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   <news:title>空撮画像から車線だけを正確に抜き出す技術の衝撃（Aerial LaneNet: Lane Marking Semantic Segmentation in Aerial Imagery using Wavelet-Enhanced Cost-sensitive Symmetric Fully Convolutional Neural Networks）</news:title>
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   <news:title>意味的ローカリティを用いたニューラルネットワークメモリプリフェッチ (A neural network memory prefetcher using semantic locality)</news:title>
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   <news:title>教師なしセマンティック深層ハッシュ（UNSUPERVISED SEMANTIC DEEP HASHING）</news:title>
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   <news:title>ランダム演算子のスペクトル多重度と局所統計の結びつき（Global multiplicity bounds and Spectral Statistics for Random Operators）</news:title>
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   <news:title>生物学的活性化関数による深層学習の改善（Deep learning improved by biological activation functions）</news:title>
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   <news:title>高次元線形モデルにおける交絡因子検出（Confounder Detection in High Dimensional Linear Models using First Moments of Spectral Measures）</news:title>
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   <news:title>音声の表現学習における異ドメイン構音データ活用（ACOUSTIC FEATURE LEARNING USING CROSS-DOMAIN ARTICULATORY MEASUREMENTS）</news:title>
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   <news:title>学習可能な画像暗号化（Learnable Image Encryption）</news:title>
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   <news:genres>Blog</news:genres>
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   <news:title>音楽スタイル変換の位置づけと課題（Music Style Transfer: A Position Paper）</news:title>
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   <news:genres>Blog</news:genres>
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    <news:language>ja</news:language>
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   <news:title>スマートフォンデータから行動ルールを抽出する方法（Mining User Behavioral Rules from Smartphone Data through Association Analysis）</news:title>
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   <news:genres>Blog</news:genres>
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   <news:title>Attention-GANによる野生画像での物体変換（Attention-GAN for Object Transfiguration in Wild Images）</news:title>
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    <news:language>ja</news:language>
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   <news:title>Faster R-CNNの分類力を目覚めさせる（Revisiting RCNN: On Awakening the Classification Power of Faster RCNN）</news:title>
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   <news:genres>Blog</news:genres>
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    <news:language>ja</news:language>
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   <news:title>ロボット操作のための合成可能な深層強化学習（Composable Deep Reinforcement Learning for Robotic Manipulation）</news:title>
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   <news:genres>Blog</news:genres>
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    <news:language>ja</news:language>
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   <news:title>マルチモーダル感情分析の基礎とベンチマーク構築（Multimodal Sentiment Analysis: Addressing Key Issues and Setting up the Baselines）</news:title>
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   <news:genres>Blog</news:genres>
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   <news:title>TOMAAT: 体積医用画像解析のクラウドサービス化（TOMAAT: volumetric medical image analysis as a cloud service）</news:title>
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   <news:genres>Blog</news:genres>
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    <news:language>ja</news:language>
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   <news:title>非局所低ランクテンソル因子解析による画像復元（Nonlocal Low-Rank Tensor Factor Analysis for Image Restoration）</news:title>
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   <news:genres>Blog</news:genres>
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    <news:language>ja</news:language>
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   <news:title>ソフトウェア定義ネットワーク向けの効率的な異常検知法（Towards an Efficient Anomaly-Based Intrusion Detection for Software-Defined Networks）</news:title>
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   <news:genres>Blog</news:genres>
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   <news:title>EnvelopeNetsによる高速ニューラルアーキテクチャ構築（Fast Neural Architecture Construction using EnvelopeNets）</news:title>
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   <news:genres>Blog</news:genres>
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   <news:title>密集フェムトセル環境におけるQoSを考慮した出力割当の機械学習的アプローチ（A Machine Learning Approach for Power Allocation in HetNets Considering QoS）</news:title>
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   <news:genres>Blog</news:genres>
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    <news:language>ja</news:language>
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   <news:title>ワンビット通信で分散検出を実現する（Detection under One-Bit Messaging over Adaptive Networks）</news:title>
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   <news:genres>Blog</news:genres>
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  <lastmod>2026-04-18T04:09:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>大規模動的予測回帰の分解・再結合戦略（Large-Scale Dynamic Predictive Regressions）</news:title>
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   <news:genres>Blog</news:genres>
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  <news:news>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>長期予測のための集約戦略（Aggregating Strategies for Long-term Forecasting）</news:title>
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   <news:genres>Blog</news:genres>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>視覚と意味を同時に識別可能にするゼロショット学習（Discriminative Learning of Latent Features for Zero-Shot Recognition）</news:title>
   <news:publication_date>2026-04-18T04:09:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-04-18T03:17:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>プログラムを言葉にして学ぶ――抽象化された象徴的トレースからのコードベクトル（Code Vectors: Understanding Programs Through Embedded Abstracted Symbolic Traces）</news:title>
   <news:publication_date>2026-04-18T03:17:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-04-18T03:17:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>格子基底縮約を用いた高次元線形回帰（High Dimensional Linear Regression using Lattice Basis Reduction）</news:title>
   <news:publication_date>2026-04-18T03:17:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/680586</loc>
  <lastmod>2026-04-18T03:17:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>監督学習によるスキルミオン相認識の実用的意義（Supervised-learning approach for recognizing magnetic skyrmion phases）</news:title>
   <news:publication_date>2026-04-18T03:17:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/680584</loc>
  <lastmod>2026-04-18T03:16:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>異なる深度マップを賢く融合する半教師ありマルチスケール敵対ネットワーク（SDF-MAN: SEMI-SUPERVISED DISPARITY FUSION WITH MULTI-SCALE ADVERSARIAL NETWORKS）</news:title>
   <news:publication_date>2026-04-18T03:16:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>オンライン鎖分割とスケジューリング（On-line Chain Partitioning Approach to Scheduling）</news:title>
   <news:publication_date>2026-04-18T03:15:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>三峡ダム流域の土地利用マッピング（Land use mapping in the Three Gorges Reservoir Area based on semantic segmentation deep learning method）</news:title>
   <news:publication_date>2026-04-18T03:15:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-04-18T03:15:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次元データにおける希少特徴選択の再考（Rare Feature Selection in High Dimensions）</news:title>
   <news:publication_date>2026-04-18T03:15:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-04-18T02:23:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>LINE ARTIST：マルチスタイル スケッチから絵画生成スキーム（LINE ARTIST: A Multi-style Sketch to Painting Synthesis Scheme）</news:title>
   <news:publication_date>2026-04-18T02:23:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680574</loc>
  <lastmod>2026-04-18T02:23:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ウェブを知識ベースとして複雑な質問に答える方法（The Web as a Knowledge-base for Answering Complex Questions）</news:title>
   <news:publication_date>2026-04-18T02:23:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680572</loc>
  <lastmod>2026-04-18T02:22:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Zoom and Learn: 新領域に適応する深層ステレオマッチング（Zoom and Learn: Generalizing Deep Stereo Matching to Novel Domains）</news:title>
   <news:publication_date>2026-04-18T02:22:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680570</loc>
  <lastmod>2026-04-18T02:21:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>生態写真から蝶を自動検出・種同定する技術の実務的意義（Faster R-CNN based butterfly automatic identification）</news:title>
   <news:publication_date>2026-04-18T02:21:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680568</loc>
  <lastmod>2026-04-18T02:21:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>エントロピー最小化による適応的意思決定（Adaptive Decision Making via Entropy Minimization）</news:title>
   <news:publication_date>2026-04-18T02:21:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680566</loc>
  <lastmod>2026-04-18T02:21:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DAMPEデータを用いた電子・陽子分離の機械学習手法（A machine learning method to separate cosmic ray electrons from protons from 10 to 100 GeV using DAMPE data）</news:title>
   <news:publication_date>2026-04-18T02:21:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680564</loc>
  <lastmod>2026-04-18T02:21:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>半触覚インターフェースによる楽器学習の再定義（ShIFT: A Semi-haptic Interface for Flute Tutoring）</news:title>
   <news:publication_date>2026-04-18T02:21:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680562</loc>
  <lastmod>2026-04-18T01:29:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>盲目的量子計算は常に検証可能にできる（Blind quantum computing can always be made verifiable）</news:title>
   <news:publication_date>2026-04-18T01:29:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680560</loc>
  <lastmod>2026-04-18T01:29:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習によるスパイキングネットワークの反復動態（Learning recurrent dynamics in spiking networks）</news:title>
   <news:publication_date>2026-04-18T01:29:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680558</loc>
  <lastmod>2026-04-18T01:28:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大学卒業後の収入を決める特徴量の選択（Feature Selection of Post-Graduation Income of College Students in the United States）</news:title>
   <news:publication_date>2026-04-18T01:28:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680556</loc>
  <lastmod>2026-04-18T01:27:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>陽性と未ラベルの分類で頑健なAUC最大化と外れ値検出・特徴選択の統合（A Robust AUC Maximization Framework with Simultaneous Outlier Detection and Feature Selection for Positive-Unlabeled Classification）</news:title>
   <news:publication_date>2026-04-18T01:27:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680554</loc>
  <lastmod>2026-04-18T01:26:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>軌跡に基づくシーン理解と非パラメトリック混合モデル（Trajectory-based Scene Understanding using Dirichlet Process Mixture Model）</news:title>
   <news:publication_date>2026-04-18T01:26:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680552</loc>
  <lastmod>2026-04-18T01:26:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自己反復回帰による顔ランドマーク検出とランドマーク注意ネットワーク（Facial Landmarks Detection by Self-Iterative Regression based Landmarks-Attention Network）</news:title>
   <news:publication_date>2026-04-18T01:26:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680550</loc>
  <lastmod>2026-04-18T01:25:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>情報理論的セキュリティと隠密通信（Information-Theoretic Security or Covert Communication）</news:title>
   <news:publication_date>2026-04-18T01:25:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680547</loc>
  <lastmod>2026-04-18T00:33:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>構造的Query‑by‑Committeeの統一的枠組み（Structural query-by-committee）</news:title>
   <news:publication_date>2026-04-18T00:33:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680545</loc>
  <lastmod>2026-04-18T00:33:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多重散乱の高速かつ高精度な反転を行う深層学習（Efficient and accurate inversion of multiple scattering with deep learning）</news:title>
   <news:publication_date>2026-04-18T00:33:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680543</loc>
  <lastmod>2026-04-18T00:33:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>集中治療室における早期院内死亡予測（Early Hospital Mortality Prediction using Vital Signals）</news:title>
   <news:publication_date>2026-04-18T00:33:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680541</loc>
  <lastmod>2026-04-18T00:32:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層ニューラルネットワークのスケーラブル検証に向けた二重アプローチ（A Dual Approach to Scalable Verification of Deep Networks）</news:title>
   <news:publication_date>2026-04-18T00:32:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680539</loc>
