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   <news:title>STAMPNET による教師なし多クラス物体発見（STAMPNET: UNSUPERVISED MULTI-CLASS OBJECT DISCOVERY）</news:title>
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   <news:title>ストリートシーン理解のための単一ネットワークによるパンオプティックセグメンテーション（Single Network Panoptic Segmentation for Street Scene Understanding）</news:title>
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   <news:title>混合整数計画法におけるコンフリクト駆動ヒューリスティクス（Conflict-Driven Heuristics for Mixed Integer Programming）</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>エンドポイント上でのリアルタイムマルウェア検出と自動プロセス停止（Real-time malware process detection and automated process killing）</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>視覚検索における教師なしデータ不確実性学習（Unsupervised Data Uncertainty Learning in Visual Retrieval Systems）</news:title>
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   <news:genres>Blog</news:genres>
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    <news:language>ja</news:language>
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   <news:title>オープンソース脆弱性修正データセットの作成（A Manually-Curated Dataset of Fixes to Vulnerabilities of Open-Source Software）</news:title>
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    <news:language>ja</news:language>
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   <news:title>局所Rapid Learningを用いた整数計画問題の探索高速化（Local Rapid Learning for Integer Programs）</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>Actor-Advisor: オフポリシー助言を活用する方策勾配（The Actor-Advisor: Policy Gradient With Off-Policy Advice）</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>糖尿病網膜症の眼底画像における構造・病変のセマンティックセグメンテーションのための全畳み込みニューラルネットワーク（FULLY CONVOLUTIONAL NEURAL NETWORK FOR SEMANTIC SEGMENTATION OF ANATOMICAL STRUCTURE AND PATHOLOGIES IN COLOUR FUNDUS IMAGES ASSOCIATED WITH DIABETIC RETINOPATHY）</news:title>
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   <news:genres>Blog</news:genres>
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   <news:title>少数派がより目立つ現象：人気順ランキングが生む“Few-get-richer”効果（The Few-get-richer: A Surprising Consequence of Popularity-based Rankings）</news:title>
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   <news:genres>Blog</news:genres>
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   <news:title>異種エッジ埋め込みによる友人推薦（Heterogeneous Edge Embeddings for Friend Recommendation）</news:title>
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   <news:genres>Blog</news:genres>
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   <news:title>ランダム行列による共分散推定の改良（Random Matrix Improved Covariance Estimation for a Large Class of Metrics）</news:title>
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   <news:genres>Blog</news:genres>
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   <news:title>ペナルティ付き重み付けGMMによるオンラインクラスタリング（Online Clustering by Penalized Weighted GMM）</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>多体散乱におけるRmatReact法の拡張（RmatReact methodology for reactive scattering）</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>競合リスクを伴う生存時間予測のアンサンブル手法（Ensemble Prediction of Time to Event Outcomes with Competing Risks）</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>シード入力最適化によるファジング効率改善（Optimizing seed inputs in fuzzing with machine learning）</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>DoPAMINE: 乗算性ノイズ（スペックル）除去のための二面マスクCNN（Double-sided Masked CNN for Pixel Adaptive Multiplicative Noise Despeckling）</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>全体予測・局所修正：ニューラルネットの時間並列最適制御（Predict Globally, Correct Locally: Parallel-in-Time Optimal Control of Neural Networks）</news:title>
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   <news:genres>Blog</news:genres>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>高性能な株価指数取引における深層LSTMの有効活用（High-performance stock index trading: making effective use of a deep long short-term memory network）</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>差分型RRAMによるインメモリBNN実装が拓く省電力AI（In-Memory and Error-Immune Differential RRAM Implementation of Binarized Deep Neural Networks）</news:title>
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   <news:genres>Blog</news:genres>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>ADAPTIVE POSTERIOR LEARNING：サプライズベースのメモリで少数ショット学習を効率化 (ADAPTIVE POSTERIOR LEARNING: FEW-SHOT LEARNING WITH A SURPRISE-BASED MEMORY MODULE)</news:title>
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    <news:language>ja</news:language>
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   <news:title>アスペクト別意見要約のためのオートエンコーディング変分推論への道（Towards Autoencoding Variational Inference for Aspect-based Opinion Summary）</news:title>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>写真観測データから機械学習で星形成率を推定する方法（Star Formation Rates for photometric samples of galaxies using machine learning methods）</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>シミュレータベース統計モデルのモデル選択（Model Selection for Simulator-based Statistical Models: A Kernel Approach）</news:title>
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   <news:genres>Blog</news:genres>
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  <news:news>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>繰り返し観測と相関ノイズを扱う新しいスパース推定法CLaR（Handling correlated and repeated measurements with the smoothed multivariate square-root Lasso）</news:title>
   <news:publication_date>2026-08-04T07:26:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719341</loc>
  <lastmod>2026-08-04T06:35:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SWA-Gaussianによるベイズ不確実性の簡易ベースライン（A Simple Baseline for Bayesian Uncertainty in Deep Learning）</news:title>
   <news:publication_date>2026-08-04T06:35:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719339</loc>
  <lastmod>2026-08-04T06:29:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>鳥類種識別のための音声ハッシング（CONV-CODES: AUDIO HASHING FOR BIRD SPECIES CLASSIFICATION）</news:title>
   <news:publication_date>2026-08-04T06:29:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719337</loc>
  <lastmod>2026-08-04T06:28:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>知覚的グルーピングのための学習可能な深い事前分布を持つ空間混合モデル（Spatial Mixture Models with Learnable Deep Priors for Perceptual Grouping）</news:title>
   <news:publication_date>2026-08-04T06:28:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719335</loc>
  <lastmod>2026-08-04T06:27:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CHIP: チャンネル毎に特徴を分離して解釈する手法（CHIP: Channel-wise Disentangled Interpretation of Deep Convolutional Neural Networks）</news:title>
   <news:publication_date>2026-08-04T06:27:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719333</loc>
  <lastmod>2026-08-04T06:27:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>概念ベースの自動説明手法が拓くモデル解釈の地平（Towards Automatic Concept-based Explanations）</news:title>
   <news:publication_date>2026-08-04T06:27:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719331</loc>
  <lastmod>2026-08-04T06:26:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>コスト効率の良いインセンティブ配分のための構造化反事実推論（Cost-Effective Incentive Allocation via Structured Counterfactual Inference）</news:title>
   <news:publication_date>2026-08-04T06:26:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719329</loc>
  <lastmod>2026-08-04T06:25:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>二重参照によるホログラフィック位相回復の革新（Dual-Reference Design for Holographic Coherent Diffraction Imaging）</news:title>
   <news:publication_date>2026-08-04T06:25:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719327</loc>
  <lastmod>2026-08-04T05:32:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スピーカーベリフィケーションのためのエンドツーエンド損失と全話者ハードネガティブマイニング（End-to-end losses based on speaker basis vectors and all-speaker hard negative mining for speaker verification）</news:title>
   <news:publication_date>2026-08-04T05:32:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719325</loc>
  <lastmod>2026-08-04T05:32:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>相関するノイズ対を用いたSURE拡張による深層デノイザの教師なし学習（Extending Stein’s unbiased risk estimator to train deep denoisers with correlated pairs of noisy images）</news:title>
   <news:publication_date>2026-08-04T05:32:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719323</loc>
  <lastmod>2026-08-04T05:31:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Ising-Dropoutによる賢いドロップアウトとモデル圧縮（ISING-DROPOUT: A REGULARIZATION METHOD FOR TRAINING AND COMPRESSION OF DEEP NEURAL NETWORKS）</news:title>
   <news:publication_date>2026-08-04T05:31:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719321</loc>
  <lastmod>2026-08-04T05:31:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>義肢のための人工知能 — チャレンジ解法（Artificial Intelligence for Prosthetics — challenge solutions）</news:title>
   <news:publication_date>2026-08-04T05:31:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719319</loc>
  <lastmod>2026-08-04T05:30:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>最適化アルゴリズムの収束を動的に加速するCNNの活用（Accelerating Optimization Algorithms With Dynamic Parameter Selections Using Convolutional Neural Networks For Inverse Problems In Image Processing）</news:title>
   <news:publication_date>2026-08-04T05:30:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719317</loc>
  <lastmod>2026-08-04T05:30:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>尺度不変のフラットネス指標が示す深層学習の新しい見方（A Scale Invariant Flatness Measure for Deep Network Minima）</news:title>
   <news:publication_date>2026-08-04T05:30:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719315</loc>
  <lastmod>2026-08-04T05:30:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>LSTMを用いたうっ血性心不全発症予測の有効性（Effectiveness of LSTMs in Predicting Congestive Heart Failure Onset）</news:title>
   <news:publication_date>2026-08-04T05:30:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719313</loc>
  <lastmod>2026-08-04T04:38:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>重み付きSimplex戦略の動的化が示す現場適用の道（Dynamic-Weighted Simplex Strategy for Learning Enabled Cyber Physical Systems）</news:title>
   <news:publication_date>2026-08-04T04:38:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719311</loc>
  <lastmod>2026-08-04T04:37:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>電波SETI信号の分類における機械視覚と深層学習（Machine Vision and Deep Learning for Classification of Radio SETI Signals）</news:title>
   <news:publication_date>2026-08-04T04:37:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719309</loc>
  <lastmod>2026-08-04T04:36:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>主成分モデル解析（Principal Model Analysis Based on Partial Least Squares）</news:title>
   <news:publication_date>2026-08-04T04:36:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719307</loc>
  <lastmod>2026-08-04T04:36:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Unbiased Online Recurrent Optimizationの分散解析（On the Variance of Unbiased Online Recurrent Optimization）</news:title>
   <news:publication_date>2026-08-04T04:36:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719305</loc>
  <lastmod>2026-08-04T04:36:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>小さなデータから高速にハイパーパラメータを見つける方法（Fast Hyperparameter Tuning using Bayesian Optimization with Directional Derivatives）</news:title>
   <news:publication_date>2026-08-04T04:36:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719303</loc>
  <lastmod>2026-08-04T04:36:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>最近傍補間法のL2一貫性について（On L2-consistency of nearest neighbor matching）</news:title>
   <news:publication_date>2026-08-04T04:36:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719301</loc>
  <lastmod>2026-08-04T04:35:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分類に基づく集計のバイアス補正（A Bayesian Approach for Accurate Classification-Based Aggregates）</news:title>
   <news:publication_date>2026-08-04T04:35:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719299</loc>
  <lastmod>2026-08-04T03:44:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>神経科学に学ぶ創造的デコーダ（TOWARD A NEURO-INSPIRED CREATIVE DECODER）</news:title>
   <news:publication_date>2026-08-04T03:44:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719297</loc>
  <lastmod>2026-08-04T03:44:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>根格子におけるCVPの折り畳みによる解法と深いReLUニューラルネットワーク（On the CVP for the root lattices via folding with deep ReLU neural networks）</news:title>
   <news:publication_date>2026-08-04T03:44:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719295</loc>
  <lastmod>2026-08-04T03:43:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ドメイン適応によるステンス検出の敵対的学習（Adversarial Domain Adaptation for Stance Detection）</news:title>
   <news:publication_date>2026-08-04T03:43:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719293</loc>
  <lastmod>2026-08-04T03:42:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>再帰型ニューラルネットワークの圧縮による実用的言語モデルの実装（Compression of Recurrent Neural Networks for Efficient Language Modeling）</news:title>
   <news:publication_date>2026-08-04T03:42:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719291</loc>
  <lastmod>2026-08-04T03:42:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単眼カメラで交通コーンの3D位置をリアルタイム推定する手法（Real-time 3D Traffic Cone Detection for Autonomous Driving）</news:title>
   <news:publication_date>2026-08-04T03:42:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719289</loc>
  <lastmod>2026-08-04T03:42:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DiffEqFlux.jl—Neural Differential Equationsを実現するJuliaライブラリ（DiffEqFlux.jl — A Julia Library for Neural Differential Equations）</news:title>
   <news:publication_date>2026-08-04T03:42:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719287</loc>
  <lastmod>2026-08-04T03:42:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>エンドツーエンド・アンカード音声認識の要点整理（END-TO-END ANCHORED SPEECH RECOGNITION）</news:title>
   <news:publication_date>2026-08-04T03:42:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719285</loc>
  <lastmod>2026-08-04T02:50:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>セントロイドに基づく深層距離学習による話者認識（CENTROID-BASED DEEP METRIC LEARNING FOR SPEAKER RECOGNITION）</news:title>
   <news:publication_date>2026-08-04T02:50:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719283</loc>
  <lastmod>2026-08-04T02:50:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>専門家アドバイスによる予測でのコム戦略の漸近的最適性（On the Asymptotic Optimality of the Comb Strategy for Prediction with Expert Advice）</news:title>
   <news:publication_date>2026-08-04T02:50:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719281</loc>
  <lastmod>2026-08-04T02:50:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Stack Exchangeのタグ付けネットワークのモデリングと解析 (Modeling and Analysis of Tagging Networks in Stack Exchange Communities)</news:title>
   <news:publication_date>2026-08-04T02:50:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719279</loc>
  <lastmod>2026-08-04T02:49:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>層の役割をガウス過程の視点で解く（The role of a layer in deep neural networks: a Gaussian Process perspective）</news:title>
   <news:publication_date>2026-08-04T02:49:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719277</loc>
  <lastmod>2026-08-04T02:49:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>µJy帯の電波観測が示す銀河進化の新たな視座（eMERGE Data Release 1）</news:title>
   <news:publication_date>2026-08-04T02:49:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719275</loc>
  <lastmod>2026-08-04T02:49:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層ニューラルネットワークにおけるヘッセ行列の負の固有値の意義（Negative eigenvalues of the Hessian in deep neural networks）</news:title>
   <news:publication_date>2026-08-04T02:49:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719273</loc>
  <lastmod>2026-08-04T02:48:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>孤立した矮小銀河対の発見とその意味（Discovery of an isolated dwarf–dwarf galaxy pair at z = 0.30）</news:title>
   <news:publication_date>2026-08-04T02:48:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719271</loc>
  <lastmod>2026-08-04T01:54:52Z</lastmod>
  <news:news>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-04T01:53:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-04T01:52:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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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>
   </news:publication>
   <news:title>ニューラルネットワーク帰属の因果的視点（Neural Network Attributions: A Causal Perspective）</news:title>
   <news:publication_date>2026-08-04T01:00:37Z</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>2026-08-04T01:00:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>WiFi CSIを用いた深層学習による歩容生体認証（Deep CSI Learning for Gait Biometric Sensing and Recognition）</news:title>
   <news:publication_date>2026-08-04T00:59:41Z</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>
   </news:publication>
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   <news:publication_date>2026-08-04T00:59:15Z</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>
   </news:publication>
   <news:title>多言語音声合成を非教師で実現する手法（UNSUPERVISED POLYGLOT TEXT-TO-SPEECH）</news:title>
   <news:publication_date>2026-08-04T00:59:09Z</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>骨病変病理のための生成的画像翻訳によるデータ拡張（Generative Image Translation for Data Augmentation of Bone Lesion Pathology）</news:title>
