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   <news:title>個別の快適温度を少ない問合せで学ぶ（Learning Personalized Thermal Preferences via Bayesian Active Learning with Unimodality Constraints）</news:title>
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   <news:title>予算制約下のエッジサービス配置と需要推定（Budget-constrained Edge Service Provisioning with Demand Estimation via Bandit Learning）</news:title>
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   <news:title>狭い隙間をニューラルネットワークで飛行させる—エンドツーエンド計画と制御のアプローチ（Flying through a narrow gap using neural network: an end-to-end planning and control approach）</news:title>
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    <news:language>ja</news:language>
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   <news:title>再電離期に迫る――LAGERが示したz≈7のライマンα銀河の意味（LYMAN ALPHA GALAXIES IN THE EPOCH OF REIONIZATION）</news:title>
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    <news:language>ja</news:language>
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   <news:title>等変性を持つエンティティ関係ネットワーク（Equivariant Entity-Relationship Networks）</news:title>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>個別処方型サポートベクターマシンによる再入院抑止（Prescriptive Cluster-Dependent Support Vector Machines with an Application to Reducing Hospital Readmissions）</news:title>
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   <news:title>データが乏しくても深層学習を使うための生成モデル（Generative Models For Deep Learning with Very Scarce Data）</news:title>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>屋内廊下環境における単眼カメラを用いたUAV位置推定（Localization of Unmanned Aerial Vehicles in Corridor Environments using Deep Learning）</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>サブグラフネットワークによる構造特徴空間の拡張（Subgraph Networks with Application to Structural Feature Space Expansion）</news:title>
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   <news:genres>Blog</news:genres>
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   <news:title>ココエルシビティ、平滑性とバイアスが変える分散低減確率的勾配法（Cocoercivity, Smoothness and Bias in Variance-Reduced Stochastic Gradient Methods）</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>非共有結合に対する量子機械学習補正が密度汎関数計算を変える（Non-covalent quantum machine learning corrections to density functionals）</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>HYDRAによる応力最小化型ハイパーボリック埋め込み（HYDRA: A METHOD FOR STRAIN-MINIMIZING HYPERBOLIC EMBEDDING OF NETWORK- AND DISTANCE-BASED DATA）</news:title>
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    <news:language>ja</news:language>
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   <news:title>多目的時系列解析による薬物応答モデル化（Multi-Task Time Series Analysis applied to Drug Response Modelling）</news:title>
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   <news:genres>Blog</news:genres>
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    <news:name>AI Benchmark Research</news:name>
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   <news:title>斜め落下する液滴の深い液槽への衝突（Oblique droplet impact onto a deep liquid pool）</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>市販サラダの微生物汚染評価のための統一スペクトル解析ワークフロー（A unified spectra analysis workflow for the assessment of microbial contamination of ready-to-eat green salads）</news:title>
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   <news:genres>Blog</news:genres>
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   <news:title>短期予測とマルチカメラ融合によるセマンティックグリッド（Short-Term Prediction and Multi-Camera Fusion on Semantic Grids）</news:title>
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   <news:genres>Blog</news:genres>
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   <news:title>限られたデータで機械的聴覚を改善する方法（Improving Machine Hearing on Limited Data Sets）</news:title>
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   <news:genres>Blog</news:genres>
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   <news:title>対話応答選択における階層的情報学習（Learning Multi-Level Information for Dialogue Response Selection）</news:title>
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   <news:genres>Blog</news:genres>
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   <news:title>埋め込みと規則を反復学習する知識グラフ推論（Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning）</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>後期M型矮星の均質サンプル化が示す地平（A homogeneous sample of 34 000 M7−M9.5 dwarfs brighter than J = 17.5）</news:title>
   <news:publication_date>2026-08-20T15:47:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>特徴でMCTSをバイアスする一般ゲームへの適用（Biasing MCTS with Features for General Games）</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>屋外画像における広告挿入候補スペースのデータセット（The CASE Dataset of Candidate Spaces for Advert Implantation）</news:title>
   <news:publication_date>2026-08-20T15:46:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-08-20T14:54:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>拒否者再統合による与信評価の実務課題（R´eint´egration des refus´es en Credit Scoring）</news:title>
   <news:publication_date>2026-08-20T14:54:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-08-20T14:54:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>バッチ単位の最適輸送損失による3D形状認識の高速化と精度改善（Learning with Batch-wise Optimal Transport Loss for 3D Shape Recognition）</news:title>
   <news:publication_date>2026-08-20T14:54:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/725328</loc>
  <lastmod>2026-08-20T14:53:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>PPGnetによるデバイス非依存心拍数推定（PPGnet: Deep Network for Device Independent Heart Rate Estimation from Photoplethysmogram）</news:title>
   <news:publication_date>2026-08-20T14:53:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
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  <lastmod>2026-08-20T14:53:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>情報の「写し」と「変換」を分解する（Decomposing information into copying versus transformation）</news:title>
   <news:publication_date>2026-08-20T14:53:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-08-20T14:53:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>時間的グラフにおけるノード埋め込み（Node Embedding over Temporal Graphs）</news:title>
   <news:publication_date>2026-08-20T14:53:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-08-20T14:53:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>風力タービンの稼働状態分類モデルの移植性（Transferability of Operational Status Classification Models Among Different Wind Turbine Types）</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>Deep Q-Learningにおける発散の特徴づけ（Towards Characterizing Divergence in Deep Q-Learning）</news:title>
   <news:publication_date>2026-08-20T14:52:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-08-20T14:01:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>個別化多層テンソル学習と画像解析への応用（Individualized Multilayer Tensor Learning with An Application in Imaging Analysis）</news:title>
   <news:publication_date>2026-08-20T14:01:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/725316</loc>
  <lastmod>2026-08-20T14:01:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DNNベース音源強調のための完全再構成フィルタバンクのデータ駆動設計 (DATA-DRIVEN DESIGN OF PERFECT RECONSTRUCTION FILTERBANK FOR DNN-BASED SOUND SOURCE ENHANCEMENT)</news:title>
   <news:publication_date>2026-08-20T14:01:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725314</loc>
  <lastmod>2026-08-20T14:00:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>臨床データにおける発作検出と分類の畳み込みニューラルネットワーク（Convolutional neural network for detection and classification of seizures in clinical data）</news:title>
   <news:publication_date>2026-08-20T14:00:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725312</loc>
  <lastmod>2026-08-20T14:00:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ソート演算子の連続緩和による確率的最適化（STOCHASTIC OPTIMIZATION OF SORTING NETWORKS VIA CONTINUOUS RELAXATIONS）</news:title>
   <news:publication_date>2026-08-20T14:00:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725310</loc>
  <lastmod>2026-08-20T13:59:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>脳接続性とCNNによる統合的分類アプローチ（Classification of EEG-Based Brain Connectivity Networks in Schizophrenia Using a Multi-Domain Connectome Convolutional Neural Network）</news:title>
   <news:publication_date>2026-08-20T13:59:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725308</loc>
  <lastmod>2026-08-20T13:59:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>OverSketched Newtonによるサーバレス最適化の実務的意義（OverSketched Newton: Fast Convex Optimization for Serverless Systems）</news:title>
   <news:publication_date>2026-08-20T13:59:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725306</loc>
  <lastmod>2026-08-20T13:58:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>衛星画像時系列の分離表現学習（Learning Disentangled Representations of Satellite Image Time Series）</news:title>
   <news:publication_date>2026-08-20T13:58:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725304</loc>
  <lastmod>2026-08-20T13:07:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単一入力出力層スパイキング分類器と時変重みモデルの効率化（Efficient single input-output layer spiking neural classifier with time-varying weight model）</news:title>
   <news:publication_date>2026-08-20T13:07:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725302</loc>
  <lastmod>2026-08-20T13:07:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ジオメトリ認識に基づく弱教師あり学習による3D人体姿勢推定（Weakly-Supervised Discovery of Geometry-Aware Representation for 3D Human Pose Estimation）</news:title>
   <news:publication_date>2026-08-20T13:07:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725300</loc>
  <lastmod>2026-08-20T13:06:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>超低消費電力の遠隔心電図（ECG）モニタリングシステム（Ultra Low-Power System for Remote ECG Monitoring）</news:title>
   <news:publication_date>2026-08-20T13:06:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725298</loc>
  <lastmod>2026-08-20T13:06:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>運転シーンにおける時間的ダイナミクス情報の価値（Value of Temporal Dynamics Information in Driving Scene Segmentation）</news:title>
   <news:publication_date>2026-08-20T13:06:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725296</loc>
  <lastmod>2026-08-20T13:05:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>半正則三角メッシュ上の畳み込みニューラルネットワークと脳画像への応用（Convolutional Neural Network on Semi-Regular Triangulated Meshes and its Application to Brain Image Data）</news:title>
   <news:publication_date>2026-08-20T13:05:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725294</loc>
  <lastmod>2026-08-20T13:05:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的グラフィカルモデルで先手を打つ—APTsの事前阻止フレームワーク（On Preempting Advanced Persistent Threats Using Probabilistic Graphical Models）</news:title>
   <news:publication_date>2026-08-20T13:05:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725292</loc>
  <lastmod>2026-08-20T13:05:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層的ディリクレ過程に対する正確なスライスサンプラー（Exact slice sampler for Hierarchical Dirichlet Processes）</news:title>
   <news:publication_date>2026-08-20T13:05:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725289</loc>
  <lastmod>2026-08-20T12:13:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多目的粒子群最適化で進化させる深層ニューラルネット（Evolving Deep Neural Networks by Multi-objective Particle Swarm Optimization for Image Classification）</news:title>
   <news:publication_date>2026-08-20T12:13:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725287</loc>
  <lastmod>2026-08-20T12:13:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Dual Residual Networksによる画像復元の新展開（Dual Residual Networks Leveraging the Potential of Paired Operations for Image Restoration）</news:title>
   <news:publication_date>2026-08-20T12:13:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725285</loc>
  <lastmod>2026-08-20T12:12:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>アフィン変換と非パラメトリックを同時に扱う3D画像登録のネットワーク（Networks for Joint Affine and Non-parametric Image Registration）</news:title>
   <news:publication_date>2026-08-20T12:12:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725283</loc>
  <lastmod>2026-08-20T12:11:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>入力点を中心に最大のℓp球をはめる方法（Provable Certificates for Adversarial Examples: Fitting a Ball in the Union of Polytopes）</news:title>
   <news:publication_date>2026-08-20T12:11:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725281</loc>
  <lastmod>2026-08-20T12:11:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューラルネットの解釈をフリップポイントで考える（Interpreting Neural Networks Using Flip Points）</news:title>
   <news:publication_date>2026-08-20T12:11:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725279</loc>
  <lastmod>2026-08-20T12:11:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>安全性重視のエンドツーエンド強化学習—制御バリア関数を用いた安全保証（End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks）</news:title>
   <news:publication_date>2026-08-20T12:11:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725277</loc>
  <lastmod>2026-08-20T12:11:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>信頼できる機械学習と自動化をつなぐ枠組み（A Unified Analytical Framework for Trustable Machine Learning and Automation Running with Blockchain）</news:title>
   <news:publication_date>2026-08-20T12:11:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725275</loc>
  <lastmod>2026-08-20T11:19:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>民族歌のモチーフの分散ベクトル表現（Distributed Vector Representations of Folksong Motifs）</news:title>
   <news:publication_date>2026-08-20T11:19:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725273</loc>
  <lastmod>2026-08-20T11:19:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GBMを加速する考え方と実装の要点（Accelerating Gradient Boosting Machines）</news:title>
   <news:publication_date>2026-08-20T11:19:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725271</loc>
  <lastmod>2026-08-20T11:19:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Byzantine耐性分散線形回帰（Byzantine Fault Tolerant Distributed Linear Regression）</news:title>
   <news:publication_date>2026-08-20T11:19:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725269</loc>
  <lastmod>2026-08-20T11:18:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>セグメンテーション品質評価の堅牢化（Robust Image Segmentation Quality Assessment）</news:title>
   <news:publication_date>2026-08-20T11:18:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725267</loc>
  <lastmod>2026-08-20T11:18:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>制約付きバッチ方策学習（Batch Policy Learning under Constraints）</news:title>
   <news:publication_date>2026-08-20T11:18:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725265</loc>
  <lastmod>2026-08-20T11:18:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>直接知覚におけるアフォーダンス学習が自動運転を変える（Affordance Learning In Direct Perception for Autonomous Driving）</news:title>
   <news:publication_date>2026-08-20T11:18:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725263</loc>
  <lastmod>2026-08-20T11:17:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ノイジー加速べき乗法による固有値問題の効率化（Noisy Accelerated Power Method for Eigenproblems with Applications）</news:title>
   <news:publication_date>2026-08-20T11:17:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725261</loc>
  <lastmod>2026-08-20T10:25:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>LaserNet：レンジ画像で効率的に不確かさを扱う3D物体検出（LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving）</news:title>
   <news:publication_date>2026-08-20T10:25:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725259</loc>
  <lastmod>2026-08-20T10:25:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>量子オートエンコーダによる損失なし量子データ圧縮の実現（Realization of a quantum autoencoder for lossless compression of quantum data）</news:title>
