<?xml version="1.0" encoding="UTF-8"?>
<!--generator='jetpack-16.1-beta.3'-->
<!--Jetpack_Sitemap_Buffer_News_XMLWriter-->
<?xml-stylesheet type="text/xsl" href="//aibr.jp/news-sitemap.xsl"?>
<urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9" xmlns:news="http://www.google.com/schemas/sitemap-news/0.9" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.sitemaps.org/schemas/sitemap/0.9 http://www.sitemaps.org/schemas/sitemap/0.9/sitemap.xsd">
 <url>
  <loc>https://aibr.jp/archives/721237</loc>
  <lastmod>2026-08-09T10:50:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>心血管MRIの動きアーティファクト補正に敵対的生成ネットワークを使う（CMR Motion Artifact Correction using Generative Adversarial Nets）</news:title>
   <news:publication_date>2026-08-09T10:50:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721235</loc>
  <lastmod>2026-08-09T10:50:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>不整形検出器ジオメトリの表現学習（Learning representations of irregular particle-detector geometry with distance-weighted graph networks）</news:title>
   <news:publication_date>2026-08-09T10:50:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721233</loc>
  <lastmod>2026-08-09T10:49:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自動化された肝臓および腫瘍セグメンテーションの共同深層学習アプローチ（A Joint Deep Learning Approach for Automated Liver and Tumor Segmentation）</news:title>
   <news:publication_date>2026-08-09T10:49:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721231</loc>
  <lastmod>2026-08-09T10:49:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>補助変数を選ぶ情報量基準と欠測データ解析（An information criterion for auxiliary variable selection in incomplete data analysis）</news:title>
   <news:publication_date>2026-08-09T10:49:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721229</loc>
  <lastmod>2026-08-09T10:48:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所変位場を用いたスパース弾性率再構築とクラスタリング（Sparse Elasticity Reconstruction and Clustering using Local Displacement Fields）</news:title>
   <news:publication_date>2026-08-09T10:48:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721227</loc>
  <lastmod>2026-08-09T10:48:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次元データの深層学習型射影（Deep Learning Multidimensional Projections）</news:title>
   <news:publication_date>2026-08-09T10:48:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721225</loc>
  <lastmod>2026-08-09T10:48:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>注目マップを用いた深い識別表現学習（Deep Discriminative Representation Learning with Attention Map for Scene Classification）</news:title>
   <news:publication_date>2026-08-09T10:48:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721223</loc>
  <lastmod>2026-08-09T09:57:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単一粒子追跡データにおける拡散モード分類（Classification of diffusion modes in single-particle tracking data: Feature-based versus deep-learning approach）</news:title>
   <news:publication_date>2026-08-09T09:57:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721221</loc>
  <lastmod>2026-08-09T09:57:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Public Sphere 2.0: オンライン新聞におけるターゲット化コメント（Public Sphere 2.0: Targeted Commenting in Online News Media）</news:title>
   <news:publication_date>2026-08-09T09:57:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721219</loc>
  <lastmod>2026-08-09T09:55:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>事前学習言語モデルを用いたインドネシア語会話文の固有表現認識（Pretrained language model transfer on neural named entity recognition in Indonesian conversational texts）</news:title>
   <news:publication_date>2026-08-09T09:55:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721217</loc>
  <lastmod>2026-08-09T09:55:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>リング版Learning With Errorsの概観と経営視点での示唆（RING LEARNING WITH ERRORS: A CROSSROADS BETWEEN POSTQUANTUM CRYPTOGRAPHY, MACHINE LEARNING AND NUMBER THEORY）</news:title>
   <news:publication_date>2026-08-09T09:55:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721215</loc>
  <lastmod>2026-08-09T09:55:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>射影Sinkhorn反復によるWasserstein敵対的事例（Wasserstein Adversarial Examples via Projected Sinkhorn Iterations）</news:title>
   <news:publication_date>2026-08-09T09:55:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721213</loc>
  <lastmod>2026-08-09T09:55:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>不確実な入力下のベイズ最適化（Bayesian optimisation under uncertain inputs）</news:title>
   <news:publication_date>2026-08-09T09:55:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721211</loc>
  <lastmod>2026-08-09T09:54:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>無線ネットワークにおける決定的方策とターゲットを用いた電力制御学習（Learning Deterministic Policy with Target for Power Control in Wireless Networks）</news:title>
   <news:publication_date>2026-08-09T09:54:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721209</loc>
  <lastmod>2026-08-09T09:03:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>継続的ストリーミングデータの外れ値採掘とFlinkへの実装（CONTINUOUS OUTLIER MINING OF STREAMING DATA IN FLINK）</news:title>
   <news:publication_date>2026-08-09T09:03:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721207</loc>
  <lastmod>2026-08-09T09:02:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層ReLUニューラルネットワークによるSobolevノルム近似誤差境界（Error bounds for approximations with deep ReLU neural networks in W^{s,p} norms）</news:title>
   <news:publication_date>2026-08-09T09:02:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721205</loc>
  <lastmod>2026-08-09T09:02:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ブラウザ履歴に基づくリンク予測とカテゴリ別推薦（Web Links Prediction And Category-Wise Recommendation Based On Browser History）</news:title>
   <news:publication_date>2026-08-09T09:02:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721203</loc>
  <lastmod>2026-08-09T09:02:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層適応入力正規化（Deep Adaptive Input Normalization for Time Series Forecasting）</news:title>
   <news:publication_date>2026-08-09T09:02:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721201</loc>
  <lastmod>2026-08-09T09:01:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多モダリティ全心臓セグメンテーションの評価チャレンジ（Evaluation of Algorithms for Multi-Modality Whole Heart Segmentation: An Open-Access Grand Challenge）</news:title>
   <news:publication_date>2026-08-09T09:01:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721199</loc>
  <lastmod>2026-08-09T09:01:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>縦方向偏極核におけるSIDISの二ハドロン生成における単一スピン非対称性（Single-spin asymmetry in dihadron production in SIDIS off the longitudinally polarized nucleon target）</news:title>
   <news:publication_date>2026-08-09T09:01:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721197</loc>
  <lastmod>2026-08-09T09:01:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>会議音声の「誰が・何人・いつ」を同時に処理する全ニューラル手法（ALL-NEURAL ONLINE SOURCE SEPARATION, COUNTING, AND DIARIZATION FOR MEETING ANALYSIS）</news:title>
   <news:publication_date>2026-08-09T09:01:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721195</loc>
  <lastmod>2026-08-09T08:10:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時間周波数の視点でセンシング信号を学習する短時間フーリエニューラルネットワーク（STFNets: Learning Sensing Signals from the Time-Frequency Perspective with Short-Time Fourier Neural Networks）</news:title>
   <news:publication_date>2026-08-09T08:10:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721193</loc>
  <lastmod>2026-08-09T08:01:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>左心房瘢痕の自動定量化と多スケールCNNを用いたGraph-cutsフレームワークによる進展（Atrial Scar Quantification via Multi-scale CNN in the Graph-cuts Framework）</news:title>
   <news:publication_date>2026-08-09T08:01:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721191</loc>
  <lastmod>2026-08-09T08:01:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>会話文における感情検出モデルの実践──RCNNと事前学習表現の組合せ（ntuer at SemEval-2019 Task 3: Emotion Classification with Word and Sentence Representations in RCNN）</news:title>
   <news:publication_date>2026-08-09T08:01:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721189</loc>
  <lastmod>2026-08-09T08:01:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的ニューラル記号モデルによる可解性の高い視覚質問応答（Probabilistic Neural-symbolic Models for Interpretable Visual Question Answering）</news:title>
   <news:publication_date>2026-08-09T08:01:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721187</loc>
  <lastmod>2026-08-09T08:00:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>暗号資産投資におけるニューラルネットワーク積み上げ法（Stacking with Neural network for Cryptocurrency investment）</news:title>
   <news:publication_date>2026-08-09T08:00:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721185</loc>
  <lastmod>2026-08-09T08:00:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>連続学習に基づく堅牢な大規模推薦システム（Sequential Learning over Implicit Feedback for Robust Large-Scale Recommender Systems）</news:title>
   <news:publication_date>2026-08-09T08:00:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721183</loc>
  <lastmod>2026-08-09T08:00:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>安定性を考慮したベイズ最適化の手法（Stable Bayesian Optimisation via Direct Stability Quantification）</news:title>
   <news:publication_date>2026-08-09T08:00:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721181</loc>
  <lastmod>2026-08-09T07:09:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>段階的特徴集約による人体姿勢推定の改良（Cascade Feature Aggregation for Human Pose Estimation）</news:title>
   <news:publication_date>2026-08-09T07:09:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721179</loc>
  <lastmod>2026-08-09T07:09:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層マルチモーダル物体検出とセマンティックセグメンテーション（Deep Multi-modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges）</news:title>
   <news:publication_date>2026-08-09T07:09:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721177</loc>
  <lastmod>2026-08-09T07:08:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ConceptNetの経路品質予測（Predicting ConceptNet Path Quality Using Crowdsourced Assessments of Naturalness）</news:title>
   <news:publication_date>2026-08-09T07:08:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721175</loc>
  <lastmod>2026-08-09T07:07:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>二重空間─Winograd領域での同時スパース化による畳み込みニューラルネットワーク（JOINTLY SPARSE CONVOLUTIONAL NEURAL NETWORKS IN DUAL SPATIAL-WINOGRAD DOMAINS）</news:title>
   <news:publication_date>2026-08-09T07:07:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721173</loc>
  <lastmod>2026-08-09T07:07:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確からしさ等価が線形二次制御に効く理由（Certainty Equivalence is Efficient for Linear Quadratic Control）</news:title>
   <news:publication_date>2026-08-09T07:07:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721171</loc>
  <lastmod>2026-08-09T07:07:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>安定で公平な分類の設計（Stable and Fair Classification）</news:title>
   <news:publication_date>2026-08-09T07:07:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721169</loc>
  <lastmod>2026-08-09T07:07:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>対応分析をニューラルネットで拡張する（Correspondence Analysis Using Neural Networks）</news:title>
   <news:publication_date>2026-08-09T07:07:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721167</loc>
  <lastmod>2026-08-09T06:15:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多層プーリングによる深層スピーカー埋め込み学習（DEEP SPEAKER EMBEDDING LEARNING WITH MULTI-LEVEL POOLING FOR TEXT-INDEPENDENT SPEAKER VERIFICATION）</news:title>
   <news:publication_date>2026-08-09T06:15:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721165</loc>
  <lastmod>2026-08-09T06:14:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多様な機械翻訳を実現する混合モデルの工夫（Mixture Models for Diverse Machine Translation: Tricks of the Trade）</news:title>
   <news:publication_date>2026-08-09T06:14:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721163</loc>
  <lastmod>2026-08-09T06:14:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>音声文の音響と言語を同時にとらえる埋め込み（AUDIO-LINGUISTIC EMBEDDINGS FOR SPOKEN SENTENCES）</news:title>
   <news:publication_date>2026-08-09T06:14:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721161</loc>
  <lastmod>2026-08-09T06:13:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>半教師あり関係抽出のための二重検索モジュール学習（Learning Dual Retrieval Module for Semi-supervised Relation Extraction）</news:title>
   <news:publication_date>2026-08-09T06:13:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721159</loc>
  <lastmod>2026-08-09T06:13:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機会主義的文脈的バンディット学習の実践と評価（AdaLinUCB: Opportunistic Learning for Contextual Bandits）</news:title>
   <news:publication_date>2026-08-09T06:13:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721157</loc>
  <lastmod>2026-08-09T06:13:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>触覚を用いた力のシミュレーション（Simulating Forces: Learning Through Touch, Virtual Laboratories）</news:title>
   <news:publication_date>2026-08-09T06:13:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721155</loc>
  <lastmod>2026-08-09T06:12:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散型最適潮流と電圧制御のための回帰ベースのインバータ制御（Regression-based Inverter Control for Decentralized Optimal Power Flow and Voltage Regulation）</news:title>
   <news:publication_date>2026-08-09T06:12:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721153</loc>
  <lastmod>2026-08-09T05:20:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階級体論、ディオファントス解析と漸近的フェルマーの最終定理（Class Field Theory, Diophantine Analysis and The Asymptotic Fermat’s Last Theorem）</news:title>
   <news:publication_date>2026-08-09T05:20:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721151</loc>
  <lastmod>2026-08-09T05:20:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>整数値関数データ解析による麻疹予測（Integer-Valued Functional Data Analysis for Measles Forecasting）</news:title>
   <news:publication_date>2026-08-09T05:20:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721149</loc>
  <lastmod>2026-08-09T05:20:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>コーヒーショップで学ぶバイオフィジクスの教訓（Biophysics at the coffee shop: lessons learned working with George Oster）</news:title>
   <news:publication_date>2026-08-09T05:20:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721147</loc>
  <lastmod>2026-08-09T05:19:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>解析的グラフィックスタティクス（Analytical graphic statics）</news:title>
   <news:publication_date>2026-08-09T05:19:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721145</loc>
  <lastmod>2026-08-09T05:19:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スパース精度行列を持つ確率的局所相互作用モデルによる時空間補間（Stochastic Local Interaction Model with Sparse Precision Matrix for Space-Time Interpolation）</news:title>
   <news:publication_date>2026-08-09T05:19:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721143</loc>
  <lastmod>2026-08-09T05:18:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>知覚品質を保つブラックボックス攻撃（Perceptual quality-preserving black-box attack against deep learning image classifiers）</news:title>
   <news:publication_date>2026-08-09T05:18:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721141</loc>
  <lastmod>2026-08-09T05:18:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単眼内視鏡における密な深度推定（Dense Depth Estimation in Monocular Endoscopy with Self-supervised Learning Methods）</news:title>
   <news:publication_date>2026-08-09T05:18:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721139</loc>
  <lastmod>2026-08-09T04:25:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>N量子ビット系における実験的な対ペアもつれ推定（Experimental pairwise entanglement estimation for an N-qubit system）</news:title>
   <news:publication_date>2026-08-09T04:25:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721137</loc>
  <lastmod>2026-08-09T04:25:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Cryptϵによる暗号支援差分プライバシー（Cryptϵ: Crypto-Assisted Differential Privacy on Untrusted Servers）</news:title>
   <news:publication_date>2026-08-09T04:25:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721135</loc>
  <lastmod>2026-08-09T04:25:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マンモグラフィ画像分類のための敵対的データ拡張（Adversarial Augmentation for Enhancing Classification of Mammography Images）</news:title>
   <news:publication_date>2026-08-09T04:25:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721133</loc>
  <lastmod>2026-08-09T04:23:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>全文検索エンジン上でのハミング空間近傍探索の高速化（Fast and Exact Nearest Neighbor Search in Hamming Space on Full-Text Search Engines）</news:title>
   <news:publication_date>2026-08-09T04:23:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721131</loc>
  <lastmod>2026-08-09T04:23:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Fermi検出のBCU光学分類を機械学習で評価する（Evaluating the optical classification of Fermi BCUs using machine learning）</news:title>
   <news:publication_date>2026-08-09T04:23:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721129</loc>
  <lastmod>2026-08-09T04:23:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>デュアルエンド読み出しを用いた有機シンチレーターバーの位置・時間・エネルギー分解能（Interaction position, time, and energy resolution in organic scintillator bars with dual-ended readout）</news:title>
   <news:publication_date>2026-08-09T04:23:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721127</loc>
  <lastmod>2026-08-09T04:23:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>言語から目的を学ぶ：視覚ベースの指示遂行における逆強化学習（From Language to Goals: Inverse Reinforcement Learning for Vision-Based Instruction Following）</news:title>
   <news:publication_date>2026-08-09T04:23:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721125</loc>
  <lastmod>2026-08-09T03:31:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>雑音下の行列補完と凸緩和の統計的保証（Noisy Matrix Completion: Understanding Statistical Guarantees for Convex Relaxation via Nonconvex Optimization）</news:title>
   <news:publication_date>2026-08-09T03:31:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721123</loc>
  <lastmod>2026-08-09T03:31:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非平滑・非凸正則化問題に対する確率的手法の非漸近解析（Non-asymptotic Analysis of Stochastic Methods for Non-Smooth Non-Convex Regularized Problems）</news:title>
   <news:publication_date>2026-08-09T03:31:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721121</loc>
  <lastmod>2026-08-09T03:31:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>知識に基づくCT画像からの死亡率予測（Knowledge-based Analysis for Mortality Prediction from CT Images）</news:title>
   <news:publication_date>2026-08-09T03:31:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721119</loc>
  <lastmod>2026-08-09T03:30:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習率を自動で見つけるlossgrad（lossgrad: automatic learning rate in gradient descent）</news:title>
   <news:publication_date>2026-08-09T03:30:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721117</loc>
  <lastmod>2026-08-09T03:30:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>連続Q関数の学習と一般化ベンダーズカット（Learning continuous Q-functions using generalized Benders cuts）</news:title>
   <news:publication_date>2026-08-09T03:30:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721115</loc>
  <lastmod>2026-08-09T03:29:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>順序回帰における特徴関連性境界の提示（Feature Relevance Bounds for Ordinal Regression）</news:title>
   <news:publication_date>2026-08-09T03:29:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721113</loc>
  <lastmod>2026-08-09T03:29:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>年齢知覚バイアスが実年齢回帰に与える影響（On the effect of age perception biases for real age regression）</news:title>
   <news:publication_date>2026-08-09T03:29:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721111</loc>
  <lastmod>2026-08-09T02:38:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>貢献的社会資本の抽出（Contributive Social Capital Extraction）</news:title>