  <lastmod>2026-04-18T00:32:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>有限時間スループット最大化とセンシング最適化（Finite Horizon Throughput Maximization and Sensing Optimization in Wireless Powered Devices over Fading Channels）</news:title>
   <news:publication_date>2026-04-18T00:32:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680537</loc>
  <lastmod>2026-04-18T00:32:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>サービス提供者視点のAutoML：多デバイス・多テナント下でのGP‑EIによるモデル選択（AutoML from Service Provider’s Perspective: Multi-device, Multi-tenant Model Selection with GP-EI）</news:title>
   <news:publication_date>2026-04-18T00:32:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680535</loc>
  <lastmod>2026-04-18T00:31:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>長期依存性学習のためのフーリエ回帰ユニット（Learning Long Term Dependencies via Fourier Recurrent Units）</news:title>
   <news:publication_date>2026-04-18T00:31:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680533</loc>
  <lastmod>2026-04-17T23:39:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>なぜそうなのかを教えてください？知識グラフ関係の説明文抽出（Tell Me Why Is It So? Explaining Knowledge Graph Relationships by Finding Descriptive Support Passages）</news:title>
   <news:publication_date>2026-04-17T23:39:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680531</loc>
  <lastmod>2026-04-17T23:39:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>アクティブラーニングで脆弱性検査を効率化する発想（Improving Vulnerability Inspection Efficiency Using Active Learning）</news:title>
   <news:publication_date>2026-04-17T23:39:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680529</loc>
  <lastmod>2026-04-17T23:39:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多相配電網におけるトポロジー推定（Topology Estimation using Graphical Models in Multi-Phase Power Distribution Grids）</news:title>
   <news:publication_date>2026-04-17T23:39:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680527</loc>
  <lastmod>2026-04-17T23:38:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>サブキューブ混合モデルの学習を高速化する理論的進展（Beyond the Low-Degree Algorithm: Mixtures of Subcubes and Their Applications）</news:title>
   <news:publication_date>2026-04-17T23:38:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680525</loc>
  <lastmod>2026-04-17T23:38:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>生物シグナルネットワークにおける三頂点モチーフが示す微細トポロジーと機能の関係 (Analysis of Triplet Motifs in Biological Signed Oriented Graphs Suggests a Relationship Between Fine Topology and Function)</news:title>
   <news:publication_date>2026-04-17T23:38:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680523</loc>
  <lastmod>2026-04-17T23:38:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>弱凸関数の確率的モデルベース最小化（Stochastic Model-Based Minimization of Weakly Convex Functions）</news:title>
   <news:publication_date>2026-04-17T23:38:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680521</loc>
  <lastmod>2026-04-17T23:38:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SeqFace: 連続情報を活用した顔認識（SeqFace: Make full use of sequence information for face recognition）</news:title>
   <news:publication_date>2026-04-17T23:38:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680519</loc>
  <lastmod>2026-04-17T22:46:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>テンソルの凸ココクラスタリングによる可証的分割法（Provable Convex Co-clustering of Tensors）</news:title>
   <news:publication_date>2026-04-17T22:46:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680517</loc>
  <lastmod>2026-04-17T22:46:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SDP緩和の隠れた積分性と半ランダム堅牢性（Hidden Integrality and Semi-random Robustness of SDP Relaxation for Sub-Gaussian Mixture Model）</news:title>
   <news:publication_date>2026-04-17T22:46:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680515</loc>
  <lastmod>2026-04-17T22:45:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>MergeNetによる小さな路上障害物検出の革新（MergeNet: A Deep Net Architecture for Small Obstacle Discovery）</news:title>
   <news:publication_date>2026-04-17T22:45:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680513</loc>
  <lastmod>2026-04-17T22:45:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>概念学習による無監督視覚グラウンディングの学習（Learning Unsupervised Visual Grounding Through Semantic Self-Supervision）</news:title>
   <news:publication_date>2026-04-17T22:45:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680511</loc>
  <lastmod>2026-04-17T22:45:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>イベントベース視覚データのロバスト追跡（Robust event-stream pattern tracking based on correlative filter）</news:title>
   <news:publication_date>2026-04-17T22:45:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680509</loc>
  <lastmod>2026-04-17T22:44:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>可変長粒子群最適化による深層畳み込みニューラルネットワークの進化（Evolving Deep Convolutional Neural Networks by Variable-length Particle Swarm Optimization for Image Classification）</news:title>
   <news:publication_date>2026-04-17T22:44:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680507</loc>
  <lastmod>2026-04-17T22:44:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>画像ラベルのみで注目領域を検出する弱教師あり手法（Weakly Supervised Salient Object Detection Using Image Labels）</news:title>
   <news:publication_date>2026-04-17T22:44:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680505</loc>
  <lastmod>2026-04-17T21:53:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>平均回帰ポートフォリオの設計と推定（Mean Reverting Portfolios via Penalized OU-Likelihood Estimation）</news:title>
   <news:publication_date>2026-04-17T21:53:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680503</loc>
  <lastmod>2026-04-17T21:53:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>映像解析による待ち行列理論に基づくインテリジェント交通信号スケジューリング（Queuing Theory Guided Intelligent Traffic Scheduling through Video Analysis using Dirichlet Process Mixture Model）</news:title>
   <news:publication_date>2026-04-17T21:53:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680501</loc>
  <lastmod>2026-04-17T21:53:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>粗い微分方程式の解の定義について（On the definition of a solution to a rough differential equation）</news:title>
   <news:publication_date>2026-04-17T21:53:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680499</loc>
  <lastmod>2026-04-17T21:52:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>小規模・大規模の著者認証に関するニューラルネットワークの実験（Experiments with Neural Networks for Small and Large Scale Authorship Verification）</news:title>
   <news:publication_date>2026-04-17T21:52:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680497</loc>
  <lastmod>2026-04-17T21:52:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>潜在自己相関の最大化がタンパク質動力学の変分符号化に与える利点（Variational Encoding of Protein Dynamics Benefits from Maximizing Latent Autocorrelation）</news:title>
   <news:publication_date>2026-04-17T21:52:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680495</loc>
  <lastmod>2026-04-17T21:52:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ピクセル単位クラスタ学習による提案不要のインスタンスセグメンテーション（Learning to Cluster for Proposal-Free Instance Segmentation）</news:title>
   <news:publication_date>2026-04-17T21:52:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680493</loc>
  <lastmod>2026-04-17T21:52:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>制約付き深層学習と条件付き勾配法の応用（Constrained Deep Learning using Conditional Gradient and Applications in Computer Vision）</news:title>
   <news:publication_date>2026-04-17T21:52:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680491</loc>
  <lastmod>2026-04-17T21:01:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>二部型スピンガラスとニューラルネットにおけるレプリカ対称性の破れ（Replica Symmetry Breaking in Bipartite Spin Glasses and Neural Networks）</news:title>
   <news:publication_date>2026-04-17T21:01:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680489</loc>
  <lastmod>2026-04-17T21:00:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>通信圧縮と分散学習の両立で変わる分散学習の実務適用（Communication Compression for Decentralized Training）</news:title>
   <news:publication_date>2026-04-17T21:00:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680487</loc>
  <lastmod>2026-04-17T21:00:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次元・モデルミススペシフィケーション下の大規模モデル選択（Large-Scale Model Selection with Misspecification）</news:title>
   <news:publication_date>2026-04-17T21:00:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680485</loc>
  <lastmod>2026-04-17T20:59:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Blaschke unwinding AFDに基づく心電図圧縮法の実用化可能性（A Novel Blaschke Unwinding Adaptive Fourier Decomposition Based Signal Compression Algorithm With Application on ECG Signals）</news:title>
   <news:publication_date>2026-04-17T20:59:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680483</loc>
  <lastmod>2026-04-17T20:59:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>人工マイクロスイマーの強化学習による自律化（Reinforcement Learning of Artificial Microswimmers）</news:title>
   <news:publication_date>2026-04-17T20:59:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680481</loc>
  <lastmod>2026-04-17T20:59:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>物体インスタンスの弱教師ありセグメンテーションを切り貼りで学ぶ（Learning to Segment by Cut and Paste）</news:title>
   <news:publication_date>2026-04-17T20:59:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680479</loc>
  <lastmod>2026-04-17T20:59:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>成長するデータベースの差分プライバシー（Differential Privacy for Growing Databases）</news:title>
   <news:publication_date>2026-04-17T20:59:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680477</loc>
  <lastmod>2026-04-17T20:07:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層成分解析と交互方向ニューラルネットワーク（Deep Component Analysis via Alternating Direction Neural Networks）</news:title>
   <news:publication_date>2026-04-17T20:07:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680475</loc>
  <lastmod>2026-04-17T20:07:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>意思決定支援におけるテキスト感情認識のための深層学習（Deep learning for affective computing: text-based emotion recognition in decision support）</news:title>
   <news:publication_date>2026-04-17T20:07:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680473</loc>
  <lastmod>2026-04-17T20:07:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習で条件付き現金給付（CCT）政策をどう評価するか（Evaluating Conditional Cash Transfer Policies with Machine Learning Methods）</news:title>
   <news:publication_date>2026-04-17T20:07:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680471</loc>
  <lastmod>2026-04-17T20:06:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オンラインデータのテキスト分類におけるコーパス統計（Corpus Statistics in Text Classification of Online Data）</news:title>
   <news:publication_date>2026-04-17T20:06:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680469</loc>
  <lastmod>2026-04-17T20:06:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DUNE 35トン試作検出器の光検出器システムの時間性能（Photon detector system timing performance in the DUNE 35-ton prototype）</news:title>
   <news:publication_date>2026-04-17T20:06:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680467</loc>
  <lastmod>2026-04-17T20:05:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>経済・金融時系列予測：ARIMA vs. LSTM（Forecasting Economic and Financial Time Series: ARIMA vs. LSTM）</news:title>
   <news:publication_date>2026-04-17T20:05:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680465</loc>
  <lastmod>2026-04-17T20:05:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>再帰型逆伝播の復活と改良（Reviving and Improving Recurrent Back-Propagation）</news:title>
   <news:publication_date>2026-04-17T20:05:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680463</loc>
  <lastmod>2026-04-17T19:13:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>経験的ゲーム理論解析の一般化手法（A Generalised Method for Empirical Game Theoretic Analysis）</news:title>
   <news:publication_date>2026-04-17T19:13:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680461</loc>
  <lastmod>2026-04-17T19:13:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>対数出力の整合で堅牢化する手法（Adversarial Logit Pairing）</news:title>
   <news:publication_date>2026-04-17T19:13:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680459</loc>
  <lastmod>2026-04-17T19:13:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューラル機械翻訳のためのTensor2Tensor（Tensor2Tensor for Neural Machine Translation）</news:title>
   <news:publication_date>2026-04-17T19:13:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680457</loc>
  <lastmod>2026-04-17T19:11:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>セマンティック敵対的事例とその示唆（Semantic Adversarial Examples）</news:title>
   <news:publication_date>2026-04-17T19:11:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680455</loc>
  <lastmod>2026-04-17T19:11:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>電弱ゲージボソンのパートン分布関数（Electroweak Gauge Boson Parton Distribution Functions）</news:title>
   <news:publication_date>2026-04-17T19:11:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680453</loc>
  <lastmod>2026-04-17T19:11:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Snap MLによる階層的機械学習フレームワークの要点解説（Snap ML: A Hierarchical Framework for Machine Learning）</news:title>
   <news:publication_date>2026-04-17T19:11:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680451</loc>