   <news:publication_date>2026-08-20T10:25:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725257</loc>
  <lastmod>2026-08-20T10:25:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所経験を活かしたグローバル動作計画（Using Local Experiences for Global Motion Planning）</news:title>
   <news:publication_date>2026-08-20T10:25:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725255</loc>
  <lastmod>2026-08-20T10:24:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ハイブリッド空間における効率的な内積近似（Efficient Inner Product Approximation in Hybrid Spaces）</news:title>
   <news:publication_date>2026-08-20T10:24:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725253</loc>
  <lastmod>2026-08-20T10:24:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Polyakの学習率を用いた確率的勾配降下法（Stochastic Gradient Descent with Polyak’s Learning Rate）</news:title>
   <news:publication_date>2026-08-20T10:24:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725251</loc>
  <lastmod>2026-08-20T10:24:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>量子回路のパラメータ化とキュービット品質の時間変動への対処（Addressing Temporal Variations in Qubit Quality Metrics for Parameterized Quantum Circuits）</news:title>
   <news:publication_date>2026-08-20T10:24:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725249</loc>
  <lastmod>2026-08-20T10:23:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>エネルギー基底モデルによる暗黙的生成とモデリング（Implicit Generation and Modeling with Energy-Based Models）</news:title>
   <news:publication_date>2026-08-20T10:23:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725247</loc>
  <lastmod>2026-08-20T09:32:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>写真から自在に鉛筆画を作る技術の要点（Im2Pencil: Controllable Pencil Illustration from Photographs）</news:title>
   <news:publication_date>2026-08-20T09:32:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725245</loc>
  <lastmod>2026-08-20T09:32:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オンライン継続学習のための勾配ベースサンプル選択（Gradient based sample selection for online continual learning）</news:title>
   <news:publication_date>2026-08-20T09:32:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/725243</loc>
  <lastmod>2026-08-20T09:32:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>TATi—熱力学解析ツールキット（TATi-Thermodynamic Analytics ToolkIt: TensorFlow-based software for posterior sampling in machine learning applications）</news:title>
   <news:publication_date>2026-08-20T09:32:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725241</loc>
  <lastmod>2026-08-20T09:30:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>磁気選択的増強で学習が速い分子スピンバルブのシナプス（Fast learning synapses with molecular spin valves via selective magnetic potentiation）</news:title>
   <news:publication_date>2026-08-20T09:30:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725239</loc>
  <lastmod>2026-08-20T09:30:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>統計的部分多様体に対する近似情報検定（Approximate Information Tests on Statistical Submanifolds）</news:title>
   <news:publication_date>2026-08-20T09:30:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725237</loc>
  <lastmod>2026-08-20T09:30:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Column2Vecによるデータベーススキーマの分散表現（Column2Vec: Structural Understanding via Distributed Representations of Database Schemas）</news:title>
   <news:publication_date>2026-08-20T09:30:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725235</loc>
  <lastmod>2026-08-20T09:29:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的最適化におけるより良いモデルの重要性（The importance of better models in stochastic optimization）</news:title>
   <news:publication_date>2026-08-20T09:29:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725233</loc>
  <lastmod>2026-08-20T08:38:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オンライン食事調査における省略食品を促すレコメンダーの検証（Validation of a recommender system for prompting omitted foods in online dietary assessment surveys）</news:title>
   <news:publication_date>2026-08-20T08:38:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725231</loc>
  <lastmod>2026-08-20T08:38:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>敵対的運転に対する単一ステップオプション（Single-step Options for Adversary Driving）</news:title>
   <news:publication_date>2026-08-20T08:38:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725229</loc>
  <lastmod>2026-08-20T08:37:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所特徴のバッグモデルでCNNを近似するとImageNetで驚くほど高精度である（Approximating CNNs with Bag-of-Local-Features Models Works Surprisingly Well on ImageNet）</news:title>
   <news:publication_date>2026-08-20T08:37:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725227</loc>
  <lastmod>2026-08-20T08:36:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DC-SPP-YOLOによる物体検出の改良（DC-SPP-YOLO: Dense Connection and Spatial Pyramid Pooling Based YOLO for Object Detection）</news:title>
   <news:publication_date>2026-08-20T08:36:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725225</loc>
  <lastmod>2026-08-20T08:36:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Wikipediaをグラフで扱うためのデータセット整備（A graph-structured dataset for Wikipedia research）</news:title>
   <news:publication_date>2026-08-20T08:36:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725223</loc>
  <lastmod>2026-08-20T08:36:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>EHAAS: エネルギーハーベスタをセンサーに使う場認識（EHAAS: Energy Harvesters As A Sensor for Place Recognition on Wearables）</news:title>
   <news:publication_date>2026-08-20T08:36:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725221</loc>
  <lastmod>2026-08-20T08:35:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次元データを扱う文脈付きバンディットの高速化と次元削減の実践（Contextual Bandits with Random Projection）</news:title>
   <news:publication_date>2026-08-20T08:35:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725219</loc>
  <lastmod>2026-08-20T07:44:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>広帯域JVLAデータによる高ダイナミックレンジ電波画像生成手法（A Procedure for Making High Dynamic-Range Radio Images）</news:title>
   <news:publication_date>2026-08-20T07:44:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725217</loc>
  <lastmod>2026-08-20T07:43:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ロゼット植物における葉のセグメンテーションとカウントのためのデータ拡張（Data Augmentation for Leaf Segmentation and Counting Tasks in Rosette Plants）</news:title>
   <news:publication_date>2026-08-20T07:43:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725215</loc>
  <lastmod>2026-08-20T07:42:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ブラックホール誕生の電磁ウィンドウ（Electromagnetic Window into the Dawn of Black Holes）</news:title>
   <news:publication_date>2026-08-20T07:42:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725213</loc>
  <lastmod>2026-08-20T07:41:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>OCGANによるワン・クラス異常検知の再定義（OCGAN: One-class Novelty Detection Using GANs with Constrained Latent Representations）</news:title>
   <news:publication_date>2026-08-20T07:41:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725211</loc>
  <lastmod>2026-08-20T07:41:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>修正なしランジュヴァンアルゴリズムの高速収束（Rapid Convergence of the Unadjusted Langevin Algorithm: Isoperimetry Suffices）</news:title>
   <news:publication_date>2026-08-20T07:41:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725209</loc>
  <lastmod>2026-08-20T07:41:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高抗力の恒星間天体と銀河の動的ストリーム（High-Drag Interstellar Objects And Galactic Dynamical Streams）</news:title>
   <news:publication_date>2026-08-20T07:41:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725207</loc>
  <lastmod>2026-08-20T07:40:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動的ミニバッチ再サンプリングがもたらす損失関数のノイズと最適化への影響（Traversing the noise of dynamic mini-batch sub-sampled loss functions: A visual guide）</news:title>
   <news:publication_date>2026-08-20T07:40:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725205</loc>
  <lastmod>2026-08-20T06:49:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>圧縮センシングCT再構成のための畳み込みスパースコーディング (Convolutional Sparse Coding for Compressed Sensing CT Reconstruction)</news:title>
   <news:publication_date>2026-08-20T06:49:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725203</loc>
  <lastmod>2026-08-20T06:49:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習型畳み込み変換による点群ジオメトリの損失圧縮（LEARNING CONVOLUTIONAL TRANSFORMS FOR LOSSY POINT CLOUD GEOMETRY COMPRESSION）</news:title>
   <news:publication_date>2026-08-20T06:49:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725201</loc>
  <lastmod>2026-08-20T06:48:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単一ハドロン生成におけるビームヘリシティ非対称性（Beam-helicity asymmetries for single-hadron production in semi-inclusive deep-inelastic scattering from unpolarized hydrogen and deuterium targets）</news:title>
   <news:publication_date>2026-08-20T06:48:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725199</loc>
  <lastmod>2026-08-20T06:48:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>表面欠陥検出のためのセグメンテーションベース深層学習（Segmentation-Based Deep-Learning Approach for Surface-Defect Detection）</news:title>
   <news:publication_date>2026-08-20T06:48:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725197</loc>
  <lastmod>2026-08-20T06:48:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>やさしく触れるロボット学習 — Curiosityで学ぶ衝撃最小化（Learning Gentle Object Manipulation with Curiosity-Driven Deep Reinforcement Learning）</news:title>
   <news:publication_date>2026-08-20T06:48:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725195</loc>
  <lastmod>2026-08-20T06:47:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>強化学習で熱力学経路を最適化する（Optimizing thermodynamic trajectories using evolutionary and gradient-based reinforcement learning）</news:title>
   <news:publication_date>2026-08-20T06:47:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725193</loc>
  <lastmod>2026-08-20T05:55:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単一画像から集合画像へ──弱教師あり学習による高精度3D顔再構成（Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set）</news:title>
   <news:publication_date>2026-08-20T05:55:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725191</loc>
  <lastmod>2026-08-20T05:55:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>トポロジーに基づく代表データセットで学習資源を削減する（Topology-based Representative Datasets to Reduce Neural Network Training Resources）</news:title>
   <news:publication_date>2026-08-20T05:55:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725189</loc>
  <lastmod>2026-08-20T05:54:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単眼（モノキュラー）視差推定におけるドメイン変換と曖昧性学習による新規ネットワーク（A NOVEL MONOCULAR DISPARITY ESTIMATION NETWORK WITH DOMAIN TRANSFORMATION AND AMBIGUITY LEARNING）</news:title>
   <news:publication_date>2026-08-20T05:54:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725187</loc>
  <lastmod>2026-08-20T05:54:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高精度なメンタルストレスの早期検出（Early Detection of Mental Stress Using Advanced Neuroimaging and Artificial Intelligence）</news:title>
   <news:publication_date>2026-08-20T05:54:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725185</loc>
  <lastmod>2026-08-20T05:54:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ラベル順位のための選好ルール発掘（Preference rules for label ranking: Mining patterns in multi-target relations）</news:title>
   <news:publication_date>2026-08-20T05:54:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725183</loc>
  <lastmod>2026-08-20T05:54:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SpiNNaker 2上の報酬ベース構造可塑性の効率化（Efficient Reward-Based Structural Plasticity on a SpiNNaker 2 Prototype）</news:title>
   <news:publication_date>2026-08-20T05:54:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725181</loc>
  <lastmod>2026-08-20T05:53:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>実数階（実次数）全方位トータルバリエーションと最適構造の学習（REAL ORDER (AN)-ISOTROPIC TOTAL VARIATION IN IMAGE PROCESSING - PART II: LEARNING OF OPTIMAL STRUCTURES）</news:title>
   <news:publication_date>2026-08-20T05:53:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725179</loc>
  <lastmod>2026-08-20T05:02:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習を用いた超高速量子磁性の研究（Investigating ultrafast quantum magnetism with machine learning）</news:title>
   <news:publication_date>2026-08-20T05:02:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725177</loc>
  <lastmod>2026-08-20T05:02:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>三次元点群からの個体樹冠分割を実現する可変クラス・グラフカット法（Three-dimensional Segmentation of Trees Through a Flexible Multi-Class Graph Cut Algorithm (MCGC))</news:title>
   <news:publication_date>2026-08-20T05:02:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725175</loc>
  <lastmod>2026-08-20T05:01:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深い八元数ネットワーク（Deep Octonion Networks）</news:title>
   <news:publication_date>2026-08-20T05:01:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725173</loc>
  <lastmod>2026-08-20T05:00:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>構造的ジャンプLSTMによるニューラル速読（Neural Speed Reading with Structural-Jump-LSTM）</news:title>
   <news:publication_date>2026-08-20T05:00:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725171</loc>
  <lastmod>2026-08-20T05:00:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-20T05:00:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725169</loc>
  <lastmod>2026-08-20T05:00:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スパイク列の依存性特徴の解析（A study of dependency features of spike trains through copulas）</news:title>
   <news:publication_date>2026-08-20T05:00:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725167</loc>
  <lastmod>2026-08-20T05:00:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>リアルタイム道路走行画像のための事前学習ImageNetアーキテクチャ擁護（In Defense of Pre-trained ImageNet Architectures for Real-time Semantic Segmentation of Road-driving Images）</news:title>
   <news:publication_date>2026-08-20T05:00:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725165</loc>
  <lastmod>2026-08-20T04:08:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>履歴の時間減衰を学習する注意機構と話者指標による音声言語理解（Decay-Function-Free Time-Aware Attention to Context and Speaker Indicator for Spoken Language Understanding）</news:title>
   <news:publication_date>2026-08-20T04:08:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725163</loc>
  <lastmod>2026-08-20T04:07:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>サイバーセキュリティ意識向上のためのゲーミフィケーション手法（Gamification Techniques for Raising Cyber Security Awareness）</news:title>
   <news:publication_date>2026-08-20T04:07:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725161</loc>
  <lastmod>2026-08-20T04:07:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ツイートの感情強度推定におけるExperts Model（Affect in Tweets Using Experts Model）</news:title>
   <news:publication_date>2026-08-20T04:07:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725159</loc>
  <lastmod>2026-08-20T04:07:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>条件付き深層畳み込み敵対的生成ネットワークによるナノフォトニクス設計（DESIGNING NANOPHOTONIC STRUCTURES USING CONDITIONAL-DEEP CONVOLUTIONAL GENERATIVE ADVERSARIAL NETWORKS）</news:title>
   <news:publication_date>2026-08-20T04:07:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725157</loc>
  <lastmod>2026-08-20T04:06:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>系統的レンジングによるFlyeye望遠鏡発見NEO（近地球天体）の追跡手法（A SYSTEMATIC RANGING TECHNIQUE FOR FOLLOW-UPS OF NEOS DETECTED WITH THE FLYEYE TELESCOPE）</news:title>
   <news:publication_date>2026-08-20T04:06:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725155</loc>