   <news:publication_date>2026-08-09T02:38:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721109</loc>
  <lastmod>2026-08-09T02:37:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>初期視覚系の輪郭統合を捉えるSparse Deep Predictive Coding（Sparse Deep Predictive Coding captures contour integration capabilities of the early visual system）</news:title>
   <news:publication_date>2026-08-09T02:37:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721107</loc>
  <lastmod>2026-08-09T02:36:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>点ターゲットのオンライン学習によるフィルタリング（Filtering Point Targets via Online Learning of Motion Models）</news:title>
   <news:publication_date>2026-08-09T02:36:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721105</loc>
  <lastmod>2026-08-09T02:36:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>調査における能動的行列因子分解（Active Matrix Factorization for Surveys）</news:title>
   <news:publication_date>2026-08-09T02:36:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721103</loc>
  <lastmod>2026-08-09T02:36:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>advertorch: PyTorchベースの敵対的堅牢性ツールボックス（advertorch v0.1: An Adversarial Robustness Toolbox based on PyTorch）</news:title>
   <news:publication_date>2026-08-09T02:36:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721101</loc>
  <lastmod>2026-08-09T02:36:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>A最適サブサンプリングによる適応反復ヘッシアン・スケッチ（Adaptive Iterative Hessian Sketch via A-Optimal Subsampling）</news:title>
   <news:publication_date>2026-08-09T02:36:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721099</loc>
  <lastmod>2026-08-09T01:44:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>信頼領域を超えて：ロバストMDPのためのタイトなベイズ的あいまい性集合（Beyond Confidence Regions: Tight Bayesian Ambiguity Sets for Robust MDPs）</news:title>
   <news:publication_date>2026-08-09T01:44:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721097</loc>
  <lastmod>2026-08-09T01:43:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ガイアDR2を用いたρオフィクス星域の会員候補調査（Census of ρ Oph candidate members from Gaia Data Release 2）</news:title>
   <news:publication_date>2026-08-09T01:43:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721095</loc>
  <lastmod>2026-08-09T01:43:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ヒルベルト空間はもっと小さくできるのか（Could the Hilbert Space Be a Smaller Place? A Neural Network Perspective）</news:title>
   <news:publication_date>2026-08-09T01:43:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721093</loc>
  <lastmod>2026-08-09T01:42:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>人間の発達段階を模倣するベイズニューラルネットワーク（Emulating Human Developmental Stages with Bayesian Neural Networks）</news:title>
   <news:publication_date>2026-08-09T01:42:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721091</loc>
  <lastmod>2026-08-09T01:42:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>光ネットワークにおけるジャミング攻撃の検出と防御を機械学習で強化する方法（On Detecting and Preventing Jamming Attacks with Machine Learning in Optical Networks）</news:title>
   <news:publication_date>2026-08-09T01:42:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721089</loc>
  <lastmod>2026-08-09T01:42:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>行列に対する能動確率的推論による確率的最適化の前処理（Active Probabilistic Inference on Matrices for Pre-Conditioning in Stochastic Optimization）</news:title>
   <news:publication_date>2026-08-09T01:42:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721087</loc>
  <lastmod>2026-08-09T01:41:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ヒューリスティクはどこから来るのか（Where Do Heuristics Come From?）</news:title>
   <news:publication_date>2026-08-09T01:41:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721085</loc>
  <lastmod>2026-08-09T00:50:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散データのデータ協調解析（Data collaboration analysis for distributed datasets）</news:title>
   <news:publication_date>2026-08-09T00:50:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721083</loc>
  <lastmod>2026-08-09T00:50:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スケール不変の適応オンライン学習法（Adaptive scale-invariant online algorithms for learning linear models）</news:title>
   <news:publication_date>2026-08-09T00:50:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721081</loc>
  <lastmod>2026-08-09T00:50:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>経験のレア度で学習を優先する好奇心駆動型優先付け（Curiosity-Driven Experience Prioritization via Density Estimation）</news:title>
   <news:publication_date>2026-08-09T00:50:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721079</loc>
  <lastmod>2026-08-09T00:49:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>効率的なハプティック形状探索の学習（LEARNING EFFICIENT HAPTIC SHAPE EXPLORATION WITH A RIGID TACTILE SENSOR ARRAY）</news:title>
   <news:publication_date>2026-08-09T00:49:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721077</loc>
  <lastmod>2026-08-09T00:49:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ノイズのあるマルチラベル半教師付き次元削減（Noisy multi-label semi-supervised dimensionality reduction）</news:title>
   <news:publication_date>2026-08-09T00:49:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721075</loc>
  <lastmod>2026-08-09T00:49:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>パッチベース出力空間敵対学習による視神経乳頭と杯の同時セグメンテーション（Patch-based Output Space Adversarial Learning for Joint Optic Disc and Cup Segmentation）</news:title>
   <news:publication_date>2026-08-09T00:49:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721073</loc>
  <lastmod>2026-08-09T00:48:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>C2UCBの後悔境界の検証（A Note on Bounding Regret of the C2UCB Contextual Combinatorial Bandit）</news:title>
   <news:publication_date>2026-08-09T00:48:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721071</loc>
  <lastmod>2026-08-08T23:55:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>協調型マルチエージェント強化学習における行動価値ネットワークの因子分解の解析（Analysing Factorizations of Action-Value Networks for Cooperative Multi-Agent Reinforcement Learning）</news:title>
   <news:publication_date>2026-08-08T23:55:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721069</loc>
  <lastmod>2026-08-08T23:55:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>解釈可能なニューラル注意型推薦システム（NAIRS: A Neural Attentive Interpretable Recommendation System）</news:title>
   <news:publication_date>2026-08-08T23:55:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721067</loc>
  <lastmod>2026-08-08T23:54:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Petaﬂops級スーパーコンピュータ「Zhores」の設計と初期評価（“Zhores” —Petaﬂops supercomputer for data-driven modeling, machine learning and artificial intelligence）</news:title>
   <news:publication_date>2026-08-08T23:54:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721065</loc>
  <lastmod>2026-08-08T23:54:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>空間適応型フィルタユニットによるコンパクトで効率的な深層ニューラルネットワーク（Spatially-Adaptive Filter Units for Compact and Efficient Deep Neural Networks）</news:title>
   <news:publication_date>2026-08-08T23:54:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721063</loc>
  <lastmod>2026-08-08T23:53:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>音声映像イベント局所化のための二重モダリティSeq2Seqネットワーク（DUAL-MODALITY SEQ2SEQ NETWORK FOR AUDIO-VISUAL EVENT LOCALIZATION）</news:title>
   <news:publication_date>2026-08-08T23:53:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721061</loc>
  <lastmod>2026-08-08T23:53:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Windows向けTLS実装の状態機械学習による脆弱性検出（Identification of Bugs and Vulnerabilities in TLS Implementation for Windows Operating System Using State Machine Learning）</news:title>
   <news:publication_date>2026-08-08T23:53:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721059</loc>
  <lastmod>2026-08-08T23:53:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>テクスチャ画像や属性からの触覚振動生成（Vibrotactile Signal Generation from Texture Images or Attributes using Generative Adversarial Network）</news:title>
   <news:publication_date>2026-08-08T23:53:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721057</loc>
  <lastmod>2026-08-08T23:02:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>FPGAベースCNNアクセラレータのためのエンドツーエンドコンパイラDNNVM（DNNVM: End-to-End Compiler Leveraging Heterogeneous Optimizations on FPGA-Based CNN Accelerators）</news:title>
   <news:publication_date>2026-08-08T23:02:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721055</loc>
  <lastmod>2026-08-08T23:01:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>線形解析による超新星コア内の高速対生成ニュートリノ振動の検討（Linear Analysis of Fast-Pairwise Collective Neutrino Oscillations in Core-Collapse Supernovae based on the Results of Boltzmann Simulations）</news:title>
   <news:publication_date>2026-08-08T23:01:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721053</loc>
  <lastmod>2026-08-08T23:01:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>クラウド負荷予測における簡便で実用的な時系列手法（Easily implementable time series forecasting techniques for resource provisioning in cloud computing）</news:title>
   <news:publication_date>2026-08-08T23:01:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721051</loc>
  <lastmod>2026-08-08T23:00:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>携帯電話データに基づく顧客の性別・年齢予測（Predicting customer&amp;#039;s gender and age depending on mobile phone data）</news:title>
   <news:publication_date>2026-08-08T23:00:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721049</loc>
  <lastmod>2026-08-08T23:00:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>不完全で誤った教師付き情報下での学習（Learning with Inadequate and Incorrect Supervision）</news:title>
   <news:publication_date>2026-08-08T23:00:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721047</loc>
  <lastmod>2026-08-08T23:00:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オンザフライ適応による非線形二重スケールシミュレーション（On-the-fly adaptivity for nonlinear twoscale simulations using artificial neural networks and reduced order modeling）</news:title>
   <news:publication_date>2026-08-08T23:00:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721045</loc>
  <lastmod>2026-08-08T23:00:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非凸ペナルティと非凸性制御による希薄信号の完全復元（Perfect reconstruction of sparse signals with piecewise continuous nonconvex penalties and nonconvexity control）</news:title>
   <news:publication_date>2026-08-08T23:00:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721043</loc>
  <lastmod>2026-08-08T22:08:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>残差記号有限オートマトンのクエリ学習アルゴリズム (Query Learning Algorithm for Residual Symbolic Finite Automata)</news:title>
   <news:publication_date>2026-08-08T22:08:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721041</loc>
  <lastmod>2026-08-08T22:08:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>メキシカンハットウェーブレットカーネルELMによる多クラス分類（Mexican Hat Wavelet Kernel ELM for Multiclass Classification）</news:title>
   <news:publication_date>2026-08-08T22:08:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721039</loc>
  <lastmod>2026-08-08T22:08:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ℓ0とTℓ1によるスパースニューラルネット学習（Learning Sparse Neural Networks via ℓ0 and Tℓ1）</news:title>
   <news:publication_date>2026-08-08T22:08:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721037</loc>
  <lastmod>2026-08-08T22:06:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフにおける敵対的訓練（Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure）</news:title>
   <news:publication_date>2026-08-08T22:06:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721035</loc>
  <lastmod>2026-08-08T22:06:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散TD(0)の有限時間解析（Finite-Time Analysis of Distributed TD(0) with Linear Function Approximation for Multi-Agent Reinforcement Learning）</news:title>
   <news:publication_date>2026-08-08T22:06:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721033</loc>
  <lastmod>2026-08-08T22:06:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>LipschitzLR: 理論に基づく適応学習率で学習を速くする（LipschitzLR: Using theoretically computed adaptive learning rates for fast convergence）</news:title>
   <news:publication_date>2026-08-08T22:06:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721031</loc>
  <lastmod>2026-08-08T22:06:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>因果フォレストによる処置効果推定（Estimating Treatment Effects with Causal Forests）</news:title>
   <news:publication_date>2026-08-08T22:06:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721029</loc>
  <lastmod>2026-08-08T21:15:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>保存-散逸形式とDoiの変分法の同値性の意義（Conservation-Dissipation Formalism for Soft Matter Physics: I. Equivalence with Doi’s Variational Approach）</news:title>
   <news:publication_date>2026-08-08T21:15:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721027</loc>
  <lastmod>2026-08-08T21:15:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オートエンコーダを用いた学習型画像圧縮の実装と評価（An Autoencoder-based Learned Image Compressor: Description of Challenge Proposal by NCTU）</news:title>
   <news:publication_date>2026-08-08T21:15:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721025</loc>
  <lastmod>2026-08-08T21:14:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>平均ケース最適還元によるスパースPCAの計算困難性の確立（Optimal Average-Case Reductions to Sparse PCA: From Weak Assumptions to Strong Hardness）</news:title>
   <news:publication_date>2026-08-08T21:14:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721023</loc>
  <lastmod>2026-08-08T21:13:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>発話単位のエンドツーエンド言語識別（UTTERANCE-LEVEL END-TO-END LANGUAGE IDENTIFICATION USING ATTENTION-BASED CNN-BLSTM）</news:title>
   <news:publication_date>2026-08-08T21:13:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721021</loc>
  <lastmod>2026-08-08T21:13:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>サンプル重み付けを自動で学ぶMeta-Weight-Net（Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting）</news:title>
   <news:publication_date>2026-08-08T21:13:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721019</loc>
  <lastmod>2026-08-08T21:13:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Gaussian Processを用いた動的ペア比較モデル（Gaussian Process Priors for Dynamic Paired Comparison Modelling）</news:title>
   <news:publication_date>2026-08-08T21:13:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721017</loc>
  <lastmod>2026-08-08T21:13:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>サブモジュラ負荷クラスタリングとロバスト主成分分析（Submodular Load Clustering with Robust Principal Component Analysis）</news:title>
   <news:publication_date>2026-08-08T21:13:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721015</loc>
  <lastmod>2026-08-08T20:21:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>成果重視と志向重視を組み合わせた割当機構（A Constrained Priority Mechanism Combining Outcome-Based and Preference-Based Matching）</news:title>
   <news:publication_date>2026-08-08T20:21:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721013</loc>
  <lastmod>2026-08-08T20:13:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所構造表現と時間的依存性の学習による人体運動予測（Human Motion Prediction via Learning Local Structure Representations and Temporal Dependencies）</news:title>
   <news:publication_date>2026-08-08T20:13:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721011</loc>
  <lastmod>2026-08-08T20:13:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>未トリミング動画における行動認識のための転移可能な自己注意表現学習（Learning Transferable Self-attentive Representations for Action Recognition in Untrimmed Videos with Weak Supervision）</news:title>
   <news:publication_date>2026-08-08T20:13:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721009</loc>
  <lastmod>2026-08-08T20:13:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>限られたセンサからの流体場復元を簡潔に実現する浅層ニューラルネットワーク（Shallow Neural Networks for Fluid Flow Reconstruction with Limited Sensors）</news:title>
   <news:publication_date>2026-08-08T20:13:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721007</loc>
  <lastmod>2026-08-08T20:12:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ハイパーボリック群における方程式解集合の形式言語化（Solutions sets to systems of equations in hyperbolic groups are EDT0L in PSPACE）</news:title>
   <news:publication_date>2026-08-08T20:12:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721005</loc>
  <lastmod>2026-08-08T20:12:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>XONNによる秘匿推論の効率化（XONN: XNOR-based Oblivious Deep Neural Network Inference）</news:title>
   <news:publication_date>2026-08-08T20:12:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721003</loc>
  <lastmod>2026-08-08T20:11:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>少数ショットで物体を分割する適応マスク型プロキシ（Adaptive Masked Proxies for Few-Shot Segmentation）</news:title>
   <news:publication_date>2026-08-08T20:11:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/721001</loc>
  <lastmod>2026-08-08T19:20:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動画顔認識における成分別特徴集約ネットワーク（Video Face Recognition: Component-wise Feature Aggregation Network (C-FAN))</news:title>
   <news:publication_date>2026-08-08T19:20:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720999</loc>
  <lastmod>2026-08-08T19:19:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模マンモグラフィCADにおける変形畳み込みネットワークの活用（Large-scale mammography CAD with Deformable Conv-Nets）</news:title>
   <news:publication_date>2026-08-08T19:19:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720997</loc>
  <lastmod>2026-08-08T19:19:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>個別化オンライン適応による運動学的シナジーのパーソナライズ（Personalized On-line Adaptation of Kinematic Synergies for Human-Prosthesis Interfaces）</news:title>
   <news:publication_date>2026-08-08T19:19:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720995</loc>
  <lastmod>2026-08-08T19:18:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>木星のアンモニア分布とVLAマップによる解析（Jupiter’s Ammonia Distribution Derived from VLA Maps at 3–37 GHz）</news:title>
   <news:publication_date>2026-08-08T19:18:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720993</loc>
  <lastmod>2026-08-08T19:18:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>扁桃体の高精度自動分割と不確実性推定を可能にするベイズ型FCNN（Accurate Automatic Segmentation of Amygdala Subnuclei and Modeling of Uncertainty via Bayesian Fully Convolutional Neural Network）</news:title>
   <news:publication_date>2026-08-08T19:18:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720991</loc>
  <lastmod>2026-08-08T19:18:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>少ないデータで歌声をコピーする技術（DATA EFFICIENT VOICE CLONING FOR NEURAL SINGING SYNTHESIS）</news:title>
   <news:publication_date>2026-08-08T19:18:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720989</loc>
  <lastmod>2026-08-08T19:17:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DeepBall: ボール検出のための深層ニューラルネットワーク（DeepBall: Deep Neural-Network Ball Detector）</news:title>
   <news:publication_date>2026-08-08T19:17:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720987</loc>
  <lastmod>2026-08-08T18:26:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>連続変動下のオンライン学習：動的敗北（ダイナミックリグレット）と削減（Online Learning with Continuous Variations: Dynamic Regret and Reductions）</news:title>
   <news:publication_date>2026-08-08T18:26:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720985</loc>
  <lastmod>2026-08-08T18:26:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>一部特徴の敵対的破壊に強いサブスペース法（Subspace Methods That Are Resistant to a Limited Number of Features Corrupted by an Adversary）</news:title>
   <news:publication_date>2026-08-08T18:26:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720983</loc>
  <lastmod>2026-08-08T18:25:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>EPICによる敗血症予測モデルの実装と有効性の検証（Accuracy of the Epic Sepsis Prediction Model in a Regional Health System）</news:title>
