:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/679687</loc>
  <lastmod>2026-04-15T16:23:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>スパイク時刻に含まれる情報：ポリクロノアス群から導かれる神経符号（On the information in spike timing: neural codes derived from polychronous groups）</news:title>
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   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/679685</loc>
  <lastmod>2026-04-15T16:22:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>複数の外的条件を考慮したJoint PLDAのスコアリング手法（Scoring Formulation for Multi-Condition Joint PLDA）</news:title>
   <news:publication_date>2026-04-15T16:22:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/679683</loc>
  <lastmod>2026-04-15T16:22:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Bit-Tacticalが示した「無駄な計算」を狙う設計革新（Bit-Tactical: Exploiting Ineffectual Computations in Convolutional Neural Networks: Which, Why, and How）</news:title>
   <news:publication_date>2026-04-15T16:22:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/679681</loc>
  <lastmod>2026-04-15T16:21:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>滑らかな関数のモジュロ1サンプルの頑健な推定と位相展開への応用（Provably robust estimation of modulo 1 samples of a smooth function with applications to phase unwrapping）</news:title>
   <news:publication_date>2026-04-15T16:21:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/679679</loc>
  <lastmod>2026-04-15T16:21:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>競争的機械学習：理論上の最良予測が競争で最良を意味しない理由（Competitive Machine Learning: Best Theoretical Prediction vs Optimization）</news:title>
   <news:publication_date>2026-04-15T16:21:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/679677</loc>
  <lastmod>2026-04-15T16:20:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>持続景観のためのノンパラメトリックリスク評価と密度推定（Nonparametric Risk Assessment and Density Estimation for Persistence Landscapes）</news:title>
   <news:publication_date>2026-04-15T16:20:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
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  <loc>https://aibr.jp/archives/679675</loc>
  <lastmod>2026-04-15T16:20:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>逐次データにおける増分決定木に基づく異常検知（Sequential Outlier Detection based on Incremental Decision Trees）</news:title>
   <news:publication_date>2026-04-15T16:20:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
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  <loc>https://aibr.jp/archives/679673</loc>
  <lastmod>2026-04-15T15:28:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>格子基底と立ち上がり波による高速ガウス過程近似（Standing Wave Decomposition Gaussian Process）</news:title>
   <news:publication_date>2026-04-15T15:28:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679671</loc>
  <lastmod>2026-04-15T15:27:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ランダム初期化ネットワークに潜む勝ち馬（The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks）</news:title>
   <news:publication_date>2026-04-15T15:27:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679669</loc>
  <lastmod>2026-04-15T15:27:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マグマだまりの力学とCO2フラックシングの影響（Mechanics of magma chamber with implication to the effect of CO2 fluxing）</news:title>
   <news:publication_date>2026-04-15T15:27:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679667</loc>
  <lastmod>2026-04-15T15:26:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時間別類似性に基づく時間毎太陽放射予測（Hourly-Similarity Based Solar Forecasting Using Multi-Model Machine Learning Blending）</news:title>
   <news:publication_date>2026-04-15T15:26:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/679665</loc>
  <lastmod>2026-04-15T15:26:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチメディア鑑定から学ぶ敵対的例の検出（Detecting Adversarial Examples – A Lesson from Multimedia Forensics）</news:title>
   <news:publication_date>2026-04-15T15:26:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/679663</loc>
  <lastmod>2026-04-15T15:26:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>凸計画を用いた敵対的事例生成について（On Generation of Adversarial Examples using Convex Programming）</news:title>
   <news:publication_date>2026-04-15T15:26:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/679661</loc>
  <lastmod>2026-04-15T15:25:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Ehrhart理論によるプライバシーと忠実度のトレードオフ (The Trade-off between Privacy and Fidelity via Ehrhart Theory)</news:title>
   <news:publication_date>2026-04-15T15:25:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679659</loc>
  <lastmod>2026-04-15T14:34:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>クォークとグルーオンのジェット断面による重イオン衝突の解明（Probing heavy ion collisions using quark and gluon jet substructure）</news:title>
   <news:publication_date>2026-04-15T14:34:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679657</loc>
  <lastmod>2026-04-15T14:33:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ヒルベルト第6問題：厳密性への果てしない道（Hilbert’s Sixth Problem: the endless road to rigour）</news:title>
   <news:publication_date>2026-04-15T14:33:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679655</loc>
  <lastmod>2026-04-15T14:33:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所的な冷凍障害（quenched disorder）を学習する手法とその意義（Learning local, quenched disorder in plasticity and other crackling noise phenomena）</news:title>
   <news:publication_date>2026-04-15T14:33:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679653</loc>
  <lastmod>2026-04-15T14:32:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>逆空間スパース表現を用いた統合的腫瘍分類フレームワーク（An Integrated Inverse Space Sparse Representation Framework for Tumor Classification）</news:title>
   <news:publication_date>2026-04-15T14:32:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679651</loc>
  <lastmod>2026-04-15T14:32:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>都市規模の電子カルテが示す薬物相互作用の年齢・性差バイアス（City-wide Electronic Health Records Reveal Gender and Age Biases in Administration of Known Drug-Drug Interactions）</news:title>
   <news:publication_date>2026-04-15T14:32:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679649</loc>
  <lastmod>2026-04-15T14:32:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>チェレンコフ望遠鏡アレイとKM3ニュートリノ望遠鏡が示す広がった源の検出可能性（On the potential of Cherenkov Telescope Arrays and KM3 Neutrino Telescopes for the detection of extended sources）</news:title>
   <news:publication_date>2026-04-15T14:32:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679647</loc>
  <lastmod>2026-04-15T14:32:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>脆弱道路利用者の意図検出と予測（Intentions of Vulnerable Road Users – Detection and Forecasting by Means of Machine Learning）</news:title>
   <news:publication_date>2026-04-15T14:32:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679645</loc>
  <lastmod>2026-04-15T13:40:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>音声認証における同型暗号によるテンプレート保護（Homomorphic Encryption for Speaker Recognition: Protection of Biometric Templates and Vendor Model Parameters）</news:title>
   <news:publication_date>2026-04-15T13:40:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679643</loc>
  <lastmod>2026-04-15T13:39:39Z</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Androidマルウェア検出の説明（Explaining Black-box Android Malware Detection）</news:title>
   <news:publication_date>2026-04-15T13:39:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679641</loc>
  <lastmod>2026-04-15T13:38:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所化された深層学習を実現するニューラルネットの構築（Construction of neural networks for realization of localized deep learning）</news:title>
   <news:publication_date>2026-04-15T13:38:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679639</loc>
  <lastmod>2026-04-15T13:38:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自転車の発進動作協調検出（Cooperative Starting Movement Detection of Cyclists Using Convolutional Neural Networks and a Boosted Stacking Ensemble）</news:title>
   <news:publication_date>2026-04-15T13:38:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679637</loc>
  <lastmod>2026-04-15T13:38:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Graph-based Implicit Feedbackを用いた協調フィルタリング（Collaborative Filtering with Graph-based Implicit Feedback）</news:title>
   <news:publication_date>2026-04-15T13:38:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679635</loc>
  <lastmod>2026-04-15T13:38:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>空間概念と語彙モデルの改善されたスケーラブルなオンライン学習（Improved and Scalable Online Learning of Spatial Concepts and Language Models with Mapping）</news:title>
   <news:publication_date>2026-04-15T13:38:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679633</loc>
  <lastmod>2026-04-15T13:37:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>知識・情報・主体性を評価するマルチエージェント強化学習：スマートビルディングの事例研究（Valuing knowledge, information and agency in Multi-agent Reinforcement Learning: a case study in smart buildings）</news:title>
   <news:publication_date>2026-04-15T13:37:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679631</loc>
  <lastmod>2026-04-15T12:46:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高度に自律化された学習による歩行者等の安全性改善（Highly Automated Learning for Improved Active Safety of Vulnerable Road Users）</news:title>
   <news:publication_date>2026-04-15T12:46:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679629</loc>
  <lastmod>2026-04-15T12:46:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>視覚ベースの自己位置推定とオドメトリを同時に学習する（Deep Auxiliary Learning for Visual Localization and Odometry）</news:title>
   <news:publication_date>2026-04-15T12:46:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679627</loc>
  <lastmod>2026-04-15T12:45:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>物体に対する把持動作の自動分類（Automated Classification of Hand-grip action on Objects using Machine Learning）</news:title>
   <news:publication_date>2026-04-15T12:45:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679625</loc>
  <lastmod>2026-04-15T12:44:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的セミスムース・ニュートン法による非滑らか非凸最適化（A Stochastic Semismooth Newton Method for Nonsmooth Nonconvex Optimization）</news:title>
   <news:publication_date>2026-04-15T12:44:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679623</loc>
  <lastmod>2026-04-15T12:44:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>注意型オートエンコーダを用いた教師なし質問検索モデル（An Unsupervised Model with Attention Autoencoders for Question Retrieval）</news:title>
   <news:publication_date>2026-04-15T12:44:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679621</loc>
  <lastmod>2026-04-15T12:44:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>RippleNetによる推薦の革新（RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems）</news:title>
   <news:publication_date>2026-04-15T12:44:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679619</loc>
  <lastmod>2026-04-15T12:44:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Malytics: マルウェア検出スキームの要点と実務的意義 (Malytics: A Malware Detection Scheme)</news:title>
   <news:publication_date>2026-04-15T12:44:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679617</loc>
  <lastmod>2026-04-15T11:52:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>偽情報の初期拡散は本物とどう違うか（Fake news propagate differently from real news even at early stages of spreading）</news:title>
   <news:publication_date>2026-04-15T11:52:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679615</loc>
  <lastmod>2026-04-15T11:52:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自然発想の適応型フォグアーキテクチャ（Adaptive Nature-inspired Fog Architecture）</news:title>
   <news:publication_date>2026-04-15T11:52:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679613</loc>
  <lastmod>2026-04-15T11:51:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動的問題のためのベイズ最適化（Bayesian Optimization for Dynamic Problems）</news:title>
   <news:publication_date>2026-04-15T11:51:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679611</loc>
  <lastmod>2026-04-15T11:51:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>視覚から触感を推定する深層学習（Deep Visuo-Tactile Learning: Estimation of Tactile Properties from Images）</news:title>
   <news:publication_date>2026-04-15T11:51:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679609</loc>
  <lastmod>2026-04-15T11:50:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューラルネットワークで解くフーリエパイティグラフィー（Solving Fourier ptychographic imaging problems via neural network modeling and TensorFlow）</news:title>
   <news:publication_date>2026-04-15T11:50:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679607</loc>
  <lastmod>2026-04-15T11:50:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>地上PM2.5のリアルタイムかつシームレスな監視（REAL-TIME AND SEAMLESS MONITORING OF GROUND-LEVEL PM2.5 USING SATELLITE REMOTE SENSING）</news:title>
   <news:publication_date>2026-04-15T11:50:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679605</loc>
  <lastmod>2026-04-15T11:49:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層的畳み込み特徴の融合による人体セグメンテーションとファッション分類（Fusing Hierarchical Convolutional Features for Human Body Segmentation and Clothing Fashion Classification）</news:title>
   <news:publication_date>2026-04-15T11:49:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679603</loc>
  <lastmod>2026-04-15T10:58:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>低精度で高精度を実現する学習法（High-Accuracy Low-Precision Training）</news:title>
   <news:publication_date>2026-04-15T10:58:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679601</loc>
  <lastmod>2026-04-15T10:58:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>クロスビュー画像合成の条件付きGANによるアプローチ（Cross-View Image Synthesis using Conditional GANs）</news:title>
   <news:publication_date>2026-04-15T10:58:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679599</loc>
  <lastmod>2026-04-15T10:57:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>離散潜在変数を用いた系列モデルの高速デコーディング（Fast Decoding in Sequence Models Using Discrete Latent Variables）</news:title>
   <news:publication_date>2026-04-15T10:57:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679597</loc>
  <lastmod>2026-04-15T10:56:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>近似推論ネットワークによる学習（Learning Approximate Inference Networks for Structured Prediction）</news:title>
   <news:publication_date>2026-04-15T10:56:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679595</loc>
  <lastmod>2026-04-15T10:56:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>判別的事前分布を学習してブラインド画像復元を強化する（Learning a Discriminative Prior for Blind Image Deblurring）</news:title>
   <news:publication_date>2026-04-15T10:56:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679593</loc>
  <lastmod>2026-04-15T10:56:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所学習埋め込みと異種書誌ネットワークによるエキスパート検索の革新（Expert Finding in Heterogeneous Bibliographic Networks with Locally-trained Embeddings）</news:title>
   <news:publication_date>2026-04-15T10:56:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679591</loc>
  <lastmod>2026-04-15T10:55:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層認識損失を用いたニューラル細粒度エンティティ型分類（Neural Fine-Grained Entity Type Classification with Hierarchy-Aware Loss）</news:title>
   <news:publication_date>2026-04-15T10:55:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679589</loc>
  <lastmod>2026-04-15T10:03:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>タスク特化視覚注目予測のためのメモリ拡張条件付き生成対抗ネットワーク（Task Specific Visual Saliency Prediction with Memory Augmented Conditional Generative Adversarial Networks）</news:title>
   <news:publication_date>2026-04-15T10:03:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679587</loc>
  <lastmod>2026-04-15T10:03:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>予測による追跡：複数人の局在化と追跡のための深層生成モデル（Tracking by Prediction: A Deep Generative Model for Multi-Person localisation and Tracking）</news:title>
   <news:publication_date>2026-04-15T10:03:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679585</loc>
  <lastmod>2026-04-15T10:03:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数層かつ複数条件のデータ統合を扱う確率モデルの設計（Joint Multiple Multi-layered Gaussian Graphical Models）</news:title>
   <news:publication_date>2026-04-15T10:03:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679583</loc>
  <lastmod>2026-04-15T10:02:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>外観変換による困難条件下でのロバストな計測位置特定（Adversarial Training for Adverse Conditions: Robust Metric Localisation using Appearance Transfer）</news:title>
   <news:publication_date>2026-04-15T10:02:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679581</loc>
  <lastmod>2026-04-15T10:02:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層意味情報を用いた顔画像の手ぶれ除去（Deep Semantic Face Deblurring）</news:title>
   <news:publication_date>2026-04-15T10:02:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679579</loc>
  <lastmod>2026-04-15T10:01:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>顧客満足度評価におけるポジティビティバイアスの検出（Positivity Bias in Customer Satisfaction Ratings）</news:title>
   <news:publication_date>2026-04-15T10:01:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679577</loc>
  <lastmod>2026-04-15T10:01:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>次元ロバストなMCMCによるベイズ逆問題の効率化（Dimension-Robust MCMC in Bayesian Inverse Problems）</news:title>
   <news:publication_date>2026-04-15T10:01:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679575</loc>
  <lastmod>2026-04-15T09:10:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>MVRチェーン図の性質（On the Properties of MVR Chain Graphs）</news:title>
   <news:publication_date>2026-04-15T09:10:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679573</loc>
  <lastmod>2026-04-15T09:10:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>BOCANetによるハードウェアオブフュスケーション攻撃の変革（Deep RNN-Oriented Paradigm Shift through BOCANet: Broken Obfuscated Circuit Attack）</news:title>
   <news:publication_date>2026-04-15T09:10:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679571</loc>
  <lastmod>2026-04-15T09:09:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SVDDを用いたハイパースペクトルデータ解析のための新しいカーネル帯域幅選定基準（A New Bandwidth Selection Criterion for Using SVDD to Analyze Hyperspectral Data）</news:title>
   <news:publication_date>2026-04-15T09:09:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679569</loc>
  <lastmod>2026-04-15T09:08:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>異常バイオマーカー濃縮のための統合機械学習パイプライン（Integrated machine learning pipeline for aberrant biomarker enrichment）</news:title>