  <lastmod>2026-04-17T19:10:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>顔を照明プローブとして使う（Faces as Lighting Probes via Unsupervised Deep Highlight Extraction）</news:title>
   <news:publication_date>2026-04-17T19:10:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680449</loc>
  <lastmod>2026-04-17T18:18:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率プログラムのネスト化がもたらす統計的影響（Nesting Probabilistic Programs）</news:title>
   <news:publication_date>2026-04-17T18:18:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680447</loc>
  <lastmod>2026-04-17T18:18:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Deep Structured Active Contoursによる建物境界の精密化（Learning deep structured active contours end-to-end）</news:title>
   <news:publication_date>2026-04-17T18:18:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680445</loc>
  <lastmod>2026-04-17T18:18:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>粒子ベースの変分的BNMFアプローチ（A particle-based variational approach to BNMF）</news:title>
   <news:publication_date>2026-04-17T18:18:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680443</loc>
  <lastmod>2026-04-17T18:17:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>EVA2によるライブコンピュータビジョンの省力化（EVA2: Exploiting Temporal Redundancy in Live Computer Vision）</news:title>
   <news:publication_date>2026-04-17T18:17:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680441</loc>
  <lastmod>2026-04-17T18:17:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>部分的なマルチマッチングの同期化（Synchronisation of Partial Multi-Matchings via Non-negative Factorisations）</news:title>
   <news:publication_date>2026-04-17T18:17:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680439</loc>
  <lastmod>2026-04-17T18:16:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>超拡散銀河の星成分の分光学的特徴づけ（Spectroscopic characterisation of the stellar content of ultra diffuse galaxies）</news:title>
   <news:publication_date>2026-04-17T18:16:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680437</loc>
  <lastmod>2026-04-17T18:16:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>合成画像の現実性最適化によるパーツ分割性能の改善（Improved Part Segmentation Performance by Optimising Realism of Synthetic Images using Cycle Generative Adversarial Networks）</news:title>
   <news:publication_date>2026-04-17T18:16:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680435</loc>
  <lastmod>2026-04-17T17:24:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次元確率反転に対する随伴モデルと機械学習の統合（High-dimensional Stochastic Inversion via Adjoint Models and Machine Learning）</news:title>
   <news:publication_date>2026-04-17T17:24:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680433</loc>
  <lastmod>2026-04-17T17:24:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>二層最適化によるハイブリッドシステムの反証探索（Two-Layered Falsification of Hybrid Systems Guided by Monte Carlo Tree Search）</news:title>
   <news:publication_date>2026-04-17T17:24:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680431</loc>
  <lastmod>2026-04-17T17:24:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフ分割ニューラルネットワークによる半教師付き分類（Graph Partition Neural Networks for Semi-Supervised Classification）</news:title>
   <news:publication_date>2026-04-17T17:24:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/680429</loc>
  <lastmod>2026-04-17T17:22:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>手書き文書におけるテキストと固有表現の同時認識（Joint Recognition of Handwritten Text and Named Entities with a Neural End-to-end Model）</news:title>
   <news:publication_date>2026-04-17T17:22:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680427</loc>
  <lastmod>2026-04-17T17:22:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>シオンガン新区における将来都市成長のシミュレーション（Simulating the future urban growth in Xiongan New Area: a upcoming big city in China）</news:title>
   <news:publication_date>2026-04-17T17:22:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680425</loc>
  <lastmod>2026-04-17T17:22:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>回転等変性を組み込んだ高解像度土地被覆マッピング（Land cover mapping at very high resolution with rotation equivariant CNNs）</news:title>
   <news:publication_date>2026-04-17T17:22:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680423</loc>
  <lastmod>2026-04-17T17:22:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>共有施設利用者の調整をデータ駆動予測で支援する（Coordinating users of shared facilities via data-driven predictive assistants and game theory）</news:title>
   <news:publication_date>2026-04-17T17:22:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680421</loc>
  <lastmod>2026-04-17T16:30:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチビュー3D物体検索に効くトリプレットセンター損失（Triplet-Center Loss for Multi-View 3D Object Retrieval）</news:title>
   <news:publication_date>2026-04-17T16:30:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680419</loc>
  <lastmod>2026-04-17T16:22:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分子グラフ畳み込みによる薬物物性予測の新展開（Chemi-Net: A molecular graph convolutional network for accurate drug property prediction）</news:title>
   <news:publication_date>2026-04-17T16:22:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680417</loc>
  <lastmod>2026-04-17T16:22:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>協力と競争で重みを決める発電予測アンサンブル（A Multi-Scheme Ensemble Using Coopetitive Soft-Gating With Application to Power Forecasting for Renewable Energy Generation）</news:title>
   <news:publication_date>2026-04-17T16:22:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680415</loc>
  <lastmod>2026-04-17T16:21:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>顔写真ベースの年齢推定データベースの公開がもたらすインパクト（The AgeGuess database: an open online resource on chronological and perceived ages of people aged 3-100）</news:title>
   <news:publication_date>2026-04-17T16:21:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680413</loc>
  <lastmod>2026-04-17T16:20:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>UVITによるラム圧剥離の観測：Abell 85のGASPジェリーフィッシュ銀河JO201の剥離ガスにおける星形成（UVIT view of ram-pressure stripping in action: Star formation in the stripped gas of the GASP jellyfish galaxy JO201 in Abell 85）</news:title>
   <news:publication_date>2026-04-17T16:20:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680411</loc>
  <lastmod>2026-04-17T16:20:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>第三世代クォークに結合する重いBSM粒子の探索（Search for heavy BSM particles coupling to third generation quarks at CMS）</news:title>
   <news:publication_date>2026-04-17T16:20:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680409</loc>
  <lastmod>2026-04-17T16:19:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模データの信頼性応用における複雑性の次元（Big Data and Reliability Applications: The Complexity Dimension）</news:title>
   <news:publication_date>2026-04-17T16:19:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680407</loc>
  <lastmod>2026-04-17T15:27:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ApolloScapeによる自動運転用大規模データセットの価値（The ApolloScape Open Dataset for Autonomous Driving and its Application）</news:title>
   <news:publication_date>2026-04-17T15:27:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680405</loc>
  <lastmod>2026-04-17T15:27:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ARMスケーラブルベクター拡張（The ARM Scalable Vector Extension）</news:title>
   <news:publication_date>2026-04-17T15:27:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680403</loc>
  <lastmod>2026-04-17T15:27:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GDPR時代におけるHCIの優先課題（Some HCI Priorities for GDPR-Compliant Machine Learning）</news:title>
   <news:publication_date>2026-04-17T15:27:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680401</loc>
  <lastmod>2026-04-17T15:26:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>巨大星周囲の塵円盤に見つかった偏光の意味（A polarized dusty disk around a massive star）</news:title>
   <news:publication_date>2026-04-17T15:26:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680399</loc>
  <lastmod>2026-04-17T15:25:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>物体の言語記述による検出と検索の統合（Object Captioning and Retrieval with Natural Language）</news:title>
   <news:publication_date>2026-04-17T15:25:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680397</loc>
  <lastmod>2026-04-17T15:25:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>エネルギー自律型モバイルネットワークの実現（Energy Sustainable Mobile Networks via Energy Routing, Learning and Foresighted Optimization）</news:title>
   <news:publication_date>2026-04-17T15:25:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680395</loc>
  <lastmod>2026-04-17T15:25:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>間質性肺疾患の病理組織セグメンテーション（Semantic Segmentation of Pathological Lung Tissue with Dilated Fully Convolutional Networks）</news:title>
   <news:publication_date>2026-04-17T15:25:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680393</loc>
  <lastmod>2026-04-17T14:33:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>現代のデータ拡張のカーネル理論（A Kernel Theory of Modern Data Augmentation）</news:title>
   <news:publication_date>2026-04-17T14:33:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680391</loc>
  <lastmod>2026-04-17T14:25:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層圧縮表現からの画像理解（TOWARDS IMAGE UNDERSTANDING FROM DEEP COMPRESSION WITHOUT DECODING）</news:title>
   <news:publication_date>2026-04-17T14:25:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680389</loc>
  <lastmod>2026-04-17T14:24:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>対称群上のガウス場：予測と学習（Gaussian field on the symmetric group: prediction and learning）</news:title>
   <news:publication_date>2026-04-17T14:24:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680387</loc>
  <lastmod>2026-04-17T14:23:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>木構造から学ぶスパース深層ネットワークの設計（Learning Sparse Deep Feedforward Networks via Tree Skeleton Expansion）</news:title>
   <news:publication_date>2026-04-17T14:23:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680385</loc>
  <lastmod>2026-04-17T14:23:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層学習の脆弱性 (Vulnerability of Deep Learning)</news:title>
   <news:publication_date>2026-04-17T14:23:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680383</loc>
  <lastmod>2026-04-17T14:22:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>作業記憶を用いた視覚推論のためのデータセットとアーキテクチャ（A Dataset and Architecture for Visual Reasoning with a Working Memory）</news:title>
   <news:publication_date>2026-04-17T14:22:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680381</loc>
  <lastmod>2026-04-17T14:22:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>線形的にサブクリティカルなプラズマにおける電子ホール不安定性（Electron hole instability in linearly sub-critical plasmas）</news:title>
   <news:publication_date>2026-04-17T14:22:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680379</loc>
  <lastmod>2026-04-17T13:31:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>周辺ビューによる実時間移動物体検知・追跡・分類（Real-time Detection, Tracking, and Classification of Moving and Stationary Objects using Multiple Fisheye Images）</news:title>
   <news:publication_date>2026-04-17T13:31:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680377</loc>
  <lastmod>2026-04-17T13:31:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>汚れたデータが示す業務リスクと選択指針（Impacts of Dirty Data: an Experimental Evaluation）</news:title>
   <news:publication_date>2026-04-17T13:31:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680375</loc>
  <lastmod>2026-04-17T13:30:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DNNの学習を評価するためのベンチマーク設計（TBD: Benchmarking and Analyzing Deep Neural Network Training）</news:title>
   <news:publication_date>2026-04-17T13:30:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680373</loc>
  <lastmod>2026-04-17T13:29:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>さまようブラックホールの速度と周囲媒質の特性を推定する手法（Characterizing the velocity of a wandering black hole and properties of the surrounding medium using convolutional neural networks）</news:title>
   <news:publication_date>2026-04-17T13:29:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680371</loc>
  <lastmod>2026-04-17T13:29:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時間的相互作用データにおける疎性・不均一性・相互応答性・コミュニティ構造のモデル化（Modelling sparsity, heterogeneity, reciprocity and community structure in temporal interaction data）</news:title>
   <news:publication_date>2026-04-17T13:29:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680369</loc>
  <lastmod>2026-04-17T13:29:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散キャッシュ対応V2Xネットワークの提案と課題（Distributed Cache Enabled V2X Network, Proposals, Research Trends and Challenging Issues）</news:title>
   <news:publication_date>2026-04-17T13:29:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680367</loc>
  <lastmod>2026-04-17T13:29:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動的構造を持つ意味伝播ネットワークの要点（Dynamic-structured Semantic Propagation Network）</news:title>
   <news:publication_date>2026-04-17T13:29:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680365</loc>
  <lastmod>2026-04-17T12:36:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>誤ったシステムモデルに対するロバスト性（ROBUSTNESS TO INCORRECT SYSTEM MODELS IN STOCHASTIC CONTROL）</news:title>
   <news:publication_date>2026-04-17T12:36:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680363</loc>