  <lastmod>2026-08-20T04:06:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>反例駆動によるPOMDP戦略改善（Counterexample-Guided Strategy Improvement for POMDPs Using Recurrent Neural Networks）</news:title>
   <news:publication_date>2026-08-20T04:06:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725153</loc>
  <lastmod>2026-08-20T04:06:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>電子カルテの階層表現で臨床予測を強化するアプローチ（Learning Hierarchical Representations of Electronic Health Records for Clinical Outcome Prediction）</news:title>
   <news:publication_date>2026-08-20T04:06:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725151</loc>
  <lastmod>2026-08-20T03:14:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>チェックすべき文の自動抽出を強化するニューラルランキング（Neural Check-Worthiness Ranking with Weak Supervision）</news:title>
   <news:publication_date>2026-08-20T03:14:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725149</loc>
  <lastmod>2026-08-20T03:14:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>連続する音楽トラックのスキップ予測を行うMulti-RNNアプローチ（Modelling Sequential Music Track Skips using a Multi-RNN Approach）</news:title>
   <news:publication_date>2026-08-20T03:14:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725147</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>グラフ誤差のモデル化と堅牢なグラフ信号処理への道（Modelling Graph Errors: Towards Robust Graph Signal Processing）</news:title>
   <news:publication_date>2026-08-20T03:14:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725145</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>データサイエンスが技術的痕跡探索にもたらす可能性（The Promise of Data Science for the Technosignatures Field）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725143</loc>
  <lastmod>2026-08-20T03:13:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>制約付きクエリグラフと重み付き集合に対する適応的多数問題 (Adaptive Majority Problems for Restricted Query Graphs and for Weighted Sets)</news:title>
   <news:publication_date>2026-08-20T03:13:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725141</loc>
  <lastmod>2026-08-20T03:13:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>偶数サイズカーネルと対称パディングによる畳み込みの改善（Convolution with even-sized kernels and symmetric padding）</news:title>
   <news:publication_date>2026-08-20T03:13:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725139</loc>
  <lastmod>2026-08-20T03:13:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>パーツベースによる形態学的演算の近似（Part-based approximations for morphological operators using asymmetric auto-encoders）</news:title>
   <news:publication_date>2026-08-20T03:13:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725137</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>継続学習でモデルを自動拡張し圧縮する仕組み（Regularize, Expand and Compress: Multi-task based Lifelong Learning via NonExpansive AutoML）</news:title>
   <news:publication_date>2026-08-20T02:21:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725135</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>機械学習による動作生成の現状と意味（Machine Learning for Data-Driven Movement Generation: a Review of the State of the Art）</news:title>
   <news:publication_date>2026-08-20T02:21:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725133</loc>
  <lastmod>2026-08-20T02:20:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分子特性予測の不確実性定量化（Uncertainty quantification of molecular property prediction with Bayesian neural networks）</news:title>
   <news:publication_date>2026-08-20T02:20:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725131</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>生成的堅牢推論によるロボット認知の強化（GRIP: Generative Robust Inference and Perception for Semantic Robot Manipulation in Adversarial Environments）</news:title>
   <news:publication_date>2026-08-20T02:19:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725129</loc>
  <lastmod>2026-08-20T02:19:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Video Object Segmentationを使った視覚サーボと深度推定（Video Object Segmentation-based Visual Servo Control and Object Depth Estimation on a Mobile Robot）</news:title>
   <news:publication_date>2026-08-20T02:19:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725127</loc>
  <lastmod>2026-08-20T02:19:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>可変な最適測定角を伴う量子相関領域の検出可能性（On the possibility to detect quantum correlation regions with the variable optimal measurement angle）</news:title>
   <news:publication_date>2026-08-20T02:19:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725125</loc>
  <lastmod>2026-08-20T02:19:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模データにおける多ラベル特徴選択の分散化（Distributed Maximization of Submodular plus Diversity Functions for Multi-label Feature Selection on Huge Datasets）</news:title>
   <news:publication_date>2026-08-20T02:19:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725123</loc>
  <lastmod>2026-08-20T01:27:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>経験的レバレッジスコアに基づくランダム特徴サンプリングの実装と理論保証（On Sampling Random Features From Empirical Leverage Scores: Implementation and Theoretical Guarantees）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-20T01:27:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Deep K-近傍法の堅牢性に関する考察（On the Robustness of Deep K-Nearest Neighbors）</news:title>
   <news:publication_date>2026-08-20T01:27:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725119</loc>
  <lastmod>2026-08-20T01:27:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分布に基づくゲーム理論的解概念の学習枠組み（A Learning Framework for Distribution-Based Game-Theoretic Solution Concepts）</news:title>
   <news:publication_date>2026-08-20T01:27:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725117</loc>
  <lastmod>2026-08-20T01:26:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>乳がん検診における放射線科医の性能向上に寄与する深層ニューラルネットワーク（Deep Neural Networks Improve Radiologists’ Performance in Breast Cancer Screening）</news:title>
   <news:publication_date>2026-08-20T01:26:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725115</loc>
  <lastmod>2026-08-20T01:26:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>地下鉱山車両のビジョンのみを用いたリアルタイム高精度自己位置推定（LookUP: Vision-Only Real-Time Precise Underground Localisation for Autonomous Mining Vehicles）</news:title>
   <news:publication_date>2026-08-20T01:26:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725113</loc>
  <lastmod>2026-08-20T01:26:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非剛体3D形状検索におけるマルチビュー・メトリック学習（NON-RIGID 3D SHAPE RETRIEVAL BASED ON MULTI-VIEW METRIC LEARNING）</news:title>
   <news:publication_date>2026-08-20T01:26:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725111</loc>
  <lastmod>2026-08-20T01:25:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>未来を予測して言語から解釈可能な計画を作る—Prospectionによるロボット制御の読み方 (Prospection: Interpretable Plans From Language By Predicting the Future)</news:title>
   <news:publication_date>2026-08-20T01:25:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725109</loc>
  <lastmod>2026-08-20T00:34:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>意図分類とスロットラベリングの高速で正確な統合設計（Simple, Fast, Accurate Intent Classification and Slot Labeling for Goal-Oriented Dialogue Systems）</news:title>
   <news:publication_date>2026-08-20T00:34:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-20T00:34:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>限定ラベルと大量非ラベルで効くGANベースのスパム検出（GANs for Semi-Supervised Opinion Spam Detection）</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>スマートフォンでのコーヒー葉害虫・病害の検出と分類（A smartphone application to detection and classification of coffee leaf miner and coffee leaf rust）</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:title>確率的コンテキスト変数による効率的オフポリシーメタ強化学習 (Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables)</news:title>
   <news:publication_date>2026-08-20T00:33:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/725101</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>2026-08-20T00:33:33Z</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>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725095</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>適応ハードスレッショルディングによる一貫したロバスト回帰（Adaptive Hard Thresholding for Near-optimal Consistent Robust Regression）</news:title>
   <news:publication_date>2026-08-19T23:41:43Z</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:publication_date>2026-08-19T23:41:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-19T23:41:28Z</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>2026-08-19T23:39:49Z</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>2026-08-19T23:39:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725085</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>2026-08-19T23:39:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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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>
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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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  <lastmod>2026-08-19T20:05:07Z</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>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-19T20:04:17Z</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>真の近接方策最適化（Truly Proximal Policy Optimization）</news:title>
   <news:publication_date>2026-08-19T20:04:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-19T20:03:54Z</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>2026-08-19T20:03:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725025</loc>
  <lastmod>2026-08-19T19:12:19Z</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>2026-08-19T19:12:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725023</loc>
  <lastmod>2026-08-19T19:12:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層視覚注意モデルによる映像圧縮の改善（Improving Video Compression With Deep Visual-Attention Models）</news:title>
   <news:publication_date>2026-08-19T19:12:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725021</loc>
  <lastmod>2026-08-19T19:12:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>3Dパースを用いた車両認識の革新（Geometry-constrained Car Recognition Using a 3D Perspective Network）</news:title>
   <news:publication_date>2026-08-19T19:12:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725019</loc>
  <lastmod>2026-08-19T19:11:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>教師なしネットワーク表現学習の比較研究 (A Comparative Study for Unsupervised Network Representation Learning)</news:title>
   <news:publication_date>2026-08-19T19:11:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725017</loc>
  <lastmod>2026-08-19T19:10:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層信念ネットワークによる特徴抽出とLSTMを用いたDDoS検知手法 (DDoS attack detection method based on feature extraction of deep belief network)</news:title>
   <news:publication_date>2026-08-19T19:10:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725015</loc>
  <lastmod>2026-08-19T19:10:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>敵対的バンディットに関する一階境界・分散・ギャップ依存境界 (On First-Order Bounds, Variance and Gap-Dependent Bounds for Adversarial Bandits)</news:title>
   <news:publication_date>2026-08-19T19:10:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725013</loc>
  <lastmod>2026-08-19T19:10:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>臨床試験のコホート選択に対するハイブリッド手法（Hybrid Approaches for Cohort Selection for Clinical Trials）</news:title>
   <news:publication_date>2026-08-19T19:10:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725011</loc>
  <lastmod>2026-08-19T18:19:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ロバスト平均推定の計算困難性（How Hard is Robust Mean Estimation?）</news:title>
   <news:publication_date>2026-08-19T18:19:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725009</loc>
  <lastmod>2026-08-19T18:18:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>イベント駆動型ビジョンによる姿勢不変物体認識（Pose-invariant object recognition for event-based vision with slow-ELM）</news:title>
   <news:publication_date>2026-08-19T18:18:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725007</loc>
  <lastmod>2026-08-19T18:18:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ドメイン横断知識転移による教師なし車両再識別（CROSS DOMAIN KNOWLEDGE TRANSFER FOR UNSUPERVISED VEHICLE RE-IDENTIFICATION）</news:title>
   <news:publication_date>2026-08-19T18:18:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725005</loc>
  <lastmod>2026-08-19T18:17:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>デモ不要で模倣学習を実現する方法（Hindsight Generative Adversarial Imitation Learning）</news:title>
   <news:publication_date>2026-08-19T18:17:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725003</loc>
  <lastmod>2026-08-19T18:17:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>クラス増分学習とDeep Model Consolidation（Class-incremental Learning via Deep Model Consolidation）</news:title>
   <news:publication_date>2026-08-19T18:17:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/725001</loc>
  <lastmod>2026-08-19T18:17:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>個人化ニューラル埋め込みによるテキスト対応協調フィルタリング（Personalized Neural Embeddings for Collaborative Filtering with Text）</news:title>
   <news:publication_date>2026-08-19T18:17:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724999</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>高次元ベルヌーイ自己回帰過程と長期依存性（High-Dimensional Bernoulli Autoregressive Process with Long-Range Dependence）</news:title>
   <news:publication_date>2026-08-19T18:16:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724997</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>2026-08-19T17:24:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724995</loc>
  <lastmod>2026-08-19T17:14:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的物体検出チャレンジが示すロボット視覚の次の一手（Probabilistic Object Detection）</news:title>
   <news:publication_date>2026-08-19T17:14:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724993</loc>
  <lastmod>2026-08-19T17:13:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>KL-UCB+方策の理論的根拠と実務的示唆（A Note on KL-UCB+ Policy for the Stochastic Bandit）</news:title>
   <news:publication_date>2026-08-19T17:13:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724991</loc>
  <lastmod>2026-08-19T17:13:10Z</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>2026-08-19T17:13:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724989</loc>
  <lastmod>2026-08-19T17:13:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非負表現に基づく識別的辞書学習による顔認識（Non-negative representation based discriminative dictionary learning for face recognition）</news:title>
   <news:publication_date>2026-08-19T17:13:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724987</loc>
  <lastmod>2026-08-19T17:12:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>低ランク識別的最小二乗回帰による画像分類の改良（Low-Rank Discriminative Least Squares Regression for Image Classification）</news:title>
   <news:publication_date>2026-08-19T17:12:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724985</loc>
  <lastmod>2026-08-19T17:12:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Fisher判別付き最小二乗回帰による画像分類の改良（Fisher Discriminative Least Squares Regression for Image Classification）</news:title>
   <news:publication_date>2026-08-19T17:12:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724983</loc>
  <lastmod>2026-08-19T16:21:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Compressed Sensingを臨床へつなぐデータ駆動学習の実装と示唆（Compressed Sensing: From Research to Clinical Practice with Data-Driven Learning）</news:title>
   <news:publication_date>2026-08-19T16:21:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724981</loc>
  <lastmod>2026-08-19T16:20:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>EEGアーティファクト除去のための機械学習：ベンチマークの確立（Machine Learning for removing EEG artifacts: Setting the benchmark）</news:title>