   <news:publication_date>2026-04-15T09:08:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679567</loc>
  <lastmod>2026-04-15T09:08:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>極端な分類問題におけるグラフ上の効率的な損失基準デコーディング（Efficient Loss-Based Decoding on Graphs for Extreme Classification）</news:title>
   <news:publication_date>2026-04-15T09:08:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679565</loc>
  <lastmod>2026-04-15T09:08:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフの深層生成モデルの学習 (Learning Deep Generative Models of Graphs)</news:title>
   <news:publication_date>2026-04-15T09:08:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679563</loc>
  <lastmod>2026-04-15T09:07:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時系列異常検知における精度・再現率の再定義（Precision and Recall for Time Series）</news:title>
   <news:publication_date>2026-04-15T09:07:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679561</loc>
  <lastmod>2026-04-15T08:15:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>報酬で促進される運動学習モデル――皮質と基底核の並列経路による学習（A model of reward-modulated motor learning with parallel cortical and basal ganglia pathways）</news:title>
   <news:publication_date>2026-04-15T08:15:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679559</loc>
  <lastmod>2026-04-15T08:15:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Kepler-78 と超短周期惑星の発見が示すもの（Kepler-78 and the Ultra-Short-Period Planets）</news:title>
   <news:publication_date>2026-04-15T08:15:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679557</loc>
  <lastmod>2026-04-15T08:14:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>埋め込み層は本当に最適解か——Deep Metric Learningにおける一般化の再検討 (Generalization in Metric Learning: Should the Embedding Layer be Embedding Layer?)</news:title>
   <news:publication_date>2026-04-15T08:14:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679555</loc>
  <lastmod>2026-04-15T08:14:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>近傍の矮小銀河における周囲銀河塵の検出と意味（Cold Circumgalactic Dust in Nearby Dwarf Galaxies）</news:title>
   <news:publication_date>2026-04-15T08:14:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679553</loc>
  <lastmod>2026-04-15T08:14:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GONetによるトラバーサビリティ推定の半教師あり学習（GONet: A Semi-Supervised Deep Learning Approach For Traversability Estimation）</news:title>
   <news:publication_date>2026-04-15T08:14:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679551</loc>
  <lastmod>2026-04-15T08:13:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>アクティブラーニングによる効率的な相図サンプリング（Efficient Phase Diagram Sampling by Active Learning）</news:title>
   <news:publication_date>2026-04-15T08:13:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679549</loc>
  <lastmod>2026-04-15T08:13:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>通信スケジューリングで分散深層学習を加速する（TicTac: Accelerating Distributed Deep Learning with Communication Scheduling）</news:title>
   <news:publication_date>2026-04-15T08:13:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679547</loc>
  <lastmod>2026-04-15T07:21:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Domain Adaptive Faster R-CNNによる現場適応型物体検出（Domain Adaptive Faster R-CNN for Object Detection in the Wild）</news:title>
   <news:publication_date>2026-04-15T07:21:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679545</loc>
  <lastmod>2026-04-15T07:21:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>近似的メトリック公平性とPACF学習の要点（Probably Approximately Correct and Fair Learning）</news:title>
   <news:publication_date>2026-04-15T07:21:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679543</loc>
  <lastmod>2026-04-15T07:20:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>外れ値に強い回帰の効率的アルゴリズム（Efficient Algorithms for Outlier-Robust Regression）</news:title>
   <news:publication_date>2026-04-15T07:20:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679541</loc>
  <lastmod>2026-04-15T07:19:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>近似到達可能性の分類器ベース手法（A Classification-based Approach for Approximate Reachability）</news:title>
   <news:publication_date>2026-04-15T07:19:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679539</loc>
  <lastmod>2026-04-15T07:19:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>連続的対称性の破れを持つモデルの最適化改善（Improving Optimization for Models With Continuous Symmetry Breaking）</news:title>
   <news:publication_date>2026-04-15T07:19:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679537</loc>
  <lastmod>2026-04-15T07:19:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>計算資源制約下の意識を通した公平性（Fairness Through Computationally-Bounded Awareness）</news:title>
   <news:publication_date>2026-04-15T07:19:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679535</loc>
  <lastmod>2026-04-15T07:18:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模領域における対話管理のための封建的強化学習（Feudal Reinforcement Learning for Dialogue Management in Large Domains）</news:title>
   <news:publication_date>2026-04-15T07:18:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679533</loc>
  <lastmod>2026-04-15T06:27:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SVMによるソフトウェア欠陥予測——コードスメルを使った実証的アプローチ（Predicting Software Defects Through SVM: An Empirical Approach）</news:title>
   <news:publication_date>2026-04-15T06:27:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679531</loc>
  <lastmod>2026-04-15T06:26:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>計算核物理における深層学習の応用（Deep Learning: A Tool for Computational Nuclear Physics）</news:title>
   <news:publication_date>2026-04-15T06:26:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679529</loc>
  <lastmod>2026-04-15T06:26:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層学習で地震位相を自動検出する革新（PhaseNet: A Deep-Neural-Network-Based Seismic Arrival Time Picking Method）</news:title>
   <news:publication_date>2026-04-15T06:26:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679527</loc>
  <lastmod>2026-04-15T06:25:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>安全な多剤併用（ポリファーマシー）への薬剤推薦手法（Drug Recommendation toward Safe Polypharmacy）</news:title>
   <news:publication_date>2026-04-15T06:25:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679525</loc>
  <lastmod>2026-04-15T06:25:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>入力‑出力のトレードオフを用いたアグリゲーション（Aggregation using input-output trade-off）</news:title>
   <news:publication_date>2026-04-15T06:25:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679523</loc>
  <lastmod>2026-04-15T06:25:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>IM-ROのための影響モデルパラメータ推定に対するベイズと機械学習アプローチ (A Bayesian and Machine Learning approach to estimating Influence Model parameters for IM-RO)</news:title>
   <news:publication_date>2026-04-15T06:25:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679521</loc>
  <lastmod>2026-04-15T06:24:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>規則優先分類器の学習（Learning Rules-First Classifiers）</news:title>
   <news:publication_date>2026-04-15T06:24:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679519</loc>
  <lastmod>2026-04-15T05:32:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>プライベート深層学習のための人工データ生成（Generating Artificial Data for Private Deep Learning）</news:title>
   <news:publication_date>2026-04-15T05:32:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679517</loc>
  <lastmod>2026-04-15T05:32:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>敵対的生成で実現する物理層の自動設計（Physical Layer Communications System Design Over-the-Air Using Adversarial Networks）</news:title>
   <news:publication_date>2026-04-15T05:32:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679515</loc>
  <lastmod>2026-04-15T05:32:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SentRNA：人間の設計戦略の事前知識を組み込んだ計算的RNA設計の改善 (SentRNA: Improving computational RNA design by incorporating a prior of human design strategies)</news:title>
   <news:publication_date>2026-04-15T05:32:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679513</loc>
  <lastmod>2026-04-15T05:31:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>決定論的重み付きケプストラム距離の適用性と解釈（Applicability and interpretation of the deterministic weighted cepstral distance）</news:title>
   <news:publication_date>2026-04-15T05:31:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679511</loc>
  <lastmod>2026-04-15T05:31:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ラベルなしデータを活用した群衆計数の学習（Leveraging Unlabeled Data for Crowd Counting by Learning to Rank）</news:title>
   <news:publication_date>2026-04-15T05:31:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679509</loc>
  <lastmod>2026-04-15T05:30:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>MACネットワークによる機械推論（COMPOSITIONAL ATTENTION NETWORKS FOR MACHINE REASONING）</news:title>
   <news:publication_date>2026-04-15T05:30:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679507</loc>
  <lastmod>2026-04-15T05:30:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習モデルによる強磁性相の組成最適化（Compositional optimization of hard-magnetic phases with machine-learning models）</news:title>
   <news:publication_date>2026-04-15T05:30:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679505</loc>
  <lastmod>2026-04-15T04:38:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチ仮説視覚慣性フローの提案（Multi-Hypothesis Visual-Inertial Flow）</news:title>
   <news:publication_date>2026-04-15T04:38:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679503</loc>
  <lastmod>2026-04-15T04:38:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>RGB-DとIMUを統合してスケール付き絶対軌跡を自己教師学習で推定する手法（Vision-Aided Absolute Trajectory Estimation Using an Unsupervised Deep Network with Online Error Correction）</news:title>
   <news:publication_date>2026-04-15T04:38:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679501</loc>
  <lastmod>2026-04-15T04:38:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層的ヒューリスティック学習による効率的な規範形成（Hierarchical Heuristic Learning towards Efficient Norm Emergence）</news:title>
   <news:publication_date>2026-04-15T04:38:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679499</loc>
  <lastmod>2026-04-15T04:37:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニッケル基超合金の設計を変えた機械学習の一手（Design of a nickel-base superalloy using a neural network）</news:title>
   <news:publication_date>2026-04-15T04:37:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679497</loc>
  <lastmod>2026-04-15T04:36:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>情報フローで結ばれる能動粒子（Active Particles Bound by Information Flows）</news:title>
   <news:publication_date>2026-04-15T04:36:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679495</loc>
  <lastmod>2026-04-15T04:36:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>意味関係を保つゼロショット学習の設計（Preserving Semantic Relations for Zero-Shot Learning）</news:title>
   <news:publication_date>2026-04-15T04:36:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679493</loc>
  <lastmod>2026-04-15T04:36:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>落入領域における銀河の星形成停止（The quench of the star formation in galaxies in the infall region of Abell 85）</news:title>
   <news:publication_date>2026-04-15T04:36:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679491</loc>
  <lastmod>2026-04-15T03:45:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>詐欺ICO識別のための深層学習システム（IcoRating: A Deep-Learning System for Scam ICO Identification）</news:title>
   <news:publication_date>2026-04-15T03:45:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679489</loc>
  <lastmod>2026-04-15T03:44:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SA-IGAによる社会的最適化を目指す強化学習（SA-IGA: A Multiagent Reinforcement Learning Method Towards Socially Optimal Outcomes）</news:title>
   <news:publication_date>2026-04-15T03:44:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679487</loc>
  <lastmod>2026-04-15T03:44:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>クロスドメイン推薦と深層ドメイン適応（Cross-domain Recommendation via Deep Domain Adaptation）</news:title>
   <news:publication_date>2026-04-15T03:44:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679485</loc>
  <lastmod>2026-04-15T03:43:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>制御認識スケジューリングのための深層強化学習（DEEPCAS: A Deep Reinforcement Learning Algorithm for Control-Aware Scheduling）</news:title>
   <news:publication_date>2026-04-15T03:43:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679483</loc>
  <lastmod>2026-04-15T03:43:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深いネットワークで学ぶ効果的な二値視覚表現（Learning Effective Binary Visual Representations with Deep Networks）</news:title>
   <news:publication_date>2026-04-15T03:43:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679481</loc>
  <lastmod>2026-04-15T03:43:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>一次導出のメタ学習アルゴリズム（On First-Order Meta-Learning Algorithms）</news:title>
   <news:publication_date>2026-04-15T03:43:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679479</loc>
  <lastmod>2026-04-15T03:43:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時系列データの潜在因子を切り分ける：Disentangled Sequential Autoencoder（Disentangled Sequential Autoencoder）</news:title>
   <news:publication_date>2026-04-15T03:43:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679477</loc>
  <lastmod>2026-04-15T02:51:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ソフトなペアワイズ類似度を用いた改良深層ハッシュ法（Improved Deep Hashing with Soft Pairwise Similarity for Multi-label Image Retrieval）</news:title>
   <news:publication_date>2026-04-15T02:51:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679475</loc>
  <lastmod>2026-04-15T02:50:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>損失関数における特徴分布の再考（Rethinking Feature Distribution for Loss Functions in Image Classification）</news:title>
   <news:publication_date>2026-04-15T02:50:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679473</loc>
  <lastmod>2026-04-15T02:50:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SLADS-Netによる動的サンプリングの実務的意義（SLADS-Net: Supervised Learning Approach for Dynamic Sampling using Deep Neural Networks）</news:title>
   <news:publication_date>2026-04-15T02:50:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679471</loc>
  <lastmod>2026-04-15T02:49:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層ネットワークの近似境界に関するいくつかの考察 (Some Approximation Bounds for Deep Networks)</news:title>
   <news:publication_date>2026-04-15T02:49:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679469</loc>
  <lastmod>2026-04-15T02:49:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多目的深層強化学習フレームワーク（A Multi-Objective Deep Reinforcement Learning Framework）</news:title>
   <news:publication_date>2026-04-15T02:49:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679467</loc>
  <lastmod>2026-04-15T02:49:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層モデルの学習：臨界点と局所開放性（Learning Deep Models: Critical Points and Local Openness）</news:title>
   <news:publication_date>2026-04-15T02:49:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679465</loc>
  <lastmod>2026-04-15T02:48:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>社会から個へ：クラウドソーシング評価の多層モデルによる簡潔な分解（From Social to Individuals: a Parsimonious Path of Multi-level Models for Crowdsourced Preference Aggregation）</news:title>
   <news:publication_date>2026-04-15T02:48:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679461</loc>
  <lastmod>2026-04-15T01:56:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ダブルチーム戦略を学ぶ深層強化学習（The Advantage of Doubling: A Deep Reinforcement Learning Approach to Studying the Double Team in the NBA）</news:title>
   <news:publication_date>2026-04-15T01:56:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679459</loc>
  <lastmod>2026-04-15T01:56:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフ上の区分的に滑らかな信号の多重解像度表現（Multiresolution Representations for Piecewise-Smooth Signals on Graphs）</news:title>
   <news:publication_date>2026-04-15T01:56:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679457</loc>
  <lastmod>2026-04-15T01:55:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>顧客対応に“心”を届けるトーン対応チャットボット（Touch Your Heart: A Tone-aware Chatbot for Customer Care on Social Media）</news:title>
   <news:publication_date>2026-04-15T01:55:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679455</loc>
  <lastmod>2026-04-15T01:55:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DBpedia n-triplesによる質問から回答への翻訳（Translating Questions into Answers using DBPedia n-triples）</news:title>
   <news:publication_date>2026-04-15T01:55:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679453</loc>
  <lastmod>2026-04-15T01:55:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>補間限界における確率的・分散的勾配降下法の高速収束（Fast Convergence for Stochastic and Distributed Gradient Descent in the Interpolation Limit）</news:title>
   <news:publication_date>2026-04-15T01:55:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679451</loc>
  <lastmod>2026-04-15T01:54:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>N-ヘテロ環カルベン配位子を持つイリジウム(III)錯体の非放射減衰と安定性（Non-radiative decay and stability of N-heterocyclic carbene iridium(III) complexes）</news:title>
   <news:publication_date>2026-04-15T01:54:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679449</loc>
  <lastmod>2026-04-15T01:54:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ネットワーク上で分布の平均（Wassersteinバリセンター）を合意的に求める手法（Distributed Computation of Wasserstein Barycenters over Networks）</news:title>
   <news:publication_date>2026-04-15T01:54:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679447</loc>
  <lastmod>2026-04-15T01:03:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>正方格子上の双極子ハイゼンベルグ模型に対する繰り込み群解析（Renormalization group analysis of dipolar Heisenberg model on square lattice）</news:title>
   <news:publication_date>2026-04-15T01:03:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679445</loc>
  <lastmod>2026-04-15T01:03:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>量子支援クラスタリング分析（Quantum-assisted cluster analysis）</news:title>
   <news:publication_date>2026-04-15T01:03:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679443</loc>
  <lastmod>2026-04-15T01:03:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文の表現学習を効率化する枠組み（AN EFFICIENT FRAMEWORK FOR LEARNING SENTENCE REPRESENTATIONS）</news:title>
   <news:publication_date>2026-04-15T01:03:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679433</loc>
  <lastmod>2026-04-15T01:01:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>集合間相互作用の深層モデル（Deep Models of Interactions Across Sets）</news:title>
