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   <news:title>電圧制御問題に対する扱いやすい楕円体近似 (A tractable ellipsoidal approximation for voltage regulation problems)</news:title>
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   <news:title>COSMOS領域におけるX線系の分光学的サーベイ（A Spectroscopic Census of X-Ray Systems in the COSMOS Field）</news:title>
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   <news:title>Stiefel上の制約付き勾配降下による量子グラフィカルモデルの学習 (Learning Quantum Graphical Models using Constrained Gradient Descent on the Stiefel Manifold)</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>空を制御する―階層型空中基地局における生存性・カバレッジ・移動則（Control over Skies: Survivability, Coverage, and Mobility Laws for Hierarchical Aerial Base Stations）</news:title>
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   <news:genres>Blog</news:genres>
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    <news:language>ja</news:language>
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   <news:title>Skew-Fit：状態を網羅する自己監督型強化学習（Skew-Fit: State-Covering Self-Supervised Reinforcement Learning）</news:title>
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   <news:title>適応的電力系統緊急制御における深層強化学習（Adaptive Power System Emergency Control using Deep Reinforcement Learning）</news:title>
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   <news:genres>Blog</news:genres>
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   <news:title>非コヒーレントMIMOの変調学習（Learning to Modulate for Non-coherent MIMO）</news:title>
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   <news:title>Deep Learningを用いた2ウェイリレーネットワークの星座最適化（Deep Learning-Based Constellation Optimization for Physical Network Coding in Two-Way Relay Networks）</news:title>
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   <news:genres>Blog</news:genres>
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    <news:name>AI Benchmark Research</news:name>
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   <news:title>胸部X線からの年齢推定（Age prediction using a large chest X-ray dataset）</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>NeuTra HMCによる悪いジオメトリの是正（NeuTra-lizing Bad Geometry in Hamiltonian Monte Carlo Using Neural Transport）</news:title>
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   <news:genres>Blog</news:genres>
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   <news:title>特徴フィードバックを持つ線形バンディット（Linear Bandits with Feature Feedback）</news:title>
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   <news:genres>Blog</news:genres>
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   <news:title>画像のプライバシー予測を深層ニューラルネットワークで行う（Image Privacy Prediction Using Deep Neural Networks）</news:title>
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   <news:genres>Blog</news:genres>
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   <news:genres>Blog</news:genres>
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   <news:title>低次元における凸体の能動学習（Active-Learning a Convex Body in Low Dimensions）</news:title>
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   <news:title>パーセレイテッド多重解像度ニューラルネットワークによる定量的磁化率逆問題の解法（Quantitative Susceptibility Inversion through Parcellated Multiresolution Neural Networks and K-Space Substitution）</news:title>
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   <news:genres>Blog</news:genres>
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    <news:language>ja</news:language>
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   <news:title>フィードバック制御のための分類器訓練（Training Classifiers For Feedback Control）</news:title>
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   <news:genres>Blog</news:genres>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>移動するプライマリユーザ偽装攻撃の検出法（Primary User Emulation Attacks: A Detection Technique Based on Kalman Filter）</news:title>
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   <news:genres>Blog</news:genres>
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  <news:news>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>視覚的雑音に強い生体認証の防御法（Robust Presentation Attack Detection through Unsupervised Adversarial Invariance）</news:title>
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   <news:genres>Blog</news:genres>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>大規模エネルギー収穫ネットワークのオンライン送信電力制御を深層学習で実現する（DEEP LEARNING BASED ONLINE POWER CONTROL FOR LARGE ENERGY HARVESTING NETWORKS）</news:title>
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   <news:genres>Blog</news:genres>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>星形成初期質量関数の変動が示す銀河進化への意味（Variations of the stellar Initial Mass Function in Semi-Analytic Models）</news:title>
   <news:publication_date>2026-08-15T23:54:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>自己対戦で学習する組合せ最適化エージェントの可能性（Learning Self-Game-Play Agents for Combinatorial Optimization Problems）</news:title>
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   <news:genres>Blog</news:genres>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>スパースJLによる特徴ハッシュの理解（Understanding Sparse JL for Feature Hashing）</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>欠測データ下の正規化されないモデルに対するインピュテーション推定法（Imputation Estimators for Unnormalized Models with Missing Data）</news:title>
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   <news:genres>Blog</news:genres>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>自動運転の頑健性と安全性を高める敵対的強化学習（Improved Robustness and Safety for Autonomous Vehicle Control with Adversarial Reinforcement Learning）</news:title>
   <news:publication_date>2026-08-15T23:53:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>RF給電バックスキャッタ通信における干渉回避ゲームの強化学習（Reinforcement Learning for Interference Avoidance Game in RF-Powered Backscatter Communications）</news:title>
   <news:publication_date>2026-08-15T23:53:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/723623</loc>
  <lastmod>2026-08-15T23:01:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スパース変分ガウス過程回帰の収束速度に関する考察（Rates of Convergence for Sparse Variational Gaussian Process Regression）</news:title>
   <news:publication_date>2026-08-15T23:01:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723621</loc>
  <lastmod>2026-08-15T22:51:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的微分同相（位相保存）登録の教師なし学習（Unsupervised Learning of Probabilistic Diffeomorphic Registration for Images and Surfaces）</news:title>
   <news:publication_date>2026-08-15T22:51:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723619</loc>
  <lastmod>2026-08-15T22:51:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>触覚で物体を識別する学習（Learning to Identify Object Instances by Touch: Tactile Recognition via Multimodal Matching）</news:title>
   <news:publication_date>2026-08-15T22:51:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723617</loc>
  <lastmod>2026-08-15T22:50:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>2Dアイジンモデルの臨界温度推定を深層学習オートエンコーダで探る（The critical temperature of the 2D-Ising model through Deep Learning Autoencoders）</news:title>
   <news:publication_date>2026-08-15T22:50:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723615</loc>
  <lastmod>2026-08-15T22:50:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高速な深度推定のための2D畳み込みによるコストシグネチャ処理（Fast Deep Stereo with 2D Convolutional Processing of Cost Signatures）</news:title>
   <news:publication_date>2026-08-15T22:50:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723613</loc>
  <lastmod>2026-08-15T22:50:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>誘導的転移によるニューラルアーキテクチャ最適化（Inductive Transfer for Neural Architecture Optimization）</news:title>
   <news:publication_date>2026-08-15T22:50:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723611</loc>
  <lastmod>2026-08-15T22:50:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深い部分空間の変分推論による教師なしデータ補完（Unsupervised Data Imputation via Variational Inference of Deep Subspaces）</news:title>
   <news:publication_date>2026-08-15T22:50:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723609</loc>
  <lastmod>2026-08-15T21:58:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>視覚情報と強化学習を組み合わせた皮膚診断支援の革新（Improving Skin Condition Classification with a Visual Symptom Checker Trained using Reinforcement Learning）</news:title>
   <news:publication_date>2026-08-15T21:58:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723607</loc>
  <lastmod>2026-08-15T21:58:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>三者対戦型GANによる難易度の高いサンプル生成と分類器強化（A Three-Player GAN: Generating Hard Samples To Improve Classification Networks）</news:title>
   <news:publication_date>2026-08-15T21:58:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723605</loc>
  <lastmod>2026-08-15T21:58:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GF-2高解像度衛星画像を用いたピクセルベースとオブジェクト指向分類の比較（Research on the pixel-based and object-oriented methods of urban feature extraction with GF-2 remote-sensing images）</news:title>
   <news:publication_date>2026-08-15T21:58:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723603</loc>
  <lastmod>2026-08-15T21:57:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深さはいつ効くのか――浅いモデルがある程度良ければ深さの利点は限定的か（Is Deeper Better only when Shallow is Good?）</news:title>
   <news:publication_date>2026-08-15T21:57:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723601</loc>
  <lastmod>2026-08-15T21:57:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>弱いラベルで路上景観セグメンテーションを強化する手法（On Boosting Semantic Street Scene Segmentation with Weak Supervision）</news:title>
   <news:publication_date>2026-08-15T21:57:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723599</loc>
  <lastmod>2026-08-15T21:57:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>3D複数物体生成の進化：Auto-Encoding Progressive GANs（Auto-Encoding Progressive Generative Adversarial Networks For 3D Multi Object Scenes）</news:title>
   <news:publication_date>2026-08-15T21:57:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723597</loc>
  <lastmod>2026-08-15T21:56:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>デバイス・エッジ協調推論を高速化する2段階プルーニング（Improving Device-Edge Cooperative Inference of Deep Learning via 2-Step Pruning）</news:title>
   <news:publication_date>2026-08-15T21:56:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723595</loc>