   <news:publication_date>2026-08-04T00:58:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
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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>因果情報に基づく意思決定の指針（A Guiding Principle for Causal Decision Problems）</news:title>
   <news:publication_date>2026-08-04T00:58:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-04T00:07:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
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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>2026-08-04T00:06:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>正則化問題における頑健学習と複雑性依存境界（Robust learning and complexity dependent bounds for regularized problems）</news:title>
   <news:publication_date>2026-08-04T00:05:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
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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:publication_date>2026-08-04T00:04:38Z</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>
  </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>2026-08-04T00:03:58Z</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>2026-08-04T00:03:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-03T23:11:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719227</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>監視データを使った教師あり学習による感染症流行検知の進化（Supervised learning improves disease outbreak detection）</news:title>
   <news:publication_date>2026-08-03T23:11:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719225</loc>
  <lastmod>2026-08-03T23:11:14Z</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が導く式変換と証明の自動復元（Neural-Network Guided Expression Transformation）</news:title>
   <news:publication_date>2026-08-03T23:11:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719223</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>マスクベースの多面表現による非構造化マルチビュー深度推定（Unstructured Multi-View Depth Estimation Using Mask-Based Multiplane Representation）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719221</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>音表現からの転移学習による発話の怒り検出（TRANSFER LEARNING FROM SOUND REPRESENTATIONS FOR ANGER DETECTION IN SPEECH）</news:title>
   <news:publication_date>2026-08-03T23:10:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719219</loc>
  <lastmod>2026-08-03T23:09:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SAPSAM—2値ラベルのみで肺CTを学習する新手法（SAPSAM - SPARSELY ANNOTATED PATHOLOGICAL SIGN ACTIVATION MAPS）</news:title>
   <news:publication_date>2026-08-03T23:09:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719217</loc>
  <lastmod>2026-08-03T23:09:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>強化学習が量子制御で輝く条件（When does reinforcement learning stand out in quantum control? A comparative study on state preparation）</news:title>
   <news:publication_date>2026-08-03T23:09:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719215</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>Mol-CycleGANによる分子最適化（Mol-CycleGAN - a generative model for molecular optimization）</news:title>
   <news:publication_date>2026-08-03T22:18:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719213</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>2026-08-03T22:08:35Z</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>2026-08-03T22:07:34Z</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>2026-08-03T22:06:56Z</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:title>複素値カーネル活性化関数の広義線形カーネル（Widely Linear Kernels for Complex-Valued Kernel Activation Functions）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>固有表現を取り込む単語埋め込みの再設計（Word Embeddings for Entity-annotated Texts）</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>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-03T21:13:46Z</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>混合データのベイズ的共クラスタリングモデル（Un modèle Bayésien de co-clustering de données mixtes）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
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 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>実用的な虹彩認識の変革点（DeepIrisNet2: Learning Deep-IrisCodes from Scratch for Segmentation-Robust Visible Wavelength and Near Infrared Iris Recognition）</news:title>
   <news:publication_date>2026-08-03T21:13:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>都市の細粒度フロー推定（UrbanFM: Inferring Fine-Grained Urban Flows）</news:title>
   <news:publication_date>2026-08-03T21:12:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-03T21:12:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層学習におけるADMMの収束性と飽和回避（On ADMM in Deep Learning: Convergence and Saturation-Avoidance）</news:title>
   <news:publication_date>2026-08-03T21:12:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719187</loc>
  <lastmod>2026-08-03T20:21:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>到達しない状態を許すマルコフ連鎖の同一性検定（Testing Markov Chains Without Hitting）</news:title>
   <news:publication_date>2026-08-03T20:21:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719185</loc>
  <lastmod>2026-08-03T20:13:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>双方向推論ネットワーク（Bidirectional Inference Networks: A Class of Deep Bayesian Networks for Health Profiling）</news:title>
   <news:publication_date>2026-08-03T20:13:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/719183</loc>
  <lastmod>2026-08-03T20:13:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-03T20:13:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719181</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>分布系統における頑健な行列補完による状態推定（Robust Matrix Completion State Estimation in Distribution Systems）</news:title>
   <news:publication_date>2026-08-03T20:13:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高速な平均推定とサブガウス誤差率（Fast Mean Estimation with Sub-Gaussian Rates）</news:title>
   <news:publication_date>2026-08-03T20:12:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-03T20:12:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>多尺度データに対する自動スペクトラルクラスタリング（AN AUTOMATED SPECTRAL CLUSTERING FOR MULTI-SCALE DATA）</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>CodedReduceによる分散学習の高速化と堅牢化（CodedReduce: A Fast and Robust Framework for Gradient Aggregation in Distributed Learning）</news:title>
   <news:publication_date>2026-08-03T19:21:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-03T19:21:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>フィードフォワード設計畳み込みニューラルネットワークによる半教師あり学習（SEMI-SUPERVISED LEARNING VIA FEEDFORWARD-DESIGNED CONVOLUTIONAL NEURAL NETWORKS）</news:title>
   <news:publication_date>2026-08-03T19:21:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <lastmod>2026-08-03T19:20:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>交換可能な生成モデルとFlowScan（Exchangeable Generative Models with Flow Scans）</news:title>
   <news:publication_date>2026-08-03T19:20:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-03T19:19:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>ポリフォニック音楽作曲におけるLSTMと強化学習（Polyphonic Music Composition with LSTM Neural Networks and Reinforcement Learning）</news:title>
   <news:publication_date>2026-08-03T19:19:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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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>2026-08-03T19:19:28Z</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>2026-08-03T18:27:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-03T18:27:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719157</loc>
  <lastmod>2026-08-03T18:26:53Z</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>2026-08-03T18:26:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719155</loc>
  <lastmod>2026-08-03T18:26:02Z</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>2026-08-03T18:26:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719153</loc>
  <lastmod>2026-08-03T18:25:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Zwicky Transient Facilityにおける機械学習の実装と応用（Machine Learning for the Zwicky Transient Facility）</news:title>
   <news:publication_date>2026-08-03T18:25:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719151</loc>
  <lastmod>2026-08-03T18:25:45Z</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>2026-08-03T18:25:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719149</loc>
  <lastmod>2026-08-03T18:25:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>メタアモータイズド変分推論と学習（Meta-Amortized Variational Inference and Learning）</news:title>
   <news:publication_date>2026-08-03T18:25:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719147</loc>
  <lastmod>2026-08-03T17:33:47Z</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>2026-08-03T17:33:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719145</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>2026-08-03T17:33:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719143</loc>
  <lastmod>2026-08-03T17:33:07Z</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>2026-08-03T17:33:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719141</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>2026-08-03T17:32:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719139</loc>
  <lastmod>2026-08-03T17:32:44Z</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>2026-08-03T17:32:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ハイペルエントロピーでつなぐ勾配と乗法的更新（Exponentiated Gradient vs. Meets Gradient Descent）</news:title>
   <news:publication_date>2026-08-03T17:32:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719135</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <lastmod>2026-08-03T16:40:54Z</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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   <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:publication_date>2026-08-03T16:39:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719125</loc>
  <lastmod>2026-08-03T16:39:13Z</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>2026-08-03T16:39:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719123</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>表現の「もつれ」を測り改善する手法（Analyzing and Improving Representations with the Soft Nearest Neighbor Loss）</news:title>
   <news:publication_date>2026-08-03T16:38:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719121</loc>
  <lastmod>2026-08-03T16:38:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時間スケールごとに価値関数を分離する手法（Separating value functions across time-scales）</news:title>
   <news:publication_date>2026-08-03T16:38:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719119</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>ZTFのデータ処理とアーカイブの設計（The Zwicky Transient Facility: Data Processing, Products, and Archive）</news:title>
   <news:publication_date>2026-08-03T15:46:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719117</loc>
  <lastmod>2026-08-03T15:46:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>画像変装で守る外部委託型ディープラーニング（Disguised-Nets: Image Disguising for Privacy-preserving Outsourced Deep Learning）</news:title>
   <news:publication_date>2026-08-03T15:46:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719115</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>近似非線形関数の入力を安定化して同型暗号下でDNN推論を可能にする手法（Stabilizing Inputs to Approximated Nonlinear Functions for Inference with Homomorphic Encryption in Deep Neural Networks）</news:title>
   <news:publication_date>2026-08-03T15:45:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719113</loc>
  <lastmod>2026-08-03T15:44:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Gibbs-ERM原理の分布依存解析（Distribution-Dependent Analysis of Gibbs-ERM Principle）</news:title>
   <news:publication_date>2026-08-03T15:44:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719111</loc>
  <lastmod>2026-08-03T15:44:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非パラメトリック有限時間LTIシステム同定（Nonparametric Finite Time LTI System Identification）</news:title>
   <news:publication_date>2026-08-03T15:44:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719109</loc>
  <lastmod>2026-08-03T15:44:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>従業員向け食事需要の予測に人工ニューラルネットワークを使う意義（An Estimation of Personnel Food Demand Quantity for Businesses by Using Artificial Neural Networks）</news:title>
   <news:publication_date>2026-08-03T15:44:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719107</loc>
  <lastmod>2026-08-03T15:44:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Hydra-A 銀河中心に潜む冷たい分子ガスの発見が示すもの（Deep and narrow CO absorption revealing molecular clouds in the Hydra-A brightest cluster galaxy）</news:title>
   <news:publication_date>2026-08-03T15:44:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719105</loc>
  <lastmod>2026-08-03T14:52:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複雑なソフトウェア解析を“DODGE”する方法 (How to “DODGE” Complex Software Analytics)</news:title>
   <news:publication_date>2026-08-03T14:52:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719103</loc>
  <lastmod>2026-08-03T14:52:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューロンの出生・消滅ダイナミクスによる大域収束（Global convergence of neuron birth-death dynamics）</news:title>
   <news:publication_date>2026-08-03T14:52:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719101</loc>
  <lastmod>2026-08-03T14:52:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>3D格子ダイマー模型の位相的性質をニューラルネットワークで探る（Probing topological properties of 3D lattice dimer model with neural networks）</news:title>
   <news:publication_date>2026-08-03T14:52:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719099</loc>
  <lastmod>2026-08-03T14:51:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>共感ロボットによるグループ学習の実証研究（Empathic Robot for Group Learning: A Field Study）</news:title>
   <news:publication_date>2026-08-03T14:51:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719097</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>インタラクティブ分子動力学とマルチパーソンVR（Interactive molecular dynamics in virtual reality from quantum chemistry to drug binding: An open-source multi-person framework）</news:title>
   <news:publication_date>2026-08-03T14:51:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719095</loc>
  <lastmod>2026-08-03T14:51:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>モジュール化されたブロック対角カーブチャ近似（Modular Block-diagonal Curvature Approximations for Feedforward Architectures）</news:title>
   <news:publication_date>2026-08-03T14:51:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719093</loc>
  <lastmod>2026-08-03T14:51:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-03T14:51:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719091</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>2026-08-03T13:59:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719089</loc>
  <lastmod>2026-08-03T13:59:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>脳波で捉える「心のさ迷い」自動検出技術（Deep Convolutional Neural Network for Automated Detection of Mind Wandering using EEG Signals）</news:title>
   <news:publication_date>2026-08-03T13:59:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719087</loc>
  <lastmod>2026-08-03T13:58:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>極大型望遠鏡が拓く基礎物理学の新地平（Early Science with ELTs）</news:title>
   <news:publication_date>2026-08-03T13:58:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719085</loc>
  <lastmod>2026-08-03T13:57:58Z</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>2026-08-03T13:57:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719083</loc>
  <lastmod>2026-08-03T13:57:52Z</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>2026-08-03T13:57:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719081</loc>
  <lastmod>2026-08-03T13:57:46Z</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>2026-08-03T13:57:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719079</loc>
  <lastmod>2026-08-03T13:57:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-03T13:57:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719077</loc>
  <lastmod>2026-08-03T13:05:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>中程度高次元線形回帰における一貫したリスク推定（Consistent Risk Estimation in Moderately High-Dimensional Linear Regression）</news:title>
   <news:publication_date>2026-08-03T13:05:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719075</loc>
  <lastmod>2026-08-03T13:05:19Z</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>2026-08-03T13:05:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719073</loc>
  <lastmod>2026-08-03T13:04:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>RNNを用いたIMMフィルタ代替モデル（An RNN-based IMM Filter Surrogate）</news:title>
   <news:publication_date>2026-08-03T13:04:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719071</loc>
  <lastmod>2026-08-03T13:04:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多様体上の距離学習（Metric Learning on Manifolds）</news:title>
   <news:publication_date>2026-08-03T13:04:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719069</loc>
  <lastmod>2026-08-03T13:03:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-03T13:03:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719067</loc>