   <news:publication_date>2026-08-08T18:25:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720981</loc>
  <lastmod>2026-08-08T18:25:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>シャドウプライスによる高速ニューラルネットワーク検証（Fast Neural Network Verification via Shadow Prices）</news:title>
   <news:publication_date>2026-08-08T18:25:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720979</loc>
  <lastmod>2026-08-08T18:24:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>RNNにおける記憶の理解と制御（Understanding and Controlling Memory in Recurrent Neural Networks）</news:title>
   <news:publication_date>2026-08-08T18:24:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720977</loc>
  <lastmod>2026-08-08T18:24:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DOM-Q-NET：構造化言語でのグラウンド強化学習 (DOM-Q-NET: Grounded RL on Structured Language)</news:title>
   <news:publication_date>2026-08-08T18:24:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720975</loc>
  <lastmod>2026-08-08T18:24:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>パラメータ最適化のための遺伝的アルゴリズムを用いた深層強化学習（Deep Reinforcement Learning using Genetic Algorithm for Parameter Optimization）</news:title>
   <news:publication_date>2026-08-08T18:24:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720973</loc>
  <lastmod>2026-08-08T17:32:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ソーシャル推薦のためのグラフニューラルネットワーク（Graph Neural Networks for Social Recommendation）</news:title>
   <news:publication_date>2026-08-08T17:32:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720971</loc>
  <lastmod>2026-08-08T17:31:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>最適な線形正則化の学習（Learning Optimal Linear Regularizers）</news:title>
   <news:publication_date>2026-08-08T17:31:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720969</loc>
  <lastmod>2026-08-08T17:31:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>最適輸送によるスケーラブルなトンプソンサンプリング（Scalable Thompson Sampling via Optimal Transport）</news:title>
   <news:publication_date>2026-08-08T17:31:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720967</loc>
  <lastmod>2026-08-08T17:31:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>銀河合体率の測定：z≈2クラスターにおけるHST観測解析（Galaxy Merger Fractions in Two Clusters at z ∼2 Using the Hubble Space Telescope）</news:title>
   <news:publication_date>2026-08-08T17:31:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720965</loc>
  <lastmod>2026-08-08T17:31:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習を用いた指向性タンパク質進化（Machine Learning-Assisted Directed Protein Evolution with Combinatorial Libraries）</news:title>
   <news:publication_date>2026-08-08T17:31:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720963</loc>
  <lastmod>2026-08-08T17:30:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スペクトル特徴選択による高精度パラメータ推定（Feature Selection for Better Spectral Characterization or: How I Learned to Start Worrying and Love Ensembles）</news:title>
   <news:publication_date>2026-08-08T17:30:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720961</loc>
  <lastmod>2026-08-08T17:30:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>銀河中心に存在する高齢星の“カスプ”の分光学的検出（Spectroscopic Detection of a Cusp of Late-type Stars around the Central Black Hole in the Milky Way）</news:title>
   <news:publication_date>2026-08-08T17:30:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720959</loc>
  <lastmod>2026-08-08T16:39:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>相互作用するAubry‑Andreモデルにおけるバタフライ効果（Butterfly effect in interacting Aubry‑Andre model: thermalization, slow scrambling, and many‑body localization）</news:title>
   <news:publication_date>2026-08-08T16:39:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720957</loc>
  <lastmod>2026-08-08T16:39:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>制約付き凸ポテンシャルによる2-ワッサースタイン近似（2-Wasserstein Approximation via Restricted Convex Potentials with Application to Improved Training for GANs）</news:title>
   <news:publication_date>2026-08-08T16:39:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720955</loc>
  <lastmod>2026-08-08T16:38:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>希薄かつ不十分な報酬からの一般化学習（Learning to Generalize from Sparse and Underspecified Rewards）</news:title>
   <news:publication_date>2026-08-08T16:38:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720953</loc>
  <lastmod>2026-08-08T16:37:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>PDS 70の遷移円盤は単一惑星で形成されうる（PDS 70: A TRANSITION DISK SCULPTED BY A SINGLE PLANET）</news:title>
   <news:publication_date>2026-08-08T16:37:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720951</loc>
  <lastmod>2026-08-08T16:37:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>表現学習における合成性の測定（MEASURING COMPOSITIONALITY IN REPRESENTATION LEARNING）</news:title>
   <news:publication_date>2026-08-08T16:37:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720949</loc>
  <lastmod>2026-08-08T16:37:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>生成的RNNによる非線形ダイナミクス同定とfMRI応用（Identifying nonlinear dynamical systems via generative recurrent neural networks with applications to fMRI）</news:title>
   <news:publication_date>2026-08-08T16:37:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720947</loc>
  <lastmod>2026-08-08T16:37:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>持続化図に対する写像の近似（Approximating Maps on Persistence Diagrams）</news:title>
   <news:publication_date>2026-08-08T16:37:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720945</loc>
  <lastmod>2026-08-08T15:46:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習によるジェット観測量の自動構築（Automating the Construction of Jet Observables with Machine Learning）</news:title>
   <news:publication_date>2026-08-08T15:46:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720943</loc>
  <lastmod>2026-08-08T15:45:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフ畳み込みネットワークの簡素化（Simplifying Graph Convolutional Networks）</news:title>
   <news:publication_date>2026-08-08T15:45:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720941</loc>
  <lastmod>2026-08-08T15:45:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>音声認識におけるスペリング補正モデルの提案（A SPELLING CORRECTION MODEL FOR END-TO-END SPEECH RECOGNITION）</news:title>
   <news:publication_date>2026-08-08T15:45:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720939</loc>
  <lastmod>2026-08-08T15:44:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>和集合ノルムクラスタリングによるガウス混合モデルの回復（Recovery of a mixture of Gaussians by sum-of-norms clustering）</news:title>
   <news:publication_date>2026-08-08T15:44:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720937</loc>
  <lastmod>2026-08-08T15:43:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>競争を通じて協調が生まれる仕組み（EMERGENT COORDINATION THROUGH COMPETITION）</news:title>
   <news:publication_date>2026-08-08T15:43:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720935</loc>
  <lastmod>2026-08-08T15:43:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>異種エージェントによるベイズ的探索と推奨政策（Bayesian Exploration with Heterogeneous Agents）</news:title>
   <news:publication_date>2026-08-08T15:43:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720933</loc>
  <lastmod>2026-08-08T15:43:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>医療データに強いエントロピック特徴選択法の実務的意義（An entropic feature selection method in perspective of Turing’s formula）</news:title>
   <news:publication_date>2026-08-08T15:43:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720931</loc>
  <lastmod>2026-08-08T14:51:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>適応勾配法による過剰パラメータ化ニューラルネットワークの全体収束（Global Convergence of Adaptive Gradient Methods for An Over-parameterized Neural Network）</news:title>
   <news:publication_date>2026-08-08T14:51:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720929</loc>
  <lastmod>2026-08-08T14:51:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>見出し生成における反復抑制と敵対報酬の新手法（A NOVEL REPETITION NORMALIZED ADVERSARIAL REWARD FOR HEADLINE GENERATION）</news:title>
   <news:publication_date>2026-08-08T14:51:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720927</loc>
  <lastmod>2026-08-08T14:50:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>適応的クロスモーダル少数ショット学習（Adaptive Cross-Modal Few-shot Learning）</news:title>
   <news:publication_date>2026-08-08T14:50:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720925</loc>
  <lastmod>2026-08-08T14:49:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>低遅延ディープクラスタリングによる話者分離の実用化可能性検討（LOW-LATENCY DEEP CLUSTERING FOR SPEECH SEPARATION）</news:title>
   <news:publication_date>2026-08-08T14:49:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720923</loc>
  <lastmod>2026-08-08T14:49:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>医療データを用いたコストセンシティブ診断と学習（Cost-Sensitive Diagnosis and Learning Leveraging Public Health Data）</news:title>
   <news:publication_date>2026-08-08T14:49:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720921</loc>
  <lastmod>2026-08-08T14:49:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大気プラズマの電磁波伝播に関する重要パラメータとEMC応用（Air plasma key parameters for electromagnetic wave propagation at and out of thermal equilibrium: applications to electromagnetic compatibility）</news:title>
   <news:publication_date>2026-08-08T14:49:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720919</loc>
  <lastmod>2026-08-08T14:48:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>知識グラフ埋め込みの変分量子回路モデル（Variational Quantum Circuit Model for Knowledge Graphs Embedding）</news:title>
   <news:publication_date>2026-08-08T14:48:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720917</loc>
  <lastmod>2026-08-08T13:56:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マシンラーニングによる非マルコフ量子動力学（Machine learning non-Markovian quantum dynamics）</news:title>
   <news:publication_date>2026-08-08T13:56:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720915</loc>
  <lastmod>2026-08-08T13:46:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>連続値深層強化学習における汎化の検証（Investigating Generalisation in Continuous Deep Reinforcement Learning）</news:title>
   <news:publication_date>2026-08-08T13:46:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720913</loc>
  <lastmod>2026-08-08T13:46:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Detector-in-Detector: マルチレベル人体パーツ検出の要点（Detector-in-Detector: Multi-Level Analysis for Human-Parts）</news:title>
   <news:publication_date>2026-08-08T13:46:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720911</loc>
  <lastmod>2026-08-08T13:45:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的条件付き勾配++（Stochastic Conditional Gradient++）</news:title>
   <news:publication_date>2026-08-08T13:45:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720909</loc>
  <lastmod>2026-08-08T13:45:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分類におけるモデル較正の評価（Evaluating model calibration in classification）</news:title>
   <news:publication_date>2026-08-08T13:45:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720907</loc>
  <lastmod>2026-08-08T13:45:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>金星の弓状波のメソスケールモデリング（Mesoscale modeling of Venus’ bow-shape waves）</news:title>
   <news:publication_date>2026-08-08T13:45:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720905</loc>
  <lastmod>2026-08-08T13:44:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>二腕協調組立におけるマスター・スレーブのコンプライアンス改善（Improving dual-arm assembly by master-slave compliance）</news:title>
   <news:publication_date>2026-08-08T13:44:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720903</loc>
  <lastmod>2026-08-08T12:53:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層生成モデルの幾何と特徴分離（Geometry of Deep Generative Models for Disentangled Representations）</news:title>
   <news:publication_date>2026-08-08T12:53:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720901</loc>
  <lastmod>2026-08-08T12:53:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DEDPULによるPositive-Unlabeled学習の革新（DEDPUL: Difference-of-Estimated-Densities-based Positive-Unlabeled Learning）</news:title>
   <news:publication_date>2026-08-08T12:53:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720899</loc>
  <lastmod>2026-08-08T12:53:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自動顔匿名化（AIM）の有効性評価（Evaluating the Effectiveness of Automated Identity Masking (AIM) Methods with Human Perception and a Deep Convolutional Neural Network (CNN))</news:title>
   <news:publication_date>2026-08-08T12:53:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720897</loc>
  <lastmod>2026-08-08T12:52:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GPUベース深層学習システムの効率的メモリ管理（Efficient Memory Management for GPU-based Deep Learning Systems）</news:title>
   <news:publication_date>2026-08-08T12:52:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720895</loc>
  <lastmod>2026-08-08T12:52:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>効率的な線形収束を示す正則化近接点法による融合型複数グラフィカルラッソ問題の解法（An Efficient Linearly Convergent Regularized Proximal Point Algorithm for Fused Multiple Graphical Lasso Problems）</news:title>
   <news:publication_date>2026-08-08T12:52:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720893</loc>
  <lastmod>2026-08-08T12:51:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>木構造インデックスと深層モデルの共同最適化（Joint Optimization of Tree-based Index and Deep Model for Recommender Systems）</news:title>
   <news:publication_date>2026-08-08T12:51:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720891</loc>
  <lastmod>2026-08-08T12:51:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>二項ガウス混合の切断下におけるEM解析（On the Analysis of EM for truncated mixtures of two Gaussians）</news:title>
   <news:publication_date>2026-08-08T12:51:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720889</loc>
  <lastmod>2026-08-08T12:00:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ハードウェア実装に向けたニューラルネットワークベースの通信アルゴリズム（Towards Hardware Implementation of Neural Network-based Communication Algorithms）</news:title>
   <news:publication_date>2026-08-08T12:00:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720887</loc>
  <lastmod>2026-08-08T11:59:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>環境画像からの大気質推定を二重チャンネルで改善する（Air Quality Measurement Based on Double-Channel Convolutional Neural Network Ensemble Learning）</news:title>
   <news:publication_date>2026-08-08T11:59:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720885</loc>
  <lastmod>2026-08-08T11:59:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>K-Meansを組み込んだラベル非依存GAN（Label-Removed Generative Adversarial Networks Incorporating with K-Means）</news:title>
   <news:publication_date>2026-08-08T11:59:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720883</loc>
  <lastmod>2026-08-08T11:59:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>無ラベル超音波動画における舌運動予測（PREDICTING TONGUE MOTION IN UNLABELED ULTRASOUND VIDEOS USING CONVOLUTIONAL LSTM NEURAL NETWORKS）</news:title>
   <news:publication_date>2026-08-08T11:59:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720881</loc>
  <lastmod>2026-08-08T11:58:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>欠損値を扱う教師あり学習の一貫性（On the consistency of supervised learning with missing values）</news:title>
   <news:publication_date>2026-08-08T11:58:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720879</loc>
  <lastmod>2026-08-08T11:58:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多忠度ベイズ最適化による二項出力の扱い（Multifidelity Bayesian Optimization for Binomial Output）</news:title>
   <news:publication_date>2026-08-08T11:58:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720877</loc>
  <lastmod>2026-08-08T11:58:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>上空画像から地上視点画像を生成する条件付きGANの応用（USING CONDITIONAL GENERATIVE ADVERSARIAL NETWORKS TO GENERATE GROUND-LEVEL VIEWS FROM OVERHEAD IMAGERY）</news:title>
   <news:publication_date>2026-08-08T11:58:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720875</loc>
  <lastmod>2026-08-08T11:07:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>生成モデルを用いた高速かつ安定した圧縮センシング復元（FAST COMPRESSIVE SENSING RECOVERY USING GENERATIVE MODELS WITH STRUCTURED LATENT VARIABLES）</news:title>
   <news:publication_date>2026-08-08T11:07:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720873</loc>
  <lastmod>2026-08-08T10:58:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>サブマトリクス検出における計算下限の普遍性（Universality of Computational Lower Bounds for Submatrix Detection）</news:title>
   <news:publication_date>2026-08-08T10:58:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720871</loc>
  <lastmod>2026-08-08T10:58:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ブラックボックスを説明するための変分情報ボトルネック法（Explaining A Black-box By Using A Deep Variational Information Bottleneck Approach）</news:title>
   <news:publication_date>2026-08-08T10:58:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720869</loc>
  <lastmod>2026-08-08T10:58:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>2D LiDAR地図予測とGRUによる動きフロー推定（2D LiDAR Map Prediction via Estimating Motion Flow with GRU）</news:title>
   <news:publication_date>2026-08-08T10:58:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720867</loc>
  <lastmod>2026-08-08T10:57:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>サインビットにこそ重要性あり—ブラックボックス攻撃の新戦略（There are No Bit Parts for Sign Bits in Black-Box Attacks）</news:title>
   <news:publication_date>2026-08-08T10:57:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720865</loc>
  <lastmod>2026-08-08T10:57:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>圧縮領域で行う外観ベースのジェスチャ認識（APPEARANCE-BASED GESTURE RECOGNITION IN THE COMPRESSED DOMAIN）</news:title>
   <news:publication_date>2026-08-08T10:57:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720863</loc>
  <lastmod>2026-08-08T10:57:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>選挙に勝つための戦略的コミュニケーションの自動生成（Winning an Election: On Emergent Strategic Communication in Multi-Agent Networks）</news:title>
   <news:publication_date>2026-08-08T10:57:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720861</loc>
  <lastmod>2026-08-08T10:06:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SPINBIS: スピントロニクスによる確率的ベイズ推論システム（Spintronics based Bayesian Inference System with Stochastic Computing）</news:title>
   <news:publication_date>2026-08-08T10:06:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720859</loc>
  <lastmod>2026-08-08T10:06:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>電子顕微鏡像から3次元原子歪みを再構築する深層学習（Reconstruction of 3-D Atomic Distortions from Electron Microscopy with Deep Learning）</news:title>
   <news:publication_date>2026-08-08T10:06:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720857</loc>
  <lastmod>2026-08-08T10:05:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>行列積状態による確率モデル化（Probabilistic Modeling with Matrix Product States）</news:title>
   <news:publication_date>2026-08-08T10:05:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720855</loc>
  <lastmod>2026-08-08T10:04:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ハイパーボリック割引と複数地平での学習（Hyperbolic Discounting and Learning over Multiple Horizons）</news:title>
   <news:publication_date>2026-08-08T10:04:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720853</loc>
  <lastmod>2026-08-08T10:04:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GPUクラスタにおける分散DNN学習のネットワーク最適化（Optimizing Network Performance for Distributed DNN Training on GPU Clusters: ImageNet/AlexNet Training in 1.5 Minutes）</news:title>
   <news:publication_date>2026-08-08T10:04:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720851</loc>
  <lastmod>2026-08-08T10:04:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>十分に正確なモデル学習（Sufficiently Accurate Model Learning）</news:title>
   <news:publication_date>2026-08-08T10:04:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720849</loc>