  <lastmod>2026-04-17T12:28:56Z</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RF異常検知のための深層予測符号化ニューラルネットワーク（Deep Predictive Coding Neural Network for RF Anomaly Detection in Wireless Networks）</news:title>
   <news:publication_date>2026-04-17T12:28:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680361</loc>
  <lastmod>2026-04-17T12:28:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ガウス過程における定数時予測分布の実現（Constant-Time Predictive Distributions for Gaussian Processes）</news:title>
   <news:publication_date>2026-04-17T12:28:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680359</loc>
  <lastmod>2026-04-17T12:27:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Deep Multiple Instance Learningによるゼロショット画像タグ付け（Deep Multiple Instance Learning for Zero-shot Image Tagging）</news:title>
   <news:publication_date>2026-04-17T12:27:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680357</loc>
  <lastmod>2026-04-17T12:26:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ゼロショット物体検出（Zero-Shot Object Detection: Learning to Simultaneously Recognize and Localize Novel Concepts）</news:title>
   <news:publication_date>2026-04-17T12:26:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680355</loc>
  <lastmod>2026-04-17T12:26:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ハライド・ペロブスカイトの安定性を機械学習で設計する（Stability Engineering of Halide Perovskite via Machine Learning）</news:title>
   <news:publication_date>2026-04-17T12:26:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680353</loc>
  <lastmod>2026-04-17T12:26:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数学校や病院で測る因果効果の読み解き方（Identifying and Estimating Principal Causal Effects in Multi-site Trials）</news:title>
   <news:publication_date>2026-04-17T12:26:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680351</loc>
  <lastmod>2026-04-17T11:35:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>交差点における転移可能な歩行者軌跡予測モデル（Transferable Pedestrian Motion Prediction Models at Intersections）</news:title>
   <news:publication_date>2026-04-17T11:35:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680349</loc>
  <lastmod>2026-04-17T11:34:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>二部ネットワークの最適クラスタリング（Optimal Bipartite Network Clustering）</news:title>
   <news:publication_date>2026-04-17T11:34:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680347</loc>
  <lastmod>2026-04-17T11:34:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>レクリエーショナルランナーの乳酸閾値推定に機械学習を使う意義（Estimation of lactate threshold with machine learning techniques in recreational runners）</news:title>
   <news:publication_date>2026-04-17T11:34:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680345</loc>
  <lastmod>2026-04-17T11:33:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的勾配で鞍点を脱出する仕組み（Escaping Saddles with Stochastic Gradients）</news:title>
   <news:publication_date>2026-04-17T11:33:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680343</loc>
  <lastmod>2026-04-17T11:33:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Deep Learningによる超短パルス再構成（Deep Learning Reconstruction of Ultra-Short Pulses）</news:title>
   <news:publication_date>2026-04-17T11:33:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680341</loc>
  <lastmod>2026-04-17T11:33:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>畳み込みニューラルネットワークのハードウェア実装効率化（Efficient Hardware Realization of Convolutional Neural Networks using Intra-Kernel Regular Pruning）</news:title>
   <news:publication_date>2026-04-17T11:33:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680339</loc>
  <lastmod>2026-04-17T11:32:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>リッジ回帰と可証的な決定論的リッジレバレッジスコアサンプリング（Ridge Regression and Provable Deterministic Ridge Leverage Score Sampling）</news:title>
   <news:publication_date>2026-04-17T11:32:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680337</loc>
  <lastmod>2026-04-17T10:41:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Higuchiのフラクタル次元とサンプルエントロピーを特徴量としたEEG機械学習によるうつ病検出（EEG machine learning with Higuchi’s fractal dimension and Sample Entropy as features for successful detection of depression）</news:title>
   <news:publication_date>2026-04-17T10:41:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680335</loc>
  <lastmod>2026-04-17T10:41:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Deep Co-Training による半教師あり画像認識の実務的意義（Deep Co-Training for Semi-Supervised Image Recognition）</news:title>
   <news:publication_date>2026-04-17T10:41:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680333</loc>
  <lastmod>2026-04-17T10:41:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Real-time Deep Pose Estimation with Geodesic Loss for Image-to-Template Rigid Registration（Real-time Deep Pose Estimation with Geodesic Loss for Image-to-Template Rigid Registration）</news:title>
   <news:publication_date>2026-04-17T10:41:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680331</loc>
  <lastmod>2026-04-17T10:40:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Chironによるプライバシー保護されたML-as-a-Serviceの実現（Chiron: Privacy-preserving Machine Learning as a Service）</news:title>
   <news:publication_date>2026-04-17T10:40:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680329</loc>
  <lastmod>2026-04-17T10:39:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>光学的電荷変換による高周波エレクトロメトリ（Electrometry by Optical Charge Conversion of Deep Defects in 4H-SiC）</news:title>
   <news:publication_date>2026-04-17T10:39:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680327</loc>
  <lastmod>2026-04-17T10:39:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>航空旅程選択予測のためのPointer Networksを用いたDeep Choice Model（Deep Choice Model Using Pointer Networks for Airline Itinerary Prediction）</news:title>
   <news:publication_date>2026-04-17T10:39:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680325</loc>
  <lastmod>2026-04-17T10:38:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習済み畳み込みニューラルネットワークの不変性の研究（Studying Invariances of Trained Convolutional Neural Networks）</news:title>
   <news:publication_date>2026-04-17T10:38:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680323</loc>
  <lastmod>2026-04-17T09:48:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習と評価の接点（Learning meets Assessment: On the relation between Item Response Theory and Bayesian Knowledge Tracing）</news:title>
   <news:publication_date>2026-04-17T09:48:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680321</loc>
  <lastmod>2026-04-17T09:47:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文脈的変調と算術的相互作用の情報理論的分解（Contrasting information theoretic decompositions of modulatory and arithmetic interactions in neural information processing systems）</news:title>
   <news:publication_date>2026-04-17T09:47:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680319</loc>
  <lastmod>2026-04-17T09:47:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CNNをFPGAへ写像するためのツールフロー調査（Toolflows for Mapping Convolutional Neural Networks on FPGAs）</news:title>
   <news:publication_date>2026-04-17T09:47:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680317</loc>
  <lastmod>2026-04-17T09:47:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>クーロンガス電気統計がKPZ方程式の大ゆらぎを制御する（Coulomb-gas electrostatics controls large fluctuations of the KPZ equation）</news:title>
   <news:publication_date>2026-04-17T09:47:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680315</loc>
  <lastmod>2026-04-17T09:47:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GossipGraDによる通信効率化で大規模学習を現実に（GossipGraD: Scalable Deep Learning using Gossip Communication based Asynchronous Gradient Descent）</news:title>
   <news:publication_date>2026-04-17T09:47:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680313</loc>
  <lastmod>2026-04-17T09:47:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ブロックチェーン上の分散型データ販売（Distributed Data Vending on Blockchain）</news:title>
   <news:publication_date>2026-04-17T09:47:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680311</loc>
  <lastmod>2026-04-17T09:46:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層構造推論ネットワークによる顔表情の部分ユニット認識（Deep Structure Inference Network for Facial Action Unit Recognition）</news:title>
   <news:publication_date>2026-04-17T09:46:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680309</loc>
  <lastmod>2026-04-17T08:55:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習による反復デコーディングでロスのある画像圧縮を改善する手法（Learned Neural Iterative Decoding for Lossy Image Compression Systems）</news:title>
   <news:publication_date>2026-04-17T08:55:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680307</loc>
  <lastmod>2026-04-17T08:55:11Z</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自己複製の実装と訓練手法（Neural Network Quine）</news:title>
   <news:publication_date>2026-04-17T08:55:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680305</loc>
  <lastmod>2026-04-17T08:54:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>限られた時系列データから構造を暗黙的に捉える手法（Capturing Structure Implicitly from Time-Series having Limited Data）</news:title>
   <news:publication_date>2026-04-17T08:54:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680303</loc>
  <lastmod>2026-04-17T08:54:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>擬似マスクを用いた物体検出の拡張（Pseudo Mask Augmented Object Detection）</news:title>
   <news:publication_date>2026-04-17T08:54:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680301</loc>
  <lastmod>2026-04-17T08:54:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習を伴う実物オプションにおける技術的不確実性（Technical Uncertainty in Real Options with Learning）</news:title>
   <news:publication_date>2026-04-17T08:54:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680299</loc>
  <lastmod>2026-04-17T08:54:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所スペクトルグラフ畳み込みを用いた点群特徴学習（Local Spectral Graph Convolution for Point Set Feature Learning）</news:title>
   <news:publication_date>2026-04-17T08:54:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680297</loc>
  <lastmod>2026-04-17T08:53:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ユーザデータ注釈の課題と実務的な道筋（CHALLENGES IN ANNOTATION OF USER DATA FOR UBIQUITOUS SYSTEMS: RESULTS FROM THE 1ST ARDUOUS WORKSHOP）</news:title>
   <news:publication_date>2026-04-17T08:53:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680295</loc>
  <lastmod>2026-04-17T08:02:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>OFDMオートエンコーダによる通信システムのエンドツーエンド学習（OFDM-Autoencoder for End-to-End Learning of Communications Systems）</news:title>
   <news:publication_date>2026-04-17T08:02:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680293</loc>
  <lastmod>2026-04-17T08:01:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非定常・非混合時系列予測の理論とアルゴリズム（Theory and Algorithms for Forecasting Time Series）</news:title>
   <news:publication_date>2026-04-17T08:01:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680291</loc>
  <lastmod>2026-04-17T08:00:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文脈依存ランキング関数を学習する深層アーキテクチャ（Deep Architectures for Learning Context-dependent Ranking Functions）</news:title>
   <news:publication_date>2026-04-17T08:00:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680289</loc>
  <lastmod>2026-04-17T07:59:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>受容野プロファイルからV1の距離モデルへ（From Receptive Profiles to a Metric Model of V1）</news:title>
   <news:publication_date>2026-04-17T07:59:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680287</loc>
  <lastmod>2026-04-17T07:59:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>モンドリアン木と森のミニマックス最適率（Minimax Optimal Rates for Mondrian Trees and Forests）</news:title>
   <news:publication_date>2026-04-17T07:59:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680285</loc>
  <lastmod>2026-04-17T07:59:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-04-17T07:07:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非把持操作による物体再配置を学習する研究（Rearrangement with Nonprehensile Manipulation Using Deep Reinforcement Learning）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/680267</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-04-17T06:13:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-04-17T06:12:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680243</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散スパース学習におけるデータ分割最適化が収束を加速する仕組み（Proximal SCOPE for distributed sparse learning: Better data partition implies faster convergence rate）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680239</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 <url>
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    <news:name>AI Benchmark Research</news:name>
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   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/680211</loc>
  <lastmod>2026-04-17T02:37:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-04-17T02:37:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680209</loc>
  <lastmod>2026-04-17T02:37:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-04-17T02:37:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680207</loc>
  <lastmod>2026-04-17T02:36:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>言語ピボットによる非対訳画像キャプション生成（Unpaired Image Captioning by Language Pivoting）</news:title>