  <lastmod>2026-08-03T13:03:41Z</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MAB学習のGNU Radio実装（GNU Radio Implementation of MALIN: “Multi-Armed bandits Learning for Internet-of-things Networks”）</news:title>
   <news:publication_date>2026-08-03T13:03:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719065</loc>
  <lastmod>2026-08-03T13:03:26Z</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>2026-08-03T13:03:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719063</loc>
  <lastmod>2026-08-03T12:11:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>全確率的勾配アルゴリズムと強化学習への応用（Total stochastic gradient algorithms and applications in reinforcement learning）</news:title>
   <news:publication_date>2026-08-03T12:11:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719061</loc>
  <lastmod>2026-08-03T12:11:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Hamamatsu R12199-01 HA MOD の低温特性評価（Characterisation of the Hamamatsu R12199-01 HA MOD photomultiplier tube for low temperature applications）</news:title>
   <news:publication_date>2026-08-03T12:11:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719059</loc>
  <lastmod>2026-08-03T12:11:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>近似修復と部分標本化トラストリージョン法による有限和最小化（Inexact restoration with subsampled trust-region methods for finite-sum minimization）</news:title>
   <news:publication_date>2026-08-03T12:11:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719057</loc>
  <lastmod>2026-08-03T12:10:21Z</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>2026-08-03T12:10:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-03T12:10:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非単調な逐次テキスト生成（Non-Monotonic Sequential Text Generation）</news:title>
   <news:publication_date>2026-08-03T12:10:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/719053</loc>
  <lastmod>2026-08-03T12:10:03Z</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確率的フォールトトレランス（The Probabilistic Fault Tolerance of Neural Networks in The Continuous Limit）</news:title>
   <news:publication_date>2026-08-03T12:10:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719051</loc>
  <lastmod>2026-08-03T12:09:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>天の川バルジの恒星年齢マップ化手法（Mapping the stellar age of the Milky Way bulge with the VVV: I. The method）</news:title>
   <news:publication_date>2026-08-03T12:09:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719049</loc>
  <lastmod>2026-08-03T11:18:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ラベルなし人間指示でロボット行動を形成する手法（INTERACTIVELY SHAPING ROBOT BEHAVIOUR WITH UNLABELED HUMAN INSTRUCTIONS）</news:title>
   <news:publication_date>2026-08-03T11:18:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719047</loc>
  <lastmod>2026-08-03T11:17:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>早期敗血症の検知に向けたMGP-TCN（Early Recognition of Sepsis with MGP-TCNs）</news:title>
   <news:publication_date>2026-08-03T11:17:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719045</loc>
  <lastmod>2026-08-03T11:17:28Z</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耐障害性強化（Enhancing Fault Tolerance of Neural Networks for Security-Critical Applications）</news:title>
   <news:publication_date>2026-08-03T11:17:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719043</loc>
  <lastmod>2026-08-03T11:16:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次元ファクトリアル隠れマルコフモデルにおける局所性の利用（Exploiting locality in high-dimensional Factorial hidden Markov models）</news:title>
   <news:publication_date>2026-08-03T11:16:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719041</loc>
  <lastmod>2026-08-03T11:16:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>変動不等式に対する普遍アルゴリズム（A Universal Algorithm for Variational Inequalities Adaptive to Smoothness and Noise）</news:title>
   <news:publication_date>2026-08-03T11:16:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719039</loc>
  <lastmod>2026-08-03T11:15:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>トレースノルム球上での低ランク射影を用いた射影勾配法の収束性（On the Convergence of Projected-Gradient Methods with Low-Rank Projections for Smooth Convex Minimization over Trace-Norm Balls and Related Problems）</news:title>
   <news:publication_date>2026-08-03T11:15:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719037</loc>
  <lastmod>2026-08-03T11:15:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>一般化ステイフェル多様体における前処理付きリーマン最適化（Riemannian optimization with a preconditioning scheme on the generalized Stiefel manifold）</news:title>
   <news:publication_date>2026-08-03T11:15:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719035</loc>
  <lastmod>2026-08-03T10:23:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>低遅延の事前中断を用いたジョブスケジューリング（Low-latency job scheduling with preemption for the development of deep learning）</news:title>
   <news:publication_date>2026-08-03T10:23:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719033</loc>
  <lastmod>2026-08-03T10:22:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>最小記述長を目的関数とする非負値行列因子分解（Minimum description length as an objective function for non-negative matrix factorization）</news:title>
   <news:publication_date>2026-08-03T10:22:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719031</loc>
  <lastmod>2026-08-03T10:22:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>変分オートエンコーダによる音声強調：準教師付き分散モデルの実装と検証（A VARIANCE MODELING FRAMEWORK BASED ON VARIATIONAL AUTOENCODERS FOR SPEECH ENHANCEMENT）</news:title>
   <news:publication_date>2026-08-03T10:22:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719029</loc>
  <lastmod>2026-08-03T10:21:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>区分的定常バンディットに挑む効率的変化点検出（Efficient Change-Point Detection for Tackling Piecewise-Stationary Bandits）</news:title>
   <news:publication_date>2026-08-03T10:21:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719027</loc>
  <lastmod>2026-08-03T10:21:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>生存フォレストの検証：比例ハザード仮定がALSの予後・予測モデルに与える影響（Survival Forests under Test: Impact of the Proportional Hazards Assumption on Prognostic and Predictive Forests for ALS Survival）</news:title>
   <news:publication_date>2026-08-03T10:21:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719025</loc>
  <lastmod>2026-08-03T10:21:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>頑健な教師あり分類と特徴選択を同時に解くプライマル・デュアル法（Robust supervised classification and feature selection using a primal-dual method）</news:title>
   <news:publication_date>2026-08-03T10:21:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719023</loc>
  <lastmod>2026-08-03T10:20:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次元非線形偏微分方程式を解く深層バックワード法（Deep backward schemes for high-dimensional nonlinear PDEs）</news:title>
   <news:publication_date>2026-08-03T10:20:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719021</loc>
  <lastmod>2026-08-03T09:29:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Gradient Boostingを用いた油圧破砕の効率向上（Gradient Boosting to Boost the Efficiency of Hydraulic Fracturing）</news:title>
   <news:publication_date>2026-08-03T09:29:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719019</loc>
  <lastmod>2026-08-03T09:29:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>シミュレーションで学ぶ学習方法（Learning to Learn in Simulation）</news:title>
   <news:publication_date>2026-08-03T09:29:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719017</loc>
  <lastmod>2026-08-03T09:29:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Relevance Factor VAEによる意味的要因の自動識別（Relevance Factor VAE: Learning and Identifying Disentangled Factors）</news:title>
   <news:publication_date>2026-08-03T09:29:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719015</loc>
  <lastmod>2026-08-03T09:29:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層的相互作用学習の大規模化（Learning Hierarchical Interactions at Scale: A Convex Optimization Approach）</news:title>
   <news:publication_date>2026-08-03T09:29:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719013</loc>
  <lastmod>2026-08-03T09:28:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SASSEによるスケーラブルで適応可能な6自由度姿勢推定（SASSE: Scalable and Adaptable 6-DOF Pose Estimation）</news:title>
   <news:publication_date>2026-08-03T09:28:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719011</loc>
  <lastmod>2026-08-03T09:28:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>音声活動検出におけるSVMアンサンブルの実用性（An Ensemble SVM-based Approach for Voice Activity Detection）</news:title>
   <news:publication_date>2026-08-03T09:28:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719009</loc>
  <lastmod>2026-08-03T09:28:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチエージェント強化学習における通信スケジューリングの学習（Learning to Schedule Communication in Multi-Agent Reinforcement Learning）</news:title>
   <news:publication_date>2026-08-03T09:28:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719007</loc>
  <lastmod>2026-08-03T08:36:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>操作学習のための機能的オブジェクト指向ネットワーク（Functional Object-Oriented Network for Manipulation Learning）</news:title>
   <news:publication_date>2026-08-03T08:36:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719005</loc>
  <lastmod>2026-08-03T08:36:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>参照リーダー：照応解析のための再帰的実体ネットワーク（The Referential Reader: A Recurrent Entity Network for Anaphora Resolution）</news:title>
   <news:publication_date>2026-08-03T08:36:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719003</loc>
  <lastmod>2026-08-03T08:35:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Operational State ChangeによるNILM向けニューラルネットワークの要点解説（Neural Network for NILM Based on Operational State Change Classification）</news:title>
   <news:publication_date>2026-08-03T08:35:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/719001</loc>
  <lastmod>2026-08-03T08:34:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>連続行動を持つ文脈付きバンディット：平滑化、ズーミング、適応（Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting）</news:title>
   <news:publication_date>2026-08-03T08:34:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718999</loc>
  <lastmod>2026-08-03T08:34:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>摂動的GAN（Perturbative GAN: GAN with Perturbation Layers）</news:title>
   <news:publication_date>2026-08-03T08:34:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718997</loc>
  <lastmod>2026-08-03T08:34:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>交差デザインから個別化治療方針を推定する（Estimating Individualized Treatment Regimes from Crossover Designs）</news:title>
   <news:publication_date>2026-08-03T08:34:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718995</loc>
  <lastmod>2026-08-03T08:33:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>効率的ラベリングのための能動的画像合成（Active Image Synthesis for Efficient Labeling）</news:title>
   <news:publication_date>2026-08-03T08:33:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718993</loc>
  <lastmod>2026-08-03T07:42:26Z</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遅延対応加速近接座標降下法（Asynchronous Delay-Aware Accelerated Proximal Coordinate Descent for Nonconvex Nonsmooth Problems）</news:title>
   <news:publication_date>2026-08-03T07:42:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718991</loc>
  <lastmod>2026-08-03T07:42:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>合成ノイズ学習により機械翻訳の誤字耐性を高める（Training on Synthetic Noise Improves Robustness to Natural Noise in Machine Translation）</news:title>
   <news:publication_date>2026-08-03T07:42:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718989</loc>
  <lastmod>2026-08-03T07:41:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>デジタル服薬データから処置を処方する学習（Learning to Prescribe Interventions for Tuberculosis Patients Using Digital Adherence Data）</news:title>
   <news:publication_date>2026-08-03T07:41:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718987</loc>
  <lastmod>2026-08-03T07:41:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>混同行列とラフ集合による分類評価の再考（Confusion matrices and rough set data analysis）</news:title>
   <news:publication_date>2026-08-03T07:41:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718985</loc>
  <lastmod>2026-08-03T07:41:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>バイナリデータの有効次元とは（What is the dimension of your binary data?）</news:title>
   <news:publication_date>2026-08-03T07:41:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718983</loc>
  <lastmod>2026-08-03T07:40:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ブートストラップ座標探索を用いた多次元尺度構成（Bootstrapped Coordinate Search for Multidimensional Scaling）</news:title>
   <news:publication_date>2026-08-03T07:40:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718981</loc>
  <lastmod>2026-08-03T07:40:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>パラメータフリーなオンライン凸最適化とサブ指数ノイズへの対応（Parameter-Free Online Convex Optimization with Sub-Exponential Noise）</news:title>
   <news:publication_date>2026-08-03T07:40:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718979</loc>
  <lastmod>2026-08-03T06:49:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動画中の物体検出と追跡を同時に行うネットワーク（TrackNet: Simultaneous Object Detection and Tracking and Its Application in Traffic Video Analysis）</news:title>
   <news:publication_date>2026-08-03T06:49:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718977</loc>
  <lastmod>2026-08-03T06:49:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>空間的事前情報と3D畳み込みで実現する頑健な脳MRI構造分割（Accurate and robust segmentation of neuroanatomy in T1-weighted MRI by combining spatial priors with deep convolutional neural networks）</news:title>
   <news:publication_date>2026-08-03T06:49:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718975</loc>
  <lastmod>2026-08-03T06:48:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ソーシャルメディアにおける暗号通貨操作の検出と分析（Identifying and Analyzing Cryptocurrency Manipulations in Social Media）</news:title>
   <news:publication_date>2026-08-03T06:48:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718973</loc>
  <lastmod>2026-08-03T06:47:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ROMANetによるオフチップメモリアクセス管理の革新（ROMANet: Fine-Grained Reuse-Driven Off-Chip Memory Access Management and Data Organization for Deep Neural Network Accelerators）</news:title>
   <news:publication_date>2026-08-03T06:47:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718971</loc>
  <lastmod>2026-08-03T06:47:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>依存データからの因果効果の同定と推定（Identification and Estimation Of Causal Effects from Dependent Data）</news:title>
   <news:publication_date>2026-08-03T06:47:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718969</loc>
  <lastmod>2026-08-03T06:47:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>PVNetによる時空間PV発電予測（PVNet: A LRCN Architecture for Spatio-Temporal Photovoltaic Power Forecasting from Numerical Weather Prediction）</news:title>
   <news:publication_date>2026-08-03T06:47:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718967</loc>
  <lastmod>2026-08-03T06:47:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オートエンコーダの汎化境界（Generalization Bounds For Unsupervised and Semi-Supervised Learning With Autoencoders）</news:title>
   <news:publication_date>2026-08-03T06:47:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718965</loc>
  <lastmod>2026-08-03T05:54:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>360度映像の長期視野予測（Very Long Term Field of View Prediction for 360-degree Video Streaming）</news:title>
   <news:publication_date>2026-08-03T05:54:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718963</loc>
  <lastmod>2026-08-03T05:53:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Blazeによるメモリ上MapReduceの高速化（Blaze: Simplified High Performance Cluster Computing）</news:title>
   <news:publication_date>2026-08-03T05:53:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718961</loc>
  <lastmod>2026-08-03T05:53:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>クラスタ解析におけるパラメータ選択の可視化ツール（Visualization tools for parameter selection in cluster analysis）</news:title>
   <news:publication_date>2026-08-03T05:53:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718959</loc>
  <lastmod>2026-08-03T05:52:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>森の中の生成：近傍を用いた生成手法（A Forest from the Trees: Generation through Neighborhoods）</news:title>
   <news:publication_date>2026-08-03T05:52:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718957</loc>
  <lastmod>2026-08-03T05:52:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ハッブル望遠鏡によるNGC 2419中心域の多重星団解析（Hubble Space Telescope photometry of multiple stellar populations in the inner parts of NGC 2419）</news:title>
   <news:publication_date>2026-08-03T05:52:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718955</loc>
  <lastmod>2026-08-03T05:51:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所学習則から導かれたスパイキングニューラルネットワーク（A SPIKING NEURAL NETWORK WITH LOCAL LEARNING RULES DERIVED FROM NONNEGATIVE SIMILARITY MATCHING）</news:title>
   <news:publication_date>2026-08-03T05:51:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718953</loc>