  <lastmod>2026-08-08T10:04:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>視覚情報から都市の安全感を予測する（Predicting city safety perception based on visual image content）</news:title>
   <news:publication_date>2026-08-08T10:04:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720847</loc>
  <lastmod>2026-08-08T09:12:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>干渉環境に適応する自己符号化器（An Adaptive Deep Learning Algorithm Based Autoencoder for Interference Channels）</news:title>
   <news:publication_date>2026-08-08T09:12:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720845</loc>
  <lastmod>2026-08-08T09:12:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>活性化関数が深層ニューラルネットワークの学習に与える影響（On the Impact of the Activation Function on Deep Neural Networks Training）</news:title>
   <news:publication_date>2026-08-08T09:12:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720843</loc>
  <lastmod>2026-08-08T09:11:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>原子スケールの動力学を自動で学ぶ――Graph Dynamical Networks（Graph Dynamical Networks for Unsupervised Learning of Atomic Scale Dynamics in Materials）</news:title>
   <news:publication_date>2026-08-08T09:11:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720841</loc>
  <lastmod>2026-08-08T09:11:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>音声から直接学ぶ単語ベクトル──文脈付き音響単語埋め込みの実装と有効性（LEARNED IN SPEECH RECOGNITION: CONTEXTUAL ACOUSTIC WORD EMBEDDINGS）</news:title>
   <news:publication_date>2026-08-08T09:11:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720839</loc>
  <lastmod>2026-08-08T09:10:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>イベントカメラ向け運動等変性ネットワークと時間正規化変換（Motion Equivariant Networks for Event Cameras with the Temporal Normalization Transform）</news:title>
   <news:publication_date>2026-08-08T09:10:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720837</loc>
  <lastmod>2026-08-08T09:10:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自律的航空収益管理：シート在庫制御とオーバーブッキングに対する深層強化学習アプローチ（A Deep Reinforcement Learning Approach to Seat Inventory Control and Overbooking）</news:title>
   <news:publication_date>2026-08-08T09:10:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720835</loc>
  <lastmod>2026-08-08T09:10:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューラルネットワークの低ビット量子化による効率的推論（Low-bit Quantization of Neural Networks for Efficient Inference）</news:title>
   <news:publication_date>2026-08-08T09:10:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720833</loc>
  <lastmod>2026-08-08T08:19:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>使える機械学習の民主化（Democratisation of Usable Machine Learning in Computer Vision）</news:title>
   <news:publication_date>2026-08-08T08:19:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720831</loc>
  <lastmod>2026-08-08T08:19:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>FreeLabel：フリーハンドの線で高品質なセグメンテーションを得るツール（FreeLabel: A Publicly Available Annotation Tool based on Freehand Traces）</news:title>
   <news:publication_date>2026-08-08T08:19:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720829</loc>
  <lastmod>2026-08-08T08:19:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>低資源感情分析のためのデータ拡張（DATA AUGMENTATION FOR LOW RESOURCE SENTIMENT ANALYSIS USING GENERATIVE ADVERSARIAL NETWORKS）</news:title>
   <news:publication_date>2026-08-08T08:19:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720827</loc>
  <lastmod>2026-08-08T08:18:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>AS0295 銀河団のChandra観測が示す合体の姿（CHANDRA OBSERVATIONS OF THE ABELL S0295 CLUSTER）</news:title>
   <news:publication_date>2026-08-08T08:18:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720825</loc>
  <lastmod>2026-08-08T08:18:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ブラックボックスモデルの解釈性を高める正則化（Regularizing Black-box Models for Improved Interpretability）</news:title>
   <news:publication_date>2026-08-08T08:18:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720823</loc>
  <lastmod>2026-08-08T08:18:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習研究における七つの誤解（Seven Myths in Machine Learning Research）</news:title>
   <news:publication_date>2026-08-08T08:18:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720821</loc>
  <lastmod>2026-08-08T08:18:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多声音楽のためのエンドツーエンド歌詞アライメント（END-TO-END LYRICS ALIGNMENT FOR POLYPHONIC MUSIC USING AN AUDIO-TO-CHARACTER RECOGNITION MODEL）</news:title>
   <news:publication_date>2026-08-08T08:18:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720819</loc>
  <lastmod>2026-08-08T07:26:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>音声クローン攻撃から音声インターフェースを守る方法（Securing Voice-driven Interfaces against Fake (Cloned) Audio Attacks）</news:title>
   <news:publication_date>2026-08-08T07:26:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720817</loc>
  <lastmod>2026-08-08T07:26:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>移動ロボットのための3D点群のマルチビュー逐次セグメンテーション（Multi-view Incremental Segmentation of 3D Point Clouds for Mobile Robots）</news:title>
   <news:publication_date>2026-08-08T07:26:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720815</loc>
  <lastmod>2026-08-08T07:25:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>メタ問題と意識理論間の知識移転：ソフトウェア技術者の視点（The meta-problem and the transfer of knowledge between theories of consciousness: a software engineer’s take）</news:title>
   <news:publication_date>2026-08-08T07:25:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720813</loc>
  <lastmod>2026-08-08T07:25:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習を使った認知モデリングの導き方（Using Machine Learning to Guide Cognitive Modeling）</news:title>
   <news:publication_date>2026-08-08T07:25:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720811</loc>
  <lastmod>2026-08-08T07:24:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>初期宇宙の10キロパーセク[CII]ハローの発見（FIRST IDENTIFICATION OF 10-kpc [C II] 158µm HALOS AROUND STAR-FORMING GALAXIES AT z = 5–7）</news:title>
   <news:publication_date>2026-08-08T07:24:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720809</loc>
  <lastmod>2026-08-08T07:24:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DIViSによるドメイン不変視覚サーボで衝突回避しつつ目標到達を実現する（Domain Invariant Visual Servoing for Collision-Free Goal Reaching）</news:title>
   <news:publication_date>2026-08-08T07:24:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720807</loc>
  <lastmod>2026-08-08T07:24:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>幅の大きなニューラルネットワークは線形モデルとして振る舞う（Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent）</news:title>
   <news:publication_date>2026-08-08T07:24:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720805</loc>
  <lastmod>2026-08-08T06:33:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Fortranで並列化されたニューラルネット実装の実用性（A parallel Fortran framework for neural networks and deep learning）</news:title>
   <news:publication_date>2026-08-08T06:33:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720803</loc>
  <lastmod>2026-08-08T06:33:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>増分クラスタ妥当性指標の拡張と比較研究（Incremental Cluster Validity Indices for Hard Partitions: Extensions and Comparative Study）</news:title>
   <news:publication_date>2026-08-08T06:33:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720801</loc>
  <lastmod>2026-08-08T06:32:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>敵対的頑健性の評価基準を明確にする（On Evaluating Adversarial Robustness）</news:title>
   <news:publication_date>2026-08-08T06:32:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720799</loc>
  <lastmod>2026-08-08T06:31:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>因果適合性への組合せ的解法 (A Combinatorial Solution to Causal Compatibility)</news:title>
   <news:publication_date>2026-08-08T06:31:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720797</loc>
  <lastmod>2026-08-08T06:31:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ハイパースペクトル画像分類における3D-2D融合CNNの提案（HybridSN: Exploring 3D-2D CNN Feature Hierarchy for Hyperspectral Image Classification）</news:title>
   <news:publication_date>2026-08-08T06:31:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720795</loc>
  <lastmod>2026-08-08T06:31:37Z</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上の近傍探索を小容量で可能にするスケッチ（Sub-linear Memory Sketches for Near Neighbor Search on Streaming Data）</news:title>
   <news:publication_date>2026-08-08T06:31:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720793</loc>
  <lastmod>2026-08-08T06:31:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>網膜OCT画像を高精度に合成する生成敵対ネットワーク（Generative Adversarial Networks Synthesize Realistic OCT Images of the Retina）</news:title>
   <news:publication_date>2026-08-08T06:31:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720791</loc>
  <lastmod>2026-08-08T05:40:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単一のベイジアンネットワークで十分か（Is a single unique Bayesian network enough to accurately represent your data?）</news:title>
   <news:publication_date>2026-08-08T05:40:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720789</loc>
  <lastmod>2026-08-08T05:40:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>トルコ語の非定型短文に対する分割方法がニューラル感情分析に与える影響（Investigating the Effect of Segmentation Methods on Neural Model based Sentiment Analysis on Informal Short Texts in Turkish）</news:title>
   <news:publication_date>2026-08-08T05:40:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720787</loc>
  <lastmod>2026-08-08T05:39:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>限定的検討（Limited Consideration）下におけるリスク下の離散選択（Discrete Choice under Risk with Limited Consideration）</news:title>
   <news:publication_date>2026-08-08T05:39:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720785</loc>
  <lastmod>2026-08-08T05:38:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>短文テキストに潜むトピックを深掘りする手法（Deep Mixtures of Unigrams for uncovering Topics in Textual Data）</news:title>
   <news:publication_date>2026-08-08T05:38:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720783</loc>
  <lastmod>2026-08-08T05:38:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>MockingbirdによるWebsite Fingerprinting防御（Mockingbird: Defending Against Deep-Learning-Based Website Fingerprinting Attacks with Adversarial Traces）</news:title>
   <news:publication_date>2026-08-08T05:38:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720781</loc>
  <lastmod>2026-08-08T05:38:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>テキスト分類の浅層・深層・アンサンブル手法（Classifying textual data: shallow, deep and ensemble methods）</news:title>
   <news:publication_date>2026-08-08T05:38:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720779</loc>
  <lastmod>2026-08-08T05:38:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>UAVを用いたセルラーネットワークにおける軌道最適化とダブルQ学習（Optimized Trajectory Design in UAV Based Cellular Networks for 3D Users: A Double Q-Learning Approach）</news:title>
   <news:publication_date>2026-08-08T05:38:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720777</loc>
  <lastmod>2026-08-08T04:46:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多様な条件下での物体認識の信頼性と性能推定（Object Recognition Under Multifarious Conditions: A Reliability Analysis and a Feature Similarity-Based Performance Estimation）</news:title>
   <news:publication_date>2026-08-08T04:46:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720775</loc>
  <lastmod>2026-08-08T04:46:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高速で効率的なハイパーパラメータ調整（Fast Efficient Hyperparameter Tuning for Policy Gradient Methods）</news:title>
   <news:publication_date>2026-08-08T04:46:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720773</loc>
  <lastmod>2026-08-08T04:45:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>コンフォーマルキャリブレーター（Conformal Calibrators）</news:title>
   <news:publication_date>2026-08-08T04:45:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720771</loc>
  <lastmod>2026-08-08T04:45:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多目的ベイジアン最適化におけるカライ・スモロドンスキー解（The Kalai-Smorodinsky solution for many-objective Bayesian optimization）</news:title>
   <news:publication_date>2026-08-08T04:45:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720769</loc>
  <lastmod>2026-08-08T04:44:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>生の単一チャネル脳波から睡眠段階を判定するIITNet（Intra- and Inter-epoch Temporal Context Network）</news:title>
   <news:publication_date>2026-08-08T04:44:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720767</loc>
  <lastmod>2026-08-08T04:44:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>量子アニーリングで拡がる自動メタマテリアル探索（Expanding the horizon of automated metamaterials discovery via quantum annealing）</news:title>
   <news:publication_date>2026-08-08T04:44:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720765</loc>
  <lastmod>2026-08-08T04:43:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>STCNによる時系列表現の階層化と並列化（STCN: STOCHASTIC TEMPORAL CONVOLUTIONAL NETWORKS）</news:title>
   <news:publication_date>2026-08-08T04:43:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720763</loc>
  <lastmod>2026-08-08T03:52:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューラル集団におけるグリッド地図の計算モデル（A computational model for grid maps in neural populations）</news:title>
   <news:publication_date>2026-08-08T03:52:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720761</loc>
  <lastmod>2026-08-08T03:52:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>可視化できるニューラル地図とそれが拓く画像ベース位置推定（A Generative Map for Image-based Camera Localization）</news:title>
   <news:publication_date>2026-08-08T03:52:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720759</loc>
  <lastmod>2026-08-08T03:52:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>LocalNormによる画像分類の堅牢化（LocalNorm: Robust Image Classification through Dynamically Regularized Normalization）</news:title>
   <news:publication_date>2026-08-08T03:52:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720757</loc>
  <lastmod>2026-08-08T03:51:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>STRIPによるトロイ攻撃の検出（STRIP: A Defence Against Trojan Attacks on Deep Neural Networks）</news:title>
   <news:publication_date>2026-08-08T03:51:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720755</loc>
  <lastmod>2026-08-08T03:51:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>メッセージ・ドロップアウト：マルチエージェント深層強化学習の効率的学習法（Message-Dropout: An Efficient Training Method for Multi-Agent Deep Reinforcement Learning）</news:title>
   <news:publication_date>2026-08-08T03:51:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720753</loc>
  <lastmod>2026-08-08T03:51:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的勾配降下法（SGD）をテキスト分類で最適化する手法（Optimizing Stochastic Gradient Descent in Text Classification）</news:title>
   <news:publication_date>2026-08-08T03:51:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720751</loc>
  <lastmod>2026-08-08T03:51:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>透過機械学習による多孔質岩の有効度予測（PREDICTION OF POROSITY AND PERMEABILITY ALTERATION BASED ON MACHINE LEARNING ALGORITHMS）</news:title>
   <news:publication_date>2026-08-08T03:51:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720749</loc>
  <lastmod>2026-08-08T02:59:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自己学習型光学信号処理器と光学ニューラルネットワークチップ（Self-learning photonic signal processor with an optical neural network chip）</news:title>
   <news:publication_date>2026-08-08T02:59:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720747</loc>
  <lastmod>2026-08-08T02:59:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>L1-L1最小化アルゴリズムを展開して設計する再帰型ニューラルネットワーク（Designing Recurrent Neural Networks by Unfolding an L1-L1 Minimization Algorithm）</news:title>
   <news:publication_date>2026-08-08T02:59:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720745</loc>
  <lastmod>2026-08-08T02:59:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グリッド対グラフ：タクシー需要供給予測のための空間分割（Grids versus Graphs: Partitioning Space for Improved Taxi Demand-Supply Forecasts）</news:title>
   <news:publication_date>2026-08-08T02:59:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720743</loc>
  <lastmod>2026-08-08T02:58:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>相互作用系の構成的表現の学習（Learning Compositional Representations of Interacting Systems with Restricted Boltzmann Machines: Comparative Study of Lattice Proteins）</news:title>
   <news:publication_date>2026-08-08T02:58:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720741</loc>
  <lastmod>2026-08-08T02:58:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>連続学習の統一的ベイジアン視点（A Unifying Bayesian View of Continual Learning）</news:title>
   <news:publication_date>2026-08-08T02:58:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720739</loc>
  <lastmod>2026-08-08T02:58:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>メモリの壁を越える設計：深層学習向けメモリ中心HPCシステムの提案 (Beyond the Memory Wall: A Case for Memory-centric HPC System for Deep Learning)</news:title>
   <news:publication_date>2026-08-08T02:58:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720737</loc>
  <lastmod>2026-08-08T02:58:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>道路速度予測の構造的再帰ニューラルネットワーク（STRUCTURAL RECURRENT NEURAL NETWORK FOR TRAFFIC SPEED PREDICTION）</news:title>
   <news:publication_date>2026-08-08T02:58:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720735</loc>
  <lastmod>2026-08-08T02:06:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CBOWだけでは足りない：CBOWと行列合成モデルの組合せ（CBOW IS NOT ALL YOU NEED: COMBINING CBOW WITH THE COMPOSITIONAL MATRIX SPACE MODEL）</news:title>
   <news:publication_date>2026-08-08T02:06:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720733</loc>
  <lastmod>2026-08-08T01:58:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>構造強化型敵対的生成ネットワークによるCS-MRI再構成の要点（SEGAN: Structure-Enhanced Generative Adversarial Network for Compressed Sensing MRI Reconstruction）</news:title>
   <news:publication_date>2026-08-08T01:58:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720731</loc>
  <lastmod>2026-08-08T01:58:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>最適化されたデータ探索による化学プロセスシミュレーションの改善（Optimized data exploration applied to the simulation of a chemical process）</news:title>
   <news:publication_date>2026-08-08T01:58:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720729</loc>
  <lastmod>2026-08-08T01:58:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>海洋内部でのMeddy下における係留高解像度温度センサーによる乱流推定（Open-ocean interior moored sensor turbulence estimates, below a Meddy）</news:title>
   <news:publication_date>2026-08-08T01:58:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720727</loc>
  <lastmod>2026-08-08T01:57:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スパース残差木とフォレスト（Sparse residual tree and forest）</news:title>
   <news:publication_date>2026-08-08T01:57:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720725</loc>
  <lastmod>2026-08-08T01:56:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DeepMIMOによる無線チャネルの学習基盤の標準化（DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications）</news:title>
   <news:publication_date>2026-08-08T01:56:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720723</loc>
  <lastmod>2026-08-08T01:56:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ロボット手術器具のセグメンテーションチャレンジ（2017 Robotic Instrument Segmentation Challenge）</news:title>
   <news:publication_date>2026-08-08T01:56:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720721</loc>
  <lastmod>2026-08-08T01:05:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューラルネットワークの不確実性で建物初期故障を見つける（Detecting and Diagnosing Incipient Building Faults Using Uncertainty Information from Deep Neural Networks）</news:title>
   <news:publication_date>2026-08-08T01:05:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720719</loc>
  <lastmod>2026-08-08T00:57:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>AuxBlocksによる敵対的攻撃防御（AuxBlocks: Defense Adversarial Example via Auxiliary Blocks）</news:title>