  <lastmod>2026-08-15T21:06:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Support and Invertibility in Domain-Invariant Representations（Support and Invertibility in Domain-Invariant Representations）</news:title>
   <news:publication_date>2026-08-15T21:06:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723593</loc>
  <lastmod>2026-08-15T21:05:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>脳病変と解剖学の同時学習（Joint Learning of Brain Lesion and Anatomy Segmentation from Heterogeneous Datasets）</news:title>
   <news:publication_date>2026-08-15T21:05:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723591</loc>
  <lastmod>2026-08-15T21:05:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ランダム行列を用いたワッサースタイン距離推定の改良（Random Matrix-Improved Estimation of the Wasserstein Distance between two Centered Gaussian Distributions）</news:title>
   <news:publication_date>2026-08-15T21:05:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723589</loc>
  <lastmod>2026-08-15T21:04:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>軽量バイオメトリクスで再現するコード読解の認知指標（A Replication Study on Code Comprehension and Expertise using Lightweight Biometric Sensors）</news:title>
   <news:publication_date>2026-08-15T21:04:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723587</loc>
  <lastmod>2026-08-15T21:04:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>実測データで学ぶ信号復調の深層学習（Deep Learning for Signal Demodulation in Physical Layer Wireless Communications: Prototype Platform, Open Dataset, and Analytics）</news:title>
   <news:publication_date>2026-08-15T21:04:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723585</loc>
  <lastmod>2026-08-15T21:04:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>光学イメージング装置の不確実性対応型性能評価（Uncertainty-aware performance assessment of optical imaging modalities with invertible neural networks）</news:title>
   <news:publication_date>2026-08-15T21:04:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723583</loc>
  <lastmod>2026-08-15T21:04:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>MLWeavingによるGLM高速化（Accelerating Generalized Linear Models with MLWeaving）</news:title>
   <news:publication_date>2026-08-15T21:04:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723581</loc>
  <lastmod>2026-08-15T20:13:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>波動関数を幾何学で読み解く—2次元ターゲット空間からの一般化力学（On the geometrical hypotheses underlying wave functions and their emerging dynamics）</news:title>
   <news:publication_date>2026-08-15T20:13:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723579</loc>
  <lastmod>2026-08-15T20:13:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次元イメージングバイオマーカーを用いた事象ベースモデルによる認知症の空間的進行推定（Event-Based Modeling with High-Dimensional Imaging Biomarkers for Estimating Spatial Progression of Dementia）</news:title>
   <news:publication_date>2026-08-15T20:13:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723577</loc>
  <lastmod>2026-08-15T20:13:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>中性子星データから状態方程式を推定する深層ニューラルネットワーク（Mapping neutron star data to the equation of state using the deep neural network）</news:title>
   <news:publication_date>2026-08-15T20:13:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723575</loc>
  <lastmod>2026-08-15T20:11:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Lyapunov制約を用いた学習型制御器の設計フレームワーク（A framework of learning controller with Lyapunov-based constraint and application）</news:title>
   <news:publication_date>2026-08-15T20:11:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723573</loc>
  <lastmod>2026-08-15T20:11:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>音声駆動ジェスチャ生成の入出力表現解析 (Analyzing Input and Output Representations for Speech-Driven Gesture Generation)</news:title>
   <news:publication_date>2026-08-15T20:11:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723571</loc>
  <lastmod>2026-08-15T20:11:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>近接最適化問題に深層学習を当てる—駐車違反巡回の近似手法（Approximating Optimisation Solutions for Travelling Officer Problem with Customised Deep Learning Network）</news:title>
   <news:publication_date>2026-08-15T20:11:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723569</loc>
  <lastmod>2026-08-15T20:11:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大余裕マージン多重カーネル学習による識別的特徴選択と表現学習 (Large-Margin Multiple Kernel Learning for Discriminative Features Selection and Representation Learning)</news:title>
   <news:publication_date>2026-08-15T20:11:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723567</loc>
  <lastmod>2026-08-15T19:19:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複素値ゲーテッドオートエンコーダによる映像予測（Complex Valued Gated Auto-encoder for Video Frame Prediction）</news:title>
   <news:publication_date>2026-08-15T19:19:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723565</loc>
  <lastmod>2026-08-15T19:09:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>光学・赤外測光データに基づくクエーサー候補選択の効率化（Efficient Selection of Quasar Candidates Based on Optical and Infrared Photometric Data Using Machine Learning）</news:title>
   <news:publication_date>2026-08-15T19:09:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723563</loc>
  <lastmod>2026-08-15T19:09:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>手持ち物の姿勢と形状をRGB画像から推定する研究（Learning to Estimate Pose and Shape of Hand-Held Objects from RGB Images）</news:title>
   <news:publication_date>2026-08-15T19:09:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723561</loc>
  <lastmod>2026-08-15T19:08:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模グラフ上でのヒューリスティクス学習（Learning Heuristics over Large Graphs via Deep Reinforcement Learning）</news:title>
   <news:publication_date>2026-08-15T19:08:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723559</loc>
  <lastmod>2026-08-15T19:08:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>知識を埋め込むルーティングでシーングラフ生成を強化する手法（Knowledge-Embedded Routing Network for Scene Graph Generation）</news:title>
   <news:publication_date>2026-08-15T19:08:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723557</loc>
  <lastmod>2026-08-15T19:08:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>3Dメッシュ変形ネットワークの実務的意義（3DN: 3D Deformation Network）</news:title>
   <news:publication_date>2026-08-15T19:08:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723555</loc>
  <lastmod>2026-08-15T19:07:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Small Data時代におけるファジィ分類器は不要か（Do we still need fuzzy classifiers for Small Data in the Era of Big Data?）</news:title>
   <news:publication_date>2026-08-15T19:07:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723553</loc>
  <lastmod>2026-08-15T18:16:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>密猟対策の予測と巡回計画（Stay Ahead of Poachers: Illegal Wildlife Poaching Prediction and Patrol Planning Under Uncertainty with Field Test Evaluations）</news:title>
   <news:publication_date>2026-08-15T18:16:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723551</loc>
  <lastmod>2026-08-15T18:15:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>証明可能なテンソルリング補完（Provable Tensor Ring Completion）</news:title>
   <news:publication_date>2026-08-15T18:15:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723549</loc>
  <lastmod>2026-08-15T18:15:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-15T18:15:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723547</loc>
  <lastmod>2026-08-15T18:14:36Z</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>2026-08-15T18:14:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723545</loc>
  <lastmod>2026-08-15T18:14:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>合成データから学ぶ群衆計数の実務応用（Learning from Synthetic Data for Crowd Counting in the Wild）</news:title>
   <news:publication_date>2026-08-15T18:14:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723543</loc>
  <lastmod>2026-08-15T18:13:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>骨格軌跡に学習される規則性による映像異常検知（Learning Regularity in Skeleton Trajectories for Anomaly Detection in Videos）</news:title>
   <news:publication_date>2026-08-15T18:13:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723541</loc>
  <lastmod>2026-08-15T18:13:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時系列分類の性能評価を見直すべきか（Should we Reload Time Series Classification Performance Evaluation ?）</news:title>
   <news:publication_date>2026-08-15T18:13:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723539</loc>
  <lastmod>2026-08-15T17:22:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習者の「混乱」を言語で特定する手法（An Identification of Learners’ Confusion through Language and Discourse Analysis）</news:title>
   <news:publication_date>2026-08-15T17:22:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723537</loc>
  <lastmod>2026-08-15T17:22:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時間認識遠隔監督による関係抽出の改良（Towards Time-Aware Distant Supervision for Relation Extraction）</news:title>
   <news:publication_date>2026-08-15T17:22:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723535</loc>
  <lastmod>2026-08-15T17:21:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層クラス表現学習に基づく属性獲得（Attribute Acquisition in Ontology based on Representation Learning of Hierarchical Classes and Attributes）</news:title>
   <news:publication_date>2026-08-15T17:21:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723533</loc>
  <lastmod>2026-08-15T17:20:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>外科トレーニングにおける視覚・触覚シミュレーション概説 (A Survey of Visuo-Haptic Simulation in Surgical Training)</news:title>
   <news:publication_date>2026-08-15T17:20:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723531</loc>
  <lastmod>2026-08-15T17:20:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>地平線下の航空機検出に深層学習を適用する意義（Below Horizon Aircraft Detection Using Deep Learning for Vision-Based Sense and Avoid）</news:title>
   <news:publication_date>2026-08-15T17:20:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723529</loc>
  <lastmod>2026-08-15T17:20:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>FastDepth: 組み込み機器での高速単眼深度推定（FastDepth: Fast Monocular Depth Estimation on Embedded Systems）</news:title>
   <news:publication_date>2026-08-15T17:20:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723527</loc>