   <news:publication_date>2026-08-19T16:20:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724979</loc>
  <lastmod>2026-08-19T16:20:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多様性を重視する対話型推薦の設計（Diversity-Promoting Deep Reinforcement Learning for Interactive Recommendation）</news:title>
   <news:publication_date>2026-08-19T16:20:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724977</loc>
  <lastmod>2026-08-19T16:19:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マスク指導型スタイル転送ネットワークによる実画像の浄化（Mask-Guided Style Transfer Network for Purifying Real Images）</news:title>
   <news:publication_date>2026-08-19T16:19:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724975</loc>
  <lastmod>2026-08-19T16:19:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>主題に寄り添う文字表現の自動生成（Trick or Treat: Thematic Reinforcement for Artistic Typography）</news:title>
   <news:publication_date>2026-08-19T16:19:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724973</loc>
  <lastmod>2026-08-19T16:19:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>嗅覚の快不快をCNNで予測する試み（POP-CNN: Predicting Odor’s Pleasantness）</news:title>
   <news:publication_date>2026-08-19T16:19:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724971</loc>
  <lastmod>2026-08-19T16:19:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>半教師あり深層学習による異常脳波検出の実用性（A semi-supervised deep learning algorithm for abnormal EEG identification）</news:title>
   <news:publication_date>2026-08-19T16:19:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724969</loc>
  <lastmod>2026-08-19T15:26:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自己重み付けマルチビュー距離学習による相互相関最大化（SELF-WEIGHTED MULTIVIEW METRIC LEARNING BY MAXIMIZING THE CROSS CORRELATIONS）</news:title>
   <news:publication_date>2026-08-19T15:26:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724967</loc>
  <lastmod>2026-08-19T15:26:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>酸素殻燃焼の多次元シミュレーション――超新星直前の対流の実像（One-, Two-, and Three-dimensional Simulations of Oxygen Shell Burning Just Before the Core-Collapse of Massive Stars）</news:title>
   <news:publication_date>2026-08-19T15:26:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724965</loc>
  <lastmod>2026-08-19T15:26:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動的ハースト指数（Dynamic Hurst Exponent in Time Series）</news:title>
   <news:publication_date>2026-08-19T15:26:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724963</loc>
  <lastmod>2026-08-19T15:25:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>差分プライベート合意に基づく分散最適化（Differentially Private Consensus-Based Distributed Optimization）</news:title>
   <news:publication_date>2026-08-19T15:25:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724961</loc>
  <lastmod>2026-08-19T15:25:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>網膜血管分割のための動的深層ネットワーク（Dynamic Deep Networks for Retinal Vessel Segmentation）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-19T15:25:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>群衆密度推定におけるウェーブレット変換と機械学習の応用（Estimation of crowd density applying wavelet transform and machine learning）</news:title>
   <news:publication_date>2026-08-19T15:25:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724957</loc>
  <lastmod>2026-08-19T15:24:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>不規則領域における都市全域の群衆流予測（Predicting Citywide Crowd Flows in Irregular Regions Using Multi-View Graph Convolutional Networks）</news:title>
   <news:publication_date>2026-08-19T15:24:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724955</loc>
  <lastmod>2026-08-19T14:33:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>視覚刺激のカテゴリ復元にBRNNを適用する研究（Category decoding of visual stimuli from human brain activity using a bidirectional recurrent neural network）</news:title>
   <news:publication_date>2026-08-19T14:33:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724953</loc>
  <lastmod>2026-08-19T14:33:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ラベルノイズを内側から直す仕組み：PENCIL（Probabilistic End-to-end Noise Correction for Learning with Noisy Labels）</news:title>
   <news:publication_date>2026-08-19T14:33:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724951</loc>
  <lastmod>2026-08-19T14:33:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>クラウドソーシングと機械学習の協働が変えるデータラベリングの現場（Modeling with the Crowd: Optimizing the Human-Machine Partnership with Zooniverse）</news:title>
   <news:publication_date>2026-08-19T14:33:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724949</loc>
  <lastmod>2026-08-19T14:32:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>デコリレーションによる深層強化学習の表現学習改善（Deep Reinforcement Learning with Decorrelation）</news:title>
   <news:publication_date>2026-08-19T14:32:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724947</loc>
  <lastmod>2026-08-19T14:32:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>粒子加速器の最適化を飛躍的に高速化する機械学習手法（Machine Learning for Orders of Magnitude Speedup in Multi-Objective Optimization of Particle Accelerator Systems）</news:title>
   <news:publication_date>2026-08-19T14:32:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724945</loc>
  <lastmod>2026-08-19T14:32:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>感情可視化ジャーナルLemotif（Lemotif: An Affective Visual Journal Using Deep Neural Networks）</news:title>
   <news:publication_date>2026-08-19T14:32:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724943</loc>
  <lastmod>2026-08-19T14:32:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層的ルーティング混合専門家モデルの要点解説（Hierarchical Routing Mixture of Experts）</news:title>
   <news:publication_date>2026-08-19T14:32:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724941</loc>
  <lastmod>2026-08-19T13:40:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>銀河分類の機械学習解析（Galaxy classification: A machine learning analysis of GAMA catalogue data）</news:title>
   <news:publication_date>2026-08-19T13:40:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724939</loc>
  <lastmod>2026-08-19T13:39:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時間変動を柔軟に扱う対比較モデル（Pairwise Comparisons with Flexible Time-Dynamics）</news:title>
   <news:publication_date>2026-08-19T13:39:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724937</loc>
  <lastmod>2026-08-19T13:39:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>集合で学ぶ複数インスタンス回帰—リモートセンシングへの応用（Learning with Sets in Multiple Instance Regression Applied to Remote Sensing）</news:title>
   <news:publication_date>2026-08-19T13:39:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724935</loc>
  <lastmod>2026-08-19T13:38:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>劣化ブロードキャストチャネルにおける深層学習（Deep Learning for the Degraded Broadcast Channel）</news:title>
   <news:publication_date>2026-08-19T13:38:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724933</loc>
  <lastmod>2026-08-19T13:38:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>軌跡分類のための異種部分列発見（Discovering Heterogeneous Subsequences for Trajectory Classification）</news:title>
   <news:publication_date>2026-08-19T13:38:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724931</loc>
  <lastmod>2026-08-19T13:38:27Z</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「人」の到達可能領域を予測する手法（Predicting Stochastic Human Forward Reachable Sets Based on Learned Human Behavior）</news:title>
   <news:publication_date>2026-08-19T13:38:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724929</loc>
  <lastmod>2026-08-19T13:38:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>合成深度画像の拡張学習によるシム2リアル方策転移（Learning to Augment Synthetic Images for Sim2Real Policy Transfer）</news:title>
   <news:publication_date>2026-08-19T13:38:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724927</loc>
  <lastmod>2026-08-19T12:46:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>視線の届かない場所での物体認識（Direct Object Recognition Without Line-of-Sight Using Optical Coherence）</news:title>
   <news:publication_date>2026-08-19T12:46:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724925</loc>
  <lastmod>2026-08-19T12:46:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>実用的な隠れ音声攻撃が示す脅威と備え（Practical Hidden Voice Attacks against Speech and Speaker Recognition Systems）</news:title>
   <news:publication_date>2026-08-19T12:46:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724923</loc>
  <lastmod>2026-08-19T12:46:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Real and Discreteを用いた深層混合モデルの新展開（A RAD approach to deep mixture models）</news:title>
   <news:publication_date>2026-08-19T12:46:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724921</loc>
  <lastmod>2026-08-19T12:45:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>部分的な閉じ込めが引き起こす超臨界流体の構造と動力学（Soft-wall induced structure and dynamics of partially confined supercritical fluids）</news:title>
   <news:publication_date>2026-08-19T12:45:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724919</loc>
  <lastmod>2026-08-19T12:45:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>アプリケーション駆動のDNNアクセラレータ設計空間探索（Software-Defined Design Space Exploration for an Efficient DNN Accelerator Architecture）</news:title>
   <news:publication_date>2026-08-19T12:45:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724917</loc>
  <lastmod>2026-08-19T12:44:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層ファンダメンタル因子モデルの実務的意義（Deep Fundamental Factor Models）</news:title>
   <news:publication_date>2026-08-19T12:44:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724915</loc>
  <lastmod>2026-08-19T12:44:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Duet v2によるパッセージ再ランキングの改良（AN UPDATED DUET MODEL FOR PASSAGE RE-RANKING）</news:title>
   <news:publication_date>2026-08-19T12:44:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724913</loc>
  <lastmod>2026-08-19T11:52:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>IllustrisTNGと深層学習で読み解く銀河形態の再現性（The Hubble Sequence at z ∼0 in the IllustrisTNG simulation with deep learning）</news:title>
   <news:publication_date>2026-08-19T11:52:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724911</loc>
  <lastmod>2026-08-19T11:52:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GOODS領域における微弱電波源の本質（Nature of Faint Radio Sources in GOODS-North and GOODS-South Fields – I. Spectral Index and Radio-FIR Correlation）</news:title>
   <news:publication_date>2026-08-19T11:52:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724909</loc>
  <lastmod>2026-08-19T11:52:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ブラックボックス分類器の多重差分公平性監査（Multi-Differential Fairness Auditor for Black Box Classifiers）</news:title>
   <news:publication_date>2026-08-19T11:52:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724907</loc>
  <lastmod>2026-08-19T11:51:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文字レベルCNNによるテキスト分類とエンコーディングの比較（Character-level Convolutional Networks for Text Classification）</news:title>
   <news:publication_date>2026-08-19T11:51:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724905</loc>
  <lastmod>2026-08-19T11:50:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時間の循環一貫性から学ぶ対応関係（Learning Correspondence from the Cycle-consistency of Time）</news:title>
   <news:publication_date>2026-08-19T11:50:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724903</loc>
  <lastmod>2026-08-19T11:50:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>モデル不確実性とパラメータ不確実性を同時に扱うベイズニューラルネットワーク（Combining model and parameter uncertainty in Bayesian Neural Networks）</news:title>
   <news:publication_date>2026-08-19T11:50:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724901</loc>
  <lastmod>2026-08-19T11:50:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>知識グラフ畳み込みネットワークによる推薦システム（Knowledge Graph Convolutional Networks for Recommender Systems）</news:title>
   <news:publication_date>2026-08-19T11:50:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724899</loc>
  <lastmod>2026-08-19T10:58:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>弱い特徴量に関する二重降下モデル（Two models of double descent for weak features）</news:title>
   <news:publication_date>2026-08-19T10:58:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724897</loc>
  <lastmod>2026-08-19T10:58:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ボコーダーを用いた歌声抽出法（A VOCODER BASED METHOD FOR SINGING VOICE EXTRACTION）</news:title>
   <news:publication_date>2026-08-19T10:58:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724895</loc>
  <lastmod>2026-08-19T10:58:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>失敗の動態を定量化する（Quantifying dynamics of failure across science, startups, and security）</news:title>
   <news:publication_date>2026-08-19T10:58:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724893</loc>
  <lastmod>2026-08-19T10:57:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>EV-IMO: 屋内高速物体のイベントカメラによる動き分割とデータセット（EV-IMO: Motion Segmentation Dataset and Learning Pipeline for Event Cameras）</news:title>
   <news:publication_date>2026-08-19T10:57:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724891</loc>
  <lastmod>2026-08-19T10:57:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>変動ガンマ過程下でのアメリカン・オプション高速評価法（A fast method for pricing American options under the variance gamma model）</news:title>
   <news:publication_date>2026-08-19T10:57:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724889</loc>
  <lastmod>2026-08-19T10:57:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Short Datathonによるデータ分析・可視化スキル育成（Short Datathon for the Interdisciplinary Development of Data Analysis and Visualization Skills）</news:title>
   <news:publication_date>2026-08-19T10:57:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724887</loc>
  <lastmod>2026-08-19T10:57:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>人間の注目を活かす画像キャプション生成の強化（Boosted Attention: Leveraging Human Attention for Image Captioning）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724885</loc>
  <lastmod>2026-08-19T10:06:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-19T10:06:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724883</loc>
  <lastmod>2026-08-19T10:06:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>指数族モデルを丸ごと学習する二つのネットワーク構成（Approximating exponential family models with a two-network architecture）</news:title>
   <news:publication_date>2026-08-19T10:06:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724881</loc>
  <lastmod>2026-08-19T10:05:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>短期気象予測の予測手法比較（A Comparison of Prediction Algorithms and Nexting for Short Term Weather Forecasts）</news:title>
   <news:publication_date>2026-08-19T10:05:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724879</loc>
  <lastmod>2026-08-19T10:04:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>言語による画像編集のビリニア表現（BILINEAR REPRESENTATION FOR LANGUAGE-BASED IMAGE EDITING USING CONDITIONAL GENERATIVE ADVERSARIAL NETWORKS）</news:title>
   <news:publication_date>2026-08-19T10:04:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724877</loc>
  <lastmod>2026-08-19T10:04:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CNNベースの絶対カメラ姿勢回帰の限界（Understanding the Limitations of CNN-based Absolute Camera Pose Regression）</news:title>
   <news:publication_date>2026-08-19T10:04:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724875</loc>
  <lastmod>2026-08-19T10:04:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>効果的なラベルノイズモデルによるDNNテキスト分類の頑強化（An Effective Label Noise Model for DNN Text Classification）</news:title>
   <news:publication_date>2026-08-19T10:04:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724873</loc>