   <news:publication_date>2026-08-08T00:57:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720717</loc>
  <lastmod>2026-08-08T00:57:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>パケットタイミングが明かす匿名性の穴（Tik-Tok: The Utility of Packet Timing in Website Fingerprinting Attacks）</news:title>
   <news:publication_date>2026-08-08T00:57:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720715</loc>
  <lastmod>2026-08-08T00:57:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CNNのユニットは言葉の「部品」を見つける（DISCOVERY OF NATURAL LANGUAGE CONCEPTS IN INDIVIDUAL UNITS OF CNNS）</news:title>
   <news:publication_date>2026-08-08T00:57:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720713</loc>
  <lastmod>2026-08-08T00:55:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>チャネル剪定の実務を速くする粗いランク付け（SPEEDING UP CONVOLUTIONAL NETWORKS PRUNING WITH COARSE RANKING）</news:title>
   <news:publication_date>2026-08-08T00:55:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720711</loc>
  <lastmod>2026-08-08T00:55:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>OC-SVMを用いた時系列変化点検出のキャリブレーション手法（A One-Class Support Vector Machine Calibration Method for Time Series Change Point Detection）</news:title>
   <news:publication_date>2026-08-08T00:55:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720709</loc>
  <lastmod>2026-08-08T00:55:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>周辺眼領域（Periocular）認識とOC-LBCPを用いたデュアルストリームCNNの応用（Periocular Recognition in the Wild with Orthogonal Combination of Local Binary Coded Pattern in Dual-stream Convolutional Neural Network）</news:title>
   <news:publication_date>2026-08-08T00:55:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720707</loc>
  <lastmod>2026-08-08T00:03:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散学習による共有スペクトラム上のチャネル割当（Distributed Learning for Channel Allocation Over a Shared Spectrum）</news:title>
   <news:publication_date>2026-08-08T00:03:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720705</loc>
  <lastmod>2026-08-08T00:03:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高速過渡現象の深層学習分類器FETCH（FETCH: A deep-learning based classifier for fast transient classification）</news:title>
   <news:publication_date>2026-08-08T00:03:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720703</loc>
  <lastmod>2026-08-08T00:03:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>プログラムスケッチ推論の学習 (Learning to Infer Program Sketches)</news:title>
   <news:publication_date>2026-08-08T00:03:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720701</loc>
  <lastmod>2026-08-08T00:02:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>通信量を大幅に削減する投影不要の最適化手法（Quantized Frank-Wolfe: Faster Optimization, Lower Communication, and Projection Free）</news:title>
   <news:publication_date>2026-08-08T00:02:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720699</loc>
  <lastmod>2026-08-08T00:02:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>タンパク質折りたたみを模擬するネイティブショートカットネットワークの形成 (Forming native shortcut networks to simulate protein folding)</news:title>
   <news:publication_date>2026-08-08T00:02:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720697</loc>
  <lastmod>2026-08-08T00:02:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>含有電子散乱における平均場と二体核効果のスーパースケーリング解析（Mean field and two-body nuclear effects in inclusive electron scattering on argon, carbon and titanium: the superscaling approach）</news:title>
   <news:publication_date>2026-08-08T00:02:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720695</loc>
  <lastmod>2026-08-08T00:01:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>意味的に解釈可能で制御可能なフィルタセット（Semantically Interpretable and Controllable Filter Sets）</news:title>
   <news:publication_date>2026-08-08T00:01:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720693</loc>
  <lastmod>2026-08-07T23:10:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ドメイン適応を“クロスグラフト”で橋渡しする手法（Unsupervised Domain Adaptation using Deep Networks with Cross-Grafted Stacks）</news:title>
   <news:publication_date>2026-08-07T23:10:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720691</loc>
  <lastmod>2026-08-07T23:00:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層学習のテスト手法の改善に向けて (Towards Improved Testing For Deep Learning)</news:title>
   <news:publication_date>2026-08-07T23:00:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720689</loc>
  <lastmod>2026-08-07T22:59:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散機械学習のための私的内積取得（Private Inner Product Retrieval）</news:title>
   <news:publication_date>2026-08-07T22:59:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720687</loc>
  <lastmod>2026-08-07T22:59:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>内生的切断バイアスの半パラメトリック補正（Semiparametric Correction for Endogenous Truncation Bias with Vox Populi Based Participation Decision）</news:title>
   <news:publication_date>2026-08-07T22:59:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720685</loc>
  <lastmod>2026-08-07T22:59:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非揮発性メモリのためのニューラルネットワーク基盤動的閾値検出（Neural Network-Based Dynamic Threshold Detection for Non-Volatile Memories）</news:title>
   <news:publication_date>2026-08-07T22:59:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720683</loc>
  <lastmod>2026-08-07T22:58:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>プロトアテンド：注目機構に基づくプロトタイプ学習（ProtoAttend: Attention-based Prototypical Learning）</news:title>
   <news:publication_date>2026-08-07T22:58:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720681</loc>
  <lastmod>2026-08-07T22:58:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Deep Modulation Embedding（Deep Modulation Embedding）</news:title>
   <news:publication_date>2026-08-07T22:58:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720679</loc>
  <lastmod>2026-08-07T22:07:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Wi‑Fi信号を用いた交通モード検出のための半教師あり深層残差ネットワーク（A SEMI-SUPERVISED DEEP RESIDUAL NETWORK FOR MODE DETECTION IN WI-FI SIGNALS）</news:title>
   <news:publication_date>2026-08-07T22:07:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720677</loc>
  <lastmod>2026-08-07T22:07:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>モノリシックからマイクロサービス移行で技術的負債は減るのか（Does Migrating a Monolithic System to Microservices Decrease the Technical Debt?）</news:title>
   <news:publication_date>2026-08-07T22:07:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720675</loc>
  <lastmod>2026-08-07T22:07:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自己教師付きランキング学習によるCNNの未ラベルデータ活用（Exploiting Unlabeled Data in CNNs by Self-supervised Learning to Rank）</news:title>
   <news:publication_date>2026-08-07T22:07:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720673</loc>
  <lastmod>2026-08-07T22:06:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Deep-learning inversionによる次世代地震速度モデル構築法（Deep-learning inversion: a next generation seismic velocity-model building method）</news:title>
   <news:publication_date>2026-08-07T22:06:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720671</loc>
  <lastmod>2026-08-07T22:05:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>シナプスとミエリン可塑性が学習に与える影響（Effects of Synaptic and Myelin Plasticity on Learning in a Network of Kuramoto Phase Oscillators）</news:title>
   <news:publication_date>2026-08-07T22:05:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720669</loc>
  <lastmod>2026-08-07T22:05:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ベイズ正則化：チホノフからホースシューへ（Bayesian Regularization: From Tikhonov to Horseshoe）</news:title>
   <news:publication_date>2026-08-07T22:05:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720667</loc>
  <lastmod>2026-08-07T22:05:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>連続時刻力学系におけるパラメータと状態推定のためのODE指向回帰（ODIN: ODE-Informed Regression for Parameter and State Inference in Time-Continuous Dynamical Systems）</news:title>
   <news:publication_date>2026-08-07T22:05:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720665</loc>
  <lastmod>2026-08-07T21:13:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数混合型データにおける共通変動（グローバル・ローカル）と固有変動の分離（Separating common (global and local) and distinct variation in multiple mixed types data sets）</news:title>
   <news:publication_date>2026-08-07T21:13:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720663</loc>
  <lastmod>2026-08-07T21:12:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習過程から知識を取り出す報酬整形の新手法（A new Potential-Based Reward Shaping for Reinforcement Learning Agent）</news:title>
   <news:publication_date>2026-08-07T21:12:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720661</loc>
  <lastmod>2026-08-07T21:12:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>拡張現実のためのステレオビジョンに基づく3次元シーン再構築の探求 (Exploring Stereovision-Based 3-D Scene Reconstruction for Augmented Reality)</news:title>
   <news:publication_date>2026-08-07T21:12:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720659</loc>
  <lastmod>2026-08-07T21:12:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オンラインマッチング最適化と多エージェント深層強化学習による配車サービス改善（Optimizing Online Matching for Ride-Sourcing Services with Multi-Agent Deep Reinforcement Learning）</news:title>
   <news:publication_date>2026-08-07T21:12:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720657</loc>
  <lastmod>2026-08-07T21:12:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>知識グラフを推薦に統合する共同学習の全体像（Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences）</news:title>
   <news:publication_date>2026-08-07T21:12:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720655</loc>
  <lastmod>2026-08-07T21:11:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>屋内サブメートル級ローカリゼーション（Robust Sub-meter Level Indoor Localization - A Logistic Regression Approach）</news:title>
   <news:publication_date>2026-08-07T21:11:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720653</loc>
  <lastmod>2026-08-07T21:11:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニュースアラートからの複数文書表現による自動バイオ監視イベント検出（Multiple Document Representations from News Alerts for Automated Bio-surveillance Event Detection）</news:title>
   <news:publication_date>2026-08-07T21:11:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720651</loc>
  <lastmod>2026-08-07T20:19:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>線形二次レギュレータ学習で√Tの後悔を効率的に達成する手法（Learning Linear-Quadratic Regulators Efficiently with only √T Regret）</news:title>
   <news:publication_date>2026-08-07T20:19:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720649</loc>
  <lastmod>2026-08-07T20:19:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>色付けされた画像の検出（Detecting Colorized Images via Convolutional Neural Networks: Toward High Accuracy and Good Generalization）</news:title>
   <news:publication_date>2026-08-07T20:19:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720647</loc>
  <lastmod>2026-08-07T20:19:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>LapEPI-Netによる高密度ライトフィールド復元（LapEPI-Net: A Laplacian Pyramid EPI structure for Learning-based Dense Light Field Reconstruction）</news:title>
   <news:publication_date>2026-08-07T20:19:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720645</loc>
  <lastmod>2026-08-07T20:18:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>推薦システムのための協調類似埋め込み（Collaborative Similarity Embedding for Recommender Systems）</news:title>
   <news:publication_date>2026-08-07T20:18:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720643</loc>
  <lastmod>2026-08-07T20:18:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>低販売商品の文脈ベース動的価格設定とオンラインクラスタリング（Context-Based Dynamic Pricing with Online Clustering）</news:title>
   <news:publication_date>2026-08-07T20:18:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720641</loc>
  <lastmod>2026-08-07T20:17:49Z</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PCB欠陥検出器と新規データセット（ONLINE PCB DEFECT DETECTOR ON A NEW PCB DEFECT DATASET）</news:title>
   <news:publication_date>2026-08-07T20:17:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720639</loc>
  <lastmod>2026-08-07T20:17:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Twitchと群衆が学ぶトロール検出（Twitch Plays Pokemon, Machine Learns Twitch）</news:title>
   <news:publication_date>2026-08-07T20:17:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720637</loc>
  <lastmod>2026-08-07T19:25:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自己教師あり視覚特徴学習の総説（Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey）</news:title>
   <news:publication_date>2026-08-07T19:25:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720635</loc>
  <lastmod>2026-08-07T19:24:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>WiSE-ALEによる広域サンプル推定で得る潜在埋め込み（WiSE-ALE: Wide Sample Estimator for Approximate Latent Embedding）</news:title>
   <news:publication_date>2026-08-07T19:24:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720633</loc>
  <lastmod>2026-08-07T19:24:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非凸・非平滑最適化のための高速ゼロ次近接確率的手法（Faster Gradient-Free Proximal Stochastic Methods for Nonconvex Nonsmooth Optimization）</news:title>
   <news:publication_date>2026-08-07T19:24:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720631</loc>
  <lastmod>2026-08-07T19:23:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>BigEarthNetによる大規模リモートセンシング画像理解の基盤革新（BIGEARTHNET: A LARGE-SCALE BENCHMARK ARCHIVE FOR REMOTE SENSING IMAGE UNDERSTANDING）</news:title>
   <news:publication_date>2026-08-07T19:23:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720629</loc>
  <lastmod>2026-08-07T19:23:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>セルフィッシュなユーザとモバイル群学習における情報鮮度の保証（Can We Achieve Fresh Information with Selfish Users in Mobile Crowd-Learning?）</news:title>
   <news:publication_date>2026-08-07T19:23:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720627</loc>
  <lastmod>2026-08-07T19:22:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ほんの少しで十分：分散学習に対する防御回避手法の実態（A Little Is Enough: Circumventing Defenses For Distributed Learning）</news:title>
   <news:publication_date>2026-08-07T19:22:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720625</loc>
  <lastmod>2026-08-07T19:22:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Deep Generalized Convolutional Sum-Product Networks（Deep Generalized Convolutional Sum-Product Networks）</news:title>
   <news:publication_date>2026-08-07T19:22:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720622</loc>
  <lastmod>2026-08-07T18:31:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>不確実な領域における神経調節された目標志向知覚（Neuromodulated Goal-Driven Perception in Uncertain Domains）</news:title>
   <news:publication_date>2026-08-07T18:31:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720620</loc>
  <lastmod>2026-08-07T18:21:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習エージェント間の通信トポロジーを活用した深層強化学習（Leveraging Communication Topologies Between Learning Agents in Deep Reinforcement Learning）</news:title>
   <news:publication_date>2026-08-07T18:21:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720618</loc>
  <lastmod>2026-08-07T18:20:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Data Management in Industry 4.0の概観と課題（Data Management in Industry 4.0: State of the Art and Open Challenges）</news:title>
   <news:publication_date>2026-08-07T18:20:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720616</loc>
  <lastmod>2026-08-07T18:19:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>過去の修正から学ぶ自動バグ修正の実用性（Getafix: Learning to Fix Bugs Automatically）</news:title>
   <news:publication_date>2026-08-07T18:19:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720614</loc>
  <lastmod>2026-08-07T18:19:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械（深層）学習が人間の学習を理解する手助けをする方法（How Machine (Deep) Learning Helps Us Understand Human Learning: the Value of Big Ideas）</news:title>
   <news:publication_date>2026-08-07T18:19:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720612</loc>
  <lastmod>2026-08-07T18:19:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非凸スパース正則化を持つラッソに対するスクリーニングルール（Screening Rules for Lasso with Non-Convex Sparse Regularizers）</news:title>
   <news:publication_date>2026-08-07T18:19:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720610</loc>
  <lastmod>2026-08-07T18:18:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>凸損失関数を外れ値に強くするe指数変換（Making Convex Loss Functions Robust to Outliers using e-Exponentiated Transformation）</news:title>
   <news:publication_date>2026-08-07T18:18:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720608</loc>
  <lastmod>2026-08-07T17:27:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所差分プライバシーを用いた分散最適化（Local Differential Privacy in Decentralized Optimization）</news:title>
   <news:publication_date>2026-08-07T17:27:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720606</loc>
  <lastmod>2026-08-07T17:27:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>可微分リザバーコンピューティングの理論的進展（Differentiable Reservoir Computing）</news:title>
   <news:publication_date>2026-08-07T17:27:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720604</loc>
  <lastmod>2026-08-07T17:27:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>交互拡散過程によるグラフ上の半教師あり学習 (Semi-supervised Learning on Graph with an Alternating Diffusion Process)</news:title>
   <news:publication_date>2026-08-07T17:27:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720602</loc>
  <lastmod>2026-08-07T17:25:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>画像超解像の深層学習サーベイ（Deep Learning for Image Super-resolution: A Survey）</news:title>
   <news:publication_date>2026-08-07T17:25:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720600</loc>
  <lastmod>2026-08-07T17:25:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>再決定化情報集合MCTSによるHanabi探索改善（Re-determinizing Information Set Monte Carlo Tree Search in Hanabi）</news:title>
   <news:publication_date>2026-08-07T17:25:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720598</loc>
  <lastmod>2026-08-07T17:25:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DC-AL GANによる偽増悪と真の腫瘍増悪の識別（DC-AL GAN: Pseudoprogression and True Tumor Progression of Glioblastoma Multiform Image Classification Based on DCGAN and AlexNet）</news:title>
   <news:publication_date>2026-08-07T17:25:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720596</loc>
  <lastmod>2026-08-07T17:25:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Res-SE-NetによるResNet改良（RES-SE-NET: BOOSTING PERFORMANCE OF RESNETS BY ENHANCING BRIDGE-CONNECTIONS）</news:title>
   <news:publication_date>2026-08-07T17:25:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720594</loc>
  <lastmod>2026-08-07T16:33:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スマートシティにおける短距離通勤者のモード選択（Short-distance commuters in the smart city）</news:title>
   <news:publication_date>2026-08-07T16:33:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720592</loc>
  <lastmod>2026-08-07T16:32:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>メラノーマ検出の自動化に向けたデータ洗浄と増強（Towards Automated Melanoma Detection with Deep Learning: Data Purification and Augmentation）</news:title>
   <news:publication_date>2026-08-07T16:32:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720590</loc>
  <lastmod>2026-08-07T16:32:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>弱監督物体検出のための最小エントロピー潜在モデル（Min-Entropy Latent Model for Weakly Supervised Object Detection）</news:title>
   <news:publication_date>2026-08-07T16:32:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720588</loc>
  <lastmod>2026-08-07T16:31:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>アットメートル解像度と90 dB動的レンジ、THz帯域を同時に実現する光学ベクトル解析（Optical vector analysis with attometer resolution, 90‐dB dynamic range and THz bandwidth）</news:title>
   <news:publication_date>2026-08-07T16:31:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720586</loc>