  <lastmod>2026-08-03T05:51:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>風力タービン駆動系軸受の監視に向けた辞書学習アプローチ (Dictionary learning approach to monitoring of wind turbine drivetrain bearings)</news:title>
   <news:publication_date>2026-08-03T05:51:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718951</loc>
  <lastmod>2026-08-03T04:59:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>細粒度時間関係抽出の枠組みと実用性（Fine-Grained Temporal Relation Extraction）</news:title>
   <news:publication_date>2026-08-03T04:59:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718949</loc>
  <lastmod>2026-08-03T04:59:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>潜在変数の真価を問い直す──時系列モデルにおけるlatent変数の役割の再検討（Re-examination of the Role of Latent Variables in Sequence Modeling）</news:title>
   <news:publication_date>2026-08-03T04:59:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718947</loc>
  <lastmod>2026-08-03T04:58:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>具現化されたマルチモーダル・マルチタスク学習（Embodied Multimodal Multitask Learning）</news:title>
   <news:publication_date>2026-08-03T04:58:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718945</loc>
  <lastmod>2026-08-03T04:58:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単一画像からの霧除去を学習で実現する手法（End-to-End Single Image Fog Removal using Enhanced Cycle Consistent Adversarial Networks）</news:title>
   <news:publication_date>2026-08-03T04:58:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718943</loc>
  <lastmod>2026-08-03T04:58:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>FLORES評価データセット：ネパール語–英語とシンハラ語–英語（The FLORES Evaluation Datasets for Low-Resource Machine Translation: Nepali–English and Sinhala–English）</news:title>
   <news:publication_date>2026-08-03T04:58:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718941</loc>
  <lastmod>2026-08-03T04:58:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>過大パラメータ化された深層ReLUネットワークに対する勾配降下法の一般化誤差境界（Generalization Error Bounds of Gradient Descent for Learning Over-parameterized Deep ReLU Networks）</news:title>
   <news:publication_date>2026-08-03T04:58:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718939</loc>
  <lastmod>2026-08-03T04:57:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ベイズ推論のためのランジュバン拡散の確率的ゼロ階離散化（Stochastic Zeroth-order Discretizations of Langevin Diffusions for Bayesian Inference）</news:title>
   <news:publication_date>2026-08-03T04:57:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718937</loc>
  <lastmod>2026-08-03T04:06:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模なセキュリティ制約付きユニットコミット問題を学習で高速化する（Learning to Solve Large-Scale Security-Constrained Unit Commitment Problems）</news:title>
   <news:publication_date>2026-08-03T04:06:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718935</loc>
  <lastmod>2026-08-03T04:06:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>挿入ベースのデコーディングと自動推定生成順序（Insertion-based Decoding with Inferred Generation Order）</news:title>
   <news:publication_date>2026-08-03T04:06:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718933</loc>
  <lastmod>2026-08-03T04:06:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>隠れノード下の分布系トポロジ同定の堅牢化（Robust Hidden Topology Identification in Distribution Systems）</news:title>
   <news:publication_date>2026-08-03T04:06:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718931</loc>
  <lastmod>2026-08-03T04:05:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>最適化の視点からみる人工知能の学際的影響評価 (Evaluation of Multidisciplinary Effects of Artificial Intelligence with Optimization Perspective)</news:title>
   <news:publication_date>2026-08-03T04:05:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718929</loc>
  <lastmod>2026-08-03T04:05:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>欺瞞による防御フレームワーク（Deception-As-Defense Framework for Cyber-Physical Systems）</news:title>
   <news:publication_date>2026-08-03T04:05:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718927</loc>
  <lastmod>2026-08-03T04:04:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>構文に依存しないプロト役割ラベリングのためのマーカー・モデル（An Argument-Marker Model for Syntax-Agnostic Proto-Role Labeling）</news:title>
   <news:publication_date>2026-08-03T04:04:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718925</loc>
  <lastmod>2026-08-03T04:04:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ブラックボックス情報漏洩の高速推定（F-BLEAU: Fast Black-box Leakage Estimation）</news:title>
   <news:publication_date>2026-08-03T04:04:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718923</loc>
  <lastmod>2026-08-03T03:13:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>教師なし機械翻訳を実用領域へ近づけた手法（An Effective Approach to Unsupervised Machine Translation）</news:title>
   <news:publication_date>2026-08-03T03:13:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718921</loc>
  <lastmod>2026-08-03T02:55:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>推薦システムの共通表記の提案（Proposed Common Notation for Teaching and Research）</news:title>
   <news:publication_date>2026-08-03T02:55:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718919</loc>
  <lastmod>2026-08-03T02:55:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習で拡張するバンプハント（Extending the Bump Hunt with Machine Learning）</news:title>
   <news:publication_date>2026-08-03T02:55:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718917</loc>
  <lastmod>2026-08-03T02:54:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>近位大腿骨骨折の自動分類とその臨床応用可能性（Precise Proximal Femur Fracture Classification for Interactive Training and Surgical Planning）</news:title>
   <news:publication_date>2026-08-03T02:54:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718915</loc>
  <lastmod>2026-08-03T02:54:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>宣言型データ分析の概説（Declarative Data Analytics: a Survey）</news:title>
   <news:publication_date>2026-08-03T02:54:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718913</loc>
  <lastmod>2026-08-03T02:53:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>注意機構が変えた自然言語処理の地殻変動（A Survey on Neural Attention Models）</news:title>
   <news:publication_date>2026-08-03T02:53:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718911</loc>
  <lastmod>2026-08-03T02:01:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>熱力学データベースの効率的開発と不確かさ定量化を実現するESPEI（ESPEI for efficient thermodynamic database development, modification, and uncertainty quantification: application to Cu-Mg）</news:title>
   <news:publication_date>2026-08-03T02:01:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718909</loc>
  <lastmod>2026-08-03T02:01:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的な導関数不要最適化と重要度サンプリングの応用（A Stochastic Derivative-Free Optimization Method with Importance Sampling: Theory and Learning to Control）</news:title>
   <news:publication_date>2026-08-03T02:01:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718907</loc>
  <lastmod>2026-08-03T02:01:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Augmented Autoencodersによる6D物体検出の新境地（Augmented Autoencoders: Implicit 3D Orientation Learning for 6D Object Detection）</news:title>
   <news:publication_date>2026-08-03T02:01:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718905</loc>
  <lastmod>2026-08-03T02:00:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>2次元全変動によるノイズ除去の新しいリスク境界 (New Risk Bounds for 2D Total Variation Denoising)</news:title>
   <news:publication_date>2026-08-03T02:00:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718903</loc>
  <lastmod>2026-08-03T02:00:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>クラス内分割によるディープなワン・クラス分類（Deep One-Class Classification Using Intra-Class Splitting）</news:title>
   <news:publication_date>2026-08-03T02:00:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718901</loc>
  <lastmod>2026-08-03T01:59:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>プレイヤーごとに利得が異なるマルチプレイヤーバンディットの実用アルゴリズム（A Practical Algorithm for Multiplayer Bandits when Arm Means Vary Among Players）</news:title>
   <news:publication_date>2026-08-03T01:59:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718899</loc>
  <lastmod>2026-08-03T01:59:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>PIPPSが示した「混沌の呪い」の克服（Probabilistic Inference for Particle-based Policy Search）</news:title>
   <news:publication_date>2026-08-03T01:59:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718897</loc>
  <lastmod>2026-08-03T01:07:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>行列版多層パーセプトロンの構築とVAEへの応用 (Constructing the Matrix Multilayer Perceptron and its Application to the VAE)</news:title>
   <news:publication_date>2026-08-03T01:07:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718895</loc>
  <lastmod>2026-08-03T01:07:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>脳の接続性をグラフ上の測地線で比べる（COMPARISON OF BRAIN CONNECTOMES USING GEODESIC DISTANCE ON MANIFOLD: A TWINS STUDY）</news:title>
   <news:publication_date>2026-08-03T01:07:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718893</loc>
  <lastmod>2026-08-03T01:07:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>臨床用語の非教師あり翻訳の実用性（Unsupervised Clinical Language Translation）</news:title>
   <news:publication_date>2026-08-03T01:07:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718891</loc>
  <lastmod>2026-08-03T01:05:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>配列空間の統計力学が示す共進化解析の効率化（Statistical mechanical properties of sequence space determine the efficiency of the various algorithms to predict interaction energies and native contacts from protein coevolution）</news:title>
   <news:publication_date>2026-08-03T01:05:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718889</loc>
  <lastmod>2026-08-03T01:05:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CapStoreによるカプセルネット推論の省エネオンチップメモリ設計（CapStore: Energy-Efficient Design and Management of the On-Chip Memory for CapsuleNet Inference Accelerators）</news:title>
   <news:publication_date>2026-08-03T01:05:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718887</loc>
  <lastmod>2026-08-03T01:05:20Z</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は安全か（Is Spiking Secure? A Comparative Study on the Security Vulnerabilities of Spiking and Deep Neural Networks）</news:title>
   <news:publication_date>2026-08-03T01:05:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718885</loc>
  <lastmod>2026-08-03T01:05:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ランダム化による理論的な敵対的頑健性の裏付け（Theoretical evidence for adversarial robustness through randomization）</news:title>
   <news:publication_date>2026-08-03T01:05:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718882</loc>
  <lastmod>2026-08-03T00:13:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Adaptive Distinguishing Sequencesを用いた能動オートマトン学習の拡張（Active Automata Learning with Adaptive Distinguishing Sequences）</news:title>
   <news:publication_date>2026-08-03T00:13:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718880</loc>
  <lastmod>2026-08-03T00:13:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>行列多様体上のリーマン適応確率的勾配法（Riemannian adaptive stochastic gradient algorithms on matrix manifolds）</news:title>
   <news:publication_date>2026-08-03T00:13:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718878</loc>
  <lastmod>2026-08-03T00:13:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マーケティング予算配分の統一フレームワーク (A Unified Framework for Marketing Budget Allocation)</news:title>
   <news:publication_date>2026-08-03T00:13:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718876</loc>
  <lastmod>2026-08-03T00:12:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>行動の自然言語化（The Natural Language of Actions）</news:title>
   <news:publication_date>2026-08-03T00:12:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718874</loc>
  <lastmod>2026-08-03T00:12:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>領域句注意を用いた高現実画像生成（Realistic Image Generation using Region-phrase Attention）</news:title>
   <news:publication_date>2026-08-03T00:12:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718872</loc>
  <lastmod>2026-08-03T00:12:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模高次元データの2次元埋め込みを高速・低メモリで実現する方法（2-D Embedding of Large and High-dimensional Data with Minimal Memory and Computational Time Requirements）</news:title>
   <news:publication_date>2026-08-03T00:12:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718870</loc>
  <lastmod>2026-08-03T00:11:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>FMCWレーダーを用いた物体検出と3次元推定（OBJECT DETECTION AND 3D ESTIMATION VIA AN FMCW RADAR USING A FULLY CONVOLUTIONAL NETWORK）</news:title>
   <news:publication_date>2026-08-03T00:11:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718868</loc>
  <lastmod>2026-08-02T23:21:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>説明可能な行動変化検出（VEDAR: Accountable Behavioural Change Detection）</news:title>
   <news:publication_date>2026-08-02T23:21:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718866</loc>
  <lastmod>2026-08-02T23:20:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スパイクベースの環境音認識の効率化（Robust Environmental Sound Recognition with Sparse Key-point Encoding and Efficient Multi-spike Learning）</news:title>
   <news:publication_date>2026-08-02T23:20:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718864</loc>
  <lastmod>2026-08-02T23:20:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>服の互換性と多様性を考慮したファッション画像インペインティング（Compatible and Diverse Fashion Image Inpainting）</news:title>
   <news:publication_date>2026-08-02T23:20:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718862</loc>
  <lastmod>2026-08-02T23:19:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複合密度ネットワークによる予測不確実性の定量化（Predictive Uncertainty Quantification with Compound Density Networks）</news:title>
   <news:publication_date>2026-08-02T23:19:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718860</loc>
  <lastmod>2026-08-02T23:19:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>WideDTAによる薬物–標的結合親和性予測の革新（WIDEDTA: PREDICTION OF DRUG-TARGET BINDING AFFINITY）</news:title>
   <news:publication_date>2026-08-02T23:19:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718858</loc>
  <lastmod>2026-08-02T23:18:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Paracosmによる自動運転シミュレーション試験フレームワーク（Paracosm: A Test Framework for Autonomous Driving Simulations）</news:title>
   <news:publication_date>2026-08-02T23:18:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718856</loc>
  <lastmod>2026-08-02T23:18:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>計算的制約と堅牢分類の限界（Computational Limitations in Robust Classification and Win-Win Results）</news:title>
   <news:publication_date>2026-08-02T23:18:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718854</loc>
  <lastmod>2026-08-02T22:27:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数の不完全なデータ源からの因果効果同定（Causal Effect Identification from Multiple Incomplete Data Sources: A General Search-based Approach）</news:title>
   <news:publication_date>2026-08-02T22:27:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718852</loc>
  <lastmod>2026-08-02T22:26:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Saliency Tubesによる時空間畳み込みの可視化（SALIENCY TUBES: VISUAL EXPLANATIONS FOR SPATIO-TEMPORAL CONVOLUTIONS）</news:title>
   <news:publication_date>2026-08-02T22:26:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718850</loc>
  <lastmod>2026-08-02T22:26:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>混合環境に強い分散学習プロトコル Hop（Hop: Heterogeneity-aware Decentralized Training）</news:title>
   <news:publication_date>2026-08-02T22:26:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718848</loc>
  <lastmod>2026-08-02T22:25:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>RetinaNetを用いた歩行者検出の実用性と限界（Towards Pedestrian Detection Using RetinaNet）</news:title>
   <news:publication_date>2026-08-02T22:25:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718846</loc>
  <lastmod>2026-08-02T22:25:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単眼網膜画像から深度推定と視神経乳頭領域の分割を行う全畳み込みネットワーク（Fully Convolutional Networks for Monocular Retinal Depth Estimation and Optic Disc-Cup Segmentation）</news:title>
   <news:publication_date>2026-08-02T22:25:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718844</loc>
  <lastmod>2026-08-02T22:25:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>フェデレーテッドラーニングの大規模運用設計（Towards Federated Learning at Scale: System Design）</news:title>
   <news:publication_date>2026-08-02T22:25:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718842</loc>
  <lastmod>2026-08-02T22:24:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>部分フィードバック下での予測マージンに基づくオンライン多クラス分類（Online Multiclass Classification Based on Prediction Margin for Partial Feedback）</news:title>
   <news:publication_date>2026-08-02T22:24:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718840</loc>
  <lastmod>2026-08-02T21:33:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>RNNがSGDで効率的に学べることの証明（Can SGD Learn Recurrent Neural Networks with Provable Generalization?）</news:title>
   <news:publication_date>2026-08-02T21:33:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718838</loc>
  <lastmod>2026-08-02T21:32:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>顔表情認識における注意付き畳み込みネットワーク（Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network）</news:title>