  <lastmod>2026-08-15T17:20:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-15T17:20:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723525</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-15T16:28:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723523</loc>
  <lastmod>2026-08-15T16:28:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>触覚ユーザインタフェースと実践学習による低侵襲手術トレーニング（Haptic User Interfaces and Practice-based Learning for Minimally Invasive Surgical Training）</news:title>
   <news:publication_date>2026-08-15T16:28:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723521</loc>
  <lastmod>2026-08-15T16:27:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-15T16:27:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-15T16:26:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723517</loc>
  <lastmod>2026-08-15T16:26:31Z</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>2026-08-15T16:26:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723515</loc>
  <lastmod>2026-08-15T16:26:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>超深宇宙ミリ波サーベイが開く新しい宇宙観（Science from an Ultra-Deep, High-Resolution Millimeter-Wave Survey）</news:title>
   <news:publication_date>2026-08-15T16:26:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723513</loc>
  <lastmod>2026-08-15T16:25:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-15T16:25:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723511</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-15T15:34:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723509</loc>
  <lastmod>2026-08-15T15:34:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>重要領域を学習して効率的な経路計画を実現する（Learn and Link: Learning Critical Regions for Efficient Planning）</news:title>
   <news:publication_date>2026-08-15T15:34:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723507</loc>
  <lastmod>2026-08-15T15:33:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Auto-Vectorizing TensorFlow Graphs: Jacobians, Auto-Batching and Beyond（Auto-Vectorizing TensorFlow Graphs: Jacobians, Auto-Batching and Beyond）</news:title>
   <news:publication_date>2026-08-15T15:33:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723505</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文脈を取り込む越境語彙写像の考え方（Context-Aware Cross-Lingual Mapping）</news:title>
   <news:publication_date>2026-08-15T15:32:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723503</loc>
  <lastmod>2026-08-15T15:32:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ステップサイズ適応による特徴関連性の学習（Learning Feature Relevance Through Step Size Adaptation in Temporal-Difference Learning）</news:title>
   <news:publication_date>2026-08-15T15:32:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723501</loc>
  <lastmod>2026-08-15T15:32:18Z</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>2026-08-15T15:32:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723499</loc>
  <lastmod>2026-08-15T15:32:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ランク付きリスト損失による深層距離学習（Ranked List Loss for Deep Metric Learning）</news:title>
   <news:publication_date>2026-08-15T15:32:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723497</loc>
  <lastmod>2026-08-15T14:40:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-15T14:40:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723495</loc>
  <lastmod>2026-08-15T14:39:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SeizureNetとてんかん発作の自動分類の革新（SeizureNet: Multi-Spectral Deep Feature Learning for Seizure Type Classification）</news:title>
   <news:publication_date>2026-08-15T14:39:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723493</loc>
  <lastmod>2026-08-15T14:39:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>計画を組み合わせた敵対的模倣学習（Dyna-AIL : Adversarial Imitation Learning by Planning）</news:title>
   <news:publication_date>2026-08-15T14:39:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723491</loc>
  <lastmod>2026-08-15T14:38:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>感情知覚ロボットによる混雑環境での社会的ナビゲーション（The Emotionally Intelligent Robot: Improving Social Navigation in Crowded Environments）</news:title>
   <news:publication_date>2026-08-15T14:38:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723489</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>協調エージェントの階層的教育方策学習（Learning Hierarchical Teaching Policies for Cooperative Agents）</news:title>
   <news:publication_date>2026-08-15T14:37:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723487</loc>
  <lastmod>2026-08-15T14:37:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>社会的相互作用における突発的かつバースト的イベントのモデル化（Markov‑Modulated Hawkes Processes for Modeling Sporadic and Bursty Event Occurrences in Social Interactions）</news:title>
   <news:publication_date>2026-08-15T14:37:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-15T14:37:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Pixel-Attentive Policy Gradientによる多指把持の進展（Pixel-Attentive Policy Gradient for Multi-Fingered Grasping in Cluttered Scenes）</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>特徴のホワイトニングと合意損失による非教師ありドメイン適応（Unsupervised Domain Adaptation using Feature-Whitening and Consensus Loss）</news:title>
   <news:publication_date>2026-08-15T13:45:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news: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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 <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>
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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>
    <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:genres>Blog</news:genres>
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 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-15T12:43:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
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   <news:publication_date>2026-08-15T12:42:33Z</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>
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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>
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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>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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    <news:name>AI Benchmark Research</news:name>
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 <url>
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    <news:name>AI Benchmark Research</news:name>
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 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
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 <url>
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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:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
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    <news:name>AI Benchmark Research</news:name>
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 </url>
 <url>
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 <url>
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    <news:name>AI Benchmark Research</news:name>
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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:news>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
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    <news:name>AI Benchmark Research</news:name>
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 </url>
 <url>
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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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 </url>
 <url>
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  <news:news>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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    <news: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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 </url>
 <url>
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    <news:name>AI Benchmark Research</news:name>
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   <news:publication_date>2026-08-15T08:42:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
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   <news:genres>Blog</news:genres>
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 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
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 <url>
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    <news:name>AI Benchmark Research</news:name>
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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:language>ja</news:language>
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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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 </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>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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    <news: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>
    <news:language>ja</news:language>
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 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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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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 </url>
 <url>
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    <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>
    <news:language>ja</news:language>
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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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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
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    <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>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 </url>
 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 </url>
 <url>
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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>
    <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:name>AI Benchmark Research</news:name>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/723265</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-14T23:36:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723263</loc>