  <lastmod>2026-08-07T16:31:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>PatchNetによる深層パッチ分類の実務的意義（PatchNet: A Tool for Deep Patch Classification）</news:title>
   <news:publication_date>2026-08-07T16:31:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720584</loc>
  <lastmod>2026-08-07T16:31:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>RF信号向けディープ分類器の敵対的事例軽減（Mitigation of Adversarial Examples in RF Deep Classiﬁers Utilizing AutoEncoder Pre-training）</news:title>
   <news:publication_date>2026-08-07T16:31:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720582</loc>
  <lastmod>2026-08-07T16:30:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ドメイン知識と深層学習を組み合わせた短文・非公式メッセージの感情分析（Combination of Domain Knowledge and Deep Learning for Sentiment Analysis of Short and Informal Messages on Social Media）</news:title>
   <news:publication_date>2026-08-07T16:30:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720580</loc>
  <lastmod>2026-08-07T15:39:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>イエメンのコレラ流行を機械学習で予測する（Forecasting the 2017-2018 Yemen Cholera Outbreak with Machine Learning）</news:title>
   <news:publication_date>2026-08-07T15:39:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720578</loc>
  <lastmod>2026-08-07T15:39:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>無線(RF)深層学習における敵対的事例：攻撃検知と物理的ロバストネス（Adversarial Examples in RF Deep Learning: Detection of the Attack and its Physical Robustness）</news:title>
   <news:publication_date>2026-08-07T15:39:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720576</loc>
  <lastmod>2026-08-07T15:38:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>トピックと数式を同時に扱う新手法の実務的意義（TopicEq: A Joint Topic and Mathematical Equation Model for Scientific Texts）</news:title>
   <news:publication_date>2026-08-07T15:38:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720574</loc>
  <lastmod>2026-08-07T15:38:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>幸福表現の特徴量に基づく解析手法の提案（CruzAffect: A feature-rich approach to characterize happiness）</news:title>
   <news:publication_date>2026-08-07T15:38:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720572</loc>
  <lastmod>2026-08-07T15:37:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>空間パズル解法に効くヒューリスティック統合法（Heuristics, Answer Set Programming and Markov Decision Process for Solving a Set of Spatial Puzzles）</news:title>
   <news:publication_date>2026-08-07T15:37:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720570</loc>
  <lastmod>2026-08-07T15:37:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューラルネットワークにおける変数の有意性検定（Significance Tests for Neural Networks）</news:title>
   <news:publication_date>2026-08-07T15:37:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720568</loc>
  <lastmod>2026-08-07T15:37:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>完全微分可能なビームサーチデコーダ（A FULLY DIFFERENTIABLE BEAM SEARCH DECODER）</news:title>
   <news:publication_date>2026-08-07T15:37:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720566</loc>
  <lastmod>2026-08-07T14:46:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>二層ニューラルネットワークの平均場理論とカーネル極限（Mean-field theory of two-layers neural networks: dimension-free bounds and kernel limit）</news:title>
   <news:publication_date>2026-08-07T14:46:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720564</loc>
  <lastmod>2026-08-07T14:46:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習システムをファイバーバンドルとしてモデル化することで実現する継続学習（Realizing Continual Learning through Modeling a Learning System as a Fiber Bundle）</news:title>
   <news:publication_date>2026-08-07T14:46:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720562</loc>
  <lastmod>2026-08-07T14:45:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>リスク感度のあるカーネル学習のための非パラメトリック合成確率最適化（Nonparametric Compositional Stochastic Optimization for Risk-Sensitive Kernel Learning）</news:title>
   <news:publication_date>2026-08-07T14:45:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720560</loc>
  <lastmod>2026-08-07T14:44:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グループテスティングの情報理論的展開（Group Testing: An Information Theory Perspective）</news:title>
   <news:publication_date>2026-08-07T14:44:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720558</loc>
  <lastmod>2026-08-07T14:44:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>サンプリングに起因するニューラル分類器の情報損失（Information Losses in Neural Classifiers from Sampling）</news:title>
   <news:publication_date>2026-08-07T14:44:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720556</loc>
  <lastmod>2026-08-07T14:44:14Z</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の再調整と再サンプリングの比較（ON RESAMPLING VS. ADJUSTING PROBABILISTIC GRAPHICAL MODELS IN ESTIMATION OF DISTRIBUTION ALGORITHMS）</news:title>
   <news:publication_date>2026-08-07T14:44:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720554</loc>
  <lastmod>2026-08-07T14:43:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>人の知識を符号化して強化学習をウォームスタートする（Encoding Human Domain Knowledge to Warm Start Reinforcement Learning）</news:title>
   <news:publication_date>2026-08-07T14:43:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720552</loc>
  <lastmod>2026-08-07T13:52:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューラルネットワークの堅牢性：確率的かつ実用的アプローチ (Robustness of Neural Networks: A Probabilistic and Practical Approach)</news:title>
   <news:publication_date>2026-08-07T13:52:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720550</loc>
  <lastmod>2026-08-07T13:52:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>逐次適応的サブモジュラリティの枠組み（Adaptive Sequence Submodularity）</news:title>
   <news:publication_date>2026-08-07T13:52:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720548</loc>
  <lastmod>2026-08-07T13:51:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オペレーショナルニューラルネットワーク（Operational Neural Networks）</news:title>
   <news:publication_date>2026-08-07T13:51:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720546</loc>
  <lastmod>2026-08-07T13:50:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>MoMにおける逆行列不要な最近傍評価の意義（Inversion-Free Evaluation of Nearest Neighbors in Method of Moments）</news:title>
   <news:publication_date>2026-08-07T13:50:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720544</loc>
  <lastmod>2026-08-07T13:50:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DeepFaultによるDNNの故障局在化と検査強化（DeepFault: Fault Localization for Deep Neural Networks）</news:title>
   <news:publication_date>2026-08-07T13:50:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720542</loc>
  <lastmod>2026-08-07T13:50:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動的スパース再パラメータ化による畳み込みニューラルネットワークの効率的学習（Parameter Efficient Training of Deep Convolutional Neural Networks by Dynamic Sparse Reparameterization）</news:title>
   <news:publication_date>2026-08-07T13:50:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720540</loc>
  <lastmod>2026-08-07T13:50:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高品質3D顔再構築のためのGAN適合（GANFIT: Generative Adversarial Network Fitting for High Fidelity 3D Face Reconstruction）</news:title>
   <news:publication_date>2026-08-07T13:50:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720538</loc>
  <lastmod>2026-08-07T12:56:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ダークマターから銀河へ：畳み込みネットワークによる写像（From Dark Matter to Galaxies with Convolutional Networks）</news:title>
   <news:publication_date>2026-08-07T12:56:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720536</loc>
  <lastmod>2026-08-07T12:56:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>二次元硬粒子の排除領域の解析（The excluded area of two-dimensional hard particles）</news:title>
   <news:publication_date>2026-08-07T12:56:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720534</loc>
  <lastmod>2026-08-07T12:55:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>若い銀河のハードな電離源を探る — HeIIλ1640放射とその示唆（Exploring Heiiλ1640 emission line properties at z∼2−4）</news:title>
   <news:publication_date>2026-08-07T12:55:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720532</loc>
  <lastmod>2026-08-07T12:55:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>背外側前頭前野の遮断が暗黙的文脈記憶主導の注意を改善する（Disruption of the prefrontal cortex improves implicit contextual memory-guided attention）</news:title>
   <news:publication_date>2026-08-07T12:55:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720530</loc>
  <lastmod>2026-08-07T12:55:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>銀河団の力学質量を深層学習で測る（A Robust and Efficient Deep Learning Method for Dynamical Mass Measurements of Galaxy Clusters）</news:title>
   <news:publication_date>2026-08-07T12:55:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720528</loc>
  <lastmod>2026-08-07T12:54:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>プラズモン色の深層学習による予測（Plasmonic colours predicted by deep learning）</news:title>
   <news:publication_date>2026-08-07T12:54:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720526</loc>
  <lastmod>2026-08-07T12:54:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層畳み込みガウス過程によるベイズ画像分類（Bayesian Image Classification with Deep Convolutional Gaussian Processes）</news:title>
   <news:publication_date>2026-08-07T12:54:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720524</loc>
  <lastmod>2026-08-07T12:02:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習によるスペクトル色あせ補正で血中酸素飽和度を推定する（Estimation of blood oxygenation with learned spectral decoloring for quantitative photoacoustic imaging）</news:title>
   <news:publication_date>2026-08-07T12:02:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720522</loc>
  <lastmod>2026-08-07T12:02:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>リスクスコアの公平性を評価する新指標 xAUC（The Fairness of Risk Scores Beyond Classification: Bipartite Ranking and the xAUC Metric）</news:title>
   <news:publication_date>2026-08-07T12:02:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720520</loc>
  <lastmod>2026-08-07T12:01:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>トリプレット深層距離学習ネットワークによるリモートセンシング画像検索の性能向上 (Enhancing Remote Sensing Image Retrieval with Triplet Deep Metric Learning Network)</news:title>
   <news:publication_date>2026-08-07T12:01:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720518</loc>
  <lastmod>2026-08-07T12:00:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>重い裾（heavy-tailed）カーネルがt-SNE可視化のクラスタ構造を細かく示す（Heavy-tailed kernels reveal a finer cluster structure in t-SNE visualisations）</news:title>
   <news:publication_date>2026-08-07T12:00:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720516</loc>
  <lastmod>2026-08-07T12:00:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>教師あり深層ニューラルネットワークによるオプション価格付けとキャリブレーション（Supervised Deep Neural Networks for Pricing/Calibration of Options）</news:title>
   <news:publication_date>2026-08-07T12:00:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720514</loc>
  <lastmod>2026-08-07T12:00:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模心臓4D MRIの教師なし形状・動態解析が示す臨床的示唆（Unsupervised shape and motion analysis of 3822 cardiac 4D MRIs of UK Biobank）</news:title>
   <news:publication_date>2026-08-07T12:00:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720512</loc>
  <lastmod>2026-08-07T11:59:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>VH過程におけるSMEFTの探索と機械学習の活用（Exploring SMEFT in VH with Machine Learning）</news:title>
   <news:publication_date>2026-08-07T11:59:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720510</loc>
  <lastmod>2026-08-07T11:08:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ラプラシアン固有関数の節点と一般化特異構造（ON NODAL AND GENERALIZED SINGULAR STRUCTURES OF LAPLACIAN EIGENFUNCTIONS AND APPLICATIONS TO INVERSE SCATTERING PROBLEMS）</news:title>
   <news:publication_date>2026-08-07T11:08:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720508</loc>
  <lastmod>2026-08-07T11:07:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>不完全かつノイズのある観測に対する頑健な強化学習（Robust Reinforcement Learning in POMDPs with Incomplete and Noisy Observations）</news:title>
   <news:publication_date>2026-08-07T11:07:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720506</loc>
  <lastmod>2026-08-07T11:06:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>タスクに適応する高速アーキテクチャ推定（Fast Task-Aware Architecture Inference）</news:title>
   <news:publication_date>2026-08-07T11:06:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720504</loc>
  <lastmod>2026-08-07T11:06:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>異種交通環境における深層強化学習ベースの高次運転行動意思決定モデル（Deep Reinforcement Learning Based High-level Driving Behavior Decision-making Model in Heterogeneous Traffic）</news:title>
   <news:publication_date>2026-08-07T11:06:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720502</loc>
  <lastmod>2026-08-07T11:06:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューラル機械翻訳における動的層集約と合意ルーティング（Dynamic Layer Aggregation for Neural Machine Translation with Routing-by-Agreement）</news:title>
   <news:publication_date>2026-08-07T11:06:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720500</loc>
  <lastmod>2026-08-07T11:06:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多変量多項式評価に基づくややホモモルフィック暗号（A Somewhat Homomorphic Encryption Scheme based on Multivariate Polynomial Evaluation）</news:title>
   <news:publication_date>2026-08-07T11:06:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720498</loc>
  <lastmod>2026-08-07T11:05:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>超強結合Jaynes–Cummings模型（Ultrastrong Jaynes-Cummings Model）</news:title>
   <news:publication_date>2026-08-07T11:05:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720496</loc>
  <lastmod>2026-08-07T10:14:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文脈認識型自己注意ネットワーク（Context-Aware Self-Attention Networks）</news:title>
   <news:publication_date>2026-08-07T10:14:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720494</loc>
  <lastmod>2026-08-07T10:14:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>双対性にもとづくコアラージュ学習（Coalgebra Learning via Duality）</news:title>
   <news:publication_date>2026-08-07T10:14:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720492</loc>
  <lastmod>2026-08-07T10:13:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>漸近的に厳密なデータ拡張（Asymptotically exact data augmentation）</news:title>
   <news:publication_date>2026-08-07T10:13:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720490</loc>
  <lastmod>2026-08-07T10:12:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>2段階トランスファー学習による異種ロボット検出と2Dカメラ画像での3D関節推定（Two-Stage Transfer Learning for Heterogeneous Robot Detection and 3D Joint Position Estimation in a 2D Camera Image Using CNN）</news:title>
   <news:publication_date>2026-08-07T10:12:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720488</loc>
  <lastmod>2026-08-07T10:12:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>プロモーター分類におけるSVMとスペクトラル埋め込みの比較（Comparison of SVM and Spectral Embedding in Promoter Biobricks’ Categorizing and Clustering）</news:title>
   <news:publication_date>2026-08-07T10:12:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720486</loc>
  <lastmod>2026-08-07T10:11:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SVMベースの深層積層ネットワーク（SVM-based Deep Stacking Networks）</news:title>
   <news:publication_date>2026-08-07T10:11:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720484</loc>
  <lastmod>2026-08-07T10:11:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>睡眠段階分類のための畳み込みネットワーク（A Convolutional Network for Sleep Stages Classification）</news:title>
   <news:publication_date>2026-08-07T10:11:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720482</loc>
  <lastmod>2026-08-07T09:20:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>制約付きリスクバジェッティング・ポートフォリオ（Constrained Risk Budgeting Portfolios）</news:title>
   <news:publication_date>2026-08-07T09:20:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720480</loc>
  <lastmod>2026-08-07T09:19:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GMMを効率的に学習する深層学習の利点（Efficient Deep Learning of GMMs）</news:title>
   <news:publication_date>2026-08-07T09:19:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720478</loc>
  <lastmod>2026-08-07T09:19:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スパイキングニューラルネットワークにおけるスパイクカウント学習則（Deep Spiking Neural Network with Spike Count based Learning Rule）</news:title>
   <news:publication_date>2026-08-07T09:19:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720476</loc>
  <lastmod>2026-08-07T09:19:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>双方向価値学習によるリスク意識型計画（Bi-directional Value Learning for Risk-aware Planning Under Uncertainty: Extended Version）</news:title>
   <news:publication_date>2026-08-07T09:19:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720474</loc>
  <lastmod>2026-08-07T09:18:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>適応的スケーリングを学習するリカレントニューラルネットワーク（Learning to Adaptively Scale Recurrent Neural Networks）</news:title>
   <news:publication_date>2026-08-07T09:18:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720472</loc>
  <lastmod>2026-08-07T09:18:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>軽量特徴融合ネットワークによる単一画像超解像（Lightweight Feature Fusion Network for Single Image Super-Resolution）</news:title>
   <news:publication_date>2026-08-07T09:18:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720470</loc>
  <lastmod>2026-08-07T09:18:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ロボットのクラウドオフロード方策（Network Offloading Policies for Cloud Robotics: a Learning-based Approach）</news:title>
   <news:publication_date>2026-08-07T09:18:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720468</loc>
  <lastmod>2026-08-07T08:27:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Lipschitz条件で安定化する生成モデルの理論と実務的示唆（Lipschitz Generative Adversarial Nets）</news:title>
   <news:publication_date>2026-08-07T08:27:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720466</loc>
  <lastmod>2026-08-07T08:27:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層的サンプリングによるネットワークの位相表現学習（Learning Topological Representation for Networks via Hierarchical Sampling）</news:title>
   <news:publication_date>2026-08-07T08:27:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720464</loc>
  <lastmod>2026-08-07T08:26:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>カーネル単位の自動ニューラルネットワーク量子化（AUTOQ: AUTOMATED KERNEL-WISE NEURAL NETWORK QUANTIZATION）</news:title>
   <news:publication_date>2026-08-07T08:26:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720462</loc>
  <lastmod>2026-08-07T08:25:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>医療画像解析における深層学習の展開と課題（Going Deep in Medical Image Analysis: Concepts, Methods, Challenges and Future Directions）</news:title>
   <news:publication_date>2026-08-07T08:25:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720460</loc>
  <lastmod>2026-08-07T08:25:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ProxSARAH による確率的合成非凸最適化の効率化（ProxSARAH: An Efficient Algorithmic Framework for Stochastic Composite Nonconvex Optimization）</news:title>
   <news:publication_date>2026-08-07T08:25:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720458</loc>
  <lastmod>2026-08-07T08:25:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>公開情報を使った犯罪分析の実務的アプローチ（Crime Analysis using Open Source Information）</news:title>
   <news:publication_date>2026-08-07T08:25:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720456</loc>
  <lastmod>2026-08-07T08:25:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分子回転ムービーを高調波で撮る技術（Molecular rotation movie filmed with high-harmonic generation）</news:title>
   <news:publication_date>2026-08-07T08:25:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720454</loc>