  <lastmod>2026-08-19T10:04:25Z</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と新規前処理でアルツハイマー病の進行を予測する（Forecasting the Progression of Alzheimer’s Disease Using Neural Networks and a Novel Pre-Processing Algorithm）</news:title>
   <news:publication_date>2026-08-19T10:04:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724871</loc>
  <lastmod>2026-08-19T09:12:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文脈認知による高度なカプセルネットワーク（Advanced Capsule Networks via Context Awareness）</news:title>
   <news:publication_date>2026-08-19T09:12:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724869</loc>
  <lastmod>2026-08-19T09:12:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>荷電粒子計測のための深掘り型APDによる高精度タイミング（Deep Diffused Avalanche Photodiodes for Charged Particles Timing）</news:title>
   <news:publication_date>2026-08-19T09:12:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724867</loc>
  <lastmod>2026-08-19T09:11:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オフラインとオンラインの深層学習による画像認識（Offline and Online Deep Learning for Image Recognition）</news:title>
   <news:publication_date>2026-08-19T09:11:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724865</loc>
  <lastmod>2026-08-19T09:10:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>MUSEFood：スマートフォンでのマルチセンサによる食品容積推定（MUSEFood: Multi-sensor-based Food Volume Estimation on Smartphones）</news:title>
   <news:publication_date>2026-08-19T09:10:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724863</loc>
  <lastmod>2026-08-19T09:10:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層構造を利用したKL正則化強化学習の学習と転移（Exploiting Hierarchy for Learning and Transfer in KL-regularized RL）</news:title>
   <news:publication_date>2026-08-19T09:10:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724861</loc>
  <lastmod>2026-08-19T09:10:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>通信効率を劇的に改善する連合学習の仕組み（Communication-Efficient Federated Deep Learning with Asynchronous Model Update and Temporally Weighted Aggregation）</news:title>
   <news:publication_date>2026-08-19T09:10:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724859</loc>
  <lastmod>2026-08-19T09:09:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>n部分トーナメントの部分自己同型写像の拡張（EXTENDING PARTIAL AUTOMORPHISMS OF n-PARTITE TOURNAMENTS）</news:title>
   <news:publication_date>2026-08-19T09:09:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724857</loc>
  <lastmod>2026-08-19T08:18:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>最近傍量子化フィルタに基づく分位点回帰による確率的エネルギー予測（Probabilistic Energy Forecasting using Quantile Regressions based on a new Nearest Neighbors Quantile Filter）</news:title>
   <news:publication_date>2026-08-19T08:18:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724855</loc>
  <lastmod>2026-08-19T08:18:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>探索駆動を階層化する新戦略：Scheduled Intrinsic Drive（Scheduled Intrinsic Drive: A Hierarchical Take on Intrinsically Motivated Exploration）</news:title>
   <news:publication_date>2026-08-19T08:18:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724853</loc>
  <lastmod>2026-08-19T08:17:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ソフトウェア工学が社会心理学の感情研究から学べること（What software engineering can learn from research on affect in social psychology）</news:title>
   <news:publication_date>2026-08-19T08:17:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724851</loc>
  <lastmod>2026-08-19T08:17:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ReLUニューラルネットワークのオンライントレーニング挙動の解析（On-line learning dynamics of ReLU neural networks using statistical physics techniques）</news:title>
   <news:publication_date>2026-08-19T08:17:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724849</loc>
  <lastmod>2026-08-19T08:17:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CRAFTによる音声プロソディ可視化の教育的転換（CRAFT: A Multifunction Online Platform for Speech Prosody Visualisation）</news:title>
   <news:publication_date>2026-08-19T08:17:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724847</loc>
  <lastmod>2026-08-19T08:17:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>手書き文字認識のためのシーケンス・トゥ・シーケンスモデル評価（Evaluating Sequence-to-Sequence Models for Handwritten Text Recognition）</news:title>
   <news:publication_date>2026-08-19T08:17:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724845</loc>
  <lastmod>2026-08-19T08:17:13Z</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>2026-08-19T08:17:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724843</loc>
  <lastmod>2026-08-19T07:25:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Eコマース向けファッションアウトフィット生成（Fashion Outfit Generation for E-commerce）</news:title>
   <news:publication_date>2026-08-19T07:25:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724841</loc>
  <lastmod>2026-08-19T07:15:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチンゲール表現によるマルコフ連鎖の分散削減（Variance reduction for additive functional of Markov chains via martingale representations）</news:title>
   <news:publication_date>2026-08-19T07:15:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724839</loc>
  <lastmod>2026-08-19T07:15:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>未知光源下での自己校正フォトメトリーステレオ（Self-calibrating Deep Photometric Stereo Networks）</news:title>
   <news:publication_date>2026-08-19T07:15:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724837</loc>
  <lastmod>2026-08-19T07:14:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>印刷可能なグラフィカルコードの複製可能性と機械学習による解析（CLONABILITY OF ANTI-COUNTERFEITING PRINTABLE GRAPHICAL CODES: A MACHINE LEARNING APPROACH）</news:title>
   <news:publication_date>2026-08-19T07:14:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724835</loc>
  <lastmod>2026-08-19T07:13:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>数値モデルの不明部分の機械学習による表現（Representing ill-known parts of a numerical model using a machine learning approach）</news:title>
   <news:publication_date>2026-08-19T07:13:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724833</loc>
  <lastmod>2026-08-19T07:13:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>IvaNetによる物体検出とセグメンテーションの同時学習（IVANET: LEARNING TO JOINTLY DETECT AND SEGMENT OBJETS WITH THE HELP OF LOCAL TOP-DOWN MODULES）</news:title>
   <news:publication_date>2026-08-19T07:13:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724831</loc>
  <lastmod>2026-08-19T07:13:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Xホールを用いたF-RANのオンライン強化学習によるコンテンツ配信最適化（Online Reinforcement Learning of X-Haul Content Delivery Mode in Fog Radio Access Networks）</news:title>
   <news:publication_date>2026-08-19T07:13:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724829</loc>
  <lastmod>2026-08-19T06:21:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>集合データ学習と集約関数の選び方（On Deep Set Learning and the Choice of Aggregations）</news:title>
   <news:publication_date>2026-08-19T06:21:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724827</loc>
  <lastmod>2026-08-19T06:21:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>IMDBとTwitterデータに対する感情分析 — 機械学習とベクトル空間による手法 (Sentiment Analysis on IMDB Movie Comments and Twitter Data by Machine Learning and Vector Space Techniques)</news:title>
   <news:publication_date>2026-08-19T06:21:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724825</loc>
  <lastmod>2026-08-19T06:21:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多系列MRIを用いた深層学習による脳転移の自動検出とセグメンテーション（Deep Learning Enables Automatic Detection and Segmentation of Brain Metastases on Multi-Sequence MRI）</news:title>
   <news:publication_date>2026-08-19T06:21:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724823</loc>
  <lastmod>2026-08-19T06:21:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>左右循環制約と適応正則化による教師なし単眼深度推定（Bilateral Cyclic Constraint and Adaptive Regularization for Unsupervised Monocular Depth Prediction）</news:title>
   <news:publication_date>2026-08-19T06:21:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724821</loc>
  <lastmod>2026-08-19T06:20:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>MRIベースのアルツハイマー病分類におけるLRPによる説明性の向上（Layer-wise relevance propagation for explaining deep neural network decisions in MRI-based Alzheimer’s disease classification）</news:title>
   <news:publication_date>2026-08-19T06:20:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724819</loc>
  <lastmod>2026-08-19T06:20:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Deep Gaussian Processesを用いたマルチフィデリティモデリング（Deep Gaussian Processes for Multi-fidelity Modeling）</news:title>
   <news:publication_date>2026-08-19T06:20:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724817</loc>
  <lastmod>2026-08-19T06:20:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>低ランク近似による双曲線埋め込みの効率化（Low-rank approximations of hyperbolic embeddings）</news:title>
   <news:publication_date>2026-08-19T06:20:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/724815</loc>
  <lastmod>2026-08-19T05:28:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>M²VAEによるマルチモーダル生成の論点整理（M²VAE – Derivation of a Multi-Modal Variational Autoencoder）</news:title>
   <news:publication_date>2026-08-19T05:28:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724813</loc>
  <lastmod>2026-08-19T05:28:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフ系列の自己回帰モデル（Autoregressive Models for Sequences of Graphs）</news:title>
   <news:publication_date>2026-08-19T05:28:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724811</loc>
  <lastmod>2026-08-19T05:28:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチユーザ分散型大規模MIMOのパイロット設計に深層学習を用いる手法（Deep Learning Based Pilot Design for Multi-user Distributed Massive MIMO Systems）</news:title>
   <news:publication_date>2026-08-19T05:28:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724809</loc>
  <lastmod>2026-08-19T05:27:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>空間適応正規化によるセマンティック画像合成（Semantic Image Synthesis with Spatially-Adaptive Normalization）</news:title>
   <news:publication_date>2026-08-19T05:27:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724807</loc>
  <lastmod>2026-08-19T05:27:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>外観ベースの視線推定における拡張畳み込みの活用（Appearance-Based Gaze Estimation Using Dilated-Convolutions）</news:title>
   <news:publication_date>2026-08-19T05:27:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724805</loc>
  <lastmod>2026-08-19T05:27:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>異種グラフに注目する表現学習の新潮流（Heterogeneous Graph Attention Network）</news:title>
   <news:publication_date>2026-08-19T05:27:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724803</loc>
  <lastmod>2026-08-19T05:27:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>パディングがLSTMとCNNの振る舞いに与える影響（Effects of Padding on LSTMs and CNNs）</news:title>
   <news:publication_date>2026-08-19T05:27:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724801</loc>
  <lastmod>2026-08-19T04:36:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>条件付き生成敵対ネットワークによる敵対的事例生成（Generating Adversarial Examples With Conditional Generative Adversarial Net）</news:title>
   <news:publication_date>2026-08-19T04:36:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724799</loc>
  <lastmod>2026-08-19T04:36:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>NGC 1052周辺で見つかった複数の恒星ストリームの検出（A tidal tale: detection of multiple stellar streams in the environment of NGC 1052）</news:title>
   <news:publication_date>2026-08-19T04:36:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724797</loc>
  <lastmod>2026-08-19T04:35:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層畳み込み構造を用いたPOI意味モデル（POI Semantic Model with a Deep Convolutional Structure）</news:title>
   <news:publication_date>2026-08-19T04:35:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724795</loc>
  <lastmod>2026-08-19T04:35:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>強連結ネットワーク上での勾配追跡を用いた分散確率的最適化（Distributed stochastic optimization with gradient tracking over strongly-connected networks）</news:title>
   <news:publication_date>2026-08-19T04:35:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724793</loc>
  <lastmod>2026-08-19T04:35:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>期待認識型プランニングの統一的枠組み（Expectation-Aware Planning）</news:title>
   <news:publication_date>2026-08-19T04:35:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724791</loc>
  <lastmod>2026-08-19T04:35:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>EEGによる感情認識と機械学習の実装と評価（Emotion Recognition with Machine Learning Using EEG Signals）</news:title>
   <news:publication_date>2026-08-19T04:35:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724789</loc>
  <lastmod>2026-08-19T04:34:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>概念ドリフト下のプロトタイプ分類器の挙動解析（Prototype-based classifiers in the presence of concept drift: A modelling framework）</news:title>
   <news:publication_date>2026-08-19T04:34:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724787</loc>
  <lastmod>2026-08-19T03:43:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散最適化におけるアニーリングによる大域解収束（Annealing for Distributed Global Optimization）</news:title>
   <news:publication_date>2026-08-19T03:43:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724785</loc>
  <lastmod>2026-08-19T03:43:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフ畳み込みによるラベルノイズクリーナ（Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly Detection）</news:title>
   <news:publication_date>2026-08-19T03:43:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724783</loc>
  <lastmod>2026-08-19T03:43:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>品質認識テンプレートマッチング（Quality-Aware Template Matching For Deep Learning）</news:title>
   <news:publication_date>2026-08-19T03:43:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724781</loc>
  <lastmod>2026-08-19T03:42:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>準確率的近似の収束速度最適性（Optimal Rate of Convergence for Quasi-Stochastic Approximation）</news:title>
   <news:publication_date>2026-08-19T03:42:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724779</loc>
  <lastmod>2026-08-19T03:41:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複雑なPolSAR画像のシーン分類に向けた自己段階学習（Complex Scene Classification of PolSAR Imagery Based on a Self-Paced Learning Approach）</news:title>
   <news:publication_date>2026-08-19T03:41:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724777</loc>
  <lastmod>2026-08-19T03:41:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>遠隔探査シーン表現のための深層モデルと擬似クラスのエンドツーエンド共同学習（An End-to-End Joint Unsupervised Learning of Deep Model and Pseudo-Classes for Remote Sensing Scene Representation）</news:title>
   <news:publication_date>2026-08-19T03:41:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724775</loc>
  <lastmod>2026-08-19T03:41:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>畳み込みによる対位法（Counterpoint by Convolution）</news:title>
   <news:publication_date>2026-08-19T03:41:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724773</loc>
  <lastmod>2026-08-19T02:50:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>可搬型加速度計と深層学習で現場の地面反力を推定する（Multidimensional ground reaction forces and moments from wearable sensor accelerations via deep learning）</news:title>
   <news:publication_date>2026-08-19T02:50:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724771</loc>
  <lastmod>2026-08-19T02:49:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>射影を用いた状態安定性から見るエピソディック学習の制御理論的解析（A Control Lyapunov Perspective on Episodic Learning via Projection to State Stability）</news:title>