  <lastmod>2026-08-14T23:36:31Z</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GANによる油彩画生成（Conditional GANs For Painting Generation）</news:title>
   <news:publication_date>2026-08-14T23:36:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-14T23:35:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Buddy CompressionによるGPUメモリ拡張の実務的意義（Buddy Compression: Enabling Larger Memory for Deep Learning and HPC Workloads on GPUs）</news:title>
   <news:publication_date>2026-08-14T23:35:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-14T22:44:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>強化型深層再帰的デノイジング自己符号化器による重力波信号の復元（Denoising Gravitational Waves with Enhanced Deep Recurrent Denoising Auto-Encoders）</news:title>
   <news:publication_date>2026-08-14T22:44:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723257</loc>
  <lastmod>2026-08-14T22:35:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>永続的学習下の文埋め込み整合が変える関係抽出の実務応用（Sentence Embedding Alignment for Lifelong Relation Extraction）</news:title>
   <news:publication_date>2026-08-14T22:35:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723255</loc>
  <lastmod>2026-08-14T22:35:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GanDef：GANベースの敵対的訓練防御（GanDef: A GAN based Adversarial Training Defense）</news:title>
   <news:publication_date>2026-08-14T22:35:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723253</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>Tactical Rewind: Self-Correction via Backtracking in Vision-and-Language Navigation（Tactical Rewind: Self-Correction via Backtracking in Vision-and-Language Navigation）</news:title>
   <news:publication_date>2026-08-14T22:33:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723251</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>超新星スペクトル自動分類の深層学習（DASH: Deep Learning for the Automated Spectral Classification of Supernovae and their Hosts）</news:title>
   <news:publication_date>2026-08-14T22:33:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723249</loc>
  <lastmod>2026-08-14T22:33:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>QCD因子分解のための機械学習テンプレート（Machine Learning Templates for QCD Factorization in the Search for Physics Beyond the Standard Model）</news:title>
   <news:publication_date>2026-08-14T22:33:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723247</loc>
  <lastmod>2026-08-14T22:32:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>事後選択（ポストセレクション）による量子メトロロジーの優位性（Quantum Advantage in Postselected Metrology）</news:title>
   <news:publication_date>2026-08-14T22:32:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723245</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>Xbox Oneのデジタルフォレンジクス解析（Forensics Analysis of Xbox One Game Console）</news:title>
   <news:publication_date>2026-08-14T21:41:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723243</loc>
  <lastmod>2026-08-14T21:41:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時系列予測のための自己回帰畳み込み再帰ニューラルネットワーク（Autoregressive Convolutional Recurrent Neural Network）</news:title>
   <news:publication_date>2026-08-14T21:41:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723241</loc>
  <lastmod>2026-08-14T21:40:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>関係プーリングによるグラフ表現の強化（Relational Pooling for Graph Representations）</news:title>
   <news:publication_date>2026-08-14T21:40:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-14T21:40:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-14T21:40:09Z</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>安全指向の深層強化学習：オンラインGaussian Process推定による探索ガイド（SAFETY-GUIDED DEEP REINFORCEMENT LEARNING VIA ONLINE GAUSSIAN PROCESS ESTIMATION）</news:title>
   <news:publication_date>2026-08-14T21:39:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>未知環境での視覚ナビゲーションにおける最適制御と学習の統合（Combining Optimal Control and Learning for Visual Navigation in Novel Environments）</news:title>
   <news:publication_date>2026-08-14T21:39:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>少ないデモから学ぶ人間行動のマルチモーダル表現（Learning multimodal representations for sample-efficient recognition of human actions）</news:title>
   <news:publication_date>2026-08-14T21:39:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-14T20:47:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-14T20:47:53Z</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:publication_date>2026-08-14T20:47:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723225</loc>
  <lastmod>2026-08-14T20:46:35Z</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>2026-08-14T20:46:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723223</loc>
  <lastmod>2026-08-14T20:46:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非微分モデルのための低レベル一次確率プログラミング言語（LF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable Models）</news:title>
   <news:publication_date>2026-08-14T20:46:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723221</loc>
  <lastmod>2026-08-14T20:46:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>一発で掴むための視覚——GQ-STNによるワンショット把持検出（GQ-STN: Optimizing One-Shot Grasp Detection based on Robustness Classifier）</news:title>
   <news:publication_date>2026-08-14T20:46:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723219</loc>
  <lastmod>2026-08-14T20:45:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>キャビティ冷却による反強磁性マグノン間エンタングルメントの増強（Enhancement of antiferromagnetic magnon-magnon entanglement by cavity cooling）</news:title>
   <news:publication_date>2026-08-14T20:45:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723217</loc>
  <lastmod>2026-08-14T19:53:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>KBQA: QAコーパスと知識ベースから学ぶ質問応答（KBQA: Learning Question Answering over QA Corpora and Knowledge Bases）</news:title>
   <news:publication_date>2026-08-14T19:53:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723215</loc>
  <lastmod>2026-08-14T19:45:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>標準コーダによるコーディング工数の定量化（The standard coder: a machine learning approach to measuring the effort required to produce source code change）</news:title>
   <news:publication_date>2026-08-14T19:45:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723213</loc>
  <lastmod>2026-08-14T19:45:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>PyTorch Geometricによる高速グラフ表現学習（FAST GRAPH REPRESENTATION LEARNING WITH PYTORCH GEOMETRIC）</news:title>
   <news:publication_date>2026-08-14T19:45:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723211</loc>
  <lastmod>2026-08-14T19:45:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高パイルアップ環境でのリアルタイム信号復元を可能にするFPGA搭載ディープラーニング（FPGA implementation of a deep learning algorithm for real-time signal reconstruction in radiation detectors under high pile-up conditions）</news:title>
   <news:publication_date>2026-08-14T19:45:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-14T19:44:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オートエンコーダが検出した異常を説明する手法（Explaining Anomalies Detected by Autoencoders Using SHAP）</news:title>
   <news:publication_date>2026-08-14T19:44:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723207</loc>
  <lastmod>2026-08-14T19:44:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>敵対的例を用いた過学習検出（Detecting Overfitting via Adversarial Examples）</news:title>
   <news:publication_date>2026-08-14T19:44:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723205</loc>
  <lastmod>2026-08-14T19:43:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>回避領域（Zone of Avoidance）における銀河同定の進化的深層学習（Evolutionary Deep Learning to Identify Galaxies in the Zone of Avoidance）</news:title>
   <news:publication_date>2026-08-14T19:43:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-14T18:52:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複素値畳み込みニューラルネットワークの圧縮（COMPRESSING COMPLEX CONVOLUTIONAL NEURAL NETWORK BASED ON AN IMPROVED DEEP COMPRESSION ALGORITHM）</news:title>
   <news:publication_date>2026-08-14T18:52:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-14T18:52:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-14T18:52:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-14T18:52:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CANetによるクラス非依存の少数ショットセグメンテーション（CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learning）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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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>
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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:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-14T17:58:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-14T17:07:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-14T17:06:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-14T17:05:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-14T16:11:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-14T16:11:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-14T16:11:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-14T15:19:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <lastmod>2026-08-14T15:18:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <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>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-14T14:23:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-14T13:29:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-14T12:37:56Z</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:publication_date>2026-08-14T12:37:32Z</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:publication_date>2026-08-14T12:37:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-14T12:36:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-14T12:36:24Z</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:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-14T11:41:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-14T11:41:01Z</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: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>