   <news:publication_date>2026-08-02T21:32:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718836</loc>
  <lastmod>2026-08-02T21:31:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>フィリピンの企業協賛IT卒業設計におけるスクラム導入の有効性（A Study of an Agile methodology with scrum approach to the Filipino company-sponsored I.T. capstone program）</news:title>
   <news:publication_date>2026-08-02T21:31:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718834</loc>
  <lastmod>2026-08-02T21:31:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>応答面の全域推定を多重等高線で行う手法（Global Fitting of the Response Surface via Estimating Multiple Contours of a Simulator）</news:title>
   <news:publication_date>2026-08-02T21:31:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718832</loc>
  <lastmod>2026-08-02T21:31:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>発作タイプ分類におけるEEGと機械学習のベンチマーク設定（Seizure Type Classification using EEG signals and Machine Learning: Setting a benchmark）</news:title>
   <news:publication_date>2026-08-02T21:31:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718830</loc>
  <lastmod>2026-08-02T21:31:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>シミュレーションで銀河とハローを探す手法（Hunting for Galaxies and Halos in simulations with VELOCIraptor）</news:title>
   <news:publication_date>2026-08-02T21:31:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718828</loc>
  <lastmod>2026-08-02T20:39:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>インパルシブ雑音に強い分散RLSの設計と応用（Study of Robust Distributed Diffusion RLS Algorithms with Side Information for Adaptive Networks）</news:title>
   <news:publication_date>2026-08-02T20:39:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718826</loc>
  <lastmod>2026-08-02T20:39:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>構文的ヒューリスティクスが招く誤判断の診断（Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference）</news:title>
   <news:publication_date>2026-08-02T20:39:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718824</loc>
  <lastmod>2026-08-02T20:39:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>BottleNet によるモバイル・クラウド協調の効率化（BottleNet: A Deep Learning Architecture for Intelligent Mobile Cloud Computing Services）</news:title>
   <news:publication_date>2026-08-02T20:39:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718822</loc>
  <lastmod>2026-08-02T20:38:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>MCMCにおける加速法の類似物は存在するか（Is There an Analog of Nesterov Acceleration for MCMC?）</news:title>
   <news:publication_date>2026-08-02T20:38:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718820</loc>
  <lastmod>2026-08-02T20:38:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>最小最大実験計画法――最小二乗回帰における統計的手法と最悪ケース手法の橋渡し（Minimax experimental design: Bridging the gap between statistical and worst-case approaches to least squares regression）</news:title>
   <news:publication_date>2026-08-02T20:38:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718818</loc>
  <lastmod>2026-08-02T20:38:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>個別用量反応曲線の反事実表現学習（Learning Counterfactual Representations for Estimating Individual Dose-Response Curves）</news:title>
   <news:publication_date>2026-08-02T20:38:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718816</loc>
  <lastmod>2026-08-02T20:38:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>敵対的ネットワークとオートエンコーダのプリマル・デュアル関係と一般化境界（Adversarial Networks and Autoencoders: The Primal-Dual Relationship and Generalization Bounds）</news:title>
   <news:publication_date>2026-08-02T20:38:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718814</loc>
  <lastmod>2026-08-02T19:47:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ベイズ的情報引き出し（Bayesian Elicitation）</news:title>
   <news:publication_date>2026-08-02T19:47:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718812</loc>
  <lastmod>2026-08-02T19:46:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非定常文脈バンディットの新アルゴリズム（A New Algorithm for Non-stationary Contextual Bandits）</news:title>
   <news:publication_date>2026-08-02T19:46:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718810</loc>
  <lastmod>2026-08-02T19:46:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ユニバーサル・レマタイザー：系列対系列モデルによる普遍的依存木バンクのレマタイジング（UNIVERSAL LEMMATIZER: A SEQUENCE TO SEQUENCE MODEL FOR LEMMATIZING UNIVERSAL DEPENDENCIES TREEBANKS）</news:title>
   <news:publication_date>2026-08-02T19:46:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718808</loc>
  <lastmod>2026-08-02T19:45:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的1次法とポテンシャル関数による非漸近解析（Stochastic first-order methods: non-asymptotic and computer-aided analyses via potential functions）</news:title>
   <news:publication_date>2026-08-02T19:45:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718806</loc>
  <lastmod>2026-08-02T19:45:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>伴奏から学ぶ自動ピッチ補正のデータ駆動手法（DEEP AUTOTUNER: A DATA-DRIVEN APPROACH TO NATURAL-SOUNDING PITCH CORRECTION FOR SINGING VOICE IN KARAOKE PERFORMANCES）</news:title>
   <news:publication_date>2026-08-02T19:45:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718804</loc>
  <lastmod>2026-08-02T19:45:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ツイートからの高解像度居住地推定（High-resolution home location prediction from tweets using deep learning with dynamic structure）</news:title>
   <news:publication_date>2026-08-02T19:45:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718802</loc>
  <lastmod>2026-08-02T19:44:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>KAUにおけるLMS利用評価と“FORCE”普及戦略の提案（Assessing the Usages of LMS at KAU and Proposing “FORCE” Strategy for the Diffusion）</news:title>
   <news:publication_date>2026-08-02T19:44:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718800</loc>
  <lastmod>2026-08-02T18:52:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>有限時間誤差境界とTD学習（Finite-Time Error Bounds For Linear Stochastic Approximation and TD Learning）</news:title>
   <news:publication_date>2026-08-02T18:52:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718798</loc>
  <lastmod>2026-08-02T18:44:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非線形パラメータ推定における最適実験設計と精密信頼領域（Optimal Experiment Design in Nonlinear Parameter Estimation with Exact Confidence Regions）</news:title>
   <news:publication_date>2026-08-02T18:44:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718796</loc>
  <lastmod>2026-08-02T18:44:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>MICADO-MCAOによる総合的なアストロメトリ誤差予算の構築（Towards an overall astrometric error budget with MICADO-MCAO）</news:title>
   <news:publication_date>2026-08-02T18:44:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718794</loc>
  <lastmod>2026-08-02T18:44:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>アブイニシオ核物理におけるベイズ最適化（Bayesian optimization in ab initio nuclear physics）</news:title>
   <news:publication_date>2026-08-02T18:44:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718792</loc>
  <lastmod>2026-08-02T18:43:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>APOGEE分光器の設計と性能（The APOGEE Spectrographs）</news:title>
   <news:publication_date>2026-08-02T18:43:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718790</loc>
  <lastmod>2026-08-02T18:42:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Depthwise Convolutionによるマルチドメイン学習の要点（Depthwise Convolution is All You Need for Learning Multiple Visual Domains）</news:title>
   <news:publication_date>2026-08-02T18:42:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718788</loc>
  <lastmod>2026-08-02T18:42:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分子特性予測における不確かさ推定と能動学習の統合（Bayesian semi-supervised learning for uncertainty-calibrated prediction of molecular properties and active learning）</news:title>
   <news:publication_date>2026-08-02T18:42:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718786</loc>
  <lastmod>2026-08-02T17:50:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>テキストコーパスから概念階層を推定する手法（Inferring Concept Hierarchies from Text Corpora via Hyperbolic Embeddings）</news:title>
   <news:publication_date>2026-08-02T17:50:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718784</loc>
  <lastmod>2026-08-02T17:50:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>層間の固有類似性を掘り起こす深層モデル圧縮（MIning Cross-Layer Inherent similarity Knowledge (MICIK)）</news:title>
   <news:publication_date>2026-08-02T17:50:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718782</loc>
  <lastmod>2026-08-02T17:49:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>有界勾配仮定を外した非凸学習における確率的勾配法の理論整理（Stochastic Gradient Descent for Nonconvex Learning without Bounded Gradient Assumptions）</news:title>
   <news:publication_date>2026-08-02T17:49:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718780</loc>
  <lastmod>2026-08-02T17:49:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Szegö最小化問題に関する考察（Notes on the Szegö minimum problem. I. Measures with deep zeroes）</news:title>
   <news:publication_date>2026-08-02T17:49:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718778</loc>
  <lastmod>2026-08-02T17:49:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>リレーショナルTucker分解によるマルチ関係リンク予測（A Relational Tucker Decomposition for Multi-Relational Link Prediction）</news:title>
   <news:publication_date>2026-08-02T17:49:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718776</loc>
  <lastmod>2026-08-02T17:48:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>部分ROC下面積を最大化する話者認証の精度向上手法（Speaker Verification By Partial AUC Optimization With Mahalanobis Distance Metric Learning）</news:title>
   <news:publication_date>2026-08-02T17:48:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718774</loc>
  <lastmod>2026-08-02T17:48:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>強く相互作用する系を通したQCDカラーの伝播（Propagation of QCD Color through Strongly Interacting Systems）</news:title>
   <news:publication_date>2026-08-02T17:48:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718772</loc>
  <lastmod>2026-08-02T16:56:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>量子版AdaBoostによる分類器学習の高速化（Quantum Speedup in Adaptive Boosting of Binary Classification）</news:title>
   <news:publication_date>2026-08-02T16:56:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718770</loc>
  <lastmod>2026-08-02T16:56:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層ニューラルネットワークの正則化としての局所ラデマッハ複雑度の実証研究（An Empirical Study on Regularization of Deep Neural Networks by Local Rademacher Complexity）</news:title>
   <news:publication_date>2026-08-02T16:56:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718768</loc>
  <lastmod>2026-08-02T16:56:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ワンビットADCを用いたMU-MIMO向け半教師あり検出器（Semi-Supervised Learning Detector for MU-MIMO Systems with One-bit ADCs）</news:title>
   <news:publication_date>2026-08-02T16:56:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718766</loc>
  <lastmod>2026-08-02T16:55:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>生涯強化学習のためのメタMDPアプローチ（A Meta-MDP Approach to Exploration for Lifelong Reinforcement Learning）</news:title>
   <news:publication_date>2026-08-02T16:55:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718764</loc>
  <lastmod>2026-08-02T16:55:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューラルによる抽出的要約と統語的圧縮（Neural Extractive Text Summarization with Syntactic Compression）</news:title>
   <news:publication_date>2026-08-02T16:55:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718762</loc>
  <lastmod>2026-08-02T16:55:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>価値認識型レコメンデーションによる利益最大化（Value-aware Recommendation based on Reinforced Profit Maximization in E-commerce Systems）</news:title>
   <news:publication_date>2026-08-02T16:55:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718760</loc>
  <lastmod>2026-08-02T16:55:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>夜間撮影画像のグロー（光輝）を除去する深層学習アーキテクチャ（DeGlow‑DeHaze for Nighttime Image Enhancement）</news:title>
   <news:publication_date>2026-08-02T16:55:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718758</loc>
  <lastmod>2026-08-02T16:03:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>屋内ロボットの実時間フリースペース分割（Real-Time Freespace Segmentation on Autonomous Robots for Detection of Obstacles and Drop-Offs）</news:title>
   <news:publication_date>2026-08-02T16:03:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718756</loc>
  <lastmod>2026-08-02T16:02:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>離散確率過程の定量的弱収束（Quantitative Weak Convergence for Discrete Stochastic Processes）</news:title>
   <news:publication_date>2026-08-02T16:02:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718754</loc>
  <lastmod>2026-08-02T16:02:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>インクリメンタル学習における最大エントロピー正則化とDropOut Sampling（Incremental Learning with Maximum Entropy Regularization: Rethinking Forgetting and Intransigence）</news:title>
   <news:publication_date>2026-08-02T16:02:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718752</loc>
  <lastmod>2026-08-02T16:01:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DeepPBM: 動画から背景を確率的に推定する手法（DEEPPBM: DEEP PROBABILISTIC BACKGROUND MODEL ESTIMATION FROM VIDEO SEQUENCES）</news:title>
   <news:publication_date>2026-08-02T16:01:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718750</loc>
  <lastmod>2026-08-02T16:01:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>遅延報酬を考慮した文脈付き多腕バンディットの非パラメトリック無作為化配分（Randomized Allocation with Nonparametric Estimation for Contextual Multi-Armed Bandits with Delayed Rewards）</news:title>
   <news:publication_date>2026-08-02T16:01:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718748</loc>
  <lastmod>2026-08-02T16:01:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>記憶を持つランダムウォークのアンダーソン様局所化転移（Anderson-like localization transition of random walks with resetting）</news:title>
   <news:publication_date>2026-08-02T16:01:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718746</loc>
  <lastmod>2026-08-02T16:00:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>量的分布性質の検査における量子優位の可能性（Distributional property testing in a quantum world）</news:title>
   <news:publication_date>2026-08-02T16:00:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718744</loc>
  <lastmod>2026-08-02T15:09:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>生成モデルにおける判別器と協調するサンプリング（Collaborative Sampling in Generative Adversarial Networks）</news:title>
   <news:publication_date>2026-08-02T15:09:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718742</loc>
  <lastmod>2026-08-02T15:09:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>皮膚病変の境界を高精度に切り出すアンサンブル深層学習（Skin Lesion Segmentation in Dermoscopic Images with Ensemble Deep Learning Methods）</news:title>
   <news:publication_date>2026-08-02T15:09:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718740</loc>
  <lastmod>2026-08-02T15:08:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層ネットの複雑さと統計的リスクを結ぶ道筋（Complexity, Statistical Risk, and Metric Entropy of Deep Nets Using Total Path Variation）</news:title>
   <news:publication_date>2026-08-02T15:08:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718738</loc>
  <lastmod>2026-08-02T15:08:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>クラウドにおける学習ベースの動的キャッシュ管理（Learning-based Dynamic Cache Management in a Cloud）</news:title>
   <news:publication_date>2026-08-02T15:08:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718736</loc>
  <lastmod>2026-08-02T15:08:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>連続的効用に対する変分ベイズ意思決定（Variational Bayesian Decision-making for Continuous Utilities）</news:title>
   <news:publication_date>2026-08-02T15:08:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718734</loc>
  <lastmod>2026-08-02T15:07:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>地理教育におけるGoogle Classroomを用いたブレンデッドラーニングの実践と評価（Google Classroom as a Tool of Support of Blended Learning for Geography Students）</news:title>
   <news:publication_date>2026-08-02T15:07:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718732</loc>
  <lastmod>2026-08-02T14:16:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>線形力学系を学習するための半パラメトリック最小二乗法の改良（Learning Linear Dynamical Systems with Semi-Parametric Least Squares）</news:title>
   <news:publication_date>2026-08-02T14:16:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718730</loc>
  <lastmod>2026-08-02T14:16:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次元半教師あり学習による平均推定の最適化探索（HIGH-DIMENSIONAL SEMI-SUPERVISED LEARNING: IN SEARCH FOR OPTIMAL INFERENCE OF THE MEAN）</news:title>
   <news:publication_date>2026-08-02T14:16:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718728</loc>
  <lastmod>2026-08-02T14:15:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数ネットワーク埋め込みによるPOI推薦の統合モデル（RELINE: Point-of-Interest Recommendations using Multiple Network Embeddings）</news:title>
   <news:publication_date>2026-08-02T14:15:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718726</loc>
  <lastmod>2026-08-02T14:14:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>食料品認識のためのドメイン不変階層埋め込み（Domain invariant hierarchical embedding for grocery products recognition）</news:title>