   <news:publication_date>2026-04-17T02:36:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680205</loc>
  <lastmod>2026-04-17T02:36:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ヒッグスとトップの共演：ttHの多レプトン最終状態での証拠 (Evidence for associated production of a Higgs boson with a top quark pair in final states with electrons, muons, and hadronically decaying τ leptons at √s = 13 TeV)</news:title>
   <news:publication_date>2026-04-17T02:36:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680203</loc>
  <lastmod>2026-04-17T02:36:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ターゲット変化検出と衛星画像解析（Targeted change detection in remote sensing images）</news:title>
   <news:publication_date>2026-04-17T02:36:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680201</loc>
  <lastmod>2026-04-17T02:36:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>肺癌の深層学習による診断支援（Computer-aided diagnosis of lung carcinoma using deep learning – a pilot study）</news:title>
   <news:publication_date>2026-04-17T02:36:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680199</loc>
  <lastmod>2026-04-17T02:35:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>整数データに特化したスコアリング手法SUSTainの概観（SUSTain: Scalable Unsupervised Scoring for Tensors and its Application to Phenotyping）</news:title>
   <news:publication_date>2026-04-17T02:35:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680197</loc>
  <lastmod>2026-04-17T01:44:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>材料探索の自律効率的実験設計（Autonomous Efficient Experiment Design for Materials Discovery with Bayesian Model Averaging）</news:title>
   <news:publication_date>2026-04-17T01:44:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680195</loc>
  <lastmod>2026-04-17T01:44:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自動車の速度と車線変更の意思決定を自動で学ぶ手法（Automated Speed and Lane Change Decision Making using Deep Reinforcement Learning）</news:title>
   <news:publication_date>2026-04-17T01:44:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680193</loc>
  <lastmod>2026-04-17T01:44:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GOODS-N深部20cm帯JVLAイメージングの成果（DEEP JVLA IMAGING OF GOODS-N AT 20CM）</news:title>
   <news:publication_date>2026-04-17T01:44:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680191</loc>
  <lastmod>2026-04-17T01:43:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>重みの平均化がもたらす広い最適解と汎化改善（Averaging Weights Leads to Wider Optima and Better Generalization）</news:title>
   <news:publication_date>2026-04-17T01:43:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680189</loc>
  <lastmod>2026-04-17T01:43:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>一般化構造CNN（Generalised Structural CNNs for time series data with arbitrary graph topology）</news:title>
   <news:publication_date>2026-04-17T01:43:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680187</loc>
  <lastmod>2026-04-17T01:42:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>3Dゲームにおける同時複数行動の模倣学習（Imitation Learning with Concurrent Actions in 3D Games）</news:title>
   <news:publication_date>2026-04-17T01:42:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680185</loc>
  <lastmod>2026-04-17T01:42:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>新たな古典新星の殻の発見（Discovery of a new classical nova shell around a nova-like cataclysmic variable）</news:title>
   <news:publication_date>2026-04-17T01:42:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680183</loc>
  <lastmod>2026-04-17T00:51:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>画像の自動彩色を変えた生成的敵対ネットワークの応用（Image Colorization using Generative Adversarial Networks）</news:title>
   <news:publication_date>2026-04-17T00:51:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680181</loc>
  <lastmod>2026-04-17T00:50:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>冗長性技術による分散最適化とストラッグラー緩和（Redundancy Techniques for Distributed Optimization）</news:title>
   <news:publication_date>2026-04-17T00:50:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680179</loc>
  <lastmod>2026-04-17T00:50:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率計算ベースのニューラルネットと2値ニューラルネットの普遍近似性と等価性（On the Universal Approximation Property and Equivalence of Stochastic Computing-based Neural Networks and Binary Neural Networks）</news:title>
   <news:publication_date>2026-04-17T00:50:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680177</loc>
  <lastmod>2026-04-17T00:49:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>電子線照射下のWS2における欠陥と相の進化の深層学習解析（Deep Learning Analysis of Defect and Phase Evolution During Electron Beam Induced Transformations in WS2）</news:title>
   <news:publication_date>2026-04-17T00:49:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680175</loc>
  <lastmod>2026-04-17T00:49:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単眼SAR画像からの地形推定に向けたCNNアプローチ（Towards Monocular Digital Elevation Model (DEM) Estimation by Convolutional Neural Networks）</news:title>
   <news:publication_date>2026-04-17T00:49:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680173</loc>
  <lastmod>2026-04-17T00:48:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>二層ライドバーグ原子・極性分子系の有効スピン相互作用（Effective spin-spin interactions in bilayers of Rydberg atoms and polar molecules）</news:title>
   <news:publication_date>2026-04-17T00:48:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680171</loc>
  <lastmod>2026-04-17T00:48:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自己相似エポック：データ配列が学習効率を変える（Self-Similar Epochs: Value in Arrangement）</news:title>
   <news:publication_date>2026-04-17T00:48:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680169</loc>
  <lastmod>2026-04-16T23:57:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>長時間文脈を捉える感情データセットの提案（The OMG-Emotion Behavior Dataset）</news:title>
   <news:publication_date>2026-04-16T23:57:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680167</loc>
  <lastmod>2026-04-16T23:57:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>測定に基づく適応プロトコルと量子強化学習（Measurement-based adaptation protocol with quantum reinforcement learning）</news:title>
   <news:publication_date>2026-04-16T23:57:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680165</loc>
  <lastmod>2026-04-16T23:56:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>経口崩壊錠の処方をニューラルネットワークで予測する（Predicting Oral Disintegrating Tablet Formulations by Neural Network Techniques）</news:title>
   <news:publication_date>2026-04-16T23:56:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680163</loc>
  <lastmod>2026-04-16T23:56:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>変化点は“スパースな説明”の導入──Variational Bayesianを用いた行列分解/補完の近似手法（Approximate Method of Variational Bayesian Matrix Factorization/Completion with Sparse Prior）</news:title>
   <news:publication_date>2026-04-16T23:56:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680161</loc>
  <lastmod>2026-04-16T23:56:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>代数的機械学習の概観（Algebraic Machine Learning）</news:title>
   <news:publication_date>2026-04-16T23:56:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680159</loc>
  <lastmod>2026-04-16T23:55:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフ上で学ぶドメイン適応（Domain Adaptation on Graphs by Learning Aligned Graph Bases）</news:title>
   <news:publication_date>2026-04-16T23:55:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680157</loc>
  <lastmod>2026-04-16T23:55:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>デザインによる透明性：視覚的推論における性能と解釈性のギャップを埋める（Transparency by Design: Closing the Gap Between Performance and Interpretability in Visual Reasoning）</news:title>
   <news:publication_date>2026-04-16T23:55:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680155</loc>
  <lastmod>2026-04-16T23:04:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>クロモスフェアのMg IIラインにおける青側増強の観測と解釈（Blue wing enhancement of the chromospheric Mg II h and k lines in a solar flare）</news:title>
   <news:publication_date>2026-04-16T23:04:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680153</loc>
  <lastmod>2026-04-16T23:03:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>木構造受容野を用いたスパースな深層フィードフォワードネットワークの構築（Building Sparse Deep Feedforward Networks using Tree Receptive Fields）</news:title>
   <news:publication_date>2026-04-16T23:03:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680151</loc>
  <lastmod>2026-04-16T23:03:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層畳み込み残差ノイズ除去ネットワークによる画像デモザイキングとノイズ除去の統合（Deep Image Demosaicking using a Cascade of Convolutional Residual Denoising Networks）</news:title>
   <news:publication_date>2026-04-16T23:03:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680149</loc>
  <lastmod>2026-04-16T23:02:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>潜在ツリー型変分オートエンコーダによる多面クラスタリングの提案（LEARNING LATENT SUPERSTRUCTURES IN VARIATIONAL AUTOENCODERS FOR DEEP MULTIDIMENSIONAL CLUSTERING）</news:title>
   <news:publication_date>2026-04-16T23:02:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680147</loc>
  <lastmod>2026-04-16T23:02:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>リアルタイム心血管MRIにおける時空間アーティファクト抑制の深層学習（Real-time Cardiovascular MR with Spatio-temporal Artifact Suppression using Deep Learning）</news:title>
   <news:publication_date>2026-04-16T23:02:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680145</loc>
  <lastmod>2026-04-16T23:01:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層ニューラルネットワークによる量子制御補正の近似（Approximation of quantum control correction scheme using deep neural networks）</news:title>
   <news:publication_date>2026-04-16T23:01:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680143</loc>
  <lastmod>2026-04-16T23:01:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチレベルの文脈を組み合わせたスーパーピクセルによる自然画像ラベリング（Combining Multi-level Contexts of Superpixel using Convolutional Neural Networks to perform Natural Scene Labeling）</news:title>
   <news:publication_date>2026-04-16T23:01:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680141</loc>
  <lastmod>2026-04-16T22:10:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>xDeepFM：明示的・暗黙的な特徴量相互作用を統合する推薦モデル（xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems）</news:title>
   <news:publication_date>2026-04-16T22:10:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680139</loc>
  <lastmod>2026-04-16T22:10:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>広帯域二層反射防止構造によるシリコン真空窓の実現（A 1.6:1 Bandwidth Two-Layer Antireflection Structure for Silicon Matched to the 190–310 GHz Atmospheric Window）</news:title>
   <news:publication_date>2026-04-16T22:10:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680137</loc>
  <lastmod>2026-04-16T22:09:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>量子制御学習における幾何学的表現と時系列表現の比較（Geometrical versus time-series representation of data in quantum control learning）</news:title>
   <news:publication_date>2026-04-16T22:09:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680135</loc>
  <lastmod>2026-04-16T22:08:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ISISの最盛期におけるアラビア語Twitter議論と示唆（ISIS at its apogee: the Arabic discourse on Twitter and what we can learn from that about ISIS support and Foreign Fighters）</news:title>
   <news:publication_date>2026-04-16T22:08:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680133</loc>
  <lastmod>2026-04-16T22:08:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>アドバーサリアル・データ・プログラミング（Adversarial Data Programming: Using GANs to Relax the Bottleneck of Curated Labeled Data）</news:title>
   <news:publication_date>2026-04-16T22:08:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680131</loc>
  <lastmod>2026-04-16T22:08:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率母関数を用いた感染症モデル入門（A primer on the use of probability generating functions in infectious disease modeling）</news:title>
   <news:publication_date>2026-04-16T22:08:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680129</loc>
  <lastmod>2026-04-16T22:08:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>畳み込み非負値行列因子分解の乗法更新則とβダイバージェンス（Multiplicative Updates for Convolutional NMF Under β-Divergence）</news:title>
   <news:publication_date>2026-04-16T22:08:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680127</loc>
  <lastmod>2026-04-16T21:16:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>C-LSTMによるFPGA上の効率的なLSTM実装（C-LSTM: Enabling Efficient LSTM using Structured Compression Techniques on FPGAs）</news:title>
   <news:publication_date>2026-04-16T21:16:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680125</loc>
  <lastmod>2026-04-16T21:13:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>アナログHTMの学習を伴わない特徴抽出とメムリスタ・CMOS回路設計（Feature extraction without learning in an analog Spatial Pooler）</news:title>
   <news:publication_date>2026-04-16T21:13:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680123</loc>
  <lastmod>2026-04-16T21:13:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>信号処理における分割凸フィッティング（Signal Processing and Piecewise Convex Estimation）</news:title>
   <news:publication_date>2026-04-16T21:13:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680121</loc>