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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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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-14T10:47:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-14T09:43:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-14T09:43:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 </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>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>地球のトロヤ群小惑星を本格探索すべき理由（The Case for a Deep Search for Earth’s Trojan Asteroids）</news:title>
   <news:publication_date>2026-08-14T08:49:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723048</loc>
  <lastmod>2026-08-14T08:48:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>クラウドにおけるVM挙動識別のための深層学習アプローチ（A Deep Learning based approach to VM behavior identification in cloud systems）</news:title>
   <news:publication_date>2026-08-14T08:48:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723046</loc>
  <lastmod>2026-08-14T08:48:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>サドル点からの脱出を目指す逐次凸近似アルゴリズム（Escaping Saddle Points with the Successive Convex Approximation Algorithm）</news:title>
   <news:publication_date>2026-08-14T08:48:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723044</loc>
  <lastmod>2026-08-14T07:57:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次元流体データと作用素から本質的ダイナミクスを明らかにする（Revealing essential dynamics from high-dimensional fluid flow data and operators）</news:title>
   <news:publication_date>2026-08-14T07:57:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723042</loc>
  <lastmod>2026-08-14T07:56:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフ時系列を扱うGated Graph Convolutional Recurrent Neural Networks（Gated Graph Convolutional Recurrent Neural Networks）</news:title>
   <news:publication_date>2026-08-14T07:56:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723040</loc>
  <lastmod>2026-08-14T07:56:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>実務に効くスキル管理ツールの運用知見（Practical Knowledge Management Tool Use in a Software Consulting Company）</news:title>
   <news:publication_date>2026-08-14T07:56:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723038</loc>
  <lastmod>2026-08-14T07:55:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>VUDSによる高赤方偏移銀河のUV・Lyα輝度関数と星形成率密度の評価（The UV and Lyα Luminosity Functions of galaxies and the Star Formation Rate Density at the end of HI reionization from the VIMOS Ultra-Deep Survey (VUDS)）</news:title>
   <news:publication_date>2026-08-14T07:55:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723036</loc>
  <lastmod>2026-08-14T07:55:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>小型銀河の未来研究と望遠鏡の発見（The Future of Dwarf Galaxy Research: What Telescopes Will Discover）</news:title>
   <news:publication_date>2026-08-14T07:55:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723034</loc>
  <lastmod>2026-08-14T07:54:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分類モデルの“写し”を作る技術（COPYING MACHINE LEARNING CLASSIFIERS）</news:title>
   <news:publication_date>2026-08-14T07:54:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723032</loc>
  <lastmod>2026-08-14T07:54:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>滑らかなカーネル正則化を学習する（Learning a smooth kernel regularizer for convolutional neural networks）</news:title>
   <news:publication_date>2026-08-14T07:54:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723030</loc>
  <lastmod>2026-08-14T07:03:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>IoTネットワークにおけるエッジ計算の資源配分を強化学習で解く（Resource Allocation for Edge Computing in IoT Networks via Reinforcement Learning）</news:title>
   <news:publication_date>2026-08-14T07:03:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723028</loc>
  <lastmod>2026-08-14T07:03:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>歩行者軌跡予測のための確率的サンプリングシミュレーション（Stochastic Sampling Simulation for Pedestrian Trajectory Prediction）</news:title>
   <news:publication_date>2026-08-14T07:03:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723026</loc>
  <lastmod>2026-08-14T07:03:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多変量時系列の未識別クラスを復元・クラスタリングする多重カーネル辞書学習（Multiple-Kernel Dictionary Learning for Reconstruction and Clustering of Unseen Multivariate Time-series）</news:title>
   <news:publication_date>2026-08-14T07:03:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723024</loc>
  <lastmod>2026-08-14T07:02:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>キラル相互作用を持つLebwohl–Lasher模型における変調構造（Modulated structures in a Lebwohl-Lasher model with chiral interactions）</news:title>
   <news:publication_date>2026-08-14T07:02:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723022</loc>
  <lastmod>2026-08-14T07:02:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非凸・非滑らか最適化のための慣性ブロック近接法（Inertial Block Proximal Methods for Non-Convex Non-Smooth Optimization）</news:title>
   <news:publication_date>2026-08-14T07:02:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723020</loc>
  <lastmod>2026-08-14T07:02:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Branch-and-Boundを機械学習で高速化する手法（Learning to Branch: Accelerating Resource Allocation in Wireless Networks）</news:title>
   <news:publication_date>2026-08-14T07:02:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723018</loc>
  <lastmod>2026-08-14T07:02:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>miniTimeCubeによる中性子散乱カメラ（miniTimeCube as a neutron scatter camera）</news:title>
   <news:publication_date>2026-08-14T07:02:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723016</loc>
  <lastmod>2026-08-14T06:10:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>六角格子データをそのまま扱う畳み込み（HexagDLy — Processing hexagonally sampled data with CNNs in PyTorch）</news:title>
   <news:publication_date>2026-08-14T06:10:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723014</loc>
  <lastmod>2026-08-14T06:10:36Z</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>2026-08-14T06:10:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723012</loc>
  <lastmod>2026-08-14T06:10:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多ブロックADMMにおけるランダム化の管理（Managing Randomization in the Multi-Block Alternating Direction Method of Multipliers for Quadratic Optimization）</news:title>
   <news:publication_date>2026-08-14T06:10:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723010</loc>
  <lastmod>2026-08-14T06:09:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Shape Completionを活用した3D Siameseトラッキングの実用性（Leveraging Shape Completion for 3D Siamese Tracking）</news:title>
   <news:publication_date>2026-08-14T06:09:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723008</loc>
  <lastmod>2026-08-14T06:09:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>中国チェッカーを理解するための探索と学習の統合（Towards Understanding Chinese Checkers with Heuristics, Monte Carlo Tree Search, and Deep Reinforcement Learning）</news:title>
   <news:publication_date>2026-08-14T06:09:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723006</loc>
  <lastmod>2026-08-14T06:09:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>金融時系列予測のためのデータ駆動型ニューラルアーキテクチャ学習（Data-driven Neural Architecture Learning for Financial Time-series Forecasting）</news:title>
   <news:publication_date>2026-08-14T06:09:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723004</loc>
  <lastmod>2026-08-14T06:08:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Maximal Leakageによる適応的データ解析の新しい枠組み（A New Approach to Adaptive Data Analysis and Learning via Maximal Leakage）</news:title>
   <news:publication_date>2026-08-14T06:08:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723002</loc>
  <lastmod>2026-08-14T05:17:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>説明可能な人工知能のためのデータに基づく対話プロトコル（A Grounded Interaction Protocol for Explainable Artificial Intelligence）</news:title>
   <news:publication_date>2026-08-14T05:17:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/723000</loc>
  <lastmod>2026-08-14T05:17:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SZZ Unleashed：SZZアルゴリズムの公開実装とJenkinsを用いたJITバグ予測の適用事例（SZZ Unleashed: An Open Implementation of the SZZ Algorithm）</news:title>
   <news:publication_date>2026-08-14T05:17:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722998</loc>
  <lastmod>2026-08-14T05:16:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>モデルの所有権を証明する手法（Your Model Belongs to You: A Blind-Watermark based Framework to Protect Intellectual Property of DNN）</news:title>
   <news:publication_date>2026-08-14T05:16:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722996</loc>
  <lastmod>2026-08-14T05:15:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>新規性検出の確率的モデリングと不正検知への応用（Probabilistic Modeling for Novelty Detection with Applications to Fraud Identification）</news:title>
   <news:publication_date>2026-08-14T05:15:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722994</loc>
  <lastmod>2026-08-14T05:15:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>OMPベースのスパース部分空間クラスタリングに対するデータ適応型の効率的アプローチ（A Novel Efficient Approach with Data-Adaptive Capability for OMP-based Sparse Subspace Clustering）</news:title>
   <news:publication_date>2026-08-14T05:15:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722992</loc>
  <lastmod>2026-08-14T05:15:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ネットワーク零和ゲームにおけるマルチエージェント学習はハミルトン系である（Multi-Agent Learning in Network Zero-Sum Games is a Hamiltonian System）</news:title>
   <news:publication_date>2026-08-14T05:15:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722990</loc>
  <lastmod>2026-08-14T05:14:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>病理報告の自動分類とTF‑IDFの現実適用（Automatic Classification of Pathology Reports using TF-IDF Features）</news:title>
   <news:publication_date>2026-08-14T05:14:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722988</loc>
  <lastmod>2026-08-14T04:22:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>教育におけるブロックチェーンの可能性（Blockchain and its Potential in Education）</news:title>
   <news:publication_date>2026-08-14T04:22:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722986</loc>
  <lastmod>2026-08-14T04:22:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフ埋め込みが与信・不正検知に与える実証的効果（Empirical effect of graph embeddings on fraud detection/ risk mitigation）</news:title>
   <news:publication_date>2026-08-14T04:22:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722984</loc>
  <lastmod>2026-08-14T04:22:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>L1-normダブルバックプロパゲーションによる敵対的防御（L1-norm double backpropagation adversarial defense）</news:title>