   <news:publication_date>2026-08-19T02:49:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724769</loc>
  <lastmod>2026-08-19T02:49:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SNS上で共有されるワクチン情報の信頼性を自動で判定する手法の実用性（Automatically applying a credibility appraisal tool to track vaccine-related communications shared on social media）</news:title>
   <news:publication_date>2026-08-19T02:49:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724767</loc>
  <lastmod>2026-08-19T02:49:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習ベースの衣服アニメーションによるバーチャルトライオン（Learning-Based Animation of Clothing for Virtual Try-On）</news:title>
   <news:publication_date>2026-08-19T02:49:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724765</loc>
  <lastmod>2026-08-19T02:49:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模かつ密な部分相関ネットワークの計算と応用（On the Computation and Applications of Large Dense Partial Correlation Networks）</news:title>
   <news:publication_date>2026-08-19T02:49:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724763</loc>
  <lastmod>2026-08-19T02:49:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>AttoNetsによるエッジ向け超小型高効率ニューラルネットワーク（AttoNets: Compact and Efficient Deep Neural Networks for the Edge via Human-Machine Collaborative Design）</news:title>
   <news:publication_date>2026-08-19T02:49:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724761</loc>
  <lastmod>2026-08-19T02:48:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>モデル・フリーによるモデル調整（Model-Free Model Reconciliation）</news:title>
   <news:publication_date>2026-08-19T02:48:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724759</loc>
  <lastmod>2026-08-19T01:57:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習の“精度”は操作できる—がん予測研究の暗部（Machine Learning: A Dark Side of Cancer Computing）</news:title>
   <news:publication_date>2026-08-19T01:57:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724757</loc>
  <lastmod>2026-08-19T01:57:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>構文認識を変えるポインタネットワーク応用（Syntax-aware Representation Learning With Pointer Networks）</news:title>
   <news:publication_date>2026-08-19T01:57:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724755</loc>
  <lastmod>2026-08-19T01:57:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>欠損データに強いRNNを用いたアルツハイマー病進行モデル化（Training recurrent neural networks robust to incomplete data: application to Alzheimer’s disease progression modeling）</news:title>
   <news:publication_date>2026-08-19T01:57:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724753</loc>
  <lastmod>2026-08-19T01:56:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>人間と機械の相互作用を最小限の仮定で設計する（Modeling and Optimization of Human-Machine Interaction Processes via the Maximum Entropy Principle）</news:title>
   <news:publication_date>2026-08-19T01:56:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724751</loc>
  <lastmod>2026-08-19T01:56:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>JWSTによるトランジット系外惑星の分光特性化（Characterizing Transiting Exoplanets with JWST）</news:title>
   <news:publication_date>2026-08-19T01:56:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724749</loc>
  <lastmod>2026-08-19T01:56:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>脳に学ぶ高スパースニューラルネットワークの訓練アルゴリズム（A Brain-inspired Algorithm for Training Highly Sparse Neural Networks）</news:title>
   <news:publication_date>2026-08-19T01:56:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724747</loc>
  <lastmod>2026-08-19T01:55:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>画像復元のための近接分割ネットワーク（Proximal Splitting Networks for Image Restoration）</news:title>
   <news:publication_date>2026-08-19T01:55:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724745</loc>
  <lastmod>2026-08-19T01:04:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深いResNetの安定化と鋭いスケーリング係数τ (Stabilize Deep ResNet with A Sharp Scaling Factor τ)</news:title>
   <news:publication_date>2026-08-19T01:04:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724743</loc>
  <lastmod>2026-08-19T01:03:57Z</lastmod>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>トピック誘導型変分オートエンコーダによる文章生成（Topic-Guided Variational Autoencoders for Text Generation）</news:title>
   <news:publication_date>2026-08-19T01:03:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <lastmod>2026-08-19T01:02:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>部分人物再識別のための対ペア空間変換ネットワーク（STNReID: Deep Convolutional Networks with Pairwise Spatial Transformer Networks for Partial Person Re-identification）</news:title>
   <news:publication_date>2026-08-19T01:02:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-19T01:02:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時系列データベースで予測を直接実行する仕組み（tspDB: Time Series Predict DB）</news:title>
   <news:publication_date>2026-08-19T01:02:37Z</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>用量探索試験におけるマルチアームド・バンディット設計の応用（On Multi-Armed Bandit Designs for Dose-Finding Trials）</news:title>
   <news:publication_date>2026-08-19T01:02:31Z</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>ゼロショット翻訳に欠けていた要素（The Missing Ingredient in Zero-Shot Neural Machine Translation）</news:title>
   <news:publication_date>2026-08-19T01:02:11Z</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>教師-生徒ネットワークを用いた深層特徴選択（Deep Feature Selection using a Teacher-Student Network）</news:title>
   <news:publication_date>2026-08-19T00:11:02Z</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>AdaGraphによる予測的・連続的ドメイン適応の統一（AdaGraph: Unifying Predictive and Continuous Domain Adaptation through Graphs）</news:title>
   <news:publication_date>2026-08-19T00:10:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-19T00:03:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層人物再識別の強力なベースラインとトレーニングの小技集 (Bag of Tricks and A Strong Baseline for Deep Person Re-identification)</news:title>
   <news:publication_date>2026-08-19T00:03:12Z</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>イベントベース映像に対する時空間フィルタの応用（Spatiotemporal Filtering for Event-Based Action Recognition）</news:title>
   <news:publication_date>2026-08-19T00:02:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>競合的かつ判別的再構成による異常検知の学習（Learning Competitive and Discriminative Reconstructions for Anomaly Detection）</news:title>
   <news:publication_date>2026-08-19T00:01:38Z</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>時系列分類に対する敵対的攻撃の脆弱性（Adversarial Attacks on Deep Neural Networks for Time Series Classification）</news:title>
   <news:publication_date>2026-08-19T00:01:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-19T00:01:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散型同時摂動勾配降下法（DSPG: Decentralized Simultaneous Perturbations Gradient Descent Scheme）</news:title>
   <news:publication_date>2026-08-19T00:01:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>コンパイラ支援によるbig.LITTLEシステムの適応的プログラムスケジューリング（Compiler-assisted Adaptive Program Scheduling in big.LITTLE Systems）</news:title>
   <news:publication_date>2026-08-18T23:09:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-18T23:09:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-18T23:08:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>リスク認知型Top-k推薦の考え方（Risk Aware Ranking for Top-k Recommendation）</news:title>
   <news:publication_date>2026-08-18T23:08:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <lastmod>2026-08-18T23:08:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>全脳イメージからの神経細胞再構築が変えたもの（RECONSTRUCTING NEURONAL ANATOMY FROM WHOLE-BRAIN IMAGES）</news:title>
   <news:publication_date>2026-08-18T23:08:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>組織標本におけるヒストパソロジー画像の折りたたみ検出の深層特徴解析（Deep Features for Tissue-Fold Detection in Histopathology Images）</news:title>
   <news:publication_date>2026-08-18T23:08:13Z</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>Zeno++：完全非同期環境下での堅牢なSGD（Zeno++: Robust Fully Asynchronous SGD）</news:title>
   <news:publication_date>2026-08-18T23:08:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>病理組織画像表現のためのパッチクラスタリング（Patch Clustering for Representation of Histopathology Images）</news:title>
   <news:publication_date>2026-08-18T23:07:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724703</loc>
  <lastmod>2026-08-18T22:16:22Z</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>2026-08-18T22:16:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724701</loc>
  <lastmod>2026-08-18T22:16:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SLSGD: 端末側分散学習のための安全で効率的な手法（SLSGD: Secure and Efficient Distributed On-device Machine Learning）</news:title>
   <news:publication_date>2026-08-18T22:16:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724699</loc>
  <lastmod>2026-08-18T22:16:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>タスク分解による反復学習型モデル予測制御の効率化（Task Decomposition for Iterative Learning Model Predictive Control）</news:title>
   <news:publication_date>2026-08-18T22:16:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-18T22:14:46Z</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>2026-08-18T22:14:46Z</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>
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   <news:publication_date>2026-08-18T22:14:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724693</loc>
  <lastmod>2026-08-18T22:14:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724691</loc>
  <lastmod>2026-08-18T22:14:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-18T21:22:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分割（パーティション）中心の分散アルゴリズムによる大規模グラフのオイラー回路探索（A Partition-centric Distributed Algorithm for Identifying Euler Circuits in Large Graphs）</news:title>
   <news:publication_date>2026-08-18T21:22:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-18T21:22:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-08-18T21:22:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <lastmod>2026-08-18T21:22:30Z</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>2026-08-18T21:22:30Z</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>2026-08-18T21:22:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-08-18T21:21:47Z</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>
  </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>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
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    <news:name>AI Benchmark Research</news:name>
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 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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    <news:language>ja</news:language>
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    <news:name>AI Benchmark Research</news:name>
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 <url>
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    <news:name>AI Benchmark Research</news:name>
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    <news:language>ja</news:language>
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    <news:name>AI Benchmark Research</news:name>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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    <news:name>AI Benchmark Research</news:name>
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    <news:name>AI Benchmark Research</news:name>
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    <news:name>AI Benchmark Research</news:name>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 <url>
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   <news: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:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 <url>
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    <news:name>AI Benchmark Research</news:name>
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    <news:name>AI Benchmark Research</news:name>
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    <news:name>AI Benchmark Research</news:name>
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    <news:name>AI Benchmark Research</news:name>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>キャンパス行動から見る大学生の成績予測（Predicting Academic Performance for College Students: A Campus Behavior Perspective）</news:title>
   <news:publication_date>2026-08-18T15:49:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724597</loc>
  <lastmod>2026-08-18T15:49:28Z</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>2026-08-18T15:49:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724595</loc>
  <lastmod>2026-08-18T15:49:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模電力網の多段階故障警報システム（MULTI-STAGE FAULT WARNING FOR LARGE ELECTRIC GRIDS USING ANOMALY DETECTION AND MACHINE LEARNING）</news:title>
   <news:publication_date>2026-08-18T15:49:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724593</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>ニューラルネットワーク量子状態による二次元フラストレートJ1-J2モデルの研究（Study of the Two-Dimensional Frustrated J1-J2 Model with Neural Network Quantum States）</news:title>
   <news:publication_date>2026-08-18T15:48:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724591</loc>
  <lastmod>2026-08-18T14:56:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>畳み込みニューラルネットワーク訓練の強スケーリング改善（Improving Strong-Scaling of CNN Training by Exploiting Finer-Grained Parallelism）</news:title>
   <news:publication_date>2026-08-18T14:56:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724589</loc>
  <lastmod>2026-08-18T14:56:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Dragonflyによるスケーラブルかつ頑健なベイズ最適化（Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly）</news:title>
   <news:publication_date>2026-08-18T14:56:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724587</loc>
  <lastmod>2026-08-18T14:55:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GEE: VAEと勾配指紋で説明可能なネットワーク異常検知（GEE: A Gradient-based Explainable Variational Autoencoder for Network Anomaly Detection）</news:title>
   <news:publication_date>2026-08-18T14:55:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724585</loc>
  <lastmod>2026-08-18T14:55:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>カメラ姿勢回帰と改善のための敵対的ネットワーク（Adversarial Networks for Camera Pose Regression and Refinement）</news:title>
   <news:publication_date>2026-08-18T14:55:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724583</loc>
  <lastmod>2026-08-18T14:54:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次元最適制御に挑む：DNNが次元の呪いを乗り越える（Rectified deep neural networks overcome the curse of dimensionality for nonsmooth value functions in zero-sum games of nonlinear stiff systems）</news:title>
   <news:publication_date>2026-08-18T14:54:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724581</loc>
  <lastmod>2026-08-18T14:54:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分解可能な不確実性を用いた群衆カウント（Crowd Counting with Decomposed Uncertainty）</news:title>
   <news:publication_date>2026-08-18T14:54:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724579</loc>
  <lastmod>2026-08-18T14:54:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>効率的な太陽電池材料の高速探索（Accelerated Discovery of Efficient Solar-cell Materials using Quantum and Machine-learning Methods）</news:title>
   <news:publication_date>2026-08-18T14:54:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724577</loc>
  <lastmod>2026-08-18T14:03:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>異方的な堅牢性の証明：非一様境界の検証（On Certifying Non-uniform Bounds against Adversarial Attacks）</news:title>
   <news:publication_date>2026-08-18T14:03:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724575</loc>