   <news:publication_date>2026-08-02T14:14:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718724</loc>
  <lastmod>2026-08-02T14:14:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>生成パラメータを持つグラフニューラルネットワークによる関係抽出（Graph Neural Networks with Generated Parameters for Relation Extraction）</news:title>
   <news:publication_date>2026-08-02T14:14:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718722</loc>
  <lastmod>2026-08-02T14:13:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>少数パラメータで実現するNLPの転移学習（Parameter-Efficient Transfer Learning for NLP）</news:title>
   <news:publication_date>2026-08-02T14:13:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718720</loc>
  <lastmod>2026-08-02T14:13:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>RGB画像と疎な深度情報の融合による密な深度補完（DFuseNet: Deep Fusion of RGB and Sparse Depth Information for Image Guided Dense Depth Completion）</news:title>
   <news:publication_date>2026-08-02T14:13:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718718</loc>
  <lastmod>2026-08-02T13:21:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニュートリノ相互作用イベントの頂点再構成に対する深層学習（DEEP LEARNING FOR VERTEX RECONSTRUCTION OF NEUTRINO-NUCLEUS INTERACTION EVENTS WITH COMBINED ENERGY AND TIME DATA）</news:title>
   <news:publication_date>2026-08-02T13:21:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718716</loc>
  <lastmod>2026-08-02T13:14:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オンライン多人数追跡における二重マッチング注意機構（Online Multi-Object Tracking with Dual Matching Attention Networks）</news:title>
   <news:publication_date>2026-08-02T13:14:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718714</loc>
  <lastmod>2026-08-02T13:13:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非対称谷が示す新しい最適化観点（Asymmetric Valleys: Beyond Sharp and Flat Local Minima）</news:title>
   <news:publication_date>2026-08-02T13:13:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718712</loc>
  <lastmod>2026-08-02T13:13:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多腕バンディットにおける標本平均のバイアス、リスク、一貫性（On the bias, risk and consistency of sample means in multi-armed bandits）</news:title>
   <news:publication_date>2026-08-02T13:13:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718710</loc>
  <lastmod>2026-08-02T13:12:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>空間インターフェース評価による強化学習でのユーザー嗜好学習（Learning User Preferences via Reinforcement Learning with Spatial Interface Valuing）</news:title>
   <news:publication_date>2026-08-02T13:12:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718708</loc>
  <lastmod>2026-08-02T13:12:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>予測を用いるスケジューリングと誤予測の代償（Scheduling with Predictions and the Price of Misprediction）</news:title>
   <news:publication_date>2026-08-02T13:12:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718706</loc>
  <lastmod>2026-08-02T13:11:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>協調フィルタリングと強化学習の融合（When Collaborative Filtering Meets Reinforcement Learning）</news:title>
   <news:publication_date>2026-08-02T13:11:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718704</loc>
  <lastmod>2026-08-02T12:20:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>RemNetによるカメラモデル識別の新展開（RemNet: Remnant Convolutional Neural Network for Camera Model Identification）</news:title>
   <news:publication_date>2026-08-02T12:20:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718702</loc>
  <lastmod>2026-08-02T12:20:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューロモルフィック回路で逆問題を解く「脳の系列」を設計できるか（Can One Design a Series of Brains for Neuromorphic Computing to solve complex inverse problems?）</news:title>
   <news:publication_date>2026-08-02T12:20:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718700</loc>
  <lastmod>2026-08-02T12:20:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Majorana星による一般三量子ビット状態のスリーテンブルの可視化（Three-Tangle of a General Three-Qubit State in the Representation of Majorana Stars）</news:title>
   <news:publication_date>2026-08-02T12:20:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718698</loc>
  <lastmod>2026-08-02T12:19:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>最小最大（ミニマックス）確率変換による教師あり分類の再考（Supervised classification via minimax probabilistic transformations）</news:title>
   <news:publication_date>2026-08-02T12:19:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718696</loc>
  <lastmod>2026-08-02T12:19:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Thompson Samplingの一次ベイズ後悔解析（First-Order Bayesian Regret Analysis of Thompson Sampling）</news:title>
   <news:publication_date>2026-08-02T12:19:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718694</loc>
  <lastmod>2026-08-02T12:19:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>医療診断のためのSVM–CoDOAハイブリッド手法（Medical Diagnosis with a Novel SVM-CoDOA Based Hybrid Approach）</news:title>
   <news:publication_date>2026-08-02T12:19:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718692</loc>
  <lastmod>2026-08-02T12:19:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自然言語処理、感情分析と臨床アナリティクス（Natural Language Processing, Sentiment Analysis and Clinical Analytics）</news:title>
   <news:publication_date>2026-08-02T12:19:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718690</loc>
  <lastmod>2026-08-02T11:27:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>信念（belief）ダイナミクスの抽出──行動から内部状態を読み解く（Belief dynamics extraction）</news:title>
   <news:publication_date>2026-08-02T11:27:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718688</loc>
  <lastmod>2026-08-02T11:27:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多解像度単語埋め込みによる大規模非構造化知識ベースからの文書検索（A Multi-Resolution Word Embedding for Document Retrieval from Large Unstructured Knowledge Bases）</news:title>
   <news:publication_date>2026-08-02T11:27:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718686</loc>
  <lastmod>2026-08-02T11:27:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動的ワークロード向け学習済みインデックス（Learned Indexes for Dynamic Workloads）</news:title>
   <news:publication_date>2026-08-02T11:27:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718684</loc>
  <lastmod>2026-08-02T11:27:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>FurcaNetによる単一チャネル音声分離のエンドツーエンド手法（FURCANET: AN END-TO-END DEEP GATED CONVOLUTIONAL, LONG SHORT-TERM MEMORY, DEEP NEURAL NETWORKS FOR SINGLE CHANNEL SPEECH SEPARATION）</news:title>
   <news:publication_date>2026-08-02T11:27:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718682</loc>
  <lastmod>2026-08-02T11:27:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>効率的なクロスモーダルハッシュ学習を可能にする共通クラスタ単一損失（Joint Cluster Unary Loss for Efficient Cross-Modal Hashing）</news:title>
   <news:publication_date>2026-08-02T11:27:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718680</loc>
  <lastmod>2026-08-02T11:26:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>エンドユーザーがどのように教え、学ぶかをロボットが推定する方法（Enabling Robots to Infer how End-Users Teach and Learn through Human-Robot Interaction）</news:title>
   <news:publication_date>2026-08-02T11:26:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718678</loc>
  <lastmod>2026-08-02T11:26:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>無限幅モデルのパラメータ積分を高速に近似する手法（Fast Approximation and Estimation Bounds of Kernel Quadrature for Infinitely Wide Models）</news:title>
   <news:publication_date>2026-08-02T11:26:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718676</loc>
  <lastmod>2026-08-02T10:35:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ペアワイズ教師–生徒ネットワークによる半教師ありハッシング（Pairwise Teacher-Student Network for Semi-Supervised Hashing）</news:title>
   <news:publication_date>2026-08-02T10:35:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718674</loc>
  <lastmod>2026-08-02T10:35:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CodedPrivateML：分散学習でデータとモデルを同時に守る仕組み（CodedPrivateML: A Fast and Privacy-Preserving Framework for Distributed Machine Learning）</news:title>
   <news:publication_date>2026-08-02T10:35:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718672</loc>
  <lastmod>2026-08-02T10:35:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Particle Flow Bayes’ Rule（Particle Flow Bayes’ Rule）</news:title>
   <news:publication_date>2026-08-02T10:35:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718670</loc>
  <lastmod>2026-08-02T10:34:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時空間分解に基づく深層ニューラルネットワークによる時系列予測（A Spatial-Temporal Decomposition Based Deep Neural Network for Time Series Forecasting）</news:title>
   <news:publication_date>2026-08-02T10:34:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718668</loc>
  <lastmod>2026-08-02T10:34:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スライディングウィンドウでのAUC推定を効率化する方法（Efficient estimation of AUC in a sliding window）</news:title>
   <news:publication_date>2026-08-02T10:34:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718666</loc>
  <lastmod>2026-08-02T10:34:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチユーザ動画ストリーミングの物理層資源認識型深層強化学習アプローチ（Multiuser Video Streaming Rate Adaptation: A Physical Layer Resource-Aware Deep Reinforcement Learning Approach）</news:title>
   <news:publication_date>2026-08-02T10:34:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718664</loc>
  <lastmod>2026-08-02T10:33:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>定常時間での弱誤差解析によるSGDの拡張的理解（Uniform–in–Time Weak Error Analysis for Stochastic Gradient Descent Algorithms via Diffusion Approximation）</news:title>
   <news:publication_date>2026-08-02T10:33:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718662</loc>
  <lastmod>2026-08-02T09:43:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>バイアス付き確率近似の非漸近解析（Non-asymptotic Analysis of Biased Stochastic Approximation Scheme）</news:title>
   <news:publication_date>2026-08-02T09:43:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718660</loc>
  <lastmod>2026-08-02T09:42:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非パラメトリック曲線整列（Nonparametric Curve Alignment）</news:title>
   <news:publication_date>2026-08-02T09:42:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718658</loc>
  <lastmod>2026-08-02T09:42:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CQTを前処理に用いた単一音声分離の実証的検証（IS CQT MORE SUITABLE FOR MONAURAL SPEECH SEPARATION THAN STFT? AN EMPIRICAL STUDY）</news:title>
   <news:publication_date>2026-08-02T09:42:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718656</loc>
  <lastmod>2026-08-02T09:41:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ノイズ付き勾配法の一般化誤差境界（ON GENERALIZATION ERROR BOUNDS OF NOISY GRADIENT METHODS FOR NON-CONVEX LEARNING）</news:title>
   <news:publication_date>2026-08-02T09:41:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718654</loc>
  <lastmod>2026-08-02T09:41:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>クロスモーダル類似検索のための協調量子化（Collaborative Quantization for Cross-Modal Similarity Search）</news:title>
   <news:publication_date>2026-08-02T09:41:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718652</loc>
  <lastmod>2026-08-02T09:41:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>監督付き量子化による類似検索の精度向上（Supervised Quantization for Similarity Search）</news:title>
   <news:publication_date>2026-08-02T09:41:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718650</loc>
  <lastmod>2026-08-02T09:41:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非凸非凹ミニマックス最適化における局所最適性とは（What is Local Optimality in Nonconvex-Nonconcave Minimax Optimization?）</news:title>
   <news:publication_date>2026-08-02T09:41:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718648</loc>
  <lastmod>2026-08-02T08:49:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単語埋め込みの合成理解：テンソル分解による解析 (Understanding Composition of Word Embeddings via Tensor Decomposition)</news:title>
   <news:publication_date>2026-08-02T08:49:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718646</loc>
  <lastmod>2026-08-02T08:48:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>周回学習による自動レーシング車の軌道追従（Multiple-Lap Path Tracking for an Autonomous Race Vehicle via Iterative Learning Control）</news:title>
   <news:publication_date>2026-08-02T08:48:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718644</loc>
  <lastmod>2026-08-02T08:48:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>信頼度トリガ検出によるリアルタイム追跡の高速化（Confidence-Triggered Detection: Accelerating Real-time Tracking-by-detection Systems）</news:title>
   <news:publication_date>2026-08-02T08:48:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718642</loc>
  <lastmod>2026-08-02T08:47:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>摂動法の最適性（On the Optimality of Perturbations in Stochastic and Adversarial Multi-armed Bandit Problems）</news:title>
   <news:publication_date>2026-08-02T08:47:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718640</loc>
  <lastmod>2026-08-02T08:47:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自然な対面場面での視線（アイコンタクト）検出と児童評価への応用（Detecting Gaze Towards Eyes in Natural Social Interactions and Its Use in Child Assessment）</news:title>
   <news:publication_date>2026-08-02T08:47:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718638</loc>
  <lastmod>2026-08-02T08:47:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>離散確率モデルの効率的学習（Efficient Learning of Discrete Graphical Models）</news:title>
   <news:publication_date>2026-08-02T08:47:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718636</loc>
  <lastmod>2026-08-02T08:47:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ループネストの到達可能な性能に向けて (Towards an Achievable Performance for the Loop Nests)</news:title>
   <news:publication_date>2026-08-02T08:47:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718634</loc>
  <lastmod>2026-08-02T07:54:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所プライバシー下の推定に関する下限理論（Lower Bounds for Locally Private Estimation via Communication Complexity）</news:title>
   <news:publication_date>2026-08-02T07:54:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718632</loc>
  <lastmod>2026-08-02T07:54:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>原子間距離分布関数から空間群を推定する機械学習法（Using a machine learning approach to determine the space group of a structure from the atomic pair distribution function (PDF)）</news:title>
   <news:publication_date>2026-08-02T07:54:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718630</loc>
  <lastmod>2026-08-02T07:53:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習ベクトル量子化モデルの敵対的攻撃に対する堅牢性（Robustness of Generalized Learning Vector Quantization Models against Adversarial Attacks）</news:title>
   <news:publication_date>2026-08-02T07:53:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718628</loc>
  <lastmod>2026-08-02T07:53:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>電力系統の時間内不均衡の予測（Forecasting Intra-Hour Imbalances in Electric Power Systems）</news:title>
   <news:publication_date>2026-08-02T07:53:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718626</loc>
  <lastmod>2026-08-02T07:53:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>空間認識型機械学習による不動産予測の新潮流（The Spatially-Conscious Machine Learning Model）</news:title>
   <news:publication_date>2026-08-02T07:53:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718624</loc>
  <lastmod>2026-08-02T07:53:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Local Outlier Factorの自動ハイパーパラメータ調整法（Automatic Hyperparameter Tuning Method for Local Outlier Factor, with Applications to Anomaly Detection）</news:title>
   <news:publication_date>2026-08-02T07:53:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718622</loc>
  <lastmod>2026-08-02T07:52:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Atariゲームにおける深層強化学習の視覚的根拠表示（Visual Rationalizations in Deep Reinforcement Learning for Atari Games）</news:title>
   <news:publication_date>2026-08-02T07:52:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/718620</loc>
  <lastmod>2026-08-02T07:01:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>浅層（Shallow）EDSLとオブジェクト指向の接点（Shallow EDSLs and Object-Oriented Programming）</news:title>
   <news:publication_date>2026-08-02T07:01:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718618</loc>
  <lastmod>2026-08-02T07:01:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>予算制約下のハイパーパラメータ調整（Hyper-parameter Tuning under a Budget Constraint）</news:title>
   <news:publication_date>2026-08-02T07:01:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718616</loc>
  <lastmod>2026-08-02T07:01:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Shieldの有効性評価：異なる脅威モデル下での検証（The Efficacy of Shield under Different Threat Models）</news:title>
   <news:publication_date>2026-08-02T07:01:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718614</loc>
  <lastmod>2026-08-02T07:00:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチビュー学習によるソフトウェア理解の応用（Applications of Multi-view Learning Approaches for Software Comprehension）</news:title>
   <news:publication_date>2026-08-02T07:00:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718612</loc>
  <lastmod>2026-08-02T07:00:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>顧客レビューにおけるジェンダーバイアスの検証（Examining the Presence of Gender Bias in Customer Reviews Using Word Embedding）</news:title>