  <lastmod>2026-08-07T07:33:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非同期共エージェントネットワークの理論と実装（Asynchronous Coagent Networks）</news:title>
   <news:publication_date>2026-08-07T07:33:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720452</loc>
  <lastmod>2026-08-07T07:33:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>敵対的環境における最大エントロピー深層強化学習による能動的知覚（Active Perception in Adversarial Scenarios using Maximum Entropy Deep Reinforcement Learning）</news:title>
   <news:publication_date>2026-08-07T07:33:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720450</loc>
  <lastmod>2026-08-07T07:33:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>専門知識をニューラルネットに組み込む手法（KINN: Incorporating Expert Knowledge in Neural Networks）</news:title>
   <news:publication_date>2026-08-07T07:33:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720448</loc>
  <lastmod>2026-08-07T07:32:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>バーデの窓における赤化と減光の地図化（Mapping the Interstellar Reddening and Extinction towards Baade’s Window Using Minimum Light Colors of ab-type RR Lyrae Stars）</news:title>
   <news:publication_date>2026-08-07T07:32:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720446</loc>
  <lastmod>2026-08-07T07:32:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>検証可能な安全なオフモデル強化学習（Verifiably Safe Off-Model Reinforcement Learning）</news:title>
   <news:publication_date>2026-08-07T07:32:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720444</loc>
  <lastmod>2026-08-07T07:32:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>都市マイクロ気象のリアルタイム高解像度予測を可能にするSRシミュレーション（Super-Resolution Simulation for Real-Time Prediction of Urban Micrometeorology）</news:title>
   <news:publication_date>2026-08-07T07:32:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720442</loc>
  <lastmod>2026-08-07T07:32:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所的にグラフの最近傍を見つける方法（Finding Nearest Neighbors in graphs locally）</news:title>
   <news:publication_date>2026-08-07T07:32:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720440</loc>
  <lastmod>2026-08-07T06:40:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非コンパクト特徴空間におけるクラス条件付きラベルノイズ下の分類（Classification with unknown class-conditional label noise on non-compact feature spaces）</news:title>
   <news:publication_date>2026-08-07T06:40:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720438</loc>
  <lastmod>2026-08-07T06:39:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>WaveletAEによる風力発電機ブレードの氷結検出（WaveletAE: A Wavelet-enhanced Autoencoder for Wind Turbine Blade Icing Detection）</news:title>
   <news:publication_date>2026-08-07T06:39:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720436</loc>
  <lastmod>2026-08-07T06:39:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>画像ベースGANを用いた時系列データ生成の簡便手法（Quick and Easy Time Series Generation with Established Image-based GANs）</news:title>
   <news:publication_date>2026-08-07T06:39:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720434</loc>
  <lastmod>2026-08-07T06:39:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時間遅延結合系を深い畳み込みニューラルネットへと読み替える発想（Coupled nonlinear delay systems as deep convolutional neural networks）</news:title>
   <news:publication_date>2026-08-07T06:39:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720432</loc>
  <lastmod>2026-08-07T06:39:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>衛星画像から標準地図レイヤを生成するGeoGAN（GeoGAN: A Conditional GAN with Reconstruction and Style Loss to Generate Standard Layer of Maps from Satellite Images）</news:title>
   <news:publication_date>2026-08-07T06:39:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720430</loc>
  <lastmod>2026-08-07T06:38:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DC最適潮流における活性制約集合の分類による高速推定（Learning for DC-OPF: Classifying active sets using neural nets）</news:title>
   <news:publication_date>2026-08-07T06:38:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720428</loc>
  <lastmod>2026-08-07T06:38:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>バッチ正規化を用いるCrossQによるサンプル効率の向上（CROSSQ: BATCH NORMALIZATION IN DEEP REINFORCEMENT LEARNING FOR GREATER SAMPLE EFFICIENCY AND SIMPLICITY）</news:title>
   <news:publication_date>2026-08-07T06:38:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720426</loc>
  <lastmod>2026-08-07T05:47:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>競争下での探索の危険性（The Perils of Exploration under Competition: A Computational Modeling Approach）</news:title>
   <news:publication_date>2026-08-07T05:47:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720424</loc>
  <lastmod>2026-08-07T05:38:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>指数修正ガウス混合モデルと分光データへの応用（Exponentially-Modified Gaussian Mixture Model）</news:title>
   <news:publication_date>2026-08-07T05:38:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720422</loc>
  <lastmod>2026-08-07T05:37:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ハイパーパラメータ選択による敵対的耐性の改善（Can Intelligent Hyperparameter Selection Improve Resistance to Adversarial Examples?）</news:title>
   <news:publication_date>2026-08-07T05:37:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720420</loc>
  <lastmod>2026-08-07T05:36:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多カメラネットワークでの分散物体追跡のスケーラブルプラットフォーム（A Scalable Platform for Distributed Object Tracking across a Many-camera Network）</news:title>
   <news:publication_date>2026-08-07T05:36:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720418</loc>
  <lastmod>2026-08-07T05:36:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的枠組みによるQuantum Clusteringの実用化（A Probabilistic framework for Quantum Clustering）</news:title>
   <news:publication_date>2026-08-07T05:36:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720416</loc>
  <lastmod>2026-08-07T05:36:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>潜在空間を敵対的に近似するオートエンコーダ（Adversarially Approximated Autoencoder for Image Generation and Manipulation）</news:title>
   <news:publication_date>2026-08-07T05:36:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720414</loc>
  <lastmod>2026-08-07T05:35:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>任意長入力に対応するテキスト分類の全畳み込みネットワーク（Fully Convolutional Networks for Text Classification）</news:title>
   <news:publication_date>2026-08-07T05:35:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720412</loc>
  <lastmod>2026-08-07T04:44:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>結び目の双曲体体積を深層学習で予測する（Deep Learning the Hyperbolic Volume of a Knot）</news:title>
   <news:publication_date>2026-08-07T04:44:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720410</loc>
  <lastmod>2026-08-07T04:44:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自己組み立て形態の制御学習（Learning to Control Self-Assembling Morphologies: A Study of Generalization via Modularity）</news:title>
   <news:publication_date>2026-08-07T04:44:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720408</loc>
  <lastmod>2026-08-07T04:43:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>トラクトグラフィーと機械学習：現状と未解決課題（Tractography and machine learning: Current state and open challenges）</news:title>
   <news:publication_date>2026-08-07T04:43:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720406</loc>
  <lastmod>2026-08-07T04:43:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ベイズモデル不確実性の簡潔な案内（A Parsimonious Tour of Bayesian Model Uncertainty）</news:title>
   <news:publication_date>2026-08-07T04:43:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720404</loc>
  <lastmod>2026-08-07T04:42:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層生成的エンドメンバーモデルによる教師なしスペクトル分解（Deep Generative Endmember Modeling: An Application to Unsupervised Spectral Unmixing）</news:title>
   <news:publication_date>2026-08-07T04:42:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720402</loc>
  <lastmod>2026-08-07T04:42:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多数のモデルを一つに重ねて格納する手法（Superposition of many models into one）</news:title>
   <news:publication_date>2026-08-07T04:42:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720400</loc>
  <lastmod>2026-08-07T04:42:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>報酬なしでロボットが目標を学ぶ仕組み（Unsupervised Visuomotor Control through Distributional Planning Networks）</news:title>
   <news:publication_date>2026-08-07T04:42:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720398</loc>
  <lastmod>2026-08-07T03:51:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>遠方で高倍率に増光した星の発見（Searching for Highly Magnified Stars at Cosmological Distances: Discovery of a Redshift 0.94 Blue Supergiant in Archival Images of the Galaxy Cluster MACS J0416.1-2403）</news:title>
   <news:publication_date>2026-08-07T03:51:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720396</loc>
  <lastmod>2026-08-07T03:50:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>MultiGrain：クラスとインスタンスの統一画像埋め込み（MultiGrain: a unified image embedding for classes and instances）</news:title>
   <news:publication_date>2026-08-07T03:50:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720394</loc>
  <lastmod>2026-08-07T03:50:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ラベル属性と階層構造を統合する階層的ゼロショット画像分類（Integrating Propositional and Relational Label Side Information for Hierarchical Zero-Shot Image Classification）</news:title>
   <news:publication_date>2026-08-07T03:50:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720392</loc>
  <lastmod>2026-08-07T03:50:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ハイパー複素数値ホップフィールド型ニューラルネットワークの広範なクラス（A Broad Class of Discrete-Time Hypercomplex-Valued Hopfield Neural Networks）</news:title>
   <news:publication_date>2026-08-07T03:50:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720390</loc>
  <lastmod>2026-08-07T03:48:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ソーシャルボットの実態とオープンコード分析（OPENBOTS—AN EMPIRICAL STUDY ON AUTOMATED PROGRAMS IN SOCIAL MEDIA）</news:title>
   <news:publication_date>2026-08-07T03:48:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720388</loc>
  <lastmod>2026-08-07T03:48:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>治療反応者の分類と単調性仮定の利点（Classifying Treatment Responders Under Causal Effect Monotonicity）</news:title>
   <news:publication_date>2026-08-07T03:48:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720386</loc>
  <lastmod>2026-08-07T03:48:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自信を持って先延ばしする（Procrastinating with Confidence: Near-Optimal, Anytime, Adaptive Algorithm Configuration）</news:title>
   <news:publication_date>2026-08-07T03:48:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720384</loc>
  <lastmod>2026-08-07T02:56:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非線形統計的逆学習問題に対するチホノフ正則化の収束解析 (Convergence Analysis of Tikhonov Regularization for Non-Linear Statistical Inverse Learning Problems)</news:title>
   <news:publication_date>2026-08-07T02:56:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720382</loc>
  <lastmod>2026-08-07T02:55:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>チャネル最大プーリング層による微細車両分類（Channel Max Pooling Layer for Fine-Grained Vehicle Classification）</news:title>
   <news:publication_date>2026-08-07T02:55:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720380</loc>
  <lastmod>2026-08-07T02:55:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>医療データ向けコスト効率の高いマルコフ連鎖ベース管理図（Cost-efficient Markov chain-based control charts for healthcare）</news:title>
   <news:publication_date>2026-08-07T02:55:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720378</loc>
  <lastmod>2026-08-07T02:54:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時間変動するスパース信号の推定を改善するSBL-DF（Sparse Bayesian Learning with Dynamic Filtering for Inference of Time-Varying Sparse Signals）</news:title>
   <news:publication_date>2026-08-07T02:54:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720376</loc>
  <lastmod>2026-08-07T02:54:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフ深層学習に基づく回路難読化解除の実行時間推定（Estimating the Circuit De-obfuscation Runtime based on Graph Deep Learning）</news:title>
   <news:publication_date>2026-08-07T02:54:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720374</loc>
  <lastmod>2026-08-07T02:54:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Sparse and noisy LiDAR completion with RGB guidance and uncertainty（Sparse and noisy LiDAR completion with RGB guidance and uncertainty）</news:title>
   <news:publication_date>2026-08-07T02:54:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720372</loc>
  <lastmod>2026-08-07T02:53:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自動ラベル付きLiDARデータ生成パイプライン（Automatic Labeled LiDAR Data Generation based on Precise Human Model）</news:title>
   <news:publication_date>2026-08-07T02:53:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720370</loc>
  <lastmod>2026-08-07T02:02:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>トポロジカル署名のシンクホーン発散による時系列分類推定 (Sinkhorn Divergence of Topological Signature Estimates for Time Series Classification)</news:title>
   <news:publication_date>2026-08-07T02:02:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720368</loc>
  <lastmod>2026-08-07T02:01:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>JNDに基づくサリエンシーチャネル注意残差ネットワークによる画質予測の革新（A Novel Just-Noticeable-Difference-based Saliency-Channel Attention Residual Network for Full-Reference Image Quality Predictions）</news:title>
   <news:publication_date>2026-08-07T02:01:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720366</loc>
  <lastmod>2026-08-07T02:01:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>画家風の写真変換を安定化する二次ポテンシャル法（Artist Style Transfer Via Quadratic Potential）</news:title>
   <news:publication_date>2026-08-07T02:01:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720364</loc>
  <lastmod>2026-08-07T02:01:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時系列予測における全結合ニューラルネットワークの汎化（Generalization in fully-connected neural networks for time series forecasting）</news:title>
   <news:publication_date>2026-08-07T02:01:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720362</loc>
  <lastmod>2026-08-07T02:00:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ソースモデルとターゲットデータを用いた系列ラベリングの転移学習（Transfer Learning for Sequence Labeling Using Source Model and Target Data）</news:title>
   <news:publication_date>2026-08-07T02:00:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720360</loc>
  <lastmod>2026-08-07T02:00:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>離散時間ヘッジにおける深層学習の実践と評価（Deep learning for discrete-time hedging in incomplete markets）</news:title>
   <news:publication_date>2026-08-07T02:00:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720358</loc>
  <lastmod>2026-08-07T02:00:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ボックスレベル分割で精度と速度を両立するマルチスペクトル歩行者検出（Box-level Segmentation Supervised Deep Neural Networks for Accurate and Real-time Multispectral Pedestrian Detection）</news:title>
   <news:publication_date>2026-08-07T02:00:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720356</loc>
  <lastmod>2026-08-07T01:08:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Cetusストリームの全容解明とStarGOの示唆（REVEALING THE COMPLICATED STORY OF THE CETUS STREAM WITH STARGO）</news:title>
   <news:publication_date>2026-08-07T01:08:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720354</loc>
  <lastmod>2026-08-07T01:08:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>RHEAに先行知識を学習してオンライン計画を効率化する手法（Learn a Prior for RHEA for Better Online Planning）</news:title>
   <news:publication_date>2026-08-07T01:08:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720352</loc>
  <lastmod>2026-08-07T01:08:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>点群のセマンティック・インスタンス分割のための構造認識損失を用いた3Dグラフ埋め込み学習（3D Graph Embedding Learning with a Structure-aware Loss Function for Point Cloud Semantic Instance Segmentation）</news:title>
   <news:publication_date>2026-08-07T01:08:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720350</loc>
  <lastmod>2026-08-07T01:07:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフ正則化による画像セマンティック埋め込み（Graph-RISE: Graph-Regularized Image Semantic Embedding）</news:title>
   <news:publication_date>2026-08-07T01:07:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720348</loc>
  <lastmod>2026-08-07T01:06:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>HyPLCによるPLCとハイブリッドプログラムの双方向翻訳（HyPLC: Hybrid Programmable Logic Controller Program Translation for Verification）</news:title>
   <news:publication_date>2026-08-07T01:06:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720346</loc>
  <lastmod>2026-08-07T01:06:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非漸近的なMonte Carlo Tree Searchの解析（Non-Asymptotic Analysis of Monte Carlo Tree Search）</news:title>
   <news:publication_date>2026-08-07T01:06:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720344</loc>
  <lastmod>2026-08-07T01:06:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>視覚共追跡における長短メモリのバランス最適化（Long and Short Memory Balancing in Visual Co-Tracking Using Q-Learning）</news:title>
   <news:publication_date>2026-08-07T01:06:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720337</loc>
  <lastmod>2026-08-07T00:14:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次元確率偏微分方程式をシミュレータ不要で解く（Simulator-free Solution of High-dimensional Stochastic Elliptic Partial Differential Equations using Deep Neural Networks）</news:title>
   <news:publication_date>2026-08-07T00:14:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720335</loc>
  <lastmod>2026-08-07T00:14:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>最大一般エントロピーを達成するオフポリシーActor-Criticと効果的な環境探索（Off-Policy Actor-Critic for Maximum General Entropy and Effective Environment Exploration）</news:title>
   <news:publication_date>2026-08-07T00:14:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720333</loc>
  <lastmod>2026-08-07T00:13:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大型電波観測における光学的赤方偏移推定手法の比較（A Comparison of Photometric Redshift Techniques for Large Radio Surveys）</news:title>
   <news:publication_date>2026-08-07T00:13:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720331</loc>
  <lastmod>2026-08-07T00:12:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>MSEとCCCの多対多写像（The Many-to-Many Mapping Between the Concordance Correlation Coefficient, and the Mean Square Error）</news:title>
   <news:publication_date>2026-08-07T00:12:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720329</loc>
  <lastmod>2026-08-07T00:12:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>転移学習が医用画像解析にもたらす示唆（Transfusion: Understanding Transfer Learning for Medical Imaging）</news:title>
   <news:publication_date>2026-08-07T00:12:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720327</loc>
  <lastmod>2026-08-07T00:12:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>データ相関を考慮したワイヤレスVRの資源管理（Data Correlation-Aware Resource Management in Wireless Virtual Reality）</news:title>
   <news:publication_date>2026-08-07T00:12:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720325</loc>
  <lastmod>2026-08-07T00:11:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>変形する軟組織を深層強化学習で操作する意義（Manipulating Soft Tissues by Deep Reinforcement Learning for Autonomous Robotic Surgery）</news:title>
   <news:publication_date>2026-08-07T00:11:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720323</loc>
  <lastmod>2026-08-06T23:20:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>屋内物体操作行為の分割による人間工学的リスク予測に向けて (Toward Ergonomic Risk Prediction via Segmentation of Indoor Object Manipulation Actions Using Spatiotemporal Convolutional Networks)</news:title>
   <news:publication_date>2026-08-06T23:20:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720321</loc>
  <lastmod>2026-08-06T23:19:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>部分的順序情報を用いたガウス有向非巡回グラフの推定と乳牛データへの応用（Estimation of Gaussian directed acyclic graphs using partial ordering information with an application to dairy cattle data）</news:title>