   <news:publication_date>2026-04-15T01:01:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679431</loc>
  <lastmod>2026-04-15T01:01:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>WNGradによる学習率自動調整がもたらす実務的インパクト（WNGrad: Learn the Learning Rate in Gradient Descent）</news:title>
   <news:publication_date>2026-04-15T01:01:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679429</loc>
  <lastmod>2026-04-15T01:01:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時間重視のバンディット学習における満足解探索（Satisﬁcing in Time-Sensitive Bandit Learning）</news:title>
   <news:publication_date>2026-04-15T01:01:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679427</loc>
  <lastmod>2026-04-15T01:01:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>トポロジカルフォトニクスの逆問題を機械学習で解く（Machine Learning Inverse Problem for Topological Photonics）</news:title>
   <news:publication_date>2026-04-15T01:01:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679425</loc>
  <lastmod>2026-04-15T00:09:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>RNNと単語埋め込みに現れる代数構造の発見（The emergent algebraic structure of RNNs and embeddings in NLP）</news:title>
   <news:publication_date>2026-04-15T00:09:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679423</loc>
  <lastmod>2026-04-15T00:09:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>巨大な中心銀河のサイズと星の殻は環境（ダークマターハロー）に依存する（A Detection of the Environmental Dependence of the Sizes and Stellar Haloes of Massive Central Galaxies）</news:title>
   <news:publication_date>2026-04-15T00:09:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679421</loc>
  <lastmod>2026-04-15T00:08:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>模倣か革新か：空間的社会ジレンマゲームにおける戦略更新態度の競合（Imitate or innovate: competition of strategy updating attitudes in spatial social dilemma games）</news:title>
   <news:publication_date>2026-04-15T00:08:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679419</loc>
  <lastmod>2026-04-15T00:07:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習で明らかになった非晶質シリコンの原子構造（Realistic atomistic structure of amorphous silicon from machine-learning-driven molecular dynamics）</news:title>
   <news:publication_date>2026-04-15T00:07:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679417</loc>
  <lastmod>2026-04-15T00:07:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>外れ値に強い確率的最適化のメタアルゴリズム「Sever」について（Sever: A Robust Meta-Algorithm for Stochastic Optimization）</news:title>
   <news:publication_date>2026-04-15T00:07:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679415</loc>
  <lastmod>2026-04-15T00:07:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層強化学習の高速化手法（Accelerated Methods for Deep Reinforcement Learning）</news:title>
   <news:publication_date>2026-04-15T00:07:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679413</loc>
  <lastmod>2026-04-14T23:15:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>病理画像の核（Nuclei）を一歩で分割する深層学習（A Deep Learning Algorithm for One-step Contour Aware Nuclei Segmentation of Histopathological Images）</news:title>
   <news:publication_date>2026-04-14T23:15:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679411</loc>
  <lastmod>2026-04-14T23:15:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>バッグ対クラス発散によるマルチインスタンス学習（A bag-to-class divergence approach to multiple-instance learning）</news:title>
   <news:publication_date>2026-04-14T23:15:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679409</loc>
  <lastmod>2026-04-14T23:14:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>投票集約の高速化 — Fast Dawid-Skene（Fast Dawid-Skene: A Fast Vote Aggregation Scheme for Sentiment Classification）</news:title>
   <news:publication_date>2026-04-14T23:14:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679407</loc>
  <lastmod>2026-04-14T23:13:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Transfer Neural AutoMLの本質と経営への示唆（Transfer Learning with Neural AutoML）</news:title>
   <news:publication_date>2026-04-14T23:13:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679405</loc>
  <lastmod>2026-04-14T23:13:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層バックプロジェクションネットワークによる超解像（Deep Back-Projection Networks For Super-Resolution）</news:title>
   <news:publication_date>2026-04-14T23:13:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679403</loc>
  <lastmod>2026-04-14T23:13:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>望み水準に基づく摂動学習オートマタ（Aspiration-based Perturbed Learning Automata）</news:title>
   <news:publication_date>2026-04-14T23:13:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679401</loc>
  <lastmod>2026-04-14T23:12:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>慣性プラットフォームのニューラルネットワークフィードバック制御（Neural network feedback controller for inertial platform）</news:title>
   <news:publication_date>2026-04-14T23:12:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679399</loc>
  <lastmod>2026-04-14T22:21:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数の連続的属性を扱う確率的ブロックモデル（Stochastic Block Models with Multiple Continuous Attributes）</news:title>
   <news:publication_date>2026-04-14T22:21:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679397</loc>
  <lastmod>2026-04-14T22:20:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時変性無線周波数干渉の分類におけるCNNとLSTMを用いたアプローチ（A CNN and LSTM-Based Approach to Classifying Transient Radio Frequency Interference）</news:title>
   <news:publication_date>2026-04-14T22:20:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679395</loc>
  <lastmod>2026-04-14T22:20:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>条件付き潜在表現による矛盾・中立・含意文の自動生成（Generating Contradictory, Neutral, and Entailing Sentences）</news:title>
   <news:publication_date>2026-04-14T22:20:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679393</loc>
  <lastmod>2026-04-14T22:19:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチスペクトル画像の変化検出に向けた再帰畳み込みニューラルネットワーク（Learning Spectral-Spatial-Temporal Features via a Recurrent Convolutional Neural Network for Change Detection in Multispectral Imagery）</news:title>
   <news:publication_date>2026-04-14T22:19:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679391</loc>
  <lastmod>2026-04-14T22:18:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>運動疲労検出モデルの実務的示唆（An Exercise Fatigue Detection Model Based on Machine Learning Methods）</news:title>
   <news:publication_date>2026-04-14T22:18:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679389</loc>
  <lastmod>2026-04-14T22:18:49Z</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>2026-04-14T22:18:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679387</loc>
  <lastmod>2026-04-14T22:18:35Z</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>2026-04-14T22:18:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679385</loc>
  <lastmod>2026-04-14T21:27:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>教師なし学習で解きほぐす表現の推論（Inferencing Based on Unsupervised Learning of Disentangled Representations）</news:title>
   <news:publication_date>2026-04-14T21:27:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679383</loc>
  <lastmod>2026-04-14T21:26:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>テキストから作業手順を抽出する深層強化学習（Extracting Action Sequences from Texts Based on Deep Reinforcement Learning）</news:title>
   <news:publication_date>2026-04-14T21:26:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679381</loc>
  <lastmod>2026-04-14T21:26:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>RGBD画像における3次元人体姿勢推定とロボットタスク学習（3D Human Pose Estimation in RGBD Images for Robotic Task Learning）</news:title>
   <news:publication_date>2026-04-14T21:26:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679379</loc>
  <lastmod>2026-04-14T21:25:36Z</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>2026-04-14T21:25:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679377</loc>
  <lastmod>2026-04-14T21:25:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>観測データの自動整合と群分けを同時に行う手法の要点（Gaussian Process Latent Variable Alignment Learning）</news:title>
   <news:publication_date>2026-04-14T21:25:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679375</loc>
  <lastmod>2026-04-14T21:25:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単眼画像からのステレオ深度推定の再定式化（Single View Stereo Matching）</news:title>
   <news:publication_date>2026-04-14T21:25:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679373</loc>
  <lastmod>2026-04-14T21:24:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>潜在変数モデルとしてのイジング分布（Ising distribution as a latent variable model）</news:title>
   <news:publication_date>2026-04-14T21:24:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679371</loc>
  <lastmod>2026-04-14T20:33:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>目標指向の視覚運動計画を学習に基づき生成する手法（Generating Goal-Directed Visuomotor Plans Based on Learning Using a Predictive Coding-type Deep Visuomotor Recurrent Neural Network Model）</news:title>
   <news:publication_date>2026-04-14T20:33:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679369</loc>
  <lastmod>2026-04-14T20:33:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチチャネル・ピラミッドによる人物再識別（Multi-Channel Pyramid Person Matching Network for Person Re-Identification）</news:title>
   <news:publication_date>2026-04-14T20:33:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679367</loc>
  <lastmod>2026-04-14T20:32:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GPSP: 異種ネットワーク埋め込みのための分割と射影（GPSP: Graph Partition and Space Projection based Approach for Heterogeneous Network Embedding）</news:title>
   <news:publication_date>2026-04-14T20:32:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/679365</loc>
  <lastmod>2026-04-14T20:32:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>類似物体の同時抽出とセグメンテーションを目指す手法（Object cosegmentation using deep Siamese network）</news:title>
   <news:publication_date>2026-04-14T20:32:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679363</loc>
  <lastmod>2026-04-14T20:32:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ピラミッド人物照合ネットワークによる再識別の革新（Pyramid Person Matching Network for Person Re-identification）</news:title>
   <news:publication_date>2026-04-14T20:32:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>フィルタ信号からのグラフ学習（Graph Learning from Filtered Signals: Graph System and Diffusion Kernel Identification）</news:title>
   <news:publication_date>2026-04-14T20:32:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679359</loc>
  <lastmod>2026-04-14T20:31:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ドメイン不変特徴を無監督で抽出する手法（EXTRACTING DOMAIN INVARIANT FEATURES BY UNSUPERVISED LEARNING FOR ROBUST AUTOMATIC SPEECH RECOGNITION）</news:title>
   <news:publication_date>2026-04-14T20:31:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679357</loc>
  <lastmod>2026-04-14T19:39:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動的RNA-seqデータの差次的発現解析（Differential Expression Analysis of Dynamical Sequencing Count Data with a Gamma Markov Chain）</news:title>
   <news:publication_date>2026-04-14T19:39:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679355</loc>
  <lastmod>2026-04-14T19:31:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>3D-CNNの可視化で脳MRIを読む（Visual Explanations From Deep 3D Convolutional Neural Networks for Alzheimer’s Disease Classification）</news:title>
   <news:publication_date>2026-04-14T19:31:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679353</loc>
  <lastmod>2026-04-14T19:31:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>合成ビジョンと視線追跡を組み合わせた航空安全の向上（Improving Aviation Safety using Synthetic Vision System integrated with Eye-tracking Devices）</news:title>
   <news:publication_date>2026-04-14T19:31:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679351</loc>
  <lastmod>2026-04-14T19:30:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動画における疎な敵対的摂動の研究（Sparse Adversarial Perturbations for Videos）</news:title>
   <news:publication_date>2026-04-14T19:30:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679349</loc>
  <lastmod>2026-04-14T19:29:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>逐次的部分ラベルデータのための最大マージン分類器（SEQUENTIAL MAXIMUM MARGIN CLASSIFIERS FOR PARTIALLY LABELED DATA）</news:title>
   <news:publication_date>2026-04-14T19:29:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679347</loc>
  <lastmod>2026-04-14T19:29:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複雑な意味経路を利用したメタグラフ埋め込み（MetaGraph2Vec: Complex Semantic Path Augmented Heterogeneous Network Embedding）</news:title>
   <news:publication_date>2026-04-14T19:29:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679345</loc>
  <lastmod>2026-04-14T18:37:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オンラインピア評価にHodgeRankを使う意義（An Application of HodgeRank to Online Peer Assessment）</news:title>
   <news:publication_date>2026-04-14T18:37:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679343</loc>
  <lastmod>2026-04-14T18:36:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>有向グラフ上の線形最適化アルゴリズムと幾何学的収束（A linear algorithm for optimization over directed graphs with geometric convergence）</news:title>
   <news:publication_date>2026-04-14T18:36:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679341</loc>
  <lastmod>2026-04-14T18:36:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>公平な分類への削減アプローチ（A Reductions Approach to Fair Classification）</news:title>
   <news:publication_date>2026-04-14T18:36:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679339</loc>
  <lastmod>2026-04-14T18:36:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>視線計測を用いた認知負荷理論の検証（Use of Eye-Tracking Technology to Investigate Cognitive Load Theory）</news:title>
   <news:publication_date>2026-04-14T18:36:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679337</loc>
  <lastmod>2026-04-14T18:36:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>敵対的摂動に強い複数カーネルk平均クラスタリング（Robust Multiple Kernel k-means Clustering using Min-Max Optimization）</news:title>
   <news:publication_date>2026-04-14T18:36:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679335</loc>
  <lastmod>2026-04-14T18:35:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>不連続性に敏感な最適制御学習（Discontinuity-Sensitive Optimal Control Learning by Mixture of Experts）</news:title>
   <news:publication_date>2026-04-14T18:35:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679333</loc>
  <lastmod>2026-04-14T17:44:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非線形次元削減と線形スムージングの統一理論（On Nonlinear Dimensionality Reduction, Linear Smoothing and Autoencoding）</news:title>
   <news:publication_date>2026-04-14T17:44:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679331</loc>
  <lastmod>2026-04-14T17:43:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ノイズ下での誘導部分グラフ検出のためのマッチドフィルタ（Matched Filters for Noisy Induced Subgraph Detection）</news:title>
   <news:publication_date>2026-04-14T17:43:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679329</loc>
  <lastmod>2026-04-14T17:43:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>畳み込み特徴量のカテゴリカル混合モデルによる画像トピック発見（Categorical Mixture Models on VGGNet activations）</news:title>
   <news:publication_date>2026-04-14T17:43:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679327</loc>
  <lastmod>2026-04-14T17:43:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>音声分類のためのマスク付き条件付きニューラルネットワーク (Masked Conditional Neural Networks for Audio Classification)</news:title>
   <news:publication_date>2026-04-14T17:43:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679325</loc>
  <lastmod>2026-04-14T17:42:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>畳み込みニューラルネットワークによるタンパク質–リガンド評価の可視化（Visualizing Convolutional Neural Network Protein-Ligand Scoring）</news:title>
   <news:publication_date>2026-04-14T17:42:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679323</loc>
  <lastmod>2026-04-14T17:42:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>任意離散系列における異常検知のゼロ境界LSTM（Arbitrary Discrete Sequence Anomaly Detection with Zero Boundary LSTM）</news:title>
   <news:publication_date>2026-04-14T17:42:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679321</loc>
  <lastmod>2026-04-14T17:42:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>セントーラス銀河群の矮小銀河距離測定に関するTRGB法の適用（Tip of the red giant branch distances to the dwarf galaxies dw1335-29 and dw1340-30 in the Centaurus group）</news:title>
   <news:publication_date>2026-04-14T17:42:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679319</loc>
  <lastmod>2026-04-14T16:51:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>定常時系列の次元削減のための確率的非凸最適化（Dimensionality Reduction for Stationary Time Series via Stochastic Nonconvex Optimization）</news:title>
   <news:publication_date>2026-04-14T16:51:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679317</loc>
  <lastmod>2026-04-14T16:42:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>小さく速い予測器を学ぶ：SMaLLの考え方と実務的意義（Learning SMaLL Predictors）</news:title>
   <news:publication_date>2026-04-14T16:42:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679315</loc>
  <lastmod>2026-04-14T16:42:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>フロンティアフィールド銀河のKormendy関係が示すもの（The Kormendy Relation of Galaxies in the Frontier Fields Clusters: Abell S1063 and MACS J1149.5+2223）</news:title>
   <news:publication_date>2026-04-14T16:42:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679313</loc>
  <lastmod>2026-04-14T16:41:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>メモリアクセスパターンの学習による高効率プリフェッチ（Learning Memory Access Patterns）</news:title>
   <news:publication_date>2026-04-14T16:41:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679311</loc>
  <lastmod>2026-04-14T16:41:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>近接サーマルイメージングによる材料認識（Deep Thermal Imaging: Proximate Material Type Recognition in the Wild through Deep Learning of Spatial Surface Temperature Patterns）</news:title>
   <news:publication_date>2026-04-14T16:41:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679309</loc>
  <lastmod>2026-04-14T16:40:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>胸部X線画像のマルチラベル分類手法の比較（Comparison of Deep Learning Approaches for Multi-Label Chest X-Ray Classification）</news:title>
   <news:publication_date>2026-04-14T16:40:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679307</loc>
  <lastmod>2026-04-14T16:40:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Deep Super Learnerが示す現実的な「深層化」戦略（Deep Super Learner: A Deep Ensemble for Classification Problems）</news:title>