   <news:publication_date>2026-08-02T07:00:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718610</loc>
  <lastmod>2026-08-02T07:00:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>競合的経験再生が変える希少報酬下の探索（COMPETITIVE EXPERIENCE REPLAY）</news:title>
   <news:publication_date>2026-08-02T07:00:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718608</loc>
  <lastmod>2026-08-02T07:00:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>映像のための微分可能文法（Differentiable Grammars for Videos）</news:title>
   <news:publication_date>2026-08-02T07:00:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718606</loc>
  <lastmod>2026-08-02T06:09:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>情報理論に基づく部分監視下の最小最大後悔（An Information-Theoretic Approach to Minimax Regret in Partial Monitoring）</news:title>
   <news:publication_date>2026-08-02T06:09:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718604</loc>
  <lastmod>2026-08-02T06:00:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DANTE：交互最小化にもとづくニューラルネットワーク訓練法（DANTE: Deep AlterNations for Training nEural networks）</news:title>
   <news:publication_date>2026-08-02T06:00:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718602</loc>
  <lastmod>2026-08-02T05:59:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>近似論理合成に強化学習を用いる技術マッピング（Approximate Logic Synthesis: A Reinforcement Learning-Based Technology Mapping Approach）</news:title>
   <news:publication_date>2026-08-02T05:59:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718600</loc>
  <lastmod>2026-08-02T05:58:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多段階モンテカルロ変分推論（Multilevel Monte Carlo Variational Inference）</news:title>
   <news:publication_date>2026-08-02T05:58:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718598</loc>
  <lastmod>2026-08-02T05:58:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>超音波散乱体の再構築のためのScatGAN（SCATGAN FOR RECONSTRUCTION OF ULTRASOUND SCATTERERS USING GENERATIVE ADVERSARIAL NETWORKS）</news:title>
   <news:publication_date>2026-08-02T05:58:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718596</loc>
  <lastmod>2026-08-02T05:58:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>人口希薄地域の公共意思決定支援：時空間犯罪予測のための不均衡対応ハイパーアンサンブル（Public decision support for low population density areas: An imbalance-aware hyper-ensemble for spatio-temporal crime prediction）</news:title>
   <news:publication_date>2026-08-02T05:58:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718594</loc>
  <lastmod>2026-08-02T05:58:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>TF-Replicator：研究者向け分散機械学習の実装抽象化（TF-Replicator: Distributed machine learning for researchers）</news:title>
   <news:publication_date>2026-08-02T05:58:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718592</loc>
  <lastmod>2026-08-02T05:06:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時系列デコンファウンダー（Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders）</news:title>
   <news:publication_date>2026-08-02T05:06:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718590</loc>
  <lastmod>2026-08-02T05:06:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>空間・時間・テキストを同時に扱う犯罪連関検出（Spatial-Temporal-Textual Point Processes for Crime Linkage Detection）</news:title>
   <news:publication_date>2026-08-02T05:06:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718588</loc>
  <lastmod>2026-08-02T05:05:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>組合せベイズ最適化とグラフ直積の活用（Combinatorial Bayesian Optimization using the Graph Cartesian Product）</news:title>
   <news:publication_date>2026-08-02T05:05:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718586</loc>
  <lastmod>2026-08-02T05:05:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>社会学習と政府学習を組み込む政策の事前評価の重要性（The Importance of Social and Government Learning in Ex Ante Policy Evaluation）</news:title>
   <news:publication_date>2026-08-02T05:05:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718584</loc>
  <lastmod>2026-08-02T05:05:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>一般化スライスド・ワッサースタイン距離（Generalized Sliced Wasserstein Distances）</news:title>
   <news:publication_date>2026-08-02T05:05:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718582</loc>
  <lastmod>2026-08-02T05:05:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CIFARのテストに訓練データが混入していた問題の是正（Do we train on test data? Purging CIFAR of near-duplicates）</news:title>
   <news:publication_date>2026-08-02T05:05:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718580</loc>
  <lastmod>2026-08-02T05:04:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>語彙分類学を用いた短文分類の解釈可能な特徴生成（tax2vec: Constructing Interpretable Features from Taxonomies for Short Text Classification）</news:title>
   <news:publication_date>2026-08-02T05:04:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718578</loc>
  <lastmod>2026-08-02T04:13:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>混合分布における正規化Wasserstein距離（Normalized Wasserstein for Mixture Distributions with Applications in Adversarial Learning and Domain Adaptation）</news:title>
   <news:publication_date>2026-08-02T04:13:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718576</loc>
  <lastmod>2026-08-02T04:13:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次の損失近似と特徴が深層学習解釈に与える影響（Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation）</news:title>
   <news:publication_date>2026-08-02T04:13:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718574</loc>
  <lastmod>2026-08-02T04:12:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ReLUネットワークに対するロバストネス証明の考え方（Robustness Certificates Against Adversarial Examples for ReLU Networks）</news:title>
   <news:publication_date>2026-08-02T04:12:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718572</loc>
  <lastmod>2026-08-02T04:12:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>圧縮動的MRIのスケーラブル学習ベースサンプリング最適化（Scalable Learning-Based Sampling Optimization for Compressive Dynamic MRI）</news:title>
   <news:publication_date>2026-08-02T04:12:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718570</loc>
  <lastmod>2026-08-02T04:12:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スパース合成正則化と深層ニューラルネットワーク（Sparse synthesis regularization with deep neural networks）</news:title>
   <news:publication_date>2026-08-02T04:12:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718568</loc>
  <lastmod>2026-08-02T04:11:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動的公平性——自動意思決定における悪循環の断ち切り（Dynamic fairness – Breaking vicious cycles in automatic decision making）</news:title>
   <news:publication_date>2026-08-02T04:11:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718566</loc>
  <lastmod>2026-08-02T04:11:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習可能な埋め込み空間による効率的ニューラルアーキテクチャ圧縮（LEARNABLE EMBEDDING SPACE FOR EFFICIENT NEURAL ARCHITECTURE COMPRESSION）</news:title>
   <news:publication_date>2026-08-02T04:11:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718564</loc>
  <lastmod>2026-08-02T03:20:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>アイコン外観の類似性学習（Learning Icons Appearance Similarity）</news:title>
   <news:publication_date>2026-08-02T03:20:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718562</loc>
  <lastmod>2026-08-02T03:20:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>エントロピーに基づくセンシング行列の学習（ENTROPY-BASED LEARNING OF SENSING MATRICES）</news:title>
   <news:publication_date>2026-08-02T03:20:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718560</loc>
  <lastmod>2026-08-02T03:20:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>木構造で拡張するWasserstein距離の新展開（Tree-Sliced Variants of Wasserstein Distances）</news:title>
   <news:publication_date>2026-08-02T03:20:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718558</loc>
  <lastmod>2026-08-02T03:19:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散確率的最適化と圧縮通信を用いたゴシップアルゴリズム（Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication）</news:title>
   <news:publication_date>2026-08-02T03:19:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718556</loc>
  <lastmod>2026-08-02T03:19:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>顔画像における敏感情報を除去する表現学習：SensitiveNetsの実践（SensitiveNets: Learning Agnostic Representations with Application to Face Images）</news:title>
   <news:publication_date>2026-08-02T03:19:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718554</loc>
  <lastmod>2026-08-02T03:19:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層強化学習による結合エンティティリンク（Joint Entity Linking with Deep Reinforcement Learning）</news:title>
   <news:publication_date>2026-08-02T03:19:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718552</loc>
  <lastmod>2026-08-02T03:19:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>視覚的に重要な関係性に再焦点を当てる（VrR-VG: Refocusing Visually-Relevant Relationships）</news:title>
   <news:publication_date>2026-08-02T03:19:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718550</loc>
  <lastmod>2026-08-02T02:27:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>離散分布の近接性検定における局所ミニマックス率（Local minimax rates for closeness testing of discrete distributions）</news:title>
   <news:publication_date>2026-08-02T02:27:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718548</loc>
  <lastmod>2026-08-02T02:27:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Generative Smoke Removal（Generative Smoke Removal）</news:title>
   <news:publication_date>2026-08-02T02:27:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718546</loc>
  <lastmod>2026-08-02T02:27:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層ハイパースペクトル・プライオリ（Deep Hyperspectral Prior: Single-Image Denoising, Inpainting, Super-Resolution）</news:title>
   <news:publication_date>2026-08-02T02:27:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718544</loc>
  <lastmod>2026-08-02T02:26:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>微分可能な最小二乗法を組み込んだエンドツーエンド車線検出（End-to-end Lane Detection through Differentiable Least-Squares Fitting）</news:title>
   <news:publication_date>2026-08-02T02:26:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718542</loc>
  <lastmod>2026-08-02T02:26:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深部大気層のパルス加熱が引き起こす振動と上向きショック――フレア再結合の準周期的変調（Pulse-beam heating of deep atmospheric layers triggering their oscillations and upwards moving shocks that can modulate the reconnection in solar flares）</news:title>
   <news:publication_date>2026-08-02T02:26:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718540</loc>
  <lastmod>2026-08-02T02:25:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Wasserstein空間上の流れとしてのMCMCダイナミクス（Understanding MCMC Dynamics as Flows on the Wasserstein Space）</news:title>
   <news:publication_date>2026-08-02T02:25:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718538</loc>
  <lastmod>2026-08-02T02:25:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>因果シミュレーションによるアップリフトモデリングの検証手法（Causal Simulations for Uplift Modeling）</news:title>
   <news:publication_date>2026-08-02T02:25:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718536</loc>
  <lastmod>2026-08-02T01:34:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Flow++によるフロー型生成モデルの改良（Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design）</news:title>
   <news:publication_date>2026-08-02T01:34:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718534</loc>
  <lastmod>2026-08-02T01:34:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>TanDEM-Xデータを用いた森林分類における深層学習の適用（DEEP LEARNING SOLUTIONS FOR TANDEM-X-BASED FOREST CLASSIFICATION）</news:title>
   <news:publication_date>2026-08-02T01:34:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718532</loc>
  <lastmod>2026-08-02T01:34:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>HSTによる深宇宙観測で偶然見つかった矮小楕円銀河の発見（Serendipitous discovery of a dwarf Galaxy in background）</news:title>
   <news:publication_date>2026-08-02T01:34:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718530</loc>
  <lastmod>2026-08-02T01:32:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>継続的強化学習における方策統合（Policy Consolidation for Continual Reinforcement Learning）</news:title>
   <news:publication_date>2026-08-02T01:32:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718528</loc>
  <lastmod>2026-08-02T01:32:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>瓦礫塊天体モデルにおけるバイスタティック全波レーダートモグラフィーの検出能力（Bistatic full-wave radar tomography detects deep interior voids, cracks and boulders in a rubble-pile asteroid model）</news:title>
   <news:publication_date>2026-08-02T01:32:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718526</loc>
  <lastmod>2026-08-02T01:32:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層ニューラルネットワークによる最適軌道推定の実務的インパクト（Deep Networks as Approximators of Optimal Transfers）</news:title>
   <news:publication_date>2026-08-02T01:32:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718524</loc>
  <lastmod>2026-08-02T01:32:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>インスタンスセグメンテーションを画像セグメンテーション注釈として (Instance Segmentation as Image Segmentation Annotation)</news:title>
   <news:publication_date>2026-08-02T01:32:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718522</loc>
  <lastmod>2026-08-02T00:39:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ProteinNetによるタンパク質構造機械学習の標準化（ProteinNet: a standardized data set for machine learning of protein structure）</news:title>
   <news:publication_date>2026-08-02T00:39:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718520</loc>
  <lastmod>2026-08-02T00:39:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非凸最適化におけるSGDの鋭い解析と鞍点脱出の実務的示唆（Sharp Analysis for Nonconvex SGD Escaping from Saddle Points）</news:title>
   <news:publication_date>2026-08-02T00:39:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718518</loc>
  <lastmod>2026-08-02T00:38:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>組合せ推薦の逐次評価と生成フレームワーク（Sequential Evaluation and Generation Framework for Combinatorial Recommender System）</news:title>
   <news:publication_date>2026-08-02T00:38:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718516</loc>
  <lastmod>2026-08-02T00:38:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>画像変換に対する不変性を用いた自然誤分類と敵対的誤分類の検出（Natural and Adversarial Error Detection using Invariance to Image Transformations）</news:title>
   <news:publication_date>2026-08-02T00:38:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718514</loc>
  <lastmod>2026-08-02T00:37:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>合体に伴う衝撃波のX線証拠：ZwCl 0008.8+5215のChandra/Suzaku観測 (Evidence for a merger induced shock wave in ZwCl 0008.8+5215 with Chandra and Suzaku)</news:title>
   <news:publication_date>2026-08-02T00:37:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718512</loc>
  <lastmod>2026-08-02T00:37:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>事前定義された均等分布クラス中心に基づく分類監視オートエンコーダ（A Classification Supervised Auto-Encoder Based on Predefined Evenly-Distributed Class Centroids）</news:title>
   <news:publication_date>2026-08-02T00:37:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718510</loc>
  <lastmod>2026-08-02T00:37:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>既知経路から混合時間を推定する方法（Estimating the Mixing Time of Ergodic Markov Chains）</news:title>
   <news:publication_date>2026-08-02T00:37:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718508</loc>
  <lastmod>2026-08-01T23:45:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>因果に基づく増分型マルチタッチアトリビューションとRNN（Causally Driven Incremental Multi Touch Attribution Using a Recurrent Neural Network）</news:title>
   <news:publication_date>2026-08-01T23:45:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718506</loc>
  <lastmod>2026-08-01T23:45:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>環境を操作する自己回帰モデルへの最適攻撃（Optimal Attack against Autoregressive Models by Manipulating the Environment）</news:title>
   <news:publication_date>2026-08-01T23:45:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718504</loc>
  <lastmod>2026-08-01T23:44:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチアームドバンディット問題とバッチUCB規則（MULTI-ARMED BANDIT PROBLEM AND BATCH UCB RULE）</news:title>
   <news:publication_date>2026-08-01T23:44:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718502</loc>
  <lastmod>2026-08-01T23:44:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>弱識別モデルに対する期待値最大化法の詳細解析（Sharp Analysis of Expectation-Maximization for Weakly Identifiable Models）</news:title>
   <news:publication_date>2026-08-01T23:44:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718500</loc>
  <lastmod>2026-08-01T23:44:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>行動表現を学習する強化学習（Learning Action Representations for Reinforcement Learning）</news:title>
   <news:publication_date>2026-08-01T23:44:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718498</loc>
  <lastmod>2026-08-01T23:43:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模多言語転移による固有表現認識（Massively Multilingual Transfer for NER）</news:title>
   <news:publication_date>2026-08-01T23:43:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718496</loc>
  <lastmod>2026-08-01T23:43:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>異なるドメイン間での単語埋め込み学習のための単純な正則化アルゴリズム（A Simple Regularization-based Algorithm for Learning Cross-Domain Word Embeddings）</news:title>