  <lastmod>2026-04-16T21:12:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>検索順位学習データセットにおける特徴選択とモデル比較（Feature Selection and Model Comparison on Microsoft Learning-to-Rank Data Sets）</news:title>
   <news:publication_date>2026-04-16T21:12:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680119</loc>
  <lastmod>2026-04-16T21:12:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>網膜OCTのトポロジー保証付きセグメンテーション（Topology guaranteed segmentation of the human retina from OCT using convolutional neural networks）</news:title>
   <news:publication_date>2026-04-16T21:12:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680117</loc>
  <lastmod>2026-04-16T21:12:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>MT-Spike：時間を使うスパイキングニューラルネットで多層学習を実現する（MT-Spike: A Multilayer Time-based Spiking Neuromorphic Architecture with Temporal Error Backpropagation）</news:title>
   <news:publication_date>2026-04-16T21:12:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680115</loc>
  <lastmod>2026-04-16T21:11:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>協働的マルチタスク訓練による敵対的攻撃への防御（Defending against Adversarial Attack towards Deep Neural Networks via Collaborative Multi-Task Training）</news:title>
   <news:publication_date>2026-04-16T21:11:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680113</loc>
  <lastmod>2026-04-16T20:20:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>特徴蒸留：敵対的例に対するDNN志向JPEG圧縮（Feature Distillation: DNN-Oriented JPEG Compression Against Adversarial Examples）</news:title>
   <news:publication_date>2026-04-16T20:20:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680111</loc>
  <lastmod>2026-04-16T20:20:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>別ラベルからのアップリフトモデリング（Uplift Modeling from Separate Labels）</news:title>
   <news:publication_date>2026-04-16T20:20:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680109</loc>
  <lastmod>2026-04-16T20:19:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>近傍を学習するランキング手法の要点（Ranking with Adaptive Neighbors）</news:title>
   <news:publication_date>2026-04-16T20:19:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680107</loc>
  <lastmod>2026-04-16T20:18:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>安定性に基づくモデル非依存プライベート学習（Model-Agnostic Private Learning via Stability）</news:title>
   <news:publication_date>2026-04-16T20:18:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680105</loc>
  <lastmod>2026-04-16T20:18:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>不正確な事前分布に対するロバスト性（Robustness to Incorrect Priors in Partially Observed Stochastic Control）</news:title>
   <news:publication_date>2026-04-16T20:18:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680103</loc>
  <lastmod>2026-04-16T20:17:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DNNに適したJPEG圧縮の再設計—DeepN-JPEGの要点と実務インパクト（DeepN-JPEG: A Deep Neural Network Favorable JPEG-based Image Compression Framework）</news:title>
   <news:publication_date>2026-04-16T20:17:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680101</loc>
  <lastmod>2026-04-16T20:17:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>バケット縮約による近似推論（Bucket Renormalization for Approximate Inference）</news:title>
   <news:publication_date>2026-04-16T20:17:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680099</loc>
  <lastmod>2026-04-16T19:25:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>顔画像における注目領域を用いた軟生体情報分類（SAF-BAGE: Salient Approach for Facial Soft-Biometric Classification - Age, Gender, and Facial Expression）</news:title>
   <news:publication_date>2026-04-16T19:25:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680097</loc>
  <lastmod>2026-04-16T19:18:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>アルゴリズムによる社会介入の提案（Thesis Proposal: Algorithmic Social Intervention）</news:title>
   <news:publication_date>2026-04-16T19:18:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680095</loc>
  <lastmod>2026-04-16T19:17:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数顕著物体の検出・ランキング・即時数の再考（Revisiting Salient Object Detection: Simultaneous Detection, Ranking, and Subitizing of Multiple Salient Objects）</news:title>
   <news:publication_date>2026-04-16T19:17:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680093</loc>
  <lastmod>2026-04-16T19:16:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>垂直メニュー選択における人間のパフォーマンス予測（Predicting Human Performance in Vertical Menu Selection Using Deep Learning）</news:title>
   <news:publication_date>2026-04-16T19:16:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680091</loc>
  <lastmod>2026-04-16T19:15:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>画像から集団感情を推定するマルチモーダル手法（A Multi-Modal Approach to Infer Image Affect）</news:title>
   <news:publication_date>2026-04-16T19:15:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680089</loc>
  <lastmod>2026-04-16T19:15:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>音楽の長期構造を学習する階層潜在ベクトルモデル（A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music）</news:title>
   <news:publication_date>2026-04-16T19:15:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680087</loc>
  <lastmod>2026-04-16T19:14:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>心理学的知見を応用した実行可能な分析（Applications of Psychological Science for Actionable Analytics）</news:title>
   <news:publication_date>2026-04-16T19:14:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680085</loc>
  <lastmod>2026-04-16T18:23:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模ソーシャルデータの落とし穴：公開Redditコーパスの欠損と研究への影響（Caveat Emptor, Computational Social Science: Large-Scale Missing Data in a Widely-Published Reddit Corpus）</news:title>
   <news:publication_date>2026-04-16T18:23:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680083</loc>
  <lastmod>2026-04-16T18:23:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>探索方策を学習するメタポリシー勾配（Learning to Explore with Meta-Policy Gradient）</news:title>
   <news:publication_date>2026-04-16T18:23:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680081</loc>
  <lastmod>2026-04-16T18:22:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非自律的敵対システムの解析（Analysis of Nonautonomous Adversarial Systems）</news:title>
   <news:publication_date>2026-04-16T18:22:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680079</loc>
  <lastmod>2026-04-16T18:21:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自動化されたデータ駆動型無機材料設計とAFLOW（Autonomous data-driven design of inorganic materials with AFLOW）</news:title>
   <news:publication_date>2026-04-16T18:21:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680077</loc>
  <lastmod>2026-04-16T18:21:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>コロイド・顆粒性ガラスにおけるスピンガラス様エージング（Spin-glass–like aging in colloidal and granular glasses）</news:title>
   <news:publication_date>2026-04-16T18:21:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680075</loc>
  <lastmod>2026-04-16T18:21:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多数の戦略的エージェントを持つシステムにおける分散学習（Decentralised Learning in Systems with Many, Many Strategic Agents）</news:title>
   <news:publication_date>2026-04-16T18:21:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680073</loc>
  <lastmod>2026-04-16T18:21:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ゼロ過剰データに効く疎カーネル付き変分ガウス過程（Variational zero-inflated Gaussian processes with sparse kernels）</news:title>
   <news:publication_date>2026-04-16T18:21:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680071</loc>
  <lastmod>2026-04-16T17:29:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習とソフトウェア定義ネットワークで守るIoTの未来（Securing the Internet of Things in the Age of Machine Learning and Software-defined Networking）</news:title>
   <news:publication_date>2026-04-16T17:29:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680069</loc>
  <lastmod>2026-04-16T17:29:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的疾患進行モデルによる臨床予測の実用化（A Probabilistic Disease Progression Model for Predicting Future Clinical Outcome）</news:title>
   <news:publication_date>2026-04-16T17:29:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680067</loc>
  <lastmod>2026-04-16T17:29:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>テンソル列（Tensor Train）を使った主成分分析による次元削減の再定義（Principal Component Analysis with Tensor Train Subspace）</news:title>
   <news:publication_date>2026-04-16T17:29:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680065</loc>
  <lastmod>2026-04-16T17:28:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>タスク簡易化によるクローズドループ物体把持学習（Comparing Task Simplifications to Learn Closed-Loop Object Picking Using Deep Reinforcement Learning）</news:title>
   <news:publication_date>2026-04-16T17:28:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680063</loc>
  <lastmod>2026-04-16T17:27:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>トランジット系惑星のJWST早期公開科学計画（Transiting Exoplanet Community Early Release Science Program for JWST）</news:title>
   <news:publication_date>2026-04-16T17:27:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680061</loc>
  <lastmod>2026-04-16T17:27:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>条件付き活性化による多様なニューロン表現（Conditional Activation for Diverse Neurons in Heterogeneous Networks）</news:title>
   <news:publication_date>2026-04-16T17:27:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680059</loc>
  <lastmod>2026-04-16T17:27:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>LCANetによるエンドツーエンドのリップリーディング（LCANet: End-to-End Lipreading with Cascaded Attention-CTC）</news:title>
   <news:publication_date>2026-04-16T17:27:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680057</loc>
  <lastmod>2026-04-16T16:27:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>麻酔薬分子は液体無秩序相に入りやすい（Common anesthetic molecules prefer to partition in liquid disorder phase in a composite multicomponent membrane）</news:title>
   <news:publication_date>2026-04-16T16:27:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680055</loc>
  <lastmod>2026-04-16T16:27:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Information-Corrected Estimation（Information-Corrected Estimation: A Generalization Error Reducing Parameter Estimation Method）</news:title>
   <news:publication_date>2026-04-16T16:27:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680053</loc>
  <lastmod>2026-04-16T16:27:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>頭部CTにおける重大所見検出の深層学習アルゴリズムの開発と検証（Development and Validation of Deep Learning Algorithms for Detection of Critical Findings in Head CT Scans）</news:title>
   <news:publication_date>2026-04-16T16:27:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680051</loc>
  <lastmod>2026-04-16T16:25:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>構造非拘束モデリング：因果グラフの敵対的学習（Structural Agnostic Modeling: Adversarial Learning of Causal Graphs）</news:title>
   <news:publication_date>2026-04-16T16:25:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680049</loc>
  <lastmod>2026-04-16T16:25:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>能動的報酬評価を考慮した強化学習とMCTSの接合（Active Reinforcement Learning with Monte-Carlo Tree Search）</news:title>
   <news:publication_date>2026-04-16T16:25:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680047</loc>
  <lastmod>2026-04-16T16:24:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>FCNの量子化が生む過学習抑制と高精度セグメンテーション（Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation）</news:title>
   <news:publication_date>2026-04-16T16:24:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680045</loc>
  <lastmod>2026-04-16T15:33:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>診断駆動型カーネル混合による非定常エミュレータ（DIAGNOSTICS-DRIVEN NONSTATIONARY EMULATORS USING KERNEL MIXTURES）</news:title>
   <news:publication_date>2026-04-16T15:33:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680043</loc>
  <lastmod>2026-04-16T15:31:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CNNがマンモグラム分類で利用する視覚プリミティブを専門家が同定する研究（Expert identification of visual primitives used by CNNs during mammogram classification）</news:title>
   <news:publication_date>2026-04-16T15:31:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680041</loc>
  <lastmod>2026-04-16T15:23:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>音声から音楽ジャンルを認識する学習（Learning to Recognize Musical Genre from Audio）</news:title>
   <news:publication_date>2026-04-16T15:23:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680039</loc>
  <lastmod>2026-04-16T15:23:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数ソースからのドメイン適応におけるターゲットシフトへの最適輸送の適用（Optimal Transport for Multi-source Domain Adaptation under Target Shift）</news:title>
   <news:publication_date>2026-04-16T15:23:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680037</loc>
  <lastmod>2026-04-16T15:21:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習に基づく多変量アウトカムの関連性測定法（A machine learning-based approach for estimating and testing associations with multivariate outcomes）</news:title>
   <news:publication_date>2026-04-16T15:21:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680035</loc>
  <lastmod>2026-04-16T15:21:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>立体3D動画の視線予測を学習で実現するモデル（A Learning-Based Visual Saliency Prediction Model for Stereoscopic 3D Video）</news:title>
   <news:publication_date>2026-04-16T15:21:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680033</loc>