   <news:publication_date>2026-04-14T16:40:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679305</loc>
  <lastmod>2026-04-14T15:49:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的モデルベース強化学習によるニューラルネットワーク制御器の合成 (Synthesizing Neural Network Controllers with Probabilistic Model-Based Reinforcement Learning)</news:title>
   <news:publication_date>2026-04-14T15:49:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679303</loc>
  <lastmod>2026-04-14T15:49:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単眼カメラだけで動きを学ぶ――密な3Dフローからの視覚オドメトリと密3Dマッピング（Learning monocular visual odometry with dense 3D mapping from dense 3D flow）</news:title>
   <news:publication_date>2026-04-14T15:49:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679301</loc>
  <lastmod>2026-04-14T15:48:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ゼロショット図式-画像ハッシング（Zero-Shot Sketch-Image Hashing）</news:title>
   <news:publication_date>2026-04-14T15:48:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679299</loc>
  <lastmod>2026-04-14T15:47:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GeoNet: 動画から深度・オプティカルフロー・カメラ姿勢を共同で学習する手法（GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose）</news:title>
   <news:publication_date>2026-04-14T15:47:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679297</loc>
  <lastmod>2026-04-14T15:47:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>低ダイナミックレンジ画像から高ダイナミックレンジを再構築するExpandNet（ExpandNet: A Deep Convolutional Neural Network for High Dynamic Range Expansion from Low Dynamic Range Content）</news:title>
   <news:publication_date>2026-04-14T15:47:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679295</loc>
  <lastmod>2026-04-14T15:47:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>目標志向のエンドツーエンド対話システムと生成型応答（An End-to-End Goal-Oriented Dialog System with a Generative Natural Language Response Generation）</news:title>
   <news:publication_date>2026-04-14T15:47:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679293</loc>
  <lastmod>2026-04-14T15:46:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>個人化された露出制御（Personalized Exposure Control Using Adaptive Metering and Reinforcement Learning）</news:title>
   <news:publication_date>2026-04-14T15:46:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679291</loc>
  <lastmod>2026-04-14T14:55:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>有限深水上の周期重力波に対するバベンコ方程式の導出と数値解法（BABENKO’S EQUATION FOR PERIODIC GRAVITY WAVES ON WATER OF FINITE DEPTH: DERIVATION AND NUMERICAL SOLUTION）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/679289</loc>
  <lastmod>2026-04-14T14:54:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>重力波トリガーに対する電磁対応探索の最適化（Optimizing searches for electromagnetic counterparts of gravitational wave triggers）</news:title>
   <news:publication_date>2026-04-14T14:54:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679287</loc>
  <lastmod>2026-04-14T14:53:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オンザガーの「理想的乱流」理論のレビュー（Review of the Onsager “Ideal Turbulence” Theory）</news:title>
   <news:publication_date>2026-04-14T14:53:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679285</loc>
  <lastmod>2026-04-14T14:53:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層情報ネットワーク（Deep Information Networks）</news:title>
   <news:publication_date>2026-04-14T14:53:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679283</loc>
  <lastmod>2026-04-14T14:53:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自転車発進検知の早期検出技術（Early Start Intention Detection of Cyclists Using Motion History Images and a Deep Residual Network）</news:title>
   <news:publication_date>2026-04-14T14:53:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679281</loc>
  <lastmod>2026-04-14T14:01:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多クラス能動学習における情報量と代表性のハイブリッド基準（Multi-class Active Learning: A Hybrid Informative and Representative Criterion Inspired Approach）</news:title>
   <news:publication_date>2026-04-14T14:01:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679279</loc>
  <lastmod>2026-04-14T14:00:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>月面クレーター自動検出における畳み込みニューラルネットワーク（Lunar Crater Identification via Deep Learning）</news:title>
   <news:publication_date>2026-04-14T14:00:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679277</loc>
  <lastmod>2026-04-14T14:00:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>回転を考慮した全畳み込み把持検出ネットワーク（Fully Convolutional Grasp Detection Network with Oriented Anchor Box）</news:title>
   <news:publication_date>2026-04-14T14:00:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679275</loc>
  <lastmod>2026-04-14T13:59:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>点群の形概念化と持続ホモロジー（Conceptualization of Object Compositions Using Persistent Homology）</news:title>
   <news:publication_date>2026-04-14T13:59:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679273</loc>
  <lastmod>2026-04-14T13:58:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ランダム生成とHarris近似による分子結晶構造の高速スクリーニング（Genarris: Random Generation of Molecular Crystal Structures and Fast Screening with a Harris Approximation）</news:title>
   <news:publication_date>2026-04-14T13:58:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679271</loc>
  <lastmod>2026-04-14T13:58:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層畳み込みニューラルネットワーク概説（A Non-Technical Survey on Deep Convolutional Neural Network Architectures）</news:title>
   <news:publication_date>2026-04-14T13:58:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679269</loc>
  <lastmod>2026-04-14T13:57:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高周波数ラジオ源カウントの再評価（9C spectral-index distributions and source-count estimates from 15 to 93 GHz — a re-assessment）</news:title>
   <news:publication_date>2026-04-14T13:57:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679267</loc>
  <lastmod>2026-04-14T13:05:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深海ロボットの連続的システム統合が変える試験文化（Robust Continuous System Integration for Critical Deep-Sea Robot Operations）</news:title>
   <news:publication_date>2026-04-14T13:05:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679265</loc>
  <lastmod>2026-04-14T12:55:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非局所性を強化したCNNによる画像ノイズ除去（Nonlocality-Reinforced Convolutional Neural Networks for Image Denoising）</news:title>
   <news:publication_date>2026-04-14T12:55:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679263</loc>
  <lastmod>2026-04-14T12:54:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>六角格子畳み込みによる回転不変性の向上（HEXACONV）</news:title>
   <news:publication_date>2026-04-14T12:54:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679261</loc>
  <lastmod>2026-04-14T12:54:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習を用いた光学顕微鏡による二次元構造の知能的同定（Intelligent Identification of Two-Dimensional Structure by Machine-Learning Optical Microscopy）</news:title>
   <news:publication_date>2026-04-14T12:54:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679259</loc>
  <lastmod>2026-04-14T12:53:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非整列データに対するコンテクスチュアル損失（The Contextual Loss for Image Transformation with Non-Aligned Data）</news:title>
   <news:publication_date>2026-04-14T12:53:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679257</loc>
  <lastmod>2026-04-14T12:53:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ハイブリッドなマルチモーダル深層学習による交通流予測（A Hybrid Method for Traffic Flow Forecasting Using Multimodal Deep Learning）</news:title>
   <news:publication_date>2026-04-14T12:53:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679255</loc>
  <lastmod>2026-04-14T12:53:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大量の短文を対話的に分類する枠組みVIPE（VIPE: A NEW INTERACTIVE CLASSIFICATION FRAMEWORK FOR LARGE SETS OF SHORT TEXTS - APPLICATION TO OPINION MINING）</news:title>
   <news:publication_date>2026-04-14T12:53:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679253</loc>
  <lastmod>2026-04-14T12:02:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>地面は平らではない：移動カメラから急勾配路上の車両を単眼で復元する手法（The Earth Ain’t Flat: Monocular Reconstruction of Vehicles on Steep and Graded Roads from a Moving Camera）</news:title>
   <news:publication_date>2026-04-14T12:02:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679251</loc>
  <lastmod>2026-04-14T12:00:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オンライン深層学習：流れるデータに応じてRBMを成長させる手法（Online Deep Learning: Growing RBM on the ﬂy）</news:title>
   <news:publication_date>2026-04-14T12:00:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679249</loc>
  <lastmod>2026-04-14T12:00:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>加速された勾配ブースティング（Accelerated Gradient Boosting）</news:title>
   <news:publication_date>2026-04-14T12:00:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679247</loc>
  <lastmod>2026-04-14T11:59:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ガウス方策を学習するための平滑化作用価値関数（Smoothed Action Value Functions for Learning Gaussian Policies）</news:title>
   <news:publication_date>2026-04-14T11:59:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679245</loc>
  <lastmod>2026-04-14T11:59:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>短期ホライズン・バイアスが示すメタ最適化の限界（Understanding Short-Horizon Bias in Stochastic Meta-Optimization）</news:title>
   <news:publication_date>2026-04-14T11:59:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679243</loc>
  <lastmod>2026-04-14T11:59:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ガウス系における依存制約に基づく部分情報分解の厳密解（Exact partial information decompositions for Gaussian systems based on dependency constraints）</news:title>
   <news:publication_date>2026-04-14T11:59:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679241</loc>
  <lastmod>2026-04-14T11:58:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Johnのウォーク：凸体からのアフィン不変ランダムウォーク（John&amp;#039;s Walk: An Affine-Invariant Random Walk for Sampling Convex Bodies）</news:title>
   <news:publication_date>2026-04-14T11:58:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679239</loc>
  <lastmod>2026-04-14T11:07:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>意図認識型マルチエージェント強化学習（Intent-aware Multi-agent Reinforcement Learning）</news:title>
   <news:publication_date>2026-04-14T11:07:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679237</loc>
  <lastmod>2026-04-14T11:07:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチモーダルな複数人行動の生成モデル（Generative Modeling of Multimodal Multi-Human Behavior）</news:title>
   <news:publication_date>2026-04-14T11:07:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679235</loc>
  <lastmod>2026-04-14T11:06:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>占有マップ予測による車両ナビゲーション（Occupancy Map Prediction Using Generative and Fully Convolutional Networks for Vehicle Navigation）</news:title>
   <news:publication_date>2026-04-14T11:06:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679233</loc>
  <lastmod>2026-04-14T11:06:05Z</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量子化による圧縮手法（Deep Neural Network Compression with Single and Multiple Level Quantization）</news:title>
   <news:publication_date>2026-04-14T11:06:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679231</loc>
  <lastmod>2026-04-14T11:05:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CNNに基づく尿沈渣粒子自動認識（CNN-Based Automatic Urinary Particles Recognition）</news:title>
   <news:publication_date>2026-04-14T11:05:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679229</loc>
  <lastmod>2026-04-14T11:05:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動作ごとの力学を学習して全身制御を改善する（Learning Task-Specific Dynamics to Improve Whole-Body Control）</news:title>
   <news:publication_date>2026-04-14T11:05:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679227</loc>
  <lastmod>2026-04-14T11:05:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>フィルタバンクによるスパース化変換の学習（Learning Filter Bank Sparsifying Transforms）</news:title>
   <news:publication_date>2026-04-14T11:05:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679225</loc>
  <lastmod>2026-04-14T10:14:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>現実的な価格制約下で購買行動を学習するオンラインアルゴリズム（An Online Algorithm for Learning Buyer Behavior under Realistic Pricing Restrictions）</news:title>
   <news:publication_date>2026-04-14T10:14:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679223</loc>
  <lastmod>2026-04-14T10:14:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Restricted Boltzmann Machineの熱力学と学習ダイナミクス（Thermodynamics of Restricted Boltzmann Machines and Related Learning Dynamics）</news:title>
   <news:publication_date>2026-04-14T10:14:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679221</loc>
  <lastmod>2026-04-14T10:13:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>シーンの要点（Gist）を利用して物体認識を高める手法（Learning Scene Gist with Convolutional Neural Networks to Improve Object Recognition）</news:title>
   <news:publication_date>2026-04-14T10:13:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679219</loc>
  <lastmod>2026-04-14T10:12:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>M 3Fusion：マルチスケール・時系列衛星データ融合による土地被覆マッピング（M 3Fusion: A Deep Learning Architecture for Multi-{Scale/Modal/Temporal} satellite data fusion）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <lastmod>2026-04-14T10:12:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>触覚による再把持：シミュレートされた触覚変換を用いた把持調整（Tactile Regrasp: Grasp Adjustments via Simulated Tactile Transformations）</news:title>
   <news:publication_date>2026-04-14T10:12:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679215</loc>
  <lastmod>2026-04-14T10:11:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>一次元ボース＝ハバード模型の有限運動量励起をスーパー格子変調分光で探る（Accessing finite momentum excitations of the one-dimensional Bose-Hubbard model using superlattice modulation spectroscopy）</news:title>
   <news:publication_date>2026-04-14T10:11:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679213</loc>
  <lastmod>2026-04-14T10:11:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>エネルギー・エントロピーの競合とSGDの有効性（Energy-entropy competition and the effectiveness of stochastic gradient descent in machine learning）</news:title>
   <news:publication_date>2026-04-14T10:11:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679211</loc>
  <lastmod>2026-04-14T09:19:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分類器ネットワークを生成モデルに変える（Making a Classifier Network Generative）</news:title>
   <news:publication_date>2026-04-14T09:19:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679209</loc>
  <lastmod>2026-04-14T09:12:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マンモグラフィにおける異常検出のための深層畳み込みニューラルネットワーク（Abnormality Detection in Mammography using Deep Convolutional Neural Networks）</news:title>
   <news:publication_date>2026-04-14T09:12:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679207</loc>
  <lastmod>2026-04-14T09:11:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分離可能データにおける勾配降下法の収束（Convergence of Gradient Descent on Separable Data）</news:title>
   <news:publication_date>2026-04-14T09:11:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679205</loc>
  <lastmod>2026-04-14T09:11:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ランク付けデータにおける差別検出と除去（On Discrimination Discovery and Removal in Ranked Data using Causal Graph）</news:title>
   <news:publication_date>2026-04-14T09:11:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679203</loc>
  <lastmod>2026-04-14T09:09:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>再電離終期におけるLyα放射の等価幅分布の制約（Texas Spectroscopic Search for Lyα Emission at the End of Reionization）</news:title>
   <news:publication_date>2026-04-14T09:09:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679201</loc>
  <lastmod>2026-04-14T09:09:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SACRE：不確実性下で要求を適応させる仕組み（SACRE: Supporting contextual requirements’ adaptation in modern self-adaptive systems in the presence of uncertainty at runtime）</news:title>
   <news:publication_date>2026-04-14T09:09:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679199</loc>
  <lastmod>2026-04-14T09:08:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>低粘性ディスクにおけるスーパーアースが作る深く広いギャップ（Deep and wide gaps by super Earths in low-viscosity discs）</news:title>
   <news:publication_date>2026-04-14T09:08:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679197</loc>
  <lastmod>2026-04-14T08:17:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層強化学習による普遍的量子制御 (Universal Quantum Control through Deep Reinforcement Learning)</news:title>
   <news:publication_date>2026-04-14T08:17:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679195</loc>
  <lastmod>2026-04-14T08:16:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高赤方偏移で見つかった「金属不足」の大型休止銀河が示すもの（Metal Deficiency in Two Massive Dead Galaxies at z ∼2）</news:title>
   <news:publication_date>2026-04-14T08:16:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679193</loc>
  <lastmod>2026-04-14T08:16:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SHELLQsによる高赤方偏移低光度クエーサー探査の成果と意味（SUBARU HIGH-Z EXPLORATION OF LOW-LUMINOSITY QUASARS (SHELLQs). IV）</news:title>
   <news:publication_date>2026-04-14T08:16:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679191</loc>
  <lastmod>2026-04-14T08:16:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所表現整合によるクレジット割当の再考（Conducting Credit Assignment by Aligning Local Distributed Representations）</news:title>
   <news:publication_date>2026-04-14T08:16:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679189</loc>
  <lastmod>2026-04-14T08:15:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>周辺分布の特異性と共変量シフトにおけるラベルの有用性（Marginal Singularity, and the Benefits of Labels in Covariate-Shift）</news:title>
   <news:publication_date>2026-04-14T08:15:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679187</loc>
  <lastmod>2026-04-14T08:15:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ノルムが重要である理由—効率的かつ精度の高い正規化手法の提示（Norm matters: efficient and accurate normalization schemes in deep networks）</news:title>
   <news:publication_date>2026-04-14T08:15:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679185</loc>
  <lastmod>2026-04-14T08:14:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>画像合成のための空間変換GAN（ST-GAN: Spatial Transformer Generative Adversarial Networks for Image Compositing）</news:title>