   <news:publication_date>2026-08-06T23:19:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720319</loc>
  <lastmod>2026-08-06T23:19:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>量子言語処理（Quantum Language Processing）</news:title>
   <news:publication_date>2026-08-06T23:19:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720317</loc>
  <lastmod>2026-08-06T23:18:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>原子スケールシミュレーションにおける教師なし機械学習の位置づけ（Perspective: Unsupervised machine learning in atomistic simulations, between predictions and understanding）</news:title>
   <news:publication_date>2026-08-06T23:18:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720315</loc>
  <lastmod>2026-08-06T23:18:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多波長衛星画像とWasserstein GANを用いた半教師ありマルチタスク学習による貧困予測（Semi-Supervised Multitask Learning on Multispectral Satellite Images Using Wasserstein Generative Adversarial Networks (GANs) for Predicting Poverty）</news:title>
   <news:publication_date>2026-08-06T23:18:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720313</loc>
  <lastmod>2026-08-06T23:18:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>バイオ医用画像における機械学習の課題と解法（Machine Learning on Biomedical Images: Interactive Learning, Transfer Learning, Class Imbalance, and Beyond）</news:title>
   <news:publication_date>2026-08-06T23:18:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720311</loc>
  <lastmod>2026-08-06T23:17:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ダストが明かすミニネプチューン誕生の証拠（Dust Unveils the Formation of a Mini‑Neptune Planet in a Protoplanetary Ring）</news:title>
   <news:publication_date>2026-08-06T23:17:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720309</loc>
  <lastmod>2026-08-06T22:25:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的ニューラルアーキテクチャ探索が変える設計実務（Probabilistic Neural Architecture Search）</news:title>
   <news:publication_date>2026-08-06T22:25:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720307</loc>
  <lastmod>2026-08-06T22:25:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ImageNet分類器はImageNetに対して一般化するか（Do ImageNet Classifiers Generalize to ImageNet?）</news:title>
   <news:publication_date>2026-08-06T22:25:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720305</loc>
  <lastmod>2026-08-06T22:25:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>開放量子多体系のニューラルネットワークによる動力学表現（Neural-Network Approach to Dissipative Quantum Many-Body Dynamics）</news:title>
   <news:publication_date>2026-08-06T22:25:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720303</loc>
  <lastmod>2026-08-06T22:24:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>木構造のトレース再構成が示す新しい展望（Reconstructing Trees from Traces）</news:title>
   <news:publication_date>2026-08-06T22:24:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720301</loc>
  <lastmod>2026-08-06T22:24:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフ構造化再帰型ニューラルネットワークとスパース化による疫学予測（A Study on Graph-Structured Recurrent Neural Networks and Sparsification with Application to Epidemic Forecasting）</news:title>
   <news:publication_date>2026-08-06T22:24:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720299</loc>
  <lastmod>2026-08-06T22:24:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Anytime Tail Averaging（Anytime Tail Averaging）</news:title>
   <news:publication_date>2026-08-06T22:24:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720297</loc>
  <lastmod>2026-08-06T22:23:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>農業コミュニティのツイートから州レベルの農業センチメントを予測する研究（Predicting State-Level Agricultural Sentiment with Tweets from Farming Communities）</news:title>
   <news:publication_date>2026-08-06T22:23:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720295</loc>
  <lastmod>2026-08-06T21:32:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>超低消費電力で動く埋め込み型ゲーティッド再帰ユニットの提案（AN OPTIMIZED RECURRENT UNIT FOR ULTRA-LOW-POWER KEYWORD SPOTTING）</news:title>
   <news:publication_date>2026-08-06T21:32:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720293</loc>
  <lastmod>2026-08-06T21:32:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>無限幅・有界ノルムのReLUネットワークは関数空間でどう見えるか（How do infinite width bounded norm networks look in function space?）</news:title>
   <news:publication_date>2026-08-06T21:32:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720291</loc>
  <lastmod>2026-08-06T21:31:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>損切りを学ぶ：故障許容的制御と最適停止の考え方（Cutting Your Losses: Learning Fault-Tolerant Control and Optimal Stopping under Adverse Risk）</news:title>
   <news:publication_date>2026-08-06T21:31:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720289</loc>
  <lastmod>2026-08-06T21:30:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ATMSeerによるAutoMLの可視化と制御性向上（ATMSeer: Increasing Transparency and Controllability in Automated Machine Learning）</news:title>
   <news:publication_date>2026-08-06T21:30:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720287</loc>
  <lastmod>2026-08-06T21:30:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Wasserstein バリセントリックによるモデルアンサンブリング（Wasserstein Barycenter Model Ensembling）</news:title>
   <news:publication_date>2026-08-06T21:30:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720285</loc>
  <lastmod>2026-08-06T21:30:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ボリューム・トゥイーニング・ネットワークによる3D医療画像非教師ありエンドツーエンド登録（Unsupervised 3D End-to-End Medical Image Registration with Volume Tweening Network）</news:title>
   <news:publication_date>2026-08-06T21:30:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720283</loc>
  <lastmod>2026-08-06T21:29:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>幾何学的概念の差分プライバート学習（Differentially Private Learning of Geometric Concepts）</news:title>
   <news:publication_date>2026-08-06T21:29:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720281</loc>
  <lastmod>2026-08-06T20:38:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>異なるデータ分布をまたぐ学習の設計思想（ADAGE: Agnostic Domain Generalization and Adaptation）</news:title>
   <news:publication_date>2026-08-06T20:38:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720279</loc>
  <lastmod>2026-08-06T20:36:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニュース駆動型株価予測の説明可能なニューラルネットワーク（Explainable Text-Driven Neural Network for Stock Prediction）</news:title>
   <news:publication_date>2026-08-06T20:36:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720277</loc>
  <lastmod>2026-08-06T20:36:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ゲーティッド画像から高密度深度を得るGated2Depth（Gated2Depth: Real-Time Dense Lidar From Gated Images）</news:title>
   <news:publication_date>2026-08-06T20:36:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720275</loc>
  <lastmod>2026-08-06T20:35:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>安定予測型の反事実的後悔最小化（Stable-Predictive Counterfactual Regret Minimization）</news:title>
   <news:publication_date>2026-08-06T20:35:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720273</loc>
  <lastmod>2026-08-06T20:35:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>低ランク位相復元の証明可能な手法（Provable Low Rank Phase Retrieval）</news:title>
   <news:publication_date>2026-08-06T20:35:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720271</loc>
  <lastmod>2026-08-06T20:35:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深い発散に基づくクラスタリング手法（Deep Divergence-Based Approach to Clustering）</news:title>
   <news:publication_date>2026-08-06T20:35:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720269</loc>
  <lastmod>2026-08-06T19:43:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>図形問題（Diagrammatic Reasoning）の自動化は可能か？（Can We Automate Diagrammatic Reasoning?）</news:title>
   <news:publication_date>2026-08-06T19:43:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720267</loc>
  <lastmod>2026-08-06T19:43:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次元データに効く部分サンプリングNewton法の理論的検証（Do Subsampled Newton Methods Work for High-Dimensional Data?）</news:title>
   <news:publication_date>2026-08-06T19:43:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720265</loc>
  <lastmod>2026-08-06T19:42:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>P2Pレンディング市場におけるLSTMを用いた貸倒率予測（Risk Prediction of Peer-to-Peer Lending Market by a LSTM Model with Macroeconomic Factor）</news:title>
   <news:publication_date>2026-08-06T19:42:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720263</loc>
  <lastmod>2026-08-06T19:41:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Project Lyra：1I/‘Oumuamuaへ到達するための計画（Project Lyra: Catching 1I/‘Oumuamua – Mission Opportunities After 2024）</news:title>
   <news:publication_date>2026-08-06T19:41:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720261</loc>
  <lastmod>2026-08-06T19:41:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ランダム初期化されたReLUネットワークにおけるサンプル分散の減衰（Sample Variance Decay in Randomly Initialized ReLU Networks）</news:title>
   <news:publication_date>2026-08-06T19:41:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720259</loc>
  <lastmod>2026-08-06T19:41:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多様な生データから学ぶ3D顔モデリング（3D Face Modeling From Diverse Raw Scan Data）</news:title>
   <news:publication_date>2026-08-06T19:41:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720257</loc>
  <lastmod>2026-08-06T19:40:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>温暖から高温の水素優勢大気を素早く解析するための簡略化化学スキーム（A reduced chemical scheme for modelling warm to hot hydrogen-dominated atmospheres）</news:title>
   <news:publication_date>2026-08-06T19:40:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720255</loc>
  <lastmod>2026-08-06T18:49:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>軌跡データから化学反応ネットワークを学習する（Learning chemical reaction networks from trajectory data）</news:title>
   <news:publication_date>2026-08-06T18:49:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720253</loc>
  <lastmod>2026-08-06T18:48:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>対話応答における知識選択の学習（Learning to Select Knowledge for Response Generation in Dialog Systems）</news:title>
   <news:publication_date>2026-08-06T18:48:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720251</loc>
  <lastmod>2026-08-06T18:48:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ソフトウェアモジュールクラスタリングと適応型ファジーTLBO（Software Module Clustering based on the Fuzzy Adaptive Teaching Learning based Optimization Algorithm）</news:title>
   <news:publication_date>2026-08-06T18:48:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720249</loc>
  <lastmod>2026-08-06T18:47:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>生体模倣バイノーラル音源定位によるロボット音声認識の強化（Enhanced Robot Speech Recognition Using Biomimetic Binaural Sound Source Localization）</news:title>
   <news:publication_date>2026-08-06T18:47:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720247</loc>
  <lastmod>2026-08-06T18:47:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>牛の再識別におけるマルチビュー埋め込み（Multi-views Embedding for Cattle Re-identification）</news:title>
   <news:publication_date>2026-08-06T18:47:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720245</loc>
  <lastmod>2026-08-06T18:47:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>骨テクスチャ解析による股関節レントゲンからの変形性関節症発症予測（Bone Texture Analysis for Prediction of Incident Radiographic Hip Osteoarthritis Using Machine Learning）</news:title>
   <news:publication_date>2026-08-06T18:47:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720243</loc>
  <lastmod>2026-08-06T18:46:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>どのニューラルネットワーク構造が人工文法学習で人間に近い振る舞いを示すか（Which Neural Network Architecture matches Human Behavior in Artificial Grammar Learning?）</news:title>
   <news:publication_date>2026-08-06T18:46:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720241</loc>
  <lastmod>2026-08-06T17:54:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>不規則ドメイン上の信号分類（Classifying Signals on Irregular Domains via Convolutional Cluster Pooling）</news:title>
   <news:publication_date>2026-08-06T17:54:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720239</loc>
  <lastmod>2026-08-06T17:48:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SHEAR-netによる単一プッシュ超音波せん断波弾性イメージングの実用化可能性（SHEAR-net: An End-to-End Deep Learning Approach for Single Push Ultrasound Shear Wave Elasticity Imaging）</news:title>
   <news:publication_date>2026-08-06T17:48:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720237</loc>
  <lastmod>2026-08-06T17:47:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>土壌LIBSスペクトルの汎化学習による微量元素予測（Machine Learning Allows Calibration Models to Predict Trace Element Concentration in Soil with Generalized LIBS Spectra）</news:title>
   <news:publication_date>2026-08-06T17:47:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720235</loc>
  <lastmod>2026-08-06T17:46:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>正確なスパース最適化の下限となる凸計画法（Lower Bound Convex Programs for Exact Sparse Optimization）</news:title>
   <news:publication_date>2026-08-06T17:46:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720233</loc>
  <lastmod>2026-08-06T17:46:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グループレベルMEG/EEGソース推定における最小Wasserstein推定（Group level MEG/EEG source imaging via optimal transport: minimum Wasserstein estimates）</news:title>
   <news:publication_date>2026-08-06T17:46:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720231</loc>
  <lastmod>2026-08-06T17:46:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層型グラフ畳み込みネットワークによる半教師付きノード分類（Hierarchical Graph Convolutional Networks for Semi-supervised Node Classification）</news:title>
   <news:publication_date>2026-08-06T17:46:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720229</loc>
  <lastmod>2026-08-06T16:54:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習における非凸最適化：勾配、確率性、鞍点（On Nonconvex Optimization for Machine Learning: Gradients, Stochasticity, and Saddle Points）</news:title>
   <news:publication_date>2026-08-06T16:54:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720227</loc>
  <lastmod>2026-08-06T16:53:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>モバイル向けDNNの構造的ベイズ圧縮（Structured Bayesian Compression for DNNs in Connected Healthcare）</news:title>
   <news:publication_date>2026-08-06T16:53:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720225</loc>
  <lastmod>2026-08-06T16:52:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自己教師あり学習で手術器具を自動ラベル化する手法（Self-Supervised Surgical Tool Segmentation using Kinematic Information）</news:title>
   <news:publication_date>2026-08-06T16:52:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720223</loc>
  <lastmod>2026-08-06T16:52:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ユークリッドカーネルの表現力とカーネル学習の効率性（On the Expressive Power of Kernel Methods and the Efficiency of Kernel Learning by Association Schemes）</news:title>
   <news:publication_date>2026-08-06T16:52:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720221</loc>
  <lastmod>2026-08-06T16:52:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文書のトピック分割と分類のニューラルモデル（SECTOR: A Neural Model for Coherent Topic Segmentation and Classification）</news:title>
   <news:publication_date>2026-08-06T16:52:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720219</loc>
  <lastmod>2026-08-06T16:51:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>人の公平感と数学的定義の隔たり（Mathematical Notions vs. Human Perception of Fairness: A Descriptive Approach to Fairness for Machine Learning）</news:title>
   <news:publication_date>2026-08-06T16:51:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720217</loc>
  <lastmod>2026-08-06T16:51:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>音声分類タスクにおける性能向上と推論改善（IMPROVING PERFORMANCE AND INFERENCE ON AUDIO CLASSIFICATION TASKS USING CAPSULE NETWORKS）</news:title>
   <news:publication_date>2026-08-06T16:51:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720215</loc>
  <lastmod>2026-08-06T15:59:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>特徴次元に最適化されたパラメトリックQ学習（Sample-Optimal Parametric Q-Learning Using Linearly Additive Features）</news:title>
   <news:publication_date>2026-08-06T15:59:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720213</loc>
  <lastmod>2026-08-06T15:59:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>間接マイクロ波ホログラフィーによる金属物体イメージングと解像度向上（Indirect Microwave Holography with Resolution Enhancement in Metallic Imaging）</news:title>
   <news:publication_date>2026-08-06T15:59:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720211</loc>
  <lastmod>2026-08-06T15:59:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散オンライン線形回帰（Distributed Online Linear Regression）</news:title>
   <news:publication_date>2026-08-06T15:59:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720209</loc>
  <lastmod>2026-08-06T15:58:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>拡張変分推論の収束性に関する考察（On the Convergence of Extended Variational Inference for Non-Gaussian Statistical Models）</news:title>
   <news:publication_date>2026-08-06T15:58:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720207</loc>
  <lastmod>2026-08-06T15:58:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>半教師あり学習における効率的な交差検証の近似（Efficient Cross-Validation for Semi-Supervised Learning）</news:title>
   <news:publication_date>2026-08-06T15:58:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720205</loc>
  <lastmod>2026-08-06T15:57:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>軽量なプライバシー保護型協調学習とIoT端末のための設計（On Lightweight Privacy-Preserving Collaborative Learning for Internet-of-Things Objects）</news:title>
   <news:publication_date>2026-08-06T15:57:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720203</loc>
  <lastmod>2026-08-06T15:05:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>幅広いニューラルネットワークのスケーリング限界（Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation）</news:title>
   <news:publication_date>2026-08-06T15:05:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720201</loc>
  <lastmod>2026-08-06T15:05:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>セッション内の連続的スキップ予測（Session-based Sequential Skip Prediction via Recurrent Neural Networks）</news:title>
   <news:publication_date>2026-08-06T15:05:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720199</loc>
  <lastmod>2026-08-06T15:04:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>集合ベースの顔認識を変えた多プロトタイプ学習（Multi-Prototype Networks for Unconstrained Set-based Face Recognition）</news:title>
   <news:publication_date>2026-08-06T15:04:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720197</loc>
  <lastmod>2026-08-06T15:03:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>一様収束（ユニフォームコンバージェンス）は深層学習の汎化を説明できないかもしれない（Uniform convergence may be unable to explain generalization in deep learning）</news:title>
   <news:publication_date>2026-08-06T15:03:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720195</loc>
  <lastmod>2026-08-06T15:03:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>候補者選別を学ぶ（Learning to Screen）</news:title>
   <news:publication_date>2026-08-06T15:03:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720193</loc>
  <lastmod>2026-08-06T15:03:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>生物輸送モデルのための偏微分方程式学習（Learning partial differential equations for biological transport models from noisy spatiotemporal data）</news:title>
   <news:publication_date>2026-08-06T15:03:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/720191</loc>
  <lastmod>2026-08-06T15:02:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>安定インスタンスによる頑健なマルチインスタンス学習（Robust Multi-Instance Learning with Stable Instances）</news:title>
   <news:publication_date>2026-08-06T15:02:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
</urlset>