  <lastmod>2026-04-16T15:21:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>データベース内でのエンティティ連携を実現するIDEL（IDEL: In-Database Entity Linking with Neural Embeddings）</news:title>
   <news:publication_date>2026-04-16T15:21:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680031</loc>
  <lastmod>2026-04-16T14:29:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>音声認識のためのリソース配慮型音声視覚結合ネットワーク設計 (Resource Aware Design of a Deep Convolutional-Recurrent Neural Network for Speech Recognition through Audio-Visual Sensor Fusion)</news:title>
   <news:publication_date>2026-04-16T14:29:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680029</loc>
  <lastmod>2026-04-16T14:28:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>独立再帰ニューラルネットワーク（IndRNN）：より長く、より深いRNNの構築（Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN）</news:title>
   <news:publication_date>2026-04-16T14:28:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680027</loc>
  <lastmod>2026-04-16T14:27:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>異種時系列イベントの共同表現学習による臨床エンドポイント予測（Learning the Joint Representation of Heterogeneous Temporal Events for Clinical Endpoint Prediction）</news:title>
   <news:publication_date>2026-04-16T14:27:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680025</loc>
  <lastmod>2026-04-16T14:26:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>整数計画による低ランクブーリアン行列近似（Low-Rank Boolean Matrix Approximation by Integer Programming）</news:title>
   <news:publication_date>2026-04-16T14:26:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680023</loc>
  <lastmod>2026-04-16T14:26:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高ダイナミックレンジ動画の視覚的顕著性融合の学習モデル（A Learning-Based Visual Saliency Fusion Model for High Dynamic Range Video）</news:title>
   <news:publication_date>2026-04-16T14:26:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680021</loc>
  <lastmod>2026-04-16T14:26:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オメガ・ケンタウリの深部X線サーベイ（A Deep X-ray Survey of the Globular Cluster Omega Centauri）</news:title>
   <news:publication_date>2026-04-16T14:26:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680019</loc>
  <lastmod>2026-04-16T14:26:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層学習ツールキットとライブラリの概観（A Survey on Deep Learning Toolkits and Libraries for Intelligent User Interfaces）</news:title>
   <news:publication_date>2026-04-16T14:26:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680017</loc>
  <lastmod>2026-04-16T13:34:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>WISERNet: カラー画像ステガノ分析のための幅広いSeparate-then-Reunionネットワーク (WISERNet: Wider Separate-then-reunion Network for Steganalysis of Color Images)</news:title>
   <news:publication_date>2026-04-16T13:34:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680015</loc>
  <lastmod>2026-04-16T13:34:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数並列RRAMによるシナプスモデルが示すSNN学習の現実解（A case for multiple and parallel RRAMs as synaptic model for training SNNs）</news:title>
   <news:publication_date>2026-04-16T13:34:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680013</loc>
  <lastmod>2026-04-16T13:34:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単眼画像からの3D人体姿勢推定を複数視点学習で強化する（Learning Monocular 3D Human Pose Estimation from Multi-view Images）</news:title>
   <news:publication_date>2026-04-16T13:34:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680011</loc>
  <lastmod>2026-04-16T13:33:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Deep k-Nearest Neighbors（Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning）</news:title>
   <news:publication_date>2026-04-16T13:33:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680009</loc>
  <lastmod>2026-04-16T13:33:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>VERSE：類似度に基づく汎用グラフ埋め込み（VERSE: Versatile Graph Embeddings from Similarity Measures）</news:title>
   <news:publication_date>2026-04-16T13:33:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680007</loc>
  <lastmod>2026-04-16T13:32:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>軽量VRを用いた条件付き自動運転のドライバー訓練（Light Virtual Reality Systems for the Training of Conditionally Automated Vehicle Drivers）</news:title>
   <news:publication_date>2026-04-16T13:32:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680005</loc>
  <lastmod>2026-04-16T13:32:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>小さな物体を見逃さない注意機構付き復帰型ニューラルネット（Feature Selective Small Object Detection via Knowledge-based Recurrent Attentive Neural Network）</news:title>
   <news:publication_date>2026-04-16T13:32:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680003</loc>
  <lastmod>2026-04-16T12:41:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>プログラム間の意味を学ぶ階層的コード埋め込み（Hierarchical Learning of Cross-Language Mappings through Distributed Vector Representations for Code）</news:title>
   <news:publication_date>2026-04-16T12:41:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680001</loc>
  <lastmod>2026-04-16T12:41:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>テキストプロンプト音声認証における深層CNN特徴抽出器（DEEP CNN BASED FEATURE EXTRACTOR FOR TEXT-PROMPTED SPEAKER RECOGNITION）</news:title>
   <news:publication_date>2026-04-16T12:41:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679999</loc>
  <lastmod>2026-04-16T12:40:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非対称系における深部障壁下融合への直接反応チャンネルの影響 (Effect of direct reaction channels on deep sub-barrier fusion in asymmetric systems)</news:title>
   <news:publication_date>2026-04-16T12:40:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679997</loc>
  <lastmod>2026-04-16T12:40:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>圧縮動画の多フレーム品質向上（Multi-Frame Quality Enhancement for Compressed Video）</news:title>
   <news:publication_date>2026-04-16T12:40:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679995</loc>
  <lastmod>2026-04-16T12:39:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グループ化線形予測と加速強化学習によるオンラインコンテンツキャッシュ（Using Grouped Linear Prediction and Accelerated Reinforcement Learning for Online Content Caching）</news:title>
   <news:publication_date>2026-04-16T12:39:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679993</loc>
  <lastmod>2026-04-16T12:39:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>RGB-DマルチモーダルRNNによる屋内シーンラベリング（Multimodal Recurrent Neural Networks with Information Transfer Layers for Indoor Scene Labeling）</news:title>
   <news:publication_date>2026-04-16T12:39:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679991</loc>
  <lastmod>2026-04-16T12:39:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>連続行動空間における方策探索の概説（Policy Search in Continuous Action Domains: an Overview）</news:title>
   <news:publication_date>2026-04-16T12:39:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679989</loc>
  <lastmod>2026-04-16T11:47:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>無限腕バンディットにおける純探索（Pure Exploration in Infinite Bandit Models）</news:title>
   <news:publication_date>2026-04-16T11:47:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679987</loc>
  <lastmod>2026-04-16T11:38:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>解釈可能なシステムログ異常検知のためのRNN注意機構（Recurrent Neural Network Attention Mechanisms for Interpretable System Log Anomaly Detection）</news:title>
   <news:publication_date>2026-04-16T11:38:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679985</loc>
  <lastmod>2026-04-16T11:37:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層型強化学習：局所方策で近似する割引版巡回セールスマン問題（Approximating Optimal Discounted TSP Using Local Policies）</news:title>
   <news:publication_date>2026-04-16T11:37:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679983</loc>
  <lastmod>2026-04-16T11:36:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単一粒子系の量子非平衡熱力学（Quantum thermodynamics of single particle systems）</news:title>
   <news:publication_date>2026-04-16T11:36:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679981</loc>
  <lastmod>2026-04-16T11:36:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>二値化された観測からの行列復元の実践的示唆（Binary Matrix Completion Using Unobserved Entries）</news:title>
   <news:publication_date>2026-04-16T11:36:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679979</loc>
  <lastmod>2026-04-16T11:36:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>完全ベイズ多次元ホークス過程のシミュレーションと較正（Simulation and Calibration of a Fully Bayesian Multidimensional Hawkes Process）</news:title>
   <news:publication_date>2026-04-16T11:36:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679977</loc>
  <lastmod>2026-04-16T11:36:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>タンパク質変異の安定性三値分類（Protein Mutation Stability Ternary Classification using Neural Networks and Rigidity Analysis）</news:title>
   <news:publication_date>2026-04-16T11:36:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679975</loc>
  <lastmod>2026-04-16T10:44:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>透明物体マッティングの学習（TOM-Net: Learning Transparent Object Matting from a Single Image）</news:title>
   <news:publication_date>2026-04-16T10:44:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679973</loc>
  <lastmod>2026-04-16T10:44:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模LDAをGPUで高速化するCuLDA_CGSの全体像（CuLDA_CGS: Solving Large-scale LDA Problems on GPUs）</news:title>
   <news:publication_date>2026-04-16T10:44:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679971</loc>
  <lastmod>2026-04-16T10:43:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>組合せセミバンディットに対するトンプソン・サンプリング（Thompson Sampling for Combinatorial Semi-Bandits）</news:title>
   <news:publication_date>2026-04-16T10:43:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679969</loc>
  <lastmod>2026-04-16T10:42:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ϵ-ドミナンスで品質予測を簡潔にする（Building Better Quality Predictors Using “ϵ-Dominance”）</news:title>
   <news:publication_date>2026-04-16T10:42:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679967</loc>
  <lastmod>2026-04-16T10:42:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>超立方体形状かつ不連続なデータに対する深層ベイズ学習（Deep Bayesian Supervised Learning given Hypercuboidally-shaped, Discontinuous Data, using Compound Tensor-Variate &amp;amp; Scalar-Variate Gaussian Processes）</news:title>
   <news:publication_date>2026-04-16T10:42:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679965</loc>
  <lastmod>2026-04-16T10:42:09Z</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・ジハード主義ヘイトスピーチの自動検出（Automatic detection of online jihadist hate speech）</news:title>
   <news:publication_date>2026-04-16T10:42:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679963</loc>
  <lastmod>2026-04-16T10:41:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Goodhart効果の類型化が示すもの（Categorizing Variants of Goodhart’s Law）</news:title>
   <news:publication_date>2026-04-16T10:41:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679961</loc>
  <lastmod>2026-04-16T09:50:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>訓練データの剪定が性能を支えた（It was the training data pruning too!）</news:title>
   <news:publication_date>2026-04-16T09:50:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679959</loc>
  <lastmod>2026-04-16T09:49:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>共相関化された歩行エンベロープ（Coregionalised Locomotion Envelopes – A Qualitative Approach）</news:title>
   <news:publication_date>2026-04-16T09:49:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679957</loc>
  <lastmod>2026-04-16T09:49:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>空間重畳による量子リザバーコンピューティングの計算能力強化 (Boosting computational power through spatial multiplexing in quantum reservoir computing)</news:title>
   <news:publication_date>2026-04-16T09:49:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679955</loc>
  <lastmod>2026-04-16T09:48:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非同期SSVEP分類のためのコンパクト畳み込みニューラルネットワーク（Compact Convolutional Neural Networks for Classification of Asynchronous Steady-state Visual Evoked Potentials）</news:title>
   <news:publication_date>2026-04-16T09:48:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679953</loc>
  <lastmod>2026-04-16T09:48:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>COPA: 欠損・大規模データに対する制約付きPARAFAC2（COPA: Constrained PARAFAC2 for Sparse &amp;amp; Large Datasets）</news:title>
   <news:publication_date>2026-04-16T09:48:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679951</loc>
  <lastmod>2026-04-16T09:47:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>方言認識をエンドツーエンドで実現する技術（Convolutional Neural Networks and Language Embeddings for End-to-End Dialect Recognition）</news:title>
   <news:publication_date>2026-04-16T09:47:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679949</loc>
  <lastmod>2026-04-16T09:46:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>胸部X線の異常検出における位置情報対応Denseネットワークの実用性（Learning to recognize Abnormalities in Chest X-Rays with Location-Aware Dense Networks）</news:title>
   <news:publication_date>2026-04-16T09:46:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679947</loc>
  <lastmod>2026-04-16T08:55:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>セトゥスIIの正体：極めて薄い矮小銀河候補の再評価（ON THE NATURE OF ULTRA-FAINT DWARF GALAXY CANDIDATES II: THE CASE OF CETUS II）</news:title>