   <news:publication_date>2026-04-14T08:14:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679183</loc>
  <lastmod>2026-04-14T07:23:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>通信効率を学習で最適化する新パラダイム：Event-triggered Learning（Event-triggered Learning for Resource-efficient Networked Control）</news:title>
   <news:publication_date>2026-04-14T07:23:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679181</loc>
  <lastmod>2026-04-14T07:23:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Tucana II 銀河の新規メンバー星の化学組成（Chemical Abundances of New Member Stars in the Tucana II Dwarf Galaxy）</news:title>
   <news:publication_date>2026-04-14T07:23:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679179</loc>
  <lastmod>2026-04-14T07:23:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>正常データだけで不正を見抜く技術の要点（One-Class Adversarial Nets for Fraud Detection）</news:title>
   <news:publication_date>2026-04-14T07:23:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679177</loc>
  <lastmod>2026-04-14T07:22:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>密な形状対応を推定するDenseReg（DenseReg: Fully Convolutional Dense Shape Regression In-the-Wild）</news:title>
   <news:publication_date>2026-04-14T07:22:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679175</loc>
  <lastmod>2026-04-14T07:22:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>アスペクトに基づく埋め込み学習（ASPEM: Embedding Learning by Aspects in Heterogeneous Information Networks）</news:title>
   <news:publication_date>2026-04-14T07:22:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679173</loc>
  <lastmod>2026-04-14T07:22:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>競合的世論最適化のためのマルチエージェント学習（Multiagent Learning for Competitive Opinion Optimization）</news:title>
   <news:publication_date>2026-04-14T07:22:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679171</loc>
  <lastmod>2026-04-14T07:21:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Hyperdrive：マルチチップでスケーラブルな二値重みCNN推論エンジン（Hyperdrive: A Multi-Chip Systolically Scalable Binary-Weight CNN Inference Engine）</news:title>
   <news:publication_date>2026-04-14T07:21:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679169</loc>
  <lastmod>2026-04-14T06:30:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>微分可能なサブモジュラ最大化（Differentiable Submodular Maximization）</news:title>
   <news:publication_date>2026-04-14T06:30:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679167</loc>
  <lastmod>2026-04-14T06:30:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>キネマティック・モーフィング・ネットワークによる操作スキル転移（Kinematic Morphing Networks for Manipulation Skill Transfer）</news:title>
   <news:publication_date>2026-04-14T06:30:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679165</loc>
  <lastmod>2026-04-14T06:29:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>WikidataとImageNetの結びつきがもたらす意味（Matching Wikidata with ImageNet）</news:title>
   <news:publication_date>2026-04-14T06:29:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679163</loc>
  <lastmod>2026-04-14T06:29:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次元データ可視化の理論的解明：t-SNEの解析（An Analysis of the t-SNE Algorithm for Data Visualization）</news:title>
   <news:publication_date>2026-04-14T06:29:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679161</loc>
  <lastmod>2026-04-14T06:28:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>臨床診断に近づく脳梗塞病変の自動セグメンテーション（Towards Clinical Diagnosis: Automated Stroke Lesion Segmentation on Multimodal MR Image Using Convolutional Neural Network）</news:title>
   <news:publication_date>2026-04-14T06:28:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679159</loc>
  <lastmod>2026-04-14T06:28:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スレート最適化による推薦の再考（Beyond Greedy Ranking: Slate Optimization via List-CVAE）</news:title>
   <news:publication_date>2026-04-14T06:28:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679157</loc>
  <lastmod>2026-04-14T06:28:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>初期化とアーキテクチャが学習開始を決める（How to Start Training: The Effect of Initialization and Architecture）</news:title>
   <news:publication_date>2026-04-14T06:28:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679155</loc>
  <lastmod>2026-04-14T05:37:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動画から指の握力を推定する二流アプローチ（Finger Grip Force Estimation from Video using Two Stream Approach）</news:title>
   <news:publication_date>2026-04-14T05:37:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679153</loc>
  <lastmod>2026-04-14T05:36:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>MDPにおける分散を考慮した後悔境界の改良（Variance-Aware Regret Bounds for Undiscounted Reinforcement Learning in MDPs）</news:title>
   <news:publication_date>2026-04-14T05:36:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679151</loc>
  <lastmod>2026-04-14T05:36:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>コールドスタート利用者向けクロスドメイン推薦（Cross-Domain Recommendation for Cold-Start Users via Neighborhood Based Feature Mapping）</news:title>
   <news:publication_date>2026-04-14T05:36:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679149</loc>
  <lastmod>2026-04-14T05:35:47Z</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「2ボックス3クロップ」アプローチによるジェンダー判定の実務的意義（2B3C: 2 Box 3 Crop of Facial Image for Gender Classification with Convolutional Networks）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679147</loc>
  <lastmod>2026-04-14T05:35:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>AdaDepth: Unsupervised Content Congruent Adaptation for Depth Estimation（AdaDepth: Unsupervised Content Congruent Adaptation for Depth Estimation）</news:title>
   <news:publication_date>2026-04-14T05:35:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679145</loc>
  <lastmod>2026-04-14T05:35:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-04-14T05:35:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679143</loc>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-04-14T05:35:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679141</loc>
  <lastmod>2026-04-14T04:43:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層的で回転共変なN–Body Networksによる原子ポテンシャル学習（N–BODY NETWORKS: A COVARIANT HIERARCHICAL NEURAL NETWORK ARCHITECTURE FOR LEARNING ATOMIC POTENTIALS）</news:title>
   <news:publication_date>2026-04-14T04:43:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-04-14T04:42:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679137</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ランサムウェア攻撃の予測手法（RAPTOR: Ransomware Attack PredicTOR）</news:title>
   <news:publication_date>2026-04-14T04:42:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679135</loc>
  <lastmod>2026-04-14T04:42:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>敵対的極端多ラベル分類の考え方（Adversarial Extreme Multi-label Classification）</news:title>
   <news:publication_date>2026-04-14T04:42:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679133</loc>
  <lastmod>2026-04-14T04:42:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所的距離尺度学習による最寄り近傍分類の改善（Local Distance Metric Learning for the Nearest Neighbor Algorithm）</news:title>
   <news:publication_date>2026-04-14T04:42:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679131</loc>
  <lastmod>2026-04-14T04:41:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>固定サイズと可変サイズのデターミナント点過程の漸近同値性（Asymptotic Equivalence of Fixed-size and Varying-size Determinantal Point Processes）</news:title>
   <news:publication_date>2026-04-14T04:41:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679129</loc>
  <lastmod>2026-04-14T04:41:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ペアごとの学習を整理する――カーネルリッジ回帰に基づく比較研究（A Comparative Study of Pairwise Learning Methods based on Kernel Ridge Regression）</news:title>
   <news:publication_date>2026-04-14T04:41:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679126</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オンザフライ非断熱分子動力学における機械学習カーネルリッジ回帰によるポテンシャルエネルギー面の導入（Inclusion of Machine Learning Kernel Ridge Regression Potential Energy Surfaces in On-the-Fly Nonadiabatic Molecular Dynamics Simulation）</news:title>
   <news:publication_date>2026-04-14T03:49:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679124</loc>
  <lastmod>2026-04-14T03:48:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文脈を超えて：微小顔検出のための意味的類似性活用（BEYOND CONTEXT: EXPLORING SEMANTIC SIMILARITY FOR TINY FACE DETECTION）</news:title>
   <news:publication_date>2026-04-14T03:48:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679122</loc>
  <lastmod>2026-04-14T03:48:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>軽い荷電ヒッグスの探索における深層学習（Deep-Learning in Search of Light Charged Higgs）</news:title>
   <news:publication_date>2026-04-14T03:48:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679120</loc>
  <lastmod>2026-04-14T03:47:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>有限アクション集合における有限回の切替制約下でのオンライン学習（Online learning over a finite action set with limited switching）</news:title>
   <news:publication_date>2026-04-14T03:47:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679118</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Wasserstein GANの改良学習法の改善点（IMPROVING THE IMPROVED TRAINING OF WASSERSTEIN GANS: A CONSISTENCY TERM AND ITS DUAL EFFECT）</news:title>
   <news:publication_date>2026-04-14T03:47:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習に基づくA/Dデクオンタイズによる極端な低照度下での画像復元（Learning-based Dequantization for Image Restoration Against Extremely Poor Illumination）</news:title>
   <news:publication_date>2026-04-14T02:55:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679114</loc>
  <lastmod>2026-04-14T02:55:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>少数ショットで検出器を移行する技術の要点（LSTD: A Low-Shot Transfer Detector for Object Detection）</news:title>
   <news:publication_date>2026-04-14T02:55:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679112</loc>
  <lastmod>2026-04-14T02:54:36Z</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>2026-04-14T02:54:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679110</loc>
  <lastmod>2026-04-14T02:54:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>変調なし環境でのチャンネル等化を変える変分オートエンコーダーの応用（Blind Channel Equalization using Variational Autoencoders）</news:title>
   <news:publication_date>2026-04-14T02:54:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679108</loc>
  <lastmod>2026-04-14T02:54:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>部分カリキュラムを用いたクロスペース表現学習（Cross-Paced Representation Learning with Partial Curricula for Sketch-based Image Retrieval）</news:title>
   <news:publication_date>2026-04-14T02:54:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679106</loc>
  <lastmod>2026-04-14T02:54:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679104</loc>
  <lastmod>2026-04-14T02:02:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>シリコン上のGeVn複合体による室温単一原子ナノエレクトロニクス（GeVn complexes for silicon-based room-temperature single-atom nanoelectronics）</news:title>
   <news:publication_date>2026-04-14T02:02:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679102</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ビザンチン耐性分散学習の統計的最適性（Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates）</news:title>
   <news:publication_date>2026-04-14T02:01:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679100</loc>
  <lastmod>2026-04-14T02:01:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>予測状態に基づく再帰型方策ネットワーク（Recurrent Predictive State Policy Networks）</news:title>
   <news:publication_date>2026-04-14T02:01:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679098</loc>
  <lastmod>2026-04-14T02:00:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>サンドイッチで学ぶ学習型ブルームフィルタの最適化（Optimizing Learned Bloom Filters by Sandwiching）</news:title>
   <news:publication_date>2026-04-14T02:00:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679096</loc>
  <lastmod>2026-04-14T02:00:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>HHLアルゴリズムの再考と量子機械学習への影響（Reconsider HHL algorithm and its related quantum machine learning algorithms）</news:title>
   <news:publication_date>2026-04-14T02:00:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679094</loc>
  <lastmod>2026-04-14T01:59:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>人間の「似ている」判断を問うデータセット（Totally Looks Like - How Humans Compare, Compared to Machines）</news:title>
   <news:publication_date>2026-04-14T01:59:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679092</loc>
  <lastmod>2026-04-14T01:59:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習サンプル効率の高い移動ロボットの目標到達（Learning Sample-Efﬁcient Target Reaching for Mobile Robots）</news:title>
   <news:publication_date>2026-04-14T01:59:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679090</loc>
  <lastmod>2026-04-14T01:08:34Z</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>2026-04-14T01:08:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679088</loc>
  <lastmod>2026-04-14T01:08:20Z</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>2026-04-14T01:08:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679086</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-04-14T01:07:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679084</loc>
  <lastmod>2026-04-14T01:07:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>連続的深層クラスタリング（Deep Continuous Clustering）</news:title>
   <news:publication_date>2026-04-14T01:07:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679082</loc>
  <lastmod>2026-04-14T01:06:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>小グループにおける公平性の定性的指標（Qualitative Measures of Equity in Small Groups）</news:title>
   <news:publication_date>2026-04-14T01:06:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679080</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-04-14T01:06:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679078</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-04-14T01:06:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679076</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>数学的・物理的な負の意味の融合（Blending Mathematical and Physical Negative-ness）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的活性化プルーニングによる敵対的防御（Stochastic Activation Pruning for Robust Adversarial Defense）</news:title>
   <news:publication_date>2026-04-14T00:14:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679070</loc>
  <lastmod>2026-04-14T00:13:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>効率的かつ高精度なMRI超解像（Efficient and Accurate MRI Super-Resolution using a Generative Adversarial Network and 3D Multi-Level Densely Connected Network）</news:title>
   <news:publication_date>2026-04-14T00:13:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679068</loc>
  <lastmod>2026-04-14T00:13:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>連続最適化によるDAG構造学習の革新（DAGs with NO TEARS: Continuous Optimization for Structure Learning）</news:title>
   <news:publication_date>2026-04-14T00:13:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679066</loc>
  <lastmod>2026-04-14T00:12:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>セッション長の階層モデルとシュリンク（Hierarchical Modeling and Shrinkage for User Session Length Prediction in Media Streaming）</news:title>
   <news:publication_date>2026-04-14T00:12:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679064</loc>
  <lastmod>2026-04-13T23:21:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>粒界における原子運動を機械学習で決定する（Machine learning determination of atomic dynamics at grain boundaries）</news:title>
   <news:publication_date>2026-04-13T23:21:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679062</loc>
  <lastmod>2026-04-13T23:21:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>橋梁管理のリアルタイム意思決定支援（A real-time decision support system for bridge management based on the rules generalized by CART decision tree and SMO algorithms）</news:title>
   <news:publication_date>2026-04-13T23:21:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679060</loc>
  <lastmod>2026-04-13T23:21:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>一枚の一人称画像から作るエゴセントリックなバスケットボール動作計画（Egocentric Basketball Motion Planning from a Single First-Person Image）</news:title>
   <news:publication_date>2026-04-13T23:21:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679058</loc>
  <lastmod>2026-04-13T23:19:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>女性の腹圧性尿失禁の病態解析と人工ニューラルネットワークによる尿道圧予測（Pathological Analysis of Stress Urinary Incontinence in Females using Artificial Neural Networks）</news:title>
   <news:publication_date>2026-04-13T23:19:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679056</loc>
  <lastmod>2026-04-13T23:19:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散準ニュートン法による非平滑正則化を伴う経験的リスク最小化（A Distributed Quasi-Newton Algorithm for Empirical Risk Minimization with Nonsmooth Regularization）</news:title>
   <news:publication_date>2026-04-13T23:19:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679054</loc>
  <lastmod>2026-04-13T23:19:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Data Curation with Deep Learning（Data Curation with Deep Learning）</news:title>
   <news:publication_date>2026-04-13T23:19:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679052</loc>
  <lastmod>2026-04-13T23:19:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>一般的な凸－凹サドルポイント問題のための原始双対アルゴリズム（A Primal‑Dual Algorithm for General Convex‑Concave Saddle Point Problems）</news:title>
   <news:publication_date>2026-04-13T23:19:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679050</loc>
  <lastmod>2026-04-13T22:27:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>精密な顔部位局在化と3D表情認識の深層学習（Accurate Facial Parts Localization and Deep Learning for 3D Facial Expression Recognition）</news:title>
   <news:publication_date>2026-04-13T22:27:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679048</loc>
  <lastmod>2026-04-13T22:26:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチステップ交通流予測の新展開（New Results on Multi-Step Traffic Flow Prediction）</news:title>
   <news:publication_date>2026-04-13T22:26:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679046</loc>
  <lastmod>2026-04-13T22:26:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非定常性を検知してオンライン予測器を適応させるSAFE（SAFE: Spectral Evolution Analysis Feature Extraction for Non-Stationary Time Series Prediction）</news:title>
   <news:publication_date>2026-04-13T22:26:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679044</loc>