   <news:publication_date>2026-08-14T04:22:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722982</loc>
  <lastmod>2026-08-14T04:21:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非対称に緩和した分布整合によるドメイン適応（Domain Adaptation with Asymmetrically-Relaxed Distribution Alignment）</news:title>
   <news:publication_date>2026-08-14T04:21:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722980</loc>
  <lastmod>2026-08-14T04:21:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>EdgeStereoによるステレオマッチングとエッジ検出の統合（EdgeStereo: An Effective Multi-Task Learning Network for Stereo Matching and Edge Detection）</news:title>
   <news:publication_date>2026-08-14T04:21:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-14T04:21:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自動運転車の時空間LSTMによる深層学習モーションプランニング（Deep Learning Based Motion Planning For Autonomous Vehicle Using Spatiotemporal LSTM Network）</news:title>
   <news:publication_date>2026-08-14T04:21:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722976</loc>
  <lastmod>2026-08-14T04:21:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>道徳性の複雑性：マルコフブランケットとグラフの道徳性の検査（The Complexity of Morality: Checking Markov Blanket Consistency with DAGs via Morality）</news:title>
   <news:publication_date>2026-08-14T04:21:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722974</loc>
  <lastmod>2026-08-14T03:29:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>異種／非定常データからの因果発見と独立変化の原理（Causal Discovery from Heterogeneous/Nonstationary Data with Independent Changes）</news:title>
   <news:publication_date>2026-08-14T03:29:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722972</loc>
  <lastmod>2026-08-14T03:21:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>共益特徴量の凸的クラスタリングによる分類改善（Convex Covariate Clustering for Classification）</news:title>
   <news:publication_date>2026-08-14T03:21:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722970</loc>
  <lastmod>2026-08-14T03:20:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多車線交通速度予測における二流多チャネル畳み込みニューラルネットワーク（Two‑Stream Multi‑Channel Convolutional Neural Network (TM‑CNN) for Multi‑Lane Traffic Speed Prediction Considering Traffic Volume Impact）</news:title>
   <news:publication_date>2026-08-14T03:20:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722968</loc>
  <lastmod>2026-08-14T03:20:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>U統計量のための濃度に基づく信頼区間（Concentration-based confidence intervals for U-statistics）</news:title>
   <news:publication_date>2026-08-14T03:20:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722966</loc>
  <lastmod>2026-08-14T03:19:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>能動的深層局所化（Deep Active Localization）</news:title>
   <news:publication_date>2026-08-14T03:19:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722964</loc>
  <lastmod>2026-08-14T03:19:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オンラインデータポイズニング攻撃（Online Data Poisoning Attacks）</news:title>
   <news:publication_date>2026-08-14T03:19:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722962</loc>
  <lastmod>2026-08-14T03:18:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>HEVC向け多フレームIn-Loopフィルタ（A DenseNet Based Approach for Multi-Frame In-Loop Filter in HEVC）</news:title>
   <news:publication_date>2026-08-14T03:18:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722960</loc>
  <lastmod>2026-08-14T02:27:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>勾配降下―上昇の収束解析（Convergence of gradient descent-ascent analyzed as a Newtonian dynamical system with dissipation）</news:title>
   <news:publication_date>2026-08-14T02:27:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722958</loc>
  <lastmod>2026-08-14T02:26:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ハードウェア向けストリーミング低ランク更新法（Streaming Batch Eigenupdates for Hardware Neuromorphic Networks）</news:title>
   <news:publication_date>2026-08-14T02:26:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722956</loc>
  <lastmod>2026-08-14T02:26:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>NVDLAをRISC-V SoCに統合しFireSimで評価する意義（Integrating NVIDIA Deep Learning Accelerator (NVDLA) with RISC-V SoC on FireSim）</news:title>
   <news:publication_date>2026-08-14T02:26:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722954</loc>
  <lastmod>2026-08-14T02:26:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習初期の「再ウィンド（rewinding）」で当たりくじを安定化する方法（Stabilizing the Lottery Ticket Hypothesis）</news:title>
   <news:publication_date>2026-08-14T02:26:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722952</loc>
  <lastmod>2026-08-14T02:25:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ウェブ規模最近傍検索を用いた敵対的画像に対する防御（Defense Against Adversarial Images using Web-Scale Nearest-Neighbor Search）</news:title>
   <news:publication_date>2026-08-14T02:25:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722950</loc>
  <lastmod>2026-08-14T02:25:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>初代星（Population III）の対崩壊（Pair-Instability）超新星探索の展望（Searches for Population III pair-instability supernovae: Predictions for ULTIMATE-Subaru and WFIRST）</news:title>
   <news:publication_date>2026-08-14T02:25:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722948</loc>
  <lastmod>2026-08-14T02:25:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>欠損特徴を伴うロジスティック回帰の期待予測と埋め込み手法（What to Expect of Classifiers? Reasoning about Logistic Regression with Missing Features）</news:title>
   <news:publication_date>2026-08-14T02:25:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722946</loc>
  <lastmod>2026-08-14T01:34:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>潜在拡散過程を用いる生成モデルの理論的保証（Theoretical guarantees for sampling and inference in generative models with latent diffusions）</news:title>
   <news:publication_date>2026-08-14T01:34:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722944</loc>
  <lastmod>2026-08-14T01:33:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフデータに対する敵対的事例の攻防（Adversarial Examples on Graph Data: Deep Insights into Attack and Defense）</news:title>
   <news:publication_date>2026-08-14T01:33:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722942</loc>
  <lastmod>2026-08-14T01:33:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>超高エネルギー中性ニュートリノの探索を目指すARIANNA実験（Targeting ultra-high energy neutrinos with the ARIANNA experiment）</news:title>
   <news:publication_date>2026-08-14T01:33:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722940</loc>
  <lastmod>2026-08-14T01:33:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>後悔するエージェント：進捗推定を用いたヒューリスティック支援ナビゲーション（The Regretful Agent: Heuristic-Aided Navigation through Progress Estimation）</news:title>
   <news:publication_date>2026-08-14T01:33:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722938</loc>
  <lastmod>2026-08-14T01:32:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>長期的未来を取り入れた強化学習における動力学モデル学習（LEARNING DYNAMICS MODEL IN REINFORCEMENT LEARNING BY INCORPORATING THE LONG TERM FUTURE）</news:title>
   <news:publication_date>2026-08-14T01:32:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722936</loc>
  <lastmod>2026-08-14T01:32:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>PRIDEを用いた金星探査機の電波掩蔽観測（Venus Express radio occultation observed by PRIDE）</news:title>
   <news:publication_date>2026-08-14T01:32:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722934</loc>
  <lastmod>2026-08-14T01:32:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>不確かなロボットシステムのための制御ラプノフ関数を用いたエピソディック学習（Episodic Learning with Control Lyapunov Functions for Uncertain Robotic Systems）</news:title>
   <news:publication_date>2026-08-14T01:32:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722932</loc>
  <lastmod>2026-08-14T00:41:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>群体フォトメトリック赤方偏移（Ensemble Photometric Redshifts）</news:title>
   <news:publication_date>2026-08-14T00:41:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722930</loc>
  <lastmod>2026-08-14T00:41:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>V2X向けハイブリッドGaussian Processベース通信アーキテクチャ（V2X System Architecture Utilizing Hybrid Gaussian Process-based Model Structures）</news:title>
   <news:publication_date>2026-08-14T00:41:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722928</loc>
  <lastmod>2026-08-14T00:41:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>QickStopによる誤情報の最速検出（QuickStop: A Markov Optimal Stopping Approach for Quickest Misinformation Detection）</news:title>
   <news:publication_date>2026-08-14T00:41:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722926</loc>
  <lastmod>2026-08-14T00:40:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>モデルプリミティブ階層的ライフロング強化学習（Model Primitive Hierarchical Lifelong Reinforcement Learning）</news:title>
   <news:publication_date>2026-08-14T00:40:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722924</loc>
  <lastmod>2026-08-14T00:40:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模画像検索のための教師なしランク保存ハッシング（Unsupervised Rank-Preserving Hashing for Large-Scale Image Retrieval）</news:title>
   <news:publication_date>2026-08-14T00:40:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722922</loc>
  <lastmod>2026-08-14T00:40:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的トラストリージョン法による非凸最適化の効率化（A Stochastic Trust Region Method for Non-convex Minimization）</news:title>
   <news:publication_date>2026-08-14T00:40:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722920</loc>
  <lastmod>2026-08-14T00:39:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>初期宇宙の“最初の爆発”をELTで追う意義（ELT Contributions to The First Explosions）</news:title>
   <news:publication_date>2026-08-14T00:39:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722918</loc>
  <lastmod>2026-08-13T23:48:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>選択的センサフュージョンによるニューラル視覚慣性測位（Selective Sensor Fusion for Neural Visual-Inertial Odometry）</news:title>
   <news:publication_date>2026-08-13T23:48:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722916</loc>
  <lastmod>2026-08-13T23:48:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自動運転車の希少事象サンプリングに対する行動駆動アプローチ（A behavior driven approach for sampling rare event situations for autonomous vehicles）</news:title>