   <news:publication_date>2026-08-01T23:43:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718494</loc>
  <lastmod>2026-08-01T22:52:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文脈をめくる――視覚認識における空間と時間の文脈推論（Lift-the-flap: what, where and when for context reasoning）</news:title>
   <news:publication_date>2026-08-01T22:52:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718492</loc>
  <lastmod>2026-08-01T22:44:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>連続近似によるバイナリニューラルネットワークの臨界初期化（CRITICAL INITIALISATION IN CONTINUOUS APPROXIMATIONS OF BINARY NEURAL NETWORKS）</news:title>
   <news:publication_date>2026-08-01T22:44:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718490</loc>
  <lastmod>2026-08-01T22:44:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>勾配最適化器の圧縮手法と実務への示唆（Compressing Gradient Optimizers via Count-Sketches）</news:title>
   <news:publication_date>2026-08-01T22:44:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718488</loc>
  <lastmod>2026-08-01T22:44:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文書の日付推定にGCNを使う試み（Dating Documents using Graph Convolution Networks）</news:title>
   <news:publication_date>2026-08-01T22:44:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718486</loc>
  <lastmod>2026-08-01T22:42:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オフポリシー評価におけるプライバシー保護（Privacy Preserving Off-Policy Evaluation）</news:title>
   <news:publication_date>2026-08-01T22:42:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718484</loc>
  <lastmod>2026-08-01T22:42:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DREAM: 対話型読解の課題とモデル（DREAM: A Challenge Dataset and Models for Dialogue-Based Reading Comprehension）</news:title>
   <news:publication_date>2026-08-01T22:42:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718482</loc>
  <lastmod>2026-08-01T22:42:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Open KBの正規化を自動化するCESI（CESI: Canonicalizing Open Knowledge Bases using Embeddings and Side Information）</news:title>
   <news:publication_date>2026-08-01T22:42:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718480</loc>
  <lastmod>2026-08-01T21:50:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>普遍的太陽光発電予測器の提案（A Novel Universal Photovoltaic Energy Predictor）</news:title>
   <news:publication_date>2026-08-01T21:50:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718478</loc>
  <lastmod>2026-08-01T21:50:11Z</lastmod>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>長文を一貫して生成するための多層潜在変数モデル（Towards Generating Long and Coherent Text with Multi-Level Latent Variable Models）</news:title>
   <news:publication_date>2026-08-01T21:50:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718476</loc>
  <lastmod>2026-08-01T21:50:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GANの圧縮と知識蒸留による実装可能性の提示（Compressing GANs using Knowledge Distillation）</news:title>
   <news:publication_date>2026-08-01T21:50:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718474</loc>
  <lastmod>2026-08-01T21:49:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>バイフィデリティデータ支援ニューラルネットワークによる非侵襲型還元モデル（BIFIDELITY DATA-ASSISTED NEURAL NETWORKS IN NONINTRUSIVE REDUCED-ORDER MODELING）</news:title>
   <news:publication_date>2026-08-01T21:49:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718472</loc>
  <lastmod>2026-08-01T21:49:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>コラボレーティブ・インテリジェンスに適した深層学習アーキテクチャ（Towards Collaborative Intelligence Friendly Architectures for Deep Learning）</news:title>
   <news:publication_date>2026-08-01T21:49:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718470</loc>
  <lastmod>2026-08-01T21:48:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層トリプレット量子化（Deep Triplet Quantization）</news:title>
   <news:publication_date>2026-08-01T21:48:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718468</loc>
  <lastmod>2026-08-01T21:48:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>排他ラッソモデルの高速解法：二重ニュートン型前処理付き近接点法（A dual Newton based preconditioned proximal point algorithm for exclusive lasso models）</news:title>
   <news:publication_date>2026-08-01T21:48:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718466</loc>
  <lastmod>2026-08-01T20:55:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>アグノスティック・フェデレーテッド・ラーニングの本質（Agnostic Federated Learning）</news:title>
   <news:publication_date>2026-08-01T20:55:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718464</loc>
  <lastmod>2026-08-01T20:54:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフ抵抗と対比較学習（Graph Resistance and Learning from Pairwise Comparisons）</news:title>
   <news:publication_date>2026-08-01T20:54:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718462</loc>
  <lastmod>2026-08-01T20:54:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>敵対的攻撃に対して理論的耐性を持つ新しいニューラルネットワーク族（A New Family of Neural Networks Provably Resistant to Adversarial Attacks）</news:title>
   <news:publication_date>2026-08-01T20:54:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718460</loc>
  <lastmod>2026-08-01T20:53:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>正常細胞とがん細胞の識別におけるオートエンコーダのノード重要度（Distinguishing between Normal and Cancer Cells Using Autoencoder Node Saliency）</news:title>
   <news:publication_date>2026-08-01T20:53:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718458</loc>
  <lastmod>2026-08-01T20:53:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>メモリ効率の高い適応最適化が変える訓練スピードとモデル規模（Memory-Efficient Adaptive Optimization）</news:title>
   <news:publication_date>2026-08-01T20:53:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718456</loc>
  <lastmod>2026-08-01T20:52:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習が分子モデリングとシミュレーションを変える（Advances of Machine Learning in Molecular Modeling and Simulation）</news:title>
   <news:publication_date>2026-08-01T20:52:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718454</loc>
  <lastmod>2026-08-01T20:52:41Z</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>2026-08-01T20:52:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718452</loc>
  <lastmod>2026-08-01T20:01:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Natural Analystsによる適応的データ解析の再定式化（Natural Analysts in Adaptive Data Analysis）</news:title>
   <news:publication_date>2026-08-01T20:01:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718450</loc>
  <lastmod>2026-08-01T20:01:02Z</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>2026-08-01T20:01:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718448</loc>
  <lastmod>2026-08-01T20:00:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Top-k分類の一貫性に関する解析（On the Consistency of Top-k Surrogate Losses）</news:title>
   <news:publication_date>2026-08-01T20:00:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718446</loc>
  <lastmod>2026-08-01T19:59:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>生成的正規化フローのための新しい畳み込み（Emerging Convolutions for Generative Normalizing Flows）</news:title>
   <news:publication_date>2026-08-01T19:59:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718444</loc>
  <lastmod>2026-08-01T19:59:27Z</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>2026-08-01T19:59:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718442</loc>
  <lastmod>2026-08-01T19:59:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ディープな顔特徴量で「美しさ」を定量化する視点（Understanding Beauty via Deep Facial Features）</news:title>
   <news:publication_date>2026-08-01T19:59:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718440</loc>
  <lastmod>2026-08-01T19:59:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>進化したTransformer（The Evolved Transformer）</news:title>
   <news:publication_date>2026-08-01T19:59:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718438</loc>
  <lastmod>2026-08-01T19:07:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-01T19:07:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718436</loc>
  <lastmod>2026-08-01T19:07:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>計算流体力学におけるデータ復元と深層イメージプライア（Data recovery in computational fluid dynamics through deep image priors）</news:title>
   <news:publication_date>2026-08-01T19:07:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718434</loc>
  <lastmod>2026-08-01T19:07:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自己内省型機械学習アーキテクチャにおける量子力学の出現（Emergent Quantum Mechanics in an Introspective Machine Learning Architecture）</news:title>
   <news:publication_date>2026-08-01T19:07:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718432</loc>
  <lastmod>2026-08-01T19:06:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>派生商品のポートフォリオ評価におけるガウス過程回帰の適用（Gaussian Process Regression for Derivative Portfolio Modeling and Application to CVA Computations）</news:title>
   <news:publication_date>2026-08-01T19:06:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718430</loc>
  <lastmod>2026-08-01T19:06:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>期待値ベース強化学習と分布ベース強化学習の比較分析（A Comparative Analysis of Expected and Distributional Reinforcement Learning）</news:title>
   <news:publication_date>2026-08-01T19:06:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718428</loc>
  <lastmod>2026-08-01T19:06:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>走査型電子顕微鏡における深層学習を用いた解像度向上（Resolution enhancement in scanning electron microscopy using deep learning）</news:title>
   <news:publication_date>2026-08-01T19:06:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718426</loc>
  <lastmod>2026-08-01T19:05:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DDSLによる幾何学信号の微分可能ラスタライズ（Deep Differentiable Simplex Layer for Learning Geometric Signals）</news:title>
   <news:publication_date>2026-08-01T19:05:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718424</loc>
  <lastmod>2026-08-01T18:13:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>STM/STSデータにおけるネマティック秩序の検出（Detecting nematic order in STM/STS data with artificial intelligence）</news:title>
   <news:publication_date>2026-08-01T18:13:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718422</loc>
  <lastmod>2026-08-01T18:06:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>コラーゲンVI関連筋ジストロフィーの自動診断のための畳み込みニューラルネットワーク（A Convolutional Neural Network for the Automatic Diagnosis of Collagen VI related Muscular Dystrophies）</news:title>
   <news:publication_date>2026-08-01T18:06:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718420</loc>
  <lastmod>2026-08-01T18:05:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>リアルタイム時間分解オゾン予測における深層畳み込みニューラルネットワークの適用（A real-time hourly ozone prediction system using deep convolutional neural network）</news:title>
   <news:publication_date>2026-08-01T18:05:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718418</loc>
  <lastmod>2026-08-01T18:05:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>道路工事現場の注視点の実世界マッピング（REAL-WORLD MAPPING OF GAZE FIXATIONS USING INSTANCE SEGMENTATION FOR ROAD CONSTRUCTION SAFETY APPLICATIONS）</news:title>
   <news:publication_date>2026-08-01T18:05:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718416</loc>
  <lastmod>2026-08-01T18:04:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Metric Gaussian Variational Inferenceの要点と経営への示唆（Metric Gaussian Variational Inference）</news:title>
   <news:publication_date>2026-08-01T18:04:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718414</loc>
  <lastmod>2026-08-01T18:04:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Wilderness Areaデータセットによるクラスタリング評価の再設計（The Wilderness Area Data Set: Adapting the Covertype data set for unsupervised learning）</news:title>
   <news:publication_date>2026-08-01T18:04:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/718412</loc>
  <lastmod>2026-08-01T18:04:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>HyperGANによる多様で高性能なニューラルネットワーク生成（HyperGAN: A Generative Model for Diverse, Performant Neural Networks）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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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>
   <news:title>限定された仮説集合からのロボット生態学的知覚のブートストラップ（Bootstrapping Robotic Ecological Perception from a Limited Set of Hypotheses Through Interactive Perception）</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>QGPを可視化する電弱プローブの実験的総覧（Shining a Light on the QGP - Electroweak Probes Experimental Summary）</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>
   </news:publication>
   <news:title>Successor Features と Generalised Policy Improvement による強化学習の転移（Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement）</news:title>
   <news:publication_date>2026-08-01T17:11:01Z</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>
   </news:publication>
   <news:title>原理に基づく原子スケール特性の機械学習（Machine-learning of atomic-scale properties based on physical principles）</news:title>
   <news:publication_date>2026-08-01T17:10:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/718398</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>自己利益的な群衆を調整する仕組み（Coordinating the Crowd: Inducing Desirable Equilibria in Non-Cooperative Systems）</news:title>
   <news:publication_date>2026-08-01T16:19:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/718396</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>局所学習のためのスパース結合を用いた直接フィードバックアライメント（Direct Feedback Alignment with Sparse Connections for Local Learning）</news:title>
   <news:publication_date>2026-08-01T16:12:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/718394</loc>
  <lastmod>2026-08-01T16:12:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>長期時系列の欠損値を埋める非自己回帰型マルチ解像度補完（NAOMI: Non-Autoregressive Multiresolution Sequence Imputation）</news:title>
   <news:publication_date>2026-08-01T16:12:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/718392</loc>
  <lastmod>2026-08-01T16:11:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>内部者脅威検知の分類器群 (Classifier Suites for Insider Threat Detection)</news:title>
   <news:publication_date>2026-08-01T16:11:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/718390</loc>
  <lastmod>2026-08-01T16:10:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>フォグ・トゥ・シングス環境を保護するためのアンサンブル学習ベースの侵入検知システム（Securing Fog-to-Things Environment Using Intrusion Detection System Based On Ensemble Learning）</news:title>
   <news:publication_date>2026-08-01T16:10:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/718388</loc>
  <lastmod>2026-08-01T16:10:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複雑な3D環境における古典的ナビゲーションと学習型ナビゲーションのベンチマーク（Benchmarking Classic and Learned Navigation in Complex 3D Environments）</news:title>
   <news:publication_date>2026-08-01T16:10:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/718386</loc>
  <lastmod>2026-08-01T16:10:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>因果メカニズムの分離を学ぶためのメタ転移目的（A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms）</news:title>
   <news:publication_date>2026-08-01T16:10:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/718384</loc>
  <lastmod>2026-08-01T15:18:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>情報ボトルネックによる転移と探索（INFOBOT: Transfer and Exploration via the Information Bottleneck）</news:title>
   <news:publication_date>2026-08-01T15:18:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/718382</loc>
  <lastmod>2026-08-01T15:18:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層ニューラルネットワークが抽出する特徴の相関について（On Correlation of Features Extracted by Deep Neural Networks）</news:title>
   <news:publication_date>2026-08-01T15:18:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/718380</loc>
  <lastmod>2026-08-01T15:18:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>準クローンネッカー積グラフィカルモデルの学習（Learning Quasi-Kronecker Product Graphical Models）</news:title>
   <news:publication_date>2026-08-01T15:18:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/718378</loc>
  <lastmod>2026-08-01T15:16:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>二値重みパーセプトロンの計算的困難さと入力スパース性の利点（Understanding the computational difficulty of a binary-weight perceptron and the advantage of input sparseness）</news:title>
   <news:publication_date>2026-08-01T15:16:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/718376</loc>
  <lastmod>2026-08-01T15:16:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>小さなハミング距離で生じる敵対的事例の単純な説明（A Simple Explanation for the Existence of Adversarial Examples with Small Hamming Distance）</news:title>
   <news:publication_date>2026-08-01T15:16:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/718374</loc>
  <lastmod>2026-08-01T15:15:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多目的二値線形計画における射影学習（Learning to Project in Multi-Objective Binary Linear Programming）</news:title>
   <news:publication_date>2026-08-01T15:15:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/718372</loc>
  <lastmod>2026-08-01T15:15:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>指数機構の長所と落とし穴：Hilbert空間と関数型PCAへの応用（Benefits and Pitfalls of the Exponential Mechanism with Applications to Hilbert Spaces and Functional PCA）</news:title>
   <news:publication_date>2026-08-01T15:15:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
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