  <lastmod>2026-04-13T22:25:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Twitterを教育活動に組み込む効果：学習成果と参加意欲の関係（The effect of twitter-mediated activities on learning outcome and student engagement: A case study）</news:title>
   <news:publication_date>2026-04-13T22:25:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679042</loc>
  <lastmod>2026-04-13T22:25:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>エントロピー正則化付き最適輸送問題の確率的貪欲アルゴリズム（Greedy stochastic algorithms for entropy-regularized optimal transport problems）</news:title>
   <news:publication_date>2026-04-13T22:25:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679040</loc>
  <lastmod>2026-04-13T22:24:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>シナプス結合のベイズ推論による深層ネットワーク正則化 (Deep Network Regularization via Bayesian Inference of Synaptic Connectivity)</news:title>
   <news:publication_date>2026-04-13T22:24:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679038</loc>
  <lastmod>2026-04-13T22:23:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数段階Spatial Transformerによる把持検出の実務的意義（Classification based Grasp Detection using Spatial Transformer Network）</news:title>
   <news:publication_date>2026-04-13T22:23:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679036</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Deep-FSMNによる高精度・低遅延音声認識の実用化（DEEP-FSMN FOR LARGE VOCABULARY CONTINUOUS SPEECH RECOGNITION）</news:title>
   <news:publication_date>2026-04-13T21:33:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679034</loc>
  <lastmod>2026-04-13T21:32:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>WHAIによる深層トピックモデルの推論改善（WHAI: Weibull Hybrid Autoencoding Inference for Deep Topic Modeling）</news:title>
   <news:publication_date>2026-04-13T21:32:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679032</loc>
  <lastmod>2026-04-13T21:31:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層学習を最適制御で捉える新視点（An Optimal Control Approach to Deep Learning and Applications to Discrete-Weight Neural Networks）</news:title>
   <news:publication_date>2026-04-13T21:31:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679030</loc>
  <lastmod>2026-04-13T21:31:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ルールベースモデルの妥当性と人間の判断（On Cognitive Preferences and the Plausibility of Rule-based Models）</news:title>
   <news:publication_date>2026-04-13T21:31:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679028</loc>
  <lastmod>2026-04-13T21:30:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>信号歪みを学習で補正するモジュールによる変調認識の改善（A Learnable Distortion Correction Module for Modulation Recognition）</news:title>
   <news:publication_date>2026-04-13T21:30:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679026</loc>
  <lastmod>2026-04-13T21:30:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>通信制約下の分散非パラメトリック回帰（Distributed Nonparametric Regression under Communication Constraints）</news:title>
   <news:publication_date>2026-04-13T21:30:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679024</loc>
  <lastmod>2026-04-13T20:39:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>浅層モデルに基づく潜在的計画の発見（Discovering Underlying Plans Based on Shallow Models）</news:title>
   <news:publication_date>2026-04-13T20:39:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679022</loc>
  <lastmod>2026-04-13T20:39:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>極大規模べき乗則グラフの設計・生成・検証（Design, Generation, and Validation of Extreme Scale Power-Law Graphs）</news:title>
   <news:publication_date>2026-04-13T20:39:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679020</loc>
  <lastmod>2026-04-13T20:38:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次不変性による自然勾配の加速（Accelerating Natural Gradient with Higher-Order Invariance）</news:title>
   <news:publication_date>2026-04-13T20:38:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679018</loc>
  <lastmod>2026-04-13T20:37:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>汎用的畳み込みと再帰ネットワークの経験的評価（An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling）</news:title>
   <news:publication_date>2026-04-13T20:37:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679016</loc>
  <lastmod>2026-04-13T20:37:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動画から学ぶ教師なし顔表現（Unsupervised Learning of Face Representations）</news:title>
   <news:publication_date>2026-04-13T20:37:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679014</loc>
  <lastmod>2026-04-13T20:37:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>AntShield: 端末上での個人情報漏洩検知の実践（AntShield: On-Device Detection of Personal Information Exposure）</news:title>
   <news:publication_date>2026-04-13T20:37:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679012</loc>
  <lastmod>2026-04-13T20:36:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非負値行列因子分解による信号・データ解析の実用化（Nonnegative Matrix Factorization for Signal and Data Analytics）</news:title>
   <news:publication_date>2026-04-13T20:36:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679010</loc>
  <lastmod>2026-04-13T19:44:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多義語埋め込みの誤認識を見抜く──Ex-RPCAによる疑似多義検出と改善（Understanding and Improving Multi-Sense Word Embeddings via Extended Robust Principal Component Analysis）</news:title>
   <news:publication_date>2026-04-13T19:44:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679008</loc>
  <lastmod>2026-04-13T19:44:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>虹彩位置検出のベンチマーク化と深層学習検出器の評価（A Benchmark for Iris Location and a Deep Learning Detector Evaluation）</news:title>
   <news:publication_date>2026-04-13T19:44:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679006</loc>
  <lastmod>2026-04-13T19:44:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>帰納的行列補完の高速・高効率解法（Fast and Sample Efficient Inductive Matrix Completion via Multi-Phase Procrustes Flow）</news:title>
   <news:publication_date>2026-04-13T19:44:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679004</loc>
  <lastmod>2026-04-13T19:42:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>過剰パラメータ化がもたらす最適化と汎化の力（On the Power of Over-parametrization in Neural Networks with Quadratic Activation）</news:title>
   <news:publication_date>2026-04-13T19:42:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679002</loc>
  <lastmod>2026-04-13T19:42:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GANを用いた医療画像の合成によるデータ拡張と肝病変分類の改善（GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification）</news:title>
   <news:publication_date>2026-04-13T19:42:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/679000</loc>
  <lastmod>2026-04-13T19:42:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層ベイズ能動半教師あり学習（Deep Bayesian Active Semi-Supervised Learning）</news:title>
   <news:publication_date>2026-04-13T19:42:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678998</loc>
  <lastmod>2026-04-13T19:41:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ロボット支援手術における器具の自動セグメンテーション（Automatic Instrument Segmentation in Robot-Assisted Surgery Using Deep Learning）</news:title>
   <news:publication_date>2026-04-13T19:41:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678996</loc>
  <lastmod>2026-04-13T18:49:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>フラustrated古典スピンモデルの機械学習 II: カーネル主成分分析（Machine Learning of Frustrated Classical Spin Models. II. Kernel Principal Component Analysis）</news:title>
   <news:publication_date>2026-04-13T18:49:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678994</loc>
  <lastmod>2026-04-13T18:49:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>結核の胸部X線解析と深層学習によるセグメンテーションと拡張（Chest X-Ray Analysis of Tuberculosis by Deep Learning with Segmentation and Augmentation）</news:title>
   <news:publication_date>2026-04-13T18:49:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678992</loc>
  <lastmod>2026-04-13T18:48:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数試合の空間的パスネットワークを階層的に分解する手法（MULTIRESOLUTION TENSOR DECOMPOSITION FOR MULTIPLE SPATIAL PASSING NETWORKS）</news:title>
   <news:publication_date>2026-04-13T18:48:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678990</loc>
  <lastmod>2026-04-13T18:47:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>監視映像における置き去り荷物のリアルタイム検出法（Real-Time Deep Learning Method for Abandoned Luggage Detection in Video）</news:title>
   <news:publication_date>2026-04-13T18:47:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678988</loc>
  <lastmod>2026-04-13T18:47:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>畑から都市へ、衛星画像で読み解く土地利用変化の予測（Enhancement of land-use change modeling using convolutional neural networks and convolutional denoising autoencoders）</news:title>
   <news:publication_date>2026-04-13T18:47:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678986</loc>
  <lastmod>2026-04-13T18:47:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>構文情報を活かす木構造ニューラルネットワーク（Tag-Enhanced Tree-Structured Neural Networks for Implicit Discourse Relation Classification）</news:title>
   <news:publication_date>2026-04-13T18:47:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678984</loc>
  <lastmod>2026-04-13T18:47:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>AlexNet以降の深層学習史と主要技術の俯瞰（The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches）</news:title>
   <news:publication_date>2026-04-13T18:47:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678982</loc>
  <lastmod>2026-04-13T17:54:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>モバイルヘルスの自動化と個別化に向けた対話型機械学習の展望（Towards Automatic &amp;amp; Personalised Mobile Health Interventions: An Interactive Machine Learning Perspective）</news:title>
   <news:publication_date>2026-04-13T17:54:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678980</loc>
  <lastmod>2026-04-13T17:53:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>観測的模倣学習（Observational Imitation Learning: OIL）</news:title>
   <news:publication_date>2026-04-13T17:53:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678978</loc>
  <lastmod>2026-04-13T17:53:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Seq2Sickによるseq2seqモデルの敵対的脆弱性評価（Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with Adversarial Examples）</news:title>
   <news:publication_date>2026-04-13T17:53:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678976</loc>
  <lastmod>2026-04-13T17:52:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>限られた音声データで鳥種を識別する多チャネルCNN手法（Audio-only Bird Species Automated Identification Method with Limited Training Data Based on Multi-Channel Deep Convolutional Neural Networks）</news:title>
   <news:publication_date>2026-04-13T17:52:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678974</loc>
  <lastmod>2026-04-13T17:52:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>車両監視向けFocal Loss密検出器（Focal Loss Dense Detector for Vehicle Surveillance）</news:title>
   <news:publication_date>2026-04-13T17:52:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678972</loc>
  <lastmod>2026-04-13T17:51:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>探索学習に関する検討 — メタ強化学習による探索学習について (Some Considerations on Learning to Explore via Meta-Reinforcement Learning)</news:title>
   <news:publication_date>2026-04-13T17:51:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678970</loc>
  <lastmod>2026-04-13T17:51:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>遅い学習者に待たされるより、古い勾配で進め──分散SGDにおける誤差と実行時間のトレードオフ（Slow and Stale Gradients Can Win the Race: Error-Runtime Trade-offs in Distributed SGD）</news:title>
   <news:publication_date>2026-04-13T17:51:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678968</loc>
  <lastmod>2026-04-13T16:59:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多目的進化アルゴリズムにおける参照点適応とクラスタリングの相互作用（A Many-Objective Evolutionary Algorithm With Two Interacting Processes: Cascade Clustering and Reference Point Incremental Learning）</news:title>
   <news:publication_date>2026-04-13T16:59:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678966</loc>
  <lastmod>2026-04-13T16:59:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模マルチエージェントUAV群の最適化のためのモデルベース確率探索（Model-Based Stochastic Search for Large Scale Optimization of Multi-Agent UAV Swarms）</news:title>
   <news:publication_date>2026-04-13T16:59:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678964</loc>
  <lastmod>2026-04-13T16:58:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>回帰オラクルを用いた実用的文脈バンディット（Practical Contextual Bandits with Regression Oracles）</news:title>
   <news:publication_date>2026-04-13T16:58:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678962</loc>
  <lastmod>2026-04-13T16:58:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非定常チャネル下における無線制御システムの学習（Learning in Wireless Control Systems over Non-Stationary Channels）</news:title>
   <news:publication_date>2026-04-13T16:58:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678960</loc>
  <lastmod>2026-04-13T16:58:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>空間化学反応の縮約モデルとしての動的ボルツマン分布の学習（Learning Dynamic Boltzmann Distributions as Reduced Models of Spatial Chemical Kinetics）</news:title>
   <news:publication_date>2026-04-13T16:58:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678958</loc>
  <lastmod>2026-04-13T16:57:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>安定性を担保する非線形システム同定の高速化（Specialized Interior Point Algorithm for Stable Nonlinear System Identification）</news:title>
   <news:publication_date>2026-04-13T16:57:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678956</loc>
  <lastmod>2026-04-13T16:57:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>連続最適化のための絶え間ない連結焼なまし法（Perpertual Coupled Simulated Annealing for Continuous Optimization）</news:title>
   <news:publication_date>2026-04-13T16:57:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678954</loc>
  <lastmod>2026-04-13T16:06:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GAN評価の定量化手法（Quantitatively Evaluating GANs with Divergences Proposed for Training）</news:title>
   <news:publication_date>2026-04-13T16:06:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678952</loc>
  <lastmod>2026-04-13T16:05:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>内視鏡カプセルロボットの無監督オドメトリと深度学習（Unsupervised Odometry and Depth Learning for Endoscopic Capsule Robots）</news:title>
   <news:publication_date>2026-04-13T16:05:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678950</loc>
  <lastmod>2026-04-13T16:05:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチエージェント模倣学習による運転シミュレーションの進化（Multi-Agent Imitation Learning for Driving Simulation）</news:title>
   <news:publication_date>2026-04-13T16:05:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678948</loc>
  <lastmod>2026-04-13T16:04:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>PRESISTANT: データ前処理を導く学習支援アシスタント（PRESISTANT: Learning based assistant for data pre-processing）</news:title>
   <news:publication_date>2026-04-13T16:04:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678946</loc>
  <lastmod>2026-04-13T16:04:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次元画像空間を覗く望遠鏡の構築（Building a Telescope to Look Into High-Dimensional Image Spaces）</news:title>
   <news:publication_date>2026-04-13T16:04:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678944</loc>
  <lastmod>2026-04-13T16:04:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散ストリーム処理に対するモデルフリー制御（Model-Free Control for Distributed Stream Data Processing using Deep Reinforcement Learning）</news:title>
   <news:publication_date>2026-04-13T16:04:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678942</loc>
  <lastmod>2026-04-13T16:03:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習が解くエンタングルメントの自由度—線形回帰を愛するまで (Machine Learning Entanglement Freedom Or: How I Learned to Stop Worrying and Love Linear Regression)</news:title>
   <news:publication_date>2026-04-13T16:03:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678940</loc>
  <lastmod>2026-04-13T15:12:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ロバスト部分空間復元の概観（An Overview of Robust Subspace Recovery）</news:title>
   <news:publication_date>2026-04-13T15:12:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678938</loc>
  <lastmod>2026-04-13T15:11:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時間的アラインメントによる制御のタスク分解学習（TACO: Learning Task Decomposition via Temporal Alignment for Control）</news:title>
   <news:publication_date>2026-04-13T15:11:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678936</loc>
  <lastmod>2026-04-13T15:10:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>タスク・モーション計画のための能動的モデル学習と多様な行動サンプリング（Active model learning and diverse action sampling for task and motion planning）</news:title>
   <news:publication_date>2026-04-13T15:10:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678934</loc>
  <lastmod>2026-04-13T15:10:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>歴史的時間と年代学習の実践的応用（Learning Historical and Chronological Time: Practical Applications）</news:title>
   <news:publication_date>2026-04-13T15:10:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678932</loc>
  <lastmod>2026-04-13T15:10:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>相互情報量に基づくハッシュ学習（Hashing with Mutual Information）</news:title>
   <news:publication_date>2026-04-13T15:10:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678930</loc>
  <lastmod>2026-04-13T15:09:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ラベル反転によるデータ汚染とその防衛（Label Sanitization against Label Flipping Poisoning Attacks）</news:title>
   <news:publication_date>2026-04-13T15:09:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678928</loc>
  <lastmod>2026-04-13T14:18:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Gradient-Boosted Treesを組み込んだ混合整数凸非線形最適化（Mixed-Integer Convex Nonlinear Optimization with Gradient-Boosted Trees Embedded）</news:title>
   <news:publication_date>2026-04-13T14:18:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678926</loc>
  <lastmod>2026-04-13T14:17:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>樹皮画像による樹種同定（Tree Species Identification from Bark Images Using Convolutional Neural Networks）</news:title>
   <news:publication_date>2026-04-13T14:17:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678924</loc>
  <lastmod>2026-04-13T14:17:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>全てのサンプルが同じではない：重要度サンプリングによる深層学習（Deep Learning with Importance Sampling）</news:title>