  <lastmod>2026-08-18T14:03:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>統計的畳み込みニューラルネットワークによる動画物体検出の高速化（SCNN: A General Distribution based Statistical Convolutional Neural Network with Application to Video Object Detection）</news:title>
   <news:publication_date>2026-08-18T14:03:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724573</loc>
  <lastmod>2026-08-18T14:02:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時系列分類における深層ニューラルネットワークのアンサンブル（Deep Neural Network Ensembles for Time Series Classification）</news:title>
   <news:publication_date>2026-08-18T14:02:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724571</loc>
  <lastmod>2026-08-18T14:01:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>政策蒸留と価値マッチングによるマルチエージェント強化学習の統合（Policy Distillation and Value Matching in Multiagent Reinforcement Learning）</news:title>
   <news:publication_date>2026-08-18T14:01:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724569</loc>
  <lastmod>2026-08-18T14:01:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>モンテカルロ・ウェーブレットによるフレーム離散化（Monte Carlo wavelets: a randomized approach to frame discretization）</news:title>
   <news:publication_date>2026-08-18T14:01:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724567</loc>
  <lastmod>2026-08-18T14:01:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>一般的M推定量の任意時点非漸近反復対数則（A nonasymptotic law of iterated logarithm for general M-estimators）</news:title>
   <news:publication_date>2026-08-18T14:01:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724565</loc>
  <lastmod>2026-08-18T14:01:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>選択的カーネルネットワーク（Selective Kernel Networks）</news:title>
   <news:publication_date>2026-08-18T14:01:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724563</loc>
  <lastmod>2026-08-18T13:09:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動画への動画挿入手法（Inserting Videos into Videos）</news:title>
   <news:publication_date>2026-08-18T13:09:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724561</loc>
  <lastmod>2026-08-18T13:09:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>固有状態の局所測定から系のハミルトニアンを復元する（Determining system Hamiltonian from eigenstate measurements without correlation functions）</news:title>
   <news:publication_date>2026-08-18T13:09:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724559</loc>
  <lastmod>2026-08-18T13:08:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数ラベルによる雲画像分割（Multi-label Cloud Segmentation Using a Deep Network）</news:title>
   <news:publication_date>2026-08-18T13:08:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724557</loc>
  <lastmod>2026-08-18T13:08:15Z</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>2026-08-18T13:08:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724555</loc>
  <lastmod>2026-08-18T13:08:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高スループットイメージング解析における表現型プロファイリング（Phenotypic Profiling of High Throughput Imaging Screens with Generic Deep Convolutional Features）</news:title>
   <news:publication_date>2026-08-18T13:08:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724553</loc>
  <lastmod>2026-08-18T13:07:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>テニスランキングの新モデル：非負行列因子分解に動機づけられたランキングモデル（A Ranking Model Motivated by Nonnegative Matrix Factorization with Applications to Tennis Tournaments）</news:title>
   <news:publication_date>2026-08-18T13:07:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724551</loc>
  <lastmod>2026-08-18T13:07:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>攻撃的発言検出の最先端手法の探索（An Exploration of State-of-the-art Methods for Offensive Language Detection）</news:title>
   <news:publication_date>2026-08-18T13:07:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724549</loc>
  <lastmod>2026-08-18T12:16:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチモーダル融合の自動設計探索（MFAS: Multimodal Fusion Architecture Search）</news:title>
   <news:publication_date>2026-08-18T12:16:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724547</loc>
  <lastmod>2026-08-18T12:16:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューロモーフィックハードウェアにおける学習の学習（Neuromorphic Hardware learns to learn）</news:title>
   <news:publication_date>2026-08-18T12:16:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724545</loc>
  <lastmod>2026-08-18T12:15:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多重バイオメトリクスによる双子識別（Twins Recognition with Multi Biometric System）</news:title>
   <news:publication_date>2026-08-18T12:15:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724543</loc>
  <lastmod>2026-08-18T12:14:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文脈対応型引用推薦モデルが変える参考文献探索（A Context-Aware Citation Recommendation Model with BERT and Graph Convolutional Networks）</news:title>
   <news:publication_date>2026-08-18T12:14:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724541</loc>
  <lastmod>2026-08-18T12:14:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的プログラミングを用いた感情コンピューティングの実践（Applying Probabilistic Programming to Affective Computing）</news:title>
   <news:publication_date>2026-08-18T12:14:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724539</loc>
  <lastmod>2026-08-18T12:14:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単眼画像からの濃密セマンティック再構築（Monocular Dense Semantic Reconstruction using Learned Encoded Scene Representations）</news:title>
   <news:publication_date>2026-08-18T12:14:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724537</loc>
  <lastmod>2026-08-18T12:14:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>国際株式市場を同時に扱うマルチモーダル深層学習（Multimodal Deep Learning for Finance: Integrating and Forecasting International Stock Markets）</news:title>
   <news:publication_date>2026-08-18T12:14:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724535</loc>
  <lastmod>2026-08-18T11:22:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>睡眠における皮質覚醒の自動検出と日中の眠気への寄与（Automatic Detection of Cortical Arousals in Sleep and their Contribution to Daytime Sleepiness）</news:title>
   <news:publication_date>2026-08-18T11:22:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724533</loc>
  <lastmod>2026-08-18T11:22:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>有限体上の重要特徴列挙を高速化するアルゴリズム（A Faster Algorithm Enumerating Relevant Features over Finite Fields）</news:title>
   <news:publication_date>2026-08-18T11:22:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724531</loc>
  <lastmod>2026-08-18T11:21:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>児童の第二言語音声能力を自動評価する技術の実用性（AUTOMATIC ASSESSMENT OF SPOKEN LANGUAGE PROFICIENCY OF NON-NATIVE CHILDREN）</news:title>
   <news:publication_date>2026-08-18T11:21:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724529</loc>
  <lastmod>2026-08-18T11:20:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模ブラックボックス最適化ベンチマーク（COCO: The Large Scale Black-Box Optimization Benchmarking）</news:title>
   <news:publication_date>2026-08-18T11:20:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/724527</loc>
  <lastmod>2026-08-18T11:20:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DFineNetによる深度補完と自己位置推定の統合（DFineNet: Ego-Motion Estimation and Depth Refinement from Sparse, Noisy Depth Input with RGB Guidance）</news:title>
   <news:publication_date>2026-08-18T11:20:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724525</loc>
  <lastmod>2026-08-18T11:20:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>品質を意識した非対応画像間翻訳（Quality-aware Unpaired Image-to-Image Translation）</news:title>
   <news:publication_date>2026-08-18T11:20:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724523</loc>
  <lastmod>2026-08-18T11:19:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次Dysthe方程式における変調波の数値シミュレーション（Numerical simulations of modulated waves in a higher-order Dysthe equation）</news:title>
   <news:publication_date>2026-08-18T11:19:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724521</loc>
  <lastmod>2026-08-18T10:26:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>車載レーダーの干渉除去に向けた深層学習アプローチ（A Deep Learning Approach for Automotive Radar Interference Mitigation）</news:title>
   <news:publication_date>2026-08-18T10:26:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724519</loc>
  <lastmod>2026-08-18T10:26:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スパイクベースの逆伝播による深層ニューラルネットワーク訓練の実現 (Enabling Spike-Based Backpropagation for Training Deep Neural Network Architectures)</news:title>
   <news:publication_date>2026-08-18T10:26:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724517</loc>
  <lastmod>2026-08-18T10:26:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>FASTのドリフトスキャンにおけるパルサー候補選別のための集合ネットワーク（Pulsar Candidate Selection Using Ensemble Networks for FAST Drift-Scan Survey）</news:title>
   <news:publication_date>2026-08-18T10:26:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724515</loc>
  <lastmod>2026-08-18T10:24:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散強化学習におけるマルチエージェント・オフポリシー アクタークリティック（A Multi-Agent Off-Policy Actor-Critic Algorithm for Distributed Reinforcement Learning）</news:title>
   <news:publication_date>2026-08-18T10:24:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724513</loc>
  <lastmod>2026-08-18T10:24:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>軽量光学フローCNNの再設計（A Lightweight Optical Flow CNN — Revisiting Data Fidelity and Regularization）</news:title>
   <news:publication_date>2026-08-18T10:24:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724511</loc>
  <lastmod>2026-08-18T10:24:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>周波数変調による異方性量子ラビモデルのシミュレーション（Simulating Anisotropic quantum Rabi model via frequency modulation）</news:title>
   <news:publication_date>2026-08-18T10:24:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724509</loc>
  <lastmod>2026-08-18T10:24:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>人と物の相互作用認識と姿勢推定のターボ学習フレームワーク（Turbo Learning Framework for Human-Object Interactions Recognition and Human Pose Estimation）</news:title>
   <news:publication_date>2026-08-18T10:24:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724507</loc>
  <lastmod>2026-08-18T09:32:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>医療画像分類のための教師なし深層転移特徴学習（UNSUPERVISED DEEP TRANSFER FEATURE LEARNING FOR MEDICAL IMAGE CLASSIFICATION）</news:title>
   <news:publication_date>2026-08-18T09:32:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724505</loc>
  <lastmod>2026-08-18T09:32:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ターゲットシフトに配慮した敵対的ドメイン適応（On Target Shift in Adversarial Domain Adaptation）</news:title>
   <news:publication_date>2026-08-18T09:32:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724503</loc>
  <lastmod>2026-08-18T09:31:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習を用いたリスクモデルの実務的意義（Machine Learning Risk Models）</news:title>
   <news:publication_date>2026-08-18T09:31:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724501</loc>
  <lastmod>2026-08-18T09:30:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>画像の逐次改良を可能にするDeep Joint Source-Channel Coding（Successive Refinement of Images with Deep Joint Source-Channel Coding）</news:title>
   <news:publication_date>2026-08-18T09:30:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724499</loc>
  <lastmod>2026-08-18T09:30:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ソフトマルチラベル学習による教師なし人物再識別（Unsupervised Person Re-identification by Soft Multilabel Learning）</news:title>
   <news:publication_date>2026-08-18T09:30:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724497</loc>
  <lastmod>2026-08-18T09:30:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>初期セントロイド問題に挑むDP-KMeans（Tackling Initial Centroid of K-Means with Distance Part (DP-KMeans)）</news:title>
   <news:publication_date>2026-08-18T09:30:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724495</loc>
  <lastmod>2026-08-18T09:30:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>太陽系の深時間サーベイ（Solar system Deep Time‑Surveys of atmospheres, surfaces, and rings）</news:title>
   <news:publication_date>2026-08-18T09:30:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724493</loc>
  <lastmod>2026-08-18T08:39:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ウィンドウ化された姿勢グラフ最適化による教師なし単眼Visual Odometryの改善（Pose Graph Optimization for Unsupervised Monocular Visual Odometry）</news:title>
   <news:publication_date>2026-08-18T08:39:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724491</loc>
  <lastmod>2026-08-18T08:39:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>相互線形回帰に基づく離散ハッシュ（MUTUAL LINEAR REGRESSION-BASED DISCRETE HASHING）</news:title>
   <news:publication_date>2026-08-18T08:39:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724489</loc>
  <lastmod>2026-08-18T08:38:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>専門家の視線を模倣する試み（Toward Imitating Visual Attention of Experts in Software Development Tasks）</news:title>
   <news:publication_date>2026-08-18T08:38:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724487</loc>
  <lastmod>2026-08-18T08:37:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動的モデルによる相関攻撃への防御研究アジェンダ（A Research Agenda: Dynamic Models to Defend Against Correlated Attacks）</news:title>
   <news:publication_date>2026-08-18T08:37:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724485</loc>
  <lastmod>2026-08-18T08:37:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散制約オンライン学習（Distributed Constrained Online Learning）</news:title>
   <news:publication_date>2026-08-18T08:37:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724483</loc>
  <lastmod>2026-08-18T08:37:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ROS2Learn：ROS 2向け強化学習フレームワーク（ROS2Learn: a reinforcement learning framework for ROS 2）</news:title>
   <news:publication_date>2026-08-18T08:37:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724481</loc>
  <lastmod>2026-08-18T08:37:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>gym-gazebo2を使ったROS 2とGazeboによる強化学習ツールキット（gym-gazebo2, a toolkit for reinforcement learning using ROS 2 and Gazebo）</news:title>
   <news:publication_date>2026-08-18T08:37:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724479</loc>
  <lastmod>2026-08-18T07:46:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>画像と言語を一つに学ぶ統合モデル（Show, Translate and Tell）</news:title>
   <news:publication_date>2026-08-18T07:46:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724477</loc>
  <lastmod>2026-08-18T07:45:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ジャマー抑圧のためのモデル駆動深層学習（Model-Driven Deep Learning Method for Jammer Suppression in Massive Connectivity Systems）</news:title>
   <news:publication_date>2026-08-18T07:45:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724475</loc>
  <lastmod>2026-08-18T07:45:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DysLexMLによるディスレクシアのスクリーニング（DysLexML: Screening Tool for Dyslexia Using Machine Learning）</news:title>
   <news:publication_date>2026-08-18T07:45:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724473</loc>
  <lastmod>2026-08-18T07:45:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模バッチ学習におけるK-FACの非効率性（Inefficiency of K-FAC for Large Batch Size Training）</news:title>
   <news:publication_date>2026-08-18T07:45:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724471</loc>
  <lastmod>2026-08-18T07:44:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>協調キャッシュ配置における学習オートマトン組み込みQ学習（Learning Automata Based Q-learning for Content Placement in Cooperative Caching）</news:title>
   <news:publication_date>2026-08-18T07:44:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724469</loc>