   <news:publication_date>2026-08-13T23:48:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722914</loc>
  <lastmod>2026-08-13T23:48:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>群体認知と多主体対話の計算モデル（MGPI: A Computational Model of Multiagent Group Perception and Interaction）</news:title>
   <news:publication_date>2026-08-13T23:48:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722912</loc>
  <lastmod>2026-08-13T23:47:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大きな横方向運動量の半包摂的深陽電子散乱における2次摂動の再検証（Large Transverse Momentum in Semi-Inclusive Deeply Inelastic Scattering Beyond Lowest Order）</news:title>
   <news:publication_date>2026-08-13T23:47:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722910</loc>
  <lastmod>2026-08-13T23:46:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時系列知識蒸留による能動的知覚の効率化（TKD: Temporal Knowledge Distillation for Active Perception）</news:title>
   <news:publication_date>2026-08-13T23:46:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722908</loc>
  <lastmod>2026-08-13T23:46:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>画像のデヘイジングをセグメンテーション向けに学習する意義（Learning of Image Dehazing Models for Segmentation Tasks）</news:title>
   <news:publication_date>2026-08-13T23:46:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722906</loc>
  <lastmod>2026-08-13T23:46:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>制約の厳しいIoT向け三値ハイブリッドニューラル・ツリーネットワーク（Ternary Hybrid Neural-Tree Networks for Highly Constrained IoT Applications）</news:title>
   <news:publication_date>2026-08-13T23:46:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722904</loc>
  <lastmod>2026-08-13T22:54:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューラルネットで導く強線酸素同位体較正（A Machine Learning Artificial Neural Network Calibration of the Strong-Line Oxygen Abundance）</news:title>
   <news:publication_date>2026-08-13T22:54:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722902</loc>
  <lastmod>2026-08-13T22:53:54Z</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CPU向けWinograd／Cook‑Toom畳み込みの高効率実装（Efficient Winograd or Cook‑Toom Convolution Kernel Implementation on Widely Used Mobile CPUs）</news:title>
   <news:publication_date>2026-08-13T22:53:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722900</loc>
  <lastmod>2026-08-13T22:53:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>再イオン化期の銀河で大量のLyman連続放射（LyC）漏洩を見つける方法（Identifying reionization-epoch galaxies with extreme levels of Lyman continuum leakage in James Webb Space Telescope surveys）</news:title>
   <news:publication_date>2026-08-13T22:53:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722898</loc>
  <lastmod>2026-08-13T22:52:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習と量子物理の融合（Machine Learning meets Quantum Physics）</news:title>
   <news:publication_date>2026-08-13T22:52:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722896</loc>
  <lastmod>2026-08-13T22:52:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>OPT-AMSGradによる非凸最適化の楽観的加速（An Optimistic Acceleration of AMSGrad for Nonconvex Optimization）</news:title>
   <news:publication_date>2026-08-13T22:52:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722894</loc>
  <lastmod>2026-08-13T22:51:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ノイズに強いデータ圧縮で尤度フリー推論を高速化する手法（Nuisance hardened data compression for fast likelihood-free inference）</news:title>
   <news:publication_date>2026-08-13T22:51:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722892</loc>
  <lastmod>2026-08-13T22:51:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>条件付きフローに基づく確率的動画生成（VIDEOFLOW: A CONDITIONAL FLOW-BASED MODEL FOR STOCHASTIC VIDEO GENERATION）</news:title>
   <news:publication_date>2026-08-13T22:51:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722890</loc>
  <lastmod>2026-08-13T22:00:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>データ増幅による性質推定の最適化（Data Amplification: Instance-Optimal Property Estimation）</news:title>
   <news:publication_date>2026-08-13T22:00:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722888</loc>
  <lastmod>2026-08-13T21:59:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>球状混合pスピンモデルにおける平均場ガラス動力学の再考（Rethinking mean-field glassy dynamics and its relation with the energy landscape: the awkward case of the spherical mixed p-spin model）</news:title>
   <news:publication_date>2026-08-13T21:59:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722886</loc>
  <lastmod>2026-08-13T21:58:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ガウス特徴を用いたデータベース整合（Database Alignment with Gaussian Features）</news:title>
   <news:publication_date>2026-08-13T21:58:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722884</loc>
  <lastmod>2026-08-13T21:58:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ドラム音源の転写に効くデータ拡張の実践（Data Augmentation for Drum Transcription with Convolutional Neural Networks）</news:title>
   <news:publication_date>2026-08-13T21:58:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722882</loc>
  <lastmod>2026-08-13T21:58:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多変量等高回帰とHardy-Krause変動の拡張（Multivariate extensions of isotonic regression and total variation denoising via entire monotonicity and Hardy-Krause variation）</news:title>
   <news:publication_date>2026-08-13T21:58:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722880</loc>
  <lastmod>2026-08-13T21:57:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Deep U-NetとWave-U-Netによる歌声分離の改善（Improving singing voice separation using Deep U-Net and Wave-U-Net with data augmentation）</news:title>
   <news:publication_date>2026-08-13T21:57:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722878</loc>
  <lastmod>2026-08-13T21:57:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>二足歩行のシミュレーションから現実世界への転送（Sim-to-Real Transfer for Biped Locomotion）</news:title>
   <news:publication_date>2026-08-13T21:57:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722876</loc>
  <lastmod>2026-08-13T21:05:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>知覚と制御を推論として統合する（Joint Perception and Control as Inference with an Object-Based Implementation）</news:title>
   <news:publication_date>2026-08-13T21:05:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722874</loc>
  <lastmod>2026-08-13T21:05:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフィカル計算による積と畳み込み（Graphical Calculus for products and convolutions）</news:title>
   <news:publication_date>2026-08-13T21:05:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722872</loc>
  <lastmod>2026-08-13T21:05:20Z</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ITSにおける効率的なミリ波インフラ配置（Efficient Millimeter-Wave Infrastructure Placement for City-Scale ITS）</news:title>
   <news:publication_date>2026-08-13T21:05:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722870</loc>
  <lastmod>2026-08-13T21:04:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>協調型マルチエージェント深層強化学習による微視的交通シミュレーション (Microscopic Traffic Simulation by Cooperative Multi-agent Deep Reinforcement Learning)</news:title>
   <news:publication_date>2026-08-13T21:04:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722868</loc>
  <lastmod>2026-08-13T21:04:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>置換なし確率的勾配降下法の収束改善（SGD Without Replacement: Sharper Rates for General Smooth Convex Functions）</news:title>
   <news:publication_date>2026-08-13T21:04:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722866</loc>
  <lastmod>2026-08-13T21:04:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機会的ビューのマテリアライゼーションを深層強化学習で学ぶ（Opportunistic View Materialization with Deep Reinforcement Learning）</news:title>
   <news:publication_date>2026-08-13T21:04:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722864</loc>
  <lastmod>2026-08-13T21:03:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>デモンストレーションから学ぶ感覚–運動連合の自律化（Learning Sensory-Motor Associations from Demonstration）</news:title>
   <news:publication_date>2026-08-13T21:03:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722862</loc>
  <lastmod>2026-08-13T20:12:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>反転授業と視線計測を用いたHCIデザイン教育（Teaching HCI Design in a Flipped Learning M.Sc. Course Using Eye-Tracking Peer Evaluation Data）</news:title>
   <news:publication_date>2026-08-13T20:12:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722860</loc>
  <lastmod>2026-08-13T20:11:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ゲルマニウム検出器のパルス形状識別に基づく深層学習（Deep learning based pulse shape discrimination for germanium detectors）</news:title>
   <news:publication_date>2026-08-13T20:11:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722858</loc>
  <lastmod>2026-08-13T20:11:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>パラメータ化アクション空間におけるハイブリッドActor–Critic（Hybrid Actor-Critic Reinforcement Learning in Parameterized Action Space）</news:title>
   <news:publication_date>2026-08-13T20:11:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722856</loc>
  <lastmod>2026-08-13T20:10:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>StreetLearn：Google Street Viewを用いた学習環境とデータセット（The StreetLearn Environment and Dataset）</news:title>
   <news:publication_date>2026-08-13T20:10:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722854</loc>
  <lastmod>2026-08-13T20:10:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>網膜血管の動脈／静脈同時分割と分類（Joint Segmentation and Classification of Retinal Arteries/Veins from Fundus Images）</news:title>
   <news:publication_date>2026-08-13T20:10:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722852</loc>
  <lastmod>2026-08-13T20:10:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ファジーROCと不確実性領域の可視化（The Fuzzy ROC）</news:title>
   <news:publication_date>2026-08-13T20:10:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722850</loc>
  <lastmod>2026-08-13T20:09:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ソフトウェア工学倫理を教えるシリアスゲームの実践と検証（A Serious Game for Introducing Software Engineering Ethics to University Students）</news:title>
   <news:publication_date>2026-08-13T20:09:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722848</loc>
  <lastmod>2026-08-13T19:18:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非定常性下での最適化とヘッジ（Learning to Optimize under Non-Stationarity）</news:title>