   <news:publication_date>2026-04-13T14:17:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678922</loc>
  <lastmod>2026-04-13T14:16:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ベイズ逆問題とモデル検証の「ブラックボックス脱却」（Beyond black-boxes in Bayesian inverse problems and model validation: applications in solid mechanics of elastography）</news:title>
   <news:publication_date>2026-04-13T14:16:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678920</loc>
  <lastmod>2026-04-13T14:16:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>少ない質問で精度と近似解を両立する半教師ありk-means（Semi-Supervised Algorithms for Approximately Optimal and Accurate Clustering）</news:title>
   <news:publication_date>2026-04-13T14:16:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678918</loc>
  <lastmod>2026-04-13T14:15:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散優先経験再生（Distributed Prioritized Experience Replay）</news:title>
   <news:publication_date>2026-04-13T14:15:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678916</loc>
  <lastmod>2026-04-13T14:15:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>JPEG画像を敵対的攻撃から守る（Protecting JPEG Images Against Adversarial Attacks）</news:title>
   <news:publication_date>2026-04-13T14:15:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678914</loc>
  <lastmod>2026-04-13T13:23:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>急変に強いスパース同定による高速モデル復元（Sparse Identification of Nonlinear Dynamics for Rapid Model Recovery）</news:title>
   <news:publication_date>2026-04-13T13:23:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678912</loc>
  <lastmod>2026-04-13T13:23:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ビッグデータに潜むバイアスの影響（Impact of Biases in Big Data）</news:title>
   <news:publication_date>2026-04-13T13:23:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678910</loc>
  <lastmod>2026-04-13T13:23:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>フィードバック頂点集合問題のパラメータ化アルゴリズムの実験的評価（Experimental Evaluation of Parameterized Algorithms for Feedback Vertex Set）</news:title>
   <news:publication_date>2026-04-13T13:23:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678908</loc>
  <lastmod>2026-04-13T13:22:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>構文情報を組み込む言語モデルの実践的意義（Syntax-Aware Language Modeling with Recurrent Neural Networks）</news:title>
   <news:publication_date>2026-04-13T13:22:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678906</loc>
  <lastmod>2026-04-13T13:22:08Z</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損失地形に関する衝撃的な発見（Essentially No Barriers in Neural Network Energy Landscape）</news:title>
   <news:publication_date>2026-04-13T13:22:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678904</loc>
  <lastmod>2026-04-13T13:21:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的ニューラルネットワークによるモリブデン系合金設計（Probabilistic design of a molybdenum-base alloy using a neural network）</news:title>
   <news:publication_date>2026-04-13T13:21:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678902</loc>
  <lastmod>2026-04-13T13:21:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>2Dハイブリッド有機無機ペロブスカイトの欠陥変異に基づく深青色発光ダイオード（Deep-blue light emitting diode based on defect variations of a 2D hybrid organic-inorganic low dimensional perovskite semiconductor）</news:title>
   <news:publication_date>2026-04-13T13:21:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678899</loc>
  <lastmod>2026-04-13T12:29:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>勾配に基づくサンプリングによる最小二乗解の高速化（Gradient-based Sampling: An Adaptive Importance Sampling for Least-squares）</news:title>
   <news:publication_date>2026-04-13T12:29:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678897</loc>
  <lastmod>2026-04-13T12:19:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>鉛直せん断水平流（VSHF）形成不安定性の解析（Vertically Sheared Horizontal Flow-Forming Instability in Stratified Turbulence）</news:title>
   <news:publication_date>2026-04-13T12:19:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678895</loc>
  <lastmod>2026-04-13T12:18:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>インターネット上の低品質音声からの声のクローン化の可能性（Can we steal your vocal identity from the Internet?: Initial investigation of cloning Obama’s voice using GAN, WaveNet and low-quality found data）</news:title>
   <news:publication_date>2026-04-13T12:18:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678893</loc>
  <lastmod>2026-04-13T12:18:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非常に質量の大きいウルフ・ライエ星Mk 34の155日X線サイクル（The 155-day X-ray cycle of the very massive Wolf-Rayet star Melnick 34 in the Large Magellanic Cloud）</news:title>
   <news:publication_date>2026-04-13T12:18:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678891</loc>
  <lastmod>2026-04-13T12:17:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Deep Cocktail Networkによるマルチソース非教師ありドメイン適応とカテゴリシフト (Deep Cocktail Network: Multi-source Unsupervised Domain Adaptation with Category Shift)</news:title>
   <news:publication_date>2026-04-13T12:17:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678889</loc>
  <lastmod>2026-04-13T12:17:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DREAMによる姿勢頑健な顔認識（Pose-Robust Face Recognition via Deep Residual Equivariant Mapping）</news:title>
   <news:publication_date>2026-04-13T12:17:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678887</loc>
  <lastmod>2026-04-13T12:17:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多インスタンス深層ニューラルネットワークによるヒッグスCP測定の要点解説（A multi-instance deep neural network classifier: application to Higgs boson CP measurement）</news:title>
   <news:publication_date>2026-04-13T12:17:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678885</loc>
  <lastmod>2026-04-13T11:25:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多変量線形回帰における非因果的アーティファクトの検出（Detecting non-causal artifacts in multivariate linear regression models）</news:title>
   <news:publication_date>2026-04-13T11:25:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678883</loc>
  <lastmod>2026-04-13T11:25:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ランダムウォークで学ぶグラフ生成の第一歩：NetGAN（NetGAN: Generating Graphs via Random Walks）</news:title>
   <news:publication_date>2026-04-13T11:25:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678881</loc>
  <lastmod>2026-04-13T11:24:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>照明変化画像系列を用いた教師なし単一画像の内在的分解（Unsupervised Deep Single-Image Intrinsic Decomposition using Illumination-Varying Image Sequences）</news:title>
   <news:publication_date>2026-04-13T11:24:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678879</loc>
  <lastmod>2026-04-13T11:24:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>21cm線と宇宙マイクロ波背景のクロスパワー解析が示す実務的示唆（Study of systematics effects on the Cross Power Spectrum of 21 cm Line and Cosmic Microwave Background using Murchison Widefield Array Data）</news:title>
   <news:publication_date>2026-04-13T11:24:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678877</loc>
  <lastmod>2026-04-13T11:24:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>目標空間の無教師学習による自発的目標探索（Unsupervised Learning of Goal Spaces for Intrinsically Motivated Goal Exploration）</news:title>
   <news:publication_date>2026-04-13T11:24:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678875</loc>
  <lastmod>2026-04-13T11:23:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>デジタルロックを機械学習へ導く—勾配ブースティングと深層ニューラルネットワークによる透過率予測（Driving Digital Rock towards Machine Learning: predicting permeability with Gradient Boosting and Deep Neural Networks）</news:title>
   <news:publication_date>2026-04-13T11:23:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678873</loc>
  <lastmod>2026-04-13T11:23:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スパース複数カーネル学習：ミラー・ストラティファビリティによるサポート同定（Sparse Multiple Kernel Learning: Support Identification via Mirror Stratiﬁability）</news:title>
   <news:publication_date>2026-04-13T11:23:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678871</loc>
  <lastmod>2026-04-13T10:32:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>畳み込み幾何行列補完（Convolutional Geometric Matrix Completion）</news:title>
   <news:publication_date>2026-04-13T10:32:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678869</loc>
  <lastmod>2026-04-13T10:31:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>量子回路学習の実務的意義（Quantum Circuit Learning）</news:title>
   <news:publication_date>2026-04-13T10:31:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678867</loc>
  <lastmod>2026-04-13T10:30:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>臨床的に意義ある時系列比較の新手法（Clinically Meaningful Comparisons Over Time: An Approach to Measuring Patient Similarity based on Subsequence Alignment）</news:title>
   <news:publication_date>2026-04-13T10:30:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678865</loc>
  <lastmod>2026-04-13T10:30:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自然言語から構造化クエリ生成のメタ学習（Natural Language to Structured Query Generation via Meta-Learning）</news:title>
   <news:publication_date>2026-04-13T10:30:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678863</loc>
  <lastmod>2026-04-13T10:29:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Eコマース検索におけるランキング制御を強化学習で最適化する手法（Reinforcement Learning to Rank in E-Commerce Search Engine: Formalization, Analysis, and Application）</news:title>
   <news:publication_date>2026-04-13T10:29:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678861</loc>
  <lastmod>2026-04-13T10:29:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次データ構造へのPCAの拡張（Extension of PCA to Higher Order Data Structures: An Introduction to Tensors, Tensor Decompositions, and Tensor PCA）</news:title>
   <news:publication_date>2026-04-13T10:29:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678859</loc>
  <lastmod>2026-04-13T09:38:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>生波形での多チャネル音源分離を実現する多解像度畳み込みオートエンコーダ（Raw Multi-Channel Audio Source Separation using Multi-Resolution Convolutional Auto-Encoders）</news:title>
   <news:publication_date>2026-04-13T09:38:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678857</loc>
  <lastmod>2026-04-13T09:37:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>検索ランキングを高速化する文脈的特徴選択（Accelerating E-Commerce Search Engine Ranking by Contextual Factor Selection）</news:title>
   <news:publication_date>2026-04-13T09:37:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678855</loc>
  <lastmod>2026-04-13T09:37:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>疎ビューCTのシノグラム合成に基づく深層ニューラルネットワーク（Deep-neural-network based sinogram synthesis for sparse-view CT image reconstruction）</news:title>
   <news:publication_date>2026-04-13T09:37:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678853</loc>
  <lastmod>2026-04-13T09:36:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Autostackerが切り拓くAutoMLの柔軟性（Autostacker: A Compositional Evolutionary Learning System）</news:title>
   <news:publication_date>2026-04-13T09:36:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678851</loc>
  <lastmod>2026-04-13T09:36:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ガウスノイズを加えた行列の特異部分空間摂動に関する最適推定（MATRICES WITH GAUSSIAN NOISE: OPTIMAL ESTIMATES FOR SINGULAR SUBSPACE PERTURBATION）</news:title>
   <news:publication_date>2026-04-13T09:36:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678849</loc>
  <lastmod>2026-04-13T09:36:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>UAVを用いた無線ネットワークの総合チュートリアル（A Tutorial on UAVs for Wireless Networks: Applications, Challenges, and Open Problems）</news:title>
   <news:publication_date>2026-04-13T09:36:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678847</loc>
  <lastmod>2026-04-13T09:36:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチモーダル検索のための相関を抑えたハッシュ符号学習（Learning Decorrelated Hashing Codes for Multimodal Retrieval）</news:title>
   <news:publication_date>2026-04-13T09:36:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678845</loc>
  <lastmod>2026-04-13T08:44:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>セミ教師ありメタラーニングで少数ショット学習を拡張する（META-LEARNING FOR SEMI-SUPERVISED FEW-SHOT CLASSIFICATION）</news:title>
   <news:publication_date>2026-04-13T08:44:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678843</loc>
  <lastmod>2026-04-13T08:43:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CEDMとFFDMを橋渡しするSD-CNN（Shallow-Deep Convolutional Neural Network）</news:title>
   <news:publication_date>2026-04-13T08:43:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678841</loc>
  <lastmod>2026-04-13T08:42:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>進化的生成対抗ネットワーク（Evolutionary Generative Adversarial Networks）</news:title>
   <news:publication_date>2026-04-13T08:42:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678839</loc>
  <lastmod>2026-04-13T08:42:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>半パラメトリック位相記憶によるナビゲーション（SEMI-PARAMETRIC TOPOLOGICAL MEMORY FOR NAVIGATION）</news:title>
   <news:publication_date>2026-04-13T08:42:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678837</loc>
  <lastmod>2026-04-13T08:42:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>静的および動的ロバストPCAと行列補完のレビュー（Static and Dynamic Robust PCA and Matrix Completion: A Review）</news:title>
   <news:publication_date>2026-04-13T08:42:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678835</loc>
  <lastmod>2026-04-13T08:41:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高レベルドメイン特化言語による量子コンピューティングの拡張（Q#: Enabling scalable quantum computing and development with a high-level domain–specific language）</news:title>
   <news:publication_date>2026-04-13T08:41:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678833</loc>
  <lastmod>2026-04-13T08:41:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>宇宙機クラスタの軌道決定におけるカーネル埋め込み手法（Kernel Embedding Approaches to Orbit Determination of Spacecraft Clusters）</news:title>
   <news:publication_date>2026-04-13T08:41:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678831</loc>
  <lastmod>2026-04-13T07:50:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ビットコイン上のポンジ・スキーム検出のためのデータマイニング（Data mining for detecting Bitcoin Ponzi schemes）</news:title>
   <news:publication_date>2026-04-13T07:50:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678829</loc>
  <lastmod>2026-04-13T07:50:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所的リサンプリング偽造検出（Resampling Forgery Detection Using Deep Learning and A-Contrario Analysis）</news:title>
   <news:publication_date>2026-04-13T07:50:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678827</loc>
  <lastmod>2026-04-13T07:49:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Bregman関数とその発散についての再検討（RE-EXAMINATION OF BREGMAN FUNCTIONS AND NEW PROPERTIES OF THEIR DIVERGENCES）</news:title>
   <news:publication_date>2026-04-13T07:49:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678825</loc>
  <lastmod>2026-04-13T07:48:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層的な模倣学習と強化学習の統合がもたらす実務的インパクト（Hierarchical Imitation and Reinforcement Learning）</news:title>
   <news:publication_date>2026-04-13T07:48:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678823</loc>
  <lastmod>2026-04-13T07:48:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>塵に隠れた超新星を赤外で見つけた意義（SPIRITS 16tn in NGC 3556）</news:title>
   <news:publication_date>2026-04-13T07:48:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678821</loc>
  <lastmod>2026-04-13T07:48:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>リッチ観測下のオラクル効率的PAC強化学習（On Oracle-Efficient PAC RL with Rich Observations）</news:title>
   <news:publication_date>2026-04-13T07:48:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678819</loc>
  <lastmod>2026-04-13T07:48:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Shinyを用いた応答面最適化ゲームの現代的更新（A shiny update to an old experiment game）</news:title>
   <news:publication_date>2026-04-13T07:48:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678817</loc>
  <lastmod>2026-04-13T06:56:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ワイル半金属における光起電効果の第一原理研究（Photogalvanic Effect in Weyl Semimetals from First Principles）</news:title>
   <news:publication_date>2026-04-13T06:56:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678815</loc>
  <lastmod>2026-04-13T06:55:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>光干渉計の遠隔教育ウェブサイト（An Educational Website on Interferometry）</news:title>
   <news:publication_date>2026-04-13T06:55:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678813</loc>
  <lastmod>2026-04-13T06:55:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>半教師ありオンライン構造学習による複合イベント認識の実用化（Semi-Supervised Online Structure Learning for Composite Event Recognition）</news:title>
   <news:publication_date>2026-04-13T06:55:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678811</loc>
  <lastmod>2026-04-13T06:54:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>計算最適輸送の数値的展開（Computational Optimal Transport）</news:title>
   <news:publication_date>2026-04-13T06:54:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678809</loc>
  <lastmod>2026-04-13T06:54:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>再構成可能なマニピュレータ・シミュレータ（Aaria: A Reconfigurable Manipulator Simulator）</news:title>
   <news:publication_date>2026-04-13T06:54:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678807</loc>
  <lastmod>2026-04-13T06:54:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スキルミオンと反スキルミオンの軌道運動と対生成（Trochoidal motion and pair generation in skyrmion and antiskyrmion dynamics under spin-orbit torques）</news:title>
   <news:publication_date>2026-04-13T06:54:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678805</loc>
  <lastmod>2026-04-13T06:53:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Alpha-Beta対称ダイバージェンスと正定値カーネルの意義（The Alpha-Beta-Symmetric Divergence and their Positive Definite Kernels）</news:title>
   <news:publication_date>2026-04-13T06:53:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678803</loc>
  <lastmod>2026-04-13T06:02:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>属性に基づく合成的計画（Composable Planning with Attributes）</news:title>
   <news:publication_date>2026-04-13T06:02:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678801</loc>
  <lastmod>2026-04-13T06:02:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ベクトル埋め込みの機能性と次元性の理解（Understand Functionality and Dimensionality of Vector Embeddings: the Distributional Hypothesis, the Pairwise Inner Product Loss and Its Bias–Variance Trade-off）</news:title>