  <lastmod>2026-08-18T07:44:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Neural Architecture Searchの分類器改良をアンサンブル学習で実現（Improving Neural Architecture Search Image Classifiers via Ensemble Learning）</news:title>
   <news:publication_date>2026-08-18T07:44:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724467</loc>
  <lastmod>2026-08-18T07:44:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>電子カルテデータでの機械学習予測を読み解く（Interpretation of machine learning predictions for patient outcomes in electronic health records）</news:title>
   <news:publication_date>2026-08-18T07:44:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724465</loc>
  <lastmod>2026-08-18T06:52:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>コンテキスト付き強化学習における後悔ゼロ探索（No-regret Exploration in Contextual Reinforcement Learning）</news:title>
   <news:publication_date>2026-08-18T06:52:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724463</loc>
  <lastmod>2026-08-18T06:52:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>中性子星合体からの高エネルギー放射（High-energy emissions from neutron star mergers）</news:title>
   <news:publication_date>2026-08-18T06:52:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724461</loc>
  <lastmod>2026-08-18T06:51:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>銀河進化を大規模構造の文脈で観測する意義（Observing Galaxy Evolution in the Context of Large-Scale Structure）</news:title>
   <news:publication_date>2026-08-18T06:51:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724459</loc>
  <lastmod>2026-08-18T06:51:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非極性二ループ重質量ピュアシングレットWilson係数の解析（The unpolarized two-loop massive pure singlet Wilson coefficients for deep-inelastic scattering）</news:title>
   <news:publication_date>2026-08-18T06:51:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724457</loc>
  <lastmod>2026-08-18T06:51:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>微細な差を見抜く注意機構：Trilinear Attention Sampling Networkによる微粒度画像認識（Looking for the Devil in the Details: Learning Trilinear Attention Sampling Network for Fine-grained Image Recognition）</news:title>
   <news:publication_date>2026-08-18T06:51:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/724455</loc>
  <lastmod>2026-08-18T06:51:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>人間の修正フィードバックに基づく探索を用いた深層強化学習（Deep Reinforcement Learning with Feedback-based Exploration）</news:title>
   <news:publication_date>2026-08-18T06:51:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724453</loc>
  <lastmod>2026-08-18T06:51:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>WFIRSTで実現する超深場観測（An Ultra Deep Field Survey with WFIRST）</news:title>
   <news:publication_date>2026-08-18T06:51:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724451</loc>
  <lastmod>2026-08-18T05:59:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ネスト化ポリヘドラルモデルによるテンソルコンパイル（Stripe: Tensor Compilation via the Nested Polyhedral Model）</news:title>
   <news:publication_date>2026-08-18T05:59:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724449</loc>
  <lastmod>2026-08-18T05:59:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>結びついた超伝導フラックス量子ビットにおける非ストクァスティック・ハミルトニアンの実証（Demonstration of nonstoquastic Hamiltonian in coupled superconducting flux qubits）</news:title>
   <news:publication_date>2026-08-18T05:59:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724447</loc>
  <lastmod>2026-08-18T05:59:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スイッチ型ネットワークによる離散データ生成（Deep Switch Networks for Generating Discrete Data and Language）</news:title>
   <news:publication_date>2026-08-18T05:59:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724445</loc>
  <lastmod>2026-08-18T05:58:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>品質独立のJPEG復元のための深い残差オートエンコーダ（Deep Residual Autoencoder for quality independent JPEG restoration）</news:title>
   <news:publication_date>2026-08-18T05:58:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724443</loc>
  <lastmod>2026-08-18T05:58:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>離散化されたAttend-Infer-Repeatによる教師なしで解釈可能なシーン発見（Unsupervised and interpretable scene discovery with Discrete-Attend-Infer-Repeat）</news:title>
   <news:publication_date>2026-08-18T05:58:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724441</loc>
  <lastmod>2026-08-18T05:58:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>銀行顧客の潜在表現学習（Learning Latent Representations of Bank Customers With The Variational Autoencoder）</news:title>
   <news:publication_date>2026-08-18T05:58:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724439</loc>
  <lastmod>2026-08-18T05:58:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>テンソルで圧縮し可視化するニューラルネット（Compression and Interpretability of Deep Neural Networks via Tucker Tensor Layer）</news:title>
   <news:publication_date>2026-08-18T05:58:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724437</loc>
  <lastmod>2026-08-18T05:06:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>MSG-GANによるGANの安定化—マルチスケール勾配で高解像度生成を安定化する（MSG-GAN: Multi-Scale Gradients for Generative Adversarial Networks）</news:title>
   <news:publication_date>2026-08-18T05:06:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724435</loc>
  <lastmod>2026-08-18T04:57:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>液体構造と核生成障壁をつなぐ過冷却の統計研究（Maximum Supercooling Studies in Ti39.5Zr39.5Ni21 and Zr80Pt20 – Connecting Liquid Structure and the Nucleation Barrier）</news:title>
   <news:publication_date>2026-08-18T04:57:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724433</loc>
  <lastmod>2026-08-18T04:57:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>実行プロパティ（インバリアント）の妥当性を学習する手法（Are My Invariants Valid? A Learning Approach）</news:title>
   <news:publication_date>2026-08-18T04:57:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724431</loc>
  <lastmod>2026-08-18T04:57:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多項式時間で解ける線形ディオファントス問題（On Polynomial-Time Solvable Linear Diophantine Problems）</news:title>
   <news:publication_date>2026-08-18T04:57:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724429</loc>
  <lastmod>2026-08-18T04:56:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>異種デモンストレーションから学ぶ個別化ベイズ埋め込みの推定（Inferring Personalized Bayesian Embeddings for Learning from Heterogeneous Demonstration）</news:title>
   <news:publication_date>2026-08-18T04:56:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724427</loc>
  <lastmod>2026-08-18T04:55:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的ビーム探索とGumbel-Top-kトリック（Stochastic Beams and Where to Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement）</news:title>
   <news:publication_date>2026-08-18T04:55:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724425</loc>
  <lastmod>2026-08-18T04:55:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多波長空間コヒーレンス顕微鏡と深層学習によるマラリア赤血球分類（Deep learning enabled multi-wavelength spatial coherence microscope for the classification of malaria-infected stages with limited labelled data size）</news:title>
   <news:publication_date>2026-08-18T04:55:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724423</loc>
  <lastmod>2026-08-18T04:04:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ルチル型GeO2：両極性ドーピングが可能な超広帯域ギャップ半導体（Rutile GeO2: an ultrawide-band-gap semiconductor with ambipolar doping）</news:title>
   <news:publication_date>2026-08-18T04:04:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724421</loc>
  <lastmod>2026-08-18T04:03:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動的ストリーム重み付けによる視聴覚話者追跡（Audiovisual Speaker Tracking using Nonlinear Dynamical Systems with Dynamic Stream Weights）</news:title>
   <news:publication_date>2026-08-18T04:03:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724419</loc>
  <lastmod>2026-08-18T04:02:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>医療IoTの通信を優先するための機械学習と資源割当の統合設計（Using Machine Learning and Big Data Analytics to Prioritize Outpatients in HetNets）</news:title>
   <news:publication_date>2026-08-18T04:02:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724417</loc>
  <lastmod>2026-08-18T04:02:17Z</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Gaussian Process状態空間モデル：部分観測ダイナミクスの学習と計画（Online Gaussian Process State-Space Model: Learning and Planning for Partially Observable Dynamical Systems）</news:title>
   <news:publication_date>2026-08-18T04:02:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724415</loc>
  <lastmod>2026-08-18T04:02:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ゴーストイメージングから学ぶイメージ不要の学習（On Learning from Ghost Imaging without Imaging）</news:title>
   <news:publication_date>2026-08-18T04:02:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724413</loc>
  <lastmod>2026-08-18T04:02:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ストリーミングデータからの質問応答に学習する記憶管理（Episodic Memory Reader: Learning What to Remember for Question Answering from Streaming Data）</news:title>
   <news:publication_date>2026-08-18T04:02:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724411</loc>
  <lastmod>2026-08-18T04:01:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>条件付き分布を直接推定する深層回帰（Deep Distribution Regression）</news:title>
   <news:publication_date>2026-08-18T04:01:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724409</loc>
  <lastmod>2026-08-18T03:10:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>事前学習モデルの適応戦略――調整するか否か（To Tune or Not to Tune? Adapting Pretrained Representations to Diverse Tasks）</news:title>
   <news:publication_date>2026-08-18T03:10:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724407</loc>
  <lastmod>2026-08-18T03:09:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>解釈性と圧縮性を両立する補正決定木（Rectified Decision Trees: Towards Interpretability, Compression and Empirical Soundness）</news:title>
   <news:publication_date>2026-08-18T03:09:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724405</loc>
  <lastmod>2026-08-18T03:08:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>再帰性が示す人間視覚の計算原理（Recurrence is required to capture the representational dynamics of the human visual system）</news:title>
   <news:publication_date>2026-08-18T03:08:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724403</loc>
  <lastmod>2026-08-18T03:08:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>低ランクカーネル学習によるグラフベースクラスタリングの革新（Low-rank Kernel Learning for Graph-based Clustering）</news:title>
   <news:publication_date>2026-08-18T03:08:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724401</loc>
  <lastmod>2026-08-18T03:08:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GANを用いた声帯波形生成による統計的パラメトリック音声合成の改善（Generative adversarial network-based glottal waveform model for statistical parametric speech synthesis）</news:title>
   <news:publication_date>2026-08-18T03:08:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724399</loc>
  <lastmod>2026-08-18T03:07:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>連続的な観測から離散的計画領域を増分学習する仕組み（Incremental Learning of Discrete Planning Domains from Continuous Perceptions）</news:title>
   <news:publication_date>2026-08-18T03:07:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724397</loc>
  <lastmod>2026-08-18T02:16:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散位相アレイ型MIMOにおけるチャネル推定とハイブリッドプレコーディング（Channel Estimation and Hybrid Precoding for Distributed Phased Arrays Based MIMO Wireless Communications）</news:title>
   <news:publication_date>2026-08-18T02:16:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724395</loc>
  <lastmod>2026-08-18T02:15:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動的ボルツマン・ソフトマックスによる強化学習の価値推定改善（Reinforcement Learning with Dynamic Boltzmann Softmax Updates）</news:title>
   <news:publication_date>2026-08-18T02:15:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724393</loc>
  <lastmod>2026-08-18T02:15:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>攻撃的言語分類の比較研究（Absit invidia verbo: Comparing Deep Learning methods for offensive language）</news:title>
   <news:publication_date>2026-08-18T02:15:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724391</loc>
  <lastmod>2026-08-18T02:13:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単一RGBカメラから服を着た人の3D復元を学習する（Learning to Reconstruct People in Clothing from a Single RGB Camera）</news:title>
   <news:publication_date>2026-08-18T02:13:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724389</loc>
  <lastmod>2026-08-18T02:13:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>顔画像のスケーラブル圧縮と深層特徴再構築（SCALABLE FACIAL IMAGE COMPRESSION WITH DEEP FEATURE RECONSTRUCTION）</news:title>
   <news:publication_date>2026-08-18T02:13:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724387</loc>
  <lastmod>2026-08-18T02:13:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Butterflyによる線形変換の高速アルゴリズム学習（Learning Fast Algorithms for Linear Transforms Using Butterfly Factorizations）</news:title>
   <news:publication_date>2026-08-18T02:13:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/724385</loc>
  <lastmod>2026-08-18T02:13:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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    <news:name>AI Benchmark Research</news:name>
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    <news:name>AI Benchmark Research</news:name>
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    <news:name>AI Benchmark Research</news:name>
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 </url>
 <url>
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    <news:name>AI Benchmark Research</news:name>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 <url>
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    <news:name>AI Benchmark Research</news:name>
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    <news:name>AI Benchmark Research</news:name>
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 </url>
 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-17T19:59:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-08-17T19:57:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>会話エージェント構築のためのNLUサービスベンチマーク（Benchmarking Natural Language Understanding Services for building Conversational Agents）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 </url>
 <url>
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   <news: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:title>最大k-プレックス問題に対する強化学習ベースの局所探索（Effective reinforcement learning based local search for the maximum k-plex problem）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
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    <news:name>AI Benchmark Research</news:name>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 </url>
 <url>
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    <news:name>AI Benchmark Research</news:name>
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    <news:name>AI Benchmark Research</news:name>
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    <news:name>AI Benchmark Research</news:name>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
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    <news:name>AI Benchmark Research</news:name>
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    <news:name>AI Benchmark Research</news:name>
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    <news:name>AI Benchmark Research</news:name>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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    <news:name>AI Benchmark Research</news:name>
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   <news:genres>Blog</news:genres>
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