   <news:publication_date>2026-08-13T19:18:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722846</loc>
  <lastmod>2026-08-13T19:18:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時系列のブラインドソース分離を動的モード分解で実現する（Time Series Source Separation using Dynamic Mode Decomposition）</news:title>
   <news:publication_date>2026-08-13T19:18:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722844</loc>
  <lastmod>2026-08-13T19:18:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ロングテール関係抽出に知識グラフ埋め込みとグラフ畳み込みネットワークを組み合わせる方法（Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks）</news:title>
   <news:publication_date>2026-08-13T19:18:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722842</loc>
  <lastmod>2026-08-13T19:17:19Z</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安全性検証とロバストネス解析（Safety Verification and Robustness Analysis of Neural Networks via Quadratic Constraints and Semidefinite Programming）</news:title>
   <news:publication_date>2026-08-13T19:17:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722840</loc>
  <lastmod>2026-08-13T19:17:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>秋季における亜熱帯カナリア海盆の深海散乱層の観測（Autumnal deep scattering layer from moored acoustic sensing in the subtropical Canary Basin）</news:title>
   <news:publication_date>2026-08-13T19:17:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722838</loc>
  <lastmod>2026-08-13T19:16:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>エッジ可変再帰によるGCNNの一般化（Generalizing Graph Convolutional Neural Networks with Edge-Variant Recursions on Graphs）</news:title>
   <news:publication_date>2026-08-13T19:16:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722836</loc>
  <lastmod>2026-08-13T19:16:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>従来型機械学習によるピッチ検出の実務インパクト（Traditional Machine Learning for Pitch Detection）</news:title>
   <news:publication_date>2026-08-13T19:16:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722834</loc>
  <lastmod>2026-08-13T18:24:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>認知神経科学のための深層学習（Deep Learning for Cognitive Neuroscience）</news:title>
   <news:publication_date>2026-08-13T18:24:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722832</loc>
  <lastmod>2026-08-13T18:24:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>デモから仕様を学ぶ因果分析（Using Causal Analysis to Learn Specifications from Task Demonstrations）</news:title>
   <news:publication_date>2026-08-13T18:24:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722830</loc>
  <lastmod>2026-08-13T18:24:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ラグエール過程と分割上の動的関係を結ぶゲートウェイ（ON A GATEWAY BETWEEN THE LAGUERRE PROCESS AND DYNAMICS ON PARTITIONS）</news:title>
   <news:publication_date>2026-08-13T18:24:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722828</loc>
  <lastmod>2026-08-13T18:23:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>交通参加者の相互作用をグラフで捉える（Graph Neural Networks for Modelling Traffic Participant Interaction）</news:title>
   <news:publication_date>2026-08-13T18:23:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722826</loc>
  <lastmod>2026-08-13T18:23:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>デジタル人文学領域におけるロシア語コーパスと単語埋め込みの評価（Russian Language Datasets in the Digital Humanities Domain and Their Evaluation with Word Embeddings）</news:title>
   <news:publication_date>2026-08-13T18:23:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722824</loc>
  <lastmod>2026-08-13T18:22:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層学習監視機構の体系的評価枠組み（Towards Structured Evaluation of Deep Neural Network Supervisors）</news:title>
   <news:publication_date>2026-08-13T18:22:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722822</loc>
  <lastmod>2026-08-13T18:22:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>半教師あり学習を用いた脳病変セグメンテーションの実用性（Semi-Supervised Brain Lesion Segmentation with an Adapted Mean Teacher Model）</news:title>
   <news:publication_date>2026-08-13T18:22:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722820</loc>
  <lastmod>2026-08-13T17:31:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>注意機構を用いた車線変更予測（Attention-based Lane Change Prediction）</news:title>
   <news:publication_date>2026-08-13T17:31:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722818</loc>
  <lastmod>2026-08-13T17:31:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>タスクパラメータ化された動作学習の一般化を高める枠組み重み付き軌道生成（Improving Task-Parameterised Movement Learning Generalisation with Frame-Weighted Trajectory Generation）</news:title>
   <news:publication_date>2026-08-13T17:31:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722816</loc>
  <lastmod>2026-08-13T17:30:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>病理組織画像における病変検出の深層学習フレームワークの機構理解（Understanding the Mechanism of Deep Learning Framework for Lesion Detection in Pathological Images with Breast Cancer）</news:title>
   <news:publication_date>2026-08-13T17:30:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722814</loc>
  <lastmod>2026-08-13T17:29:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>教師なしドメイン適応によるRGB-D階段認識（Unsupervised Domain Adaptation Learning Algorithm for RGB-D Staircase Recognition）</news:title>
   <news:publication_date>2026-08-13T17:29:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722812</loc>
  <lastmod>2026-08-13T17:29:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動画行動認識のための協調時空間特徴学習（Collaborative Spatiotemporal Feature Learning for Video Action Recognition）</news:title>
   <news:publication_date>2026-08-13T17:29:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722810</loc>
  <lastmod>2026-08-13T17:29:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>補完目的トレーニング（COMPLEMENT OBJECTIVE TRAINING）</news:title>
   <news:publication_date>2026-08-13T17:29:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722808</loc>
  <lastmod>2026-08-13T17:29:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>アルゴリズム判断が社会を変える長期影響（On the Long-term Impact of Algorithmic Decision Policies: Effort Unfairness and Feature Segregation through Social Learning）</news:title>
   <news:publication_date>2026-08-13T17:29:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722806</loc>
  <lastmod>2026-08-13T16:37:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動的時間伸縮距離を伸縮不変にする方法（Making the Dynamic Time Warping Distance Warping-Invariant）</news:title>
   <news:publication_date>2026-08-13T16:37:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722804</loc>
  <lastmod>2026-08-13T16:29:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>量子理論に触発された二値分類器の実務的意義（Binary Classifier Inspired by Quantum Theory）</news:title>
   <news:publication_date>2026-08-13T16:29:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722802</loc>
  <lastmod>2026-08-13T16:29:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スパイキングニューラルネットワークを進化させる非線形制御問題への応用（Evolving Spiking Neural Networks for Nonlinear Control Problems）</news:title>
   <news:publication_date>2026-08-13T16:29:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722800</loc>
  <lastmod>2026-08-13T16:29:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>歌声合成のスペクトル包絡予測に関する深層学習手法の比較（Analysing Deep Learning–Spectral Envelope Prediction Methods for Singing Synthesis）</news:title>
   <news:publication_date>2026-08-13T16:29:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722798</loc>
  <lastmod>2026-08-13T16:27:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深度距離学習と条件付き確率場を組み合わせたハイパースペクトル画像分類（Hyperspectral Image Classification with Deep Metric Learning and Conditional Random Field）</news:title>
   <news:publication_date>2026-08-13T16:27:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722796</loc>
  <lastmod>2026-08-13T16:27:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習による水理データの再構築（Reconstruction of Hydraulic Data by Machine Learning）</news:title>
   <news:publication_date>2026-08-13T16:27:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722794</loc>
  <lastmod>2026-08-13T16:27:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>STRIPSアクションモデルの学習（Learning STRIPS Action Models with Classical Planning）</news:title>
   <news:publication_date>2026-08-13T16:27:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722792</loc>
  <lastmod>2026-08-13T15:35:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習データが無くてもタスクモデルを作る考え方（Zero-Shot Task Transfer）</news:title>
   <news:publication_date>2026-08-13T15:35:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722790</loc>
  <lastmod>2026-08-13T15:35:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>遅延トレースによる微分可能な因果計算（Differentiable Causal Computations via Delayed Trace）</news:title>
   <news:publication_date>2026-08-13T15:35:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722788</loc>
  <lastmod>2026-08-13T15:35:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>パ ン クロマティックモデリングから学ぶ教訓（Challenges in Panchromatic Modelling with Next Generation Facilities）</news:title>
   <news:publication_date>2026-08-13T15:35:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722786</loc>
  <lastmod>2026-08-13T15:34:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ペアなし学習で低線量CTのノイズを除去するGAN（Unpaired image denoising using a generative adversarial network in X-ray CT）</news:title>
   <news:publication_date>2026-08-13T15:34:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722784</loc>
  <lastmod>2026-08-13T15:33:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的グラフフィードバックによる確率的オンライン学習の拡張（Stochastic Online Learning with Probabilistic Graph Feedback）</news:title>
   <news:publication_date>2026-08-13T15:33:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722782</loc>
  <lastmod>2026-08-13T15:33:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>合成学習による教師なしクロススペクトルステレオマッチング (Unsupervised Cross-spectral Stereo Matching by Learning to Synthesize)</news:title>
   <news:publication_date>2026-08-13T15:33:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/722780</loc>
  <lastmod>2026-08-13T15:33:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層監督付き密度回帰による自動顕微鏡細胞計数（Automatic Microscopic Cell Counting by Use of Deeply-Supervised Density Regression Model）</news:title>
   <news:publication_date>2026-08-13T15:33:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
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