   <news:publication_date>2026-04-13T06:02:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678799</loc>
  <lastmod>2026-04-13T06:01:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ストリーミング環境におけるオンライン特徴量ランキング（ONLINE FEATURE RANKING FOR INTRUSION DETECTION SYSTEMS）</news:title>
   <news:publication_date>2026-04-13T06:01:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678797</loc>
  <lastmod>2026-04-13T06:01:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多層グラフクラスタリングのためのパワーミーンラプラシアン（The Power Mean Laplacian for Multilayer Graph Clustering）</news:title>
   <news:publication_date>2026-04-13T06:01:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678795</loc>
  <lastmod>2026-04-13T06:00:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>異種な買い手行動下での頑健な反復オークション（Robust Repeated Auctions under Heterogeneous Buyer Behavior）</news:title>
   <news:publication_date>2026-04-13T06:00:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678793</loc>
  <lastmod>2026-04-13T06:00:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時空間スパイクパターンの最適局所・分散符号化（Optimal localist and distributed coding of spatiotemporal spike patterns through STDP and coincidence detection）</news:title>
   <news:publication_date>2026-04-13T06:00:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678791</loc>
  <lastmod>2026-04-13T06:00:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ノイズと疎なWebマークアップにおける欠損カテゴリ情報の推定（Inferring Missing Categorical Information in Noisy and Sparse Web Markup）</news:title>
   <news:publication_date>2026-04-13T06:00:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678789</loc>
  <lastmod>2026-04-13T05:09:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非パラメトリック時空間サブゴールモデルによる逆強化学習 (Inverse Reinforcement Learning via Nonparametric Spatio-Temporal Subgoal Modeling)</news:title>
   <news:publication_date>2026-04-13T05:09:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678787</loc>
  <lastmod>2026-04-13T05:08:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>人間配慮型経路計画を学習する方法（Learning Human-Aware Path Planning with Fully Convolutional Networks）</news:title>
   <news:publication_date>2026-04-13T05:08:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678785</loc>
  <lastmod>2026-04-13T05:08:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 </url>
 <url>
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    <news:name>AI Benchmark Research</news:name>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
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   <news:genres>Blog</news:genres>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 <url>
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    <news:name>AI Benchmark Research</news:name>
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   <news:genres>Blog</news:genres>
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    <news:name>AI Benchmark Research</news:name>
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   <news:genres>Blog</news:genres>
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 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
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   <news:genres>Blog</news:genres>
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 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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    <news:name>AI Benchmark Research</news:name>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
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 </url>
 <url>
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 </url>
 <url>
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 </url>
 <url>
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  <news:news>
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 </url>
 <url>
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  <news:news>
   <news:publication>
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 </url>
 <url>
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 </url>
 <url>
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  <news:news>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 </url>
 <url>
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  <news:news>
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 </url>
 <url>
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  <news:news>
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 </url>
 <url>
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  <news:news>
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    <news:name>AI Benchmark Research</news:name>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 </url>
 <url>
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  <news:news>
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    <news:name>AI Benchmark Research</news:name>
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 </url>
 <url>
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    <news:name>AI Benchmark Research</news:name>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-04-13T00:32:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-04-13T00:32:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-04-12T23:38:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>逆思考をモデル化する機械学習（Modeling reverse thinking for machine learning）</news:title>
   <news:publication_date>2026-04-12T23:36:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>因果推論のための深層学習（Deep Learning for Causal Inference）</news:title>
   <news:publication_date>2026-04-12T22:35:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>RNNの長期依存性学習を助ける補助損失（Learning Longer-term Dependencies in RNNs with Auxiliary Losses）</news:title>
   <news:publication_date>2026-04-12T22:33:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>構造化光学受信機における誤差補償法（Error Correction in Structured Optical Receivers）</news:title>
   <news:publication_date>2026-04-12T22:33:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>ボイド群のネットワーク化は敵対的学習に対してより頑健である（Networking the Boids is More Robust Against Adversarial Learning）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ADMMベースのネットワーク化確率的変分推論（ADMM-based Networked Stochastic Variational Inference）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>Twitterにおけるヘイトスピーチ検出──長尾（Long-tail）問題が解決を遠ざける理由（Hate Speech Detection - the Difficult Long-tail Case of Twitter）</news:title>
   <news:publication_date>2026-04-12T20:47:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678659</loc>
  <lastmod>2026-04-12T20:46:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-04-12T20:46:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Augmented CycleGANによる多対多写像の学習（Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678655</loc>
  <lastmod>2026-04-12T20:46:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-04-12T20:46:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678653</loc>
  <lastmod>2026-04-12T20:46:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>潜在空間物理学：流体の時間発展を学習する（Latent Space Physics: Towards Learning the Temporal Evolution of Fluid Flow）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678651</loc>
  <lastmod>2026-04-12T19:54:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-04-12T19:54:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散学習におけるビザンチン耐性SGDの一般化（Generalized Byzantine-tolerant SGD）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>弾性源イメージングのための深層学習の数学的枠組み (A Mathematical Framework for Deep Learning in Elastic Source Imaging)</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>初期印刷書籍のOCR精度向上：事前学習・投票・能動学習の組合せ（Improving OCR Accuracy on Early Printed Books by combining Pretraining, Voting, and Active Learning）</news:title>
   <news:publication_date>2026-04-12T19:53:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678639</loc>
  <lastmod>2026-04-12T19:52:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>初期印刷本のOCR精度向上（Improving OCR Accuracy on Early Printed Books using Deep Convolutional Networks）</news:title>
   <news:publication_date>2026-04-12T19:52:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678637</loc>
  <lastmod>2026-04-12T19:02:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs（Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs）</news:title>
   <news:publication_date>2026-04-12T19:02:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678635</loc>
  <lastmod>2026-04-12T19:02:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>監視映像における顔認識のための深層学習アーキテクチャ（Deep Learning Architectures for Face Recognition in Video Surveillance）</news:title>
   <news:publication_date>2026-04-12T19:02:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678633</loc>
  <lastmod>2026-04-12T19:02:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>行動依存ベースラインの幻影（The Mirage of Action-Dependent Baselines in Reinforcement Learning）</news:title>
   <news:publication_date>2026-04-12T19:02:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678631</loc>
  <lastmod>2026-04-12T19:00:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>立体視画像のニューラルスタイル転写（Neural Stereoscopic Image Style Transfer）</news:title>
   <news:publication_date>2026-04-12T19:00:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678629</loc>
  <lastmod>2026-04-12T19:00:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高解像度3D物体表現のための多視点シルエットと深度分解（Multi-View Silhouette and Depth Decomposition for High Resolution 3D Object Representation）</news:title>
   <news:publication_date>2026-04-12T19:00:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678627</loc>
  <lastmod>2026-04-12T19:00:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層ネットワークにおけるスペクトル普遍性の出現（The Emergence of Spectral Universality in Deep Networks）</news:title>
   <news:publication_date>2026-04-12T19:00:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678625</loc>
  <lastmod>2026-04-12T19:00:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単眼カメラによる3次元複数物体追跡（Mono-Camera 3D Multi-Object Tracking Using Deep Learning Detections and PMBM Filtering）</news:title>
   <news:publication_date>2026-04-12T19:00:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678623</loc>
  <lastmod>2026-04-12T18:08:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>実世界における反復動作の推定（Real-World Repetition Estimation by Div, Grad and Curl）</news:title>
   <news:publication_date>2026-04-12T18:08:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678621</loc>
  <lastmod>2026-04-12T18:08:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>順列に基づくモデルで1/√nの壁を破る（Breaking the 1/√n Barrier: Faster Rates for Permutation-based Models in Polynomial Time）</news:title>
   <news:publication_date>2026-04-12T18:08:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678619</loc>
  <lastmod>2026-04-12T18:08:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>照明条件を考慮したマルチスペクトル融合で歩行者検出を強化する手法（Fusion of Multispectral Data Through Illumination-aware Deep Neural Networks for Pedestrian Detection）</news:title>
   <news:publication_date>2026-04-12T18:08:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678617</loc>
  <lastmod>2026-04-12T18:06:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>有害コメント分類のための畳み込みニューラルネットワーク（Convolutional Neural Networks for Toxic Comment Classification）</news:title>
   <news:publication_date>2026-04-12T18:06:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678615</loc>
  <lastmod>2026-04-12T18:06:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>走行データからのV2V遭遇シナリオ抽出（Extraction of V2V Encountering Scenarios from Naturalistic Driving Database）</news:title>
   <news:publication_date>2026-04-12T18:06:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678613</loc>
  <lastmod>2026-04-12T18:06:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>インスタンス最適復元と制限等長性（Instance Optimal Decoding and the Restricted Isometry Property）</news:title>
   <news:publication_date>2026-04-12T18:06:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678611</loc>
  <lastmod>2026-04-12T18:06:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>異なるラベル空間を横断する対（ペア）系列分類のマルチタスク学習（Multi-task Learning of Pairwise Sequence Classification Tasks Over Disparate Label Spaces）</news:title>
   <news:publication_date>2026-04-12T18:06:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678609</loc>
  <lastmod>2026-04-12T17:14:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>RDBMSにおける浮動小数点集計の再現性確保（Reproducible Floating-Point Aggregation in RDBMSs）</news:title>
   <news:publication_date>2026-04-12T17:14:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678607</loc>
  <lastmod>2026-04-12T17:13:38Z</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相と相転移学習のパラメータ診断 (Parameter diagnostics of phases and phase transition learning by neural networks)</news:title>
   <news:publication_date>2026-04-12T17:13:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678605</loc>
  <lastmod>2026-04-12T17:13:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ENEM結果から見る物理学習の難点（Physics learning difficulties from the perspective of ENEM results）</news:title>
   <news:publication_date>2026-04-12T17:13:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678603</loc>
  <lastmod>2026-04-12T17:12:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>音声信号の深層因子分解（DEEP FACTORIZATION FOR SPEECH SIGNAL）</news:title>
   <news:publication_date>2026-04-12T17:12:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678601</loc>
  <lastmod>2026-04-12T17:12:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層ピクセルレベル事前分布を用いた逆問題解法（Solving Inverse Computational Imaging Problems using Deep Pixel-level Prior）</news:title>
   <news:publication_date>2026-04-12T17:12:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678599</loc>
  <lastmod>2026-04-12T17:11:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>少ない方が良い：標準コンパイラ最適化レベルを活用して性能とエネルギーを改善する（Less is More: Exploiting the Standard Compiler Optimization Levels for Better Performance and Energy Consumption）</news:title>
   <news:publication_date>2026-04-12T17:11:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678597</loc>
  <lastmod>2026-04-12T17:11:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>敵対的サンプルを学習に活かす能動学習（Adversarial Active Learning for Deep Networks: a Margin Based Approach）</news:title>
   <news:publication_date>2026-04-12T17:11:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678595</loc>
  <lastmod>2026-04-12T16:20:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スケルトンに基づく行動認識の時空間グラフ畳み込み（Spatio-Temporal Graph Convolution for Skeleton Based Action Recognition）</news:title>
   <news:publication_date>2026-04-12T16:20:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678593</loc>
  <lastmod>2026-04-12T16:19:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>粗から細への非剛体レジストレーション（Coarse to fine non-rigid registration: a chain of scale-specific neural networks for multimodal image alignment with application to remote sensing）</news:title>
   <news:publication_date>2026-04-12T16:19:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678591</loc>
  <lastmod>2026-04-12T16:19:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数タスクを同時学習する差分方策勾配（DiGrad: Multi-Task Reinforcement Learning with Shared Actions）</news:title>
   <news:publication_date>2026-04-12T16:19:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678589</loc>
  <lastmod>2026-04-12T16:18:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>バイオインフォマティクスと医療における深層学習の役割（Bioinformatics and Medicine in the Era of Deep Learning）</news:title>
   <news:publication_date>2026-04-12T16:18:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678587</loc>
  <lastmod>2026-04-12T16:18:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時系列重視の顧客離反予測とPU学習の実務的意義（Time-sensitive Customer Churn Prediction based on PU Learning）</news:title>
   <news:publication_date>2026-04-12T16:18:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678585</loc>
  <lastmod>2026-04-12T16:18:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>軌道に沿った線形時変モデルの同定法——滑らかな変化と不連続な変化を扱う凸最適化手法（Identification of LTV Dynamical Models with Smooth or Discontinuous Time Evolution by means of Convex Optimization）</news:title>
   <news:publication_date>2026-04-12T16:18:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678583</loc>
  <lastmod>2026-04-12T16:18:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>事前情報を必要としないマッチング畳み込みニューラルネットワーク (Matching Convolutional Neural Networks without Priors about Data)</news:title>
   <news:publication_date>2026-04-12T16:18:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678581</loc>
  <lastmod>2026-04-12T15:26:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>堅牢なオブジェクトネス転移による物体検出の改良（Robust Objectness Transfer for Object Detection）</news:title>
   <news:publication_date>2026-04-12T15:26:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678579</loc>
  <lastmod>2026-04-12T15:26:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時間変動チャネル追跡と空間時系列基底展開（Time Varying Channel Tracking with Spatial and Temporal BEM for Massive MIMO Systems）</news:title>
   <news:publication_date>2026-04-12T15:26:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678577</loc>
  <lastmod>2026-04-12T15:25:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ガウス・メタ埋め込みによる高次裾野PLDAモデルの効率的スコアリング（Gaussian meta-embeddings for efficient scoring of a heavy-tailed PLDA model）</news:title>
   <news:publication_date>2026-04-12T15:25:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678575</loc>
  <lastmod>2026-04-12T15:25:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューラルネットワークの表現学習と情報ボトルネック原理（Learning Representations for Neural Network-Based Classification Using the Information Bottleneck Principle）</news:title>
   <news:publication_date>2026-04-12T15:25:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678573</loc>
  <lastmod>2026-04-12T15:24:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>効率的な深層ニューラルネットワーク学習のためのL1ノルムバッチ正規化（L1-Norm Batch Normalization for Efficient Training of Deep Neural Networks）</news:title>
   <news:publication_date>2026-04-12T15:24:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678571</loc>
  <lastmod>2026-04-12T15:24:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>再帰地平線制御のためのマルチステップ予測モデル（On multi-step prediction models for receding horizon control）</news:title>
   <news:publication_date>2026-04-12T15:24:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
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