   <news:publication_date>2026-04-16T08:55:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679945</loc>
  <lastmod>2026-04-16T08:50:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>NASAのアステロイド・グランドチャレンジの戦略と教訓（NASA’s Asteroid Grand Challenge: Strategy, Results, and Lessons Learned）</news:title>
   <news:publication_date>2026-04-16T08:50:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679943</loc>
  <lastmod>2026-04-16T08:49:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>2次元データにおけるオニオンピーリングによる外れ値検出（Onion-Peeling Outlier Detection in 2-D data Sets）</news:title>
   <news:publication_date>2026-04-16T08:49:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679941</loc>
  <lastmod>2026-04-16T08:49:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>二部グラフ一般設定におけるスペクトルクラスタリングの解析（Analysis of spectral clustering algorithms for community detection: the general bipartite setting）</news:title>
   <news:publication_date>2026-04-16T08:49:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679939</loc>
  <lastmod>2026-04-16T08:49:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Taking Turing by surprise? — 倫理的驚きを設計するデジタルコンピュータ（Taking Turing by surprise? Designing ‘digital computers’ for morally-loaded contexts）</news:title>
   <news:publication_date>2026-04-16T08:49:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679937</loc>
  <lastmod>2026-04-16T08:48:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>トポロジー知識と中継深度が非同期コンセンサスに与える影響（Eﬀects of Topology Knowledge and Relay Depth on Asynchronous Consensus）</news:title>
   <news:publication_date>2026-04-16T08:48:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679935</loc>
  <lastmod>2026-04-16T08:48:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>加重ベイズ・ブートストラップの実務的意義（Weighted Bayesian Bootstrap for Scalable Bayes）</news:title>
   <news:publication_date>2026-04-16T08:48:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679933</loc>
  <lastmod>2026-04-16T07:56:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自己符号化器と意味ハッシュによる勾配拡張情報検索（Gradient Augmented Information Retrieval with Autoencoders and Semantic Hashing）</news:title>
   <news:publication_date>2026-04-16T07:56:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679931</loc>
  <lastmod>2026-04-16T07:56:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的・正則化されたグラフ畳み込みネットワークの検討（Probabilistic and Regularized Graph Convolutional Networks）</news:title>
   <news:publication_date>2026-04-16T07:56:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679929</loc>
  <lastmod>2026-04-16T07:55:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>カメラセンサーで粒子を識別する手法（Particle Identification In Camera Image Sensors Using Computer Vision）</news:title>
   <news:publication_date>2026-04-16T07:55:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679927</loc>
  <lastmod>2026-04-16T07:55:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Accuracy-Reliabilityを用いた経験的分散推定の実務的意義（Accuracy-Reliability Cost Function for Empirical Variance Estimation）</news:title>
   <news:publication_date>2026-04-16T07:55:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679925</loc>
  <lastmod>2026-04-16T07:54:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>概念（オントロジー）埋め込みの品質評価指標（Metrics for Evaluating Quality of Embeddings for Ontological Concepts）</news:title>
   <news:publication_date>2026-04-16T07:54:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679923</loc>
  <lastmod>2026-04-16T07:54:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分子動力学と機械学習が切り拓く新しいµオピオイド化学骨格（Machine Learning Harnesses Molecular Dynamics to Discover New µ Opioid Chemotypes）</news:title>
   <news:publication_date>2026-04-16T07:54:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679921</loc>
  <lastmod>2026-04-16T07:54:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GANの射影による画像ノイズ除去（CORRECTION BY PROJECTION: DENOISING IMAGES WITH GENERATIVE ADVERSARIAL NETWORKS）</news:title>
   <news:publication_date>2026-04-16T07:54:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679919</loc>
  <lastmod>2026-04-16T07:02:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分子特性予測のためのPotentialNet（PotentialNet for Molecular Property Prediction）</news:title>
   <news:publication_date>2026-04-16T07:02:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679917</loc>
  <lastmod>2026-04-16T07:02:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>空間特徴を用いた犯罪予測（Predicting Crime Using Spatial Features）</news:title>
   <news:publication_date>2026-04-16T07:02:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679915</loc>
  <lastmod>2026-04-16T07:02:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>誤検出率（FDR）を抑えつつ変数選択を可能にする手法（False Discovery Rate Control via Debiased Lasso）</news:title>
   <news:publication_date>2026-04-16T07:02:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679913</loc>
  <lastmod>2026-04-16T07:00:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>放射線画像解析における非類似度（ディシミラリティ）表現の提案（Dissimilarity-based representation for radiomics applications）</news:title>
   <news:publication_date>2026-04-16T07:00:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679911</loc>
  <lastmod>2026-04-16T07:00:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ノードからネットワークへ：進化的手法で再帰型ニューラルネットワークを設計する（From Nodes to Networks: Evolving Recurrent Neural Networks）</news:title>
   <news:publication_date>2026-04-16T07:00:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679909</loc>
  <lastmod>2026-04-16T07:00:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>拡張アフィニティ・プロパゲーションがもたらす「局所」と「全体」の両取り（Extended Affinity Propagation: Global Discovery and Local Insights）</news:title>
   <news:publication_date>2026-04-16T07:00:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679907</loc>
  <lastmod>2026-04-16T07:00:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ウェアラブルで外来患者の臨床悪化を予測する（Predicting Clinical Deterioration of Outpatients Using Multimodal Data Collected by Wearables）</news:title>
   <news:publication_date>2026-04-16T07:00:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679905</loc>
  <lastmod>2026-04-16T06:08:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>転移可能な分子電荷割当を深層ニューラルネットワークで実現する（Transferable Molecular Charge Assignment Using Deep Neural Networks）</news:title>
   <news:publication_date>2026-04-16T06:08:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679903</loc>
  <lastmod>2026-04-16T06:07:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Flipoutによるミニバッチ内の擬似独立重み摂動（Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches）</news:title>
   <news:publication_date>2026-04-16T06:07:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679901</loc>
  <lastmod>2026-04-16T06:07:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次元線形混合モデルを大規模に学習するための手法（Scalable Algorithms for Learning High-Dimensional Linear Mixed Models）</news:title>
   <news:publication_date>2026-04-16T06:07:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679899</loc>
  <lastmod>2026-04-16T06:06:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スマートフォンによる運転スタイル分類と離散ウェーブレット変換（Smartphone based Driving Style Classification Using Features Made by Discrete Wavelet Transformation）</news:title>
   <news:publication_date>2026-04-16T06:06:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679897</loc>
  <lastmod>2026-04-16T06:06:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スケッチ化正則化アルゴリズムの最適収束率（Optimal Rates of Sketched-regularized Algorithms for Least-Squares Regression over Hilbert Spaces）</news:title>
   <news:publication_date>2026-04-16T06:06:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679895</loc>
  <lastmod>2026-04-16T06:06:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>潜在分布を学習することで生成モデルの表現力を高める（Learning the Base Distribution in Implicit Generative Models）</news:title>
   <news:publication_date>2026-04-16T06:06:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679893</loc>
  <lastmod>2026-04-16T06:05:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>公平な機械学習の遅延影響（Delayed Impact of Fair Machine Learning）</news:title>
   <news:publication_date>2026-04-16T06:05:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679891</loc>
  <lastmod>2026-04-16T05:14:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模集団における個体追跡の実用化を前進させた技術（idtracker.ai: Tracking all individuals in large collectives of unmarked animals）</news:title>
   <news:publication_date>2026-04-16T05:14:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679889</loc>
  <lastmod>2026-04-16T05:14:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Tinderプロフィール分類にFaceNet顔埋め込みを活用する手法（CLASSIFYING ONLINE DATING PROFILES ON TINDER USING FACENET FACIAL EMBEDDINGS）</news:title>
   <news:publication_date>2026-04-16T05:14:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679887</loc>
  <lastmod>2026-04-16T05:13:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>網膜画像における糖尿病網膜症検出の再現研究（Replication study: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs）</news:title>
   <news:publication_date>2026-04-16T05:13:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679885</loc>
  <lastmod>2026-04-16T05:12:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>永続的検証データベースの設計（The Everlasting Database: Statistical Validity at a Fair Price）</news:title>
   <news:publication_date>2026-04-16T05:12:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679883</loc>
  <lastmod>2026-04-16T05:12:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ReLUモデルにおける表現学習と復元 (Representation Learning and Recovery in the ReLU Model)</news:title>
   <news:publication_date>2026-04-16T05:12:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679881</loc>
  <lastmod>2026-04-16T05:12:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自然言語をSPARQLへ変換するニューラル注意機構（Semantic Parsing Natural Language into SPARQL: Improving Target Language Representation with Neural Attention）</news:title>
   <news:publication_date>2026-04-16T05:12:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679879</loc>
  <lastmod>2026-04-16T05:12:17Z</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・無線ネットワークにおける深層学習の展望（Deep Learning in Mobile and Wireless Networking: A Survey）</news:title>
   <news:publication_date>2026-04-16T05:12:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679877</loc>
  <lastmod>2026-04-16T04:20:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Neural Conditional Gradients（Neural Conditional Gradients）</news:title>
   <news:publication_date>2026-04-16T04:20:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679875</loc>
  <lastmod>2026-04-16T04:19:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Sentinel-2画像の超解像：グローバルに適用可能な深層ニューラルネットワークの学習（Super-resolution of Sentinel-2 images: Learning a globally applicable deep neural network）</news:title>
   <news:publication_date>2026-04-16T04:19:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679873</loc>
  <lastmod>2026-04-16T04:19:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>未知の常微分方程式を学ぶ非パラメトリック手法（Learning unknown ODE models with Gaussian processes）</news:title>
   <news:publication_date>2026-04-16T04:19:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679871</loc>
  <lastmod>2026-04-16T04:18:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SO-Netによる点群解析の革新（SO-Net: Self-Organizing Network for Point Cloud Analysis）</news:title>
   <news:publication_date>2026-04-16T04:18:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679869</loc>
  <lastmod>2026-04-16T04:18:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動画物体セグメンテーションの新展開 — 再識別と注意機構によるマスク伝播（Video Object Segmentation with Joint Re-identification and Attention-Aware Mask Propagation）</news:title>
   <news:publication_date>2026-04-16T04:18:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679867</loc>
  <lastmod>2026-04-16T04:18:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>FeTa: DCAによる高速プルーニングと一般化誤差保証（FeTa: A DCA Pruning Algorithm with Generalization Error Guarantees）</news:title>
   <news:publication_date>2026-04-16T04:18:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679865</loc>
  <lastmod>2026-04-16T04:17:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>共変チャイラル摂動論におけるオクテットバリオン磁気モーメントのNNLO解析 (Octet baryon magnetic moments at next-to-next-to-leading order in covariant chiral perturbation theory)</news:title>
   <news:publication_date>2026-04-16T04:17:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679863</loc>
  <lastmod>2026-04-16T03:26:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>全方位CNNを用いた屋内プレイス認識とナビゲーション（Omnidirectional CNN for Visual Place Recognition and Navigation）</news:title>
   <news:publication_date>2026-04-16T03:26:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679861</loc>
  <lastmod>2026-04-16T03:26:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>パネルカウントデータに対するガウス過程の変分推論（Variational Inference for Gaussian Process with Panel Count Data）</news:title>
   <news:publication_date>2026-04-16T03:26:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679859</loc>
  <lastmod>2026-04-16T03:25:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>クラウドソーシングデータを活用した深層能動学習（Leveraging Crowdsourcing Data For Deep Active Learning）</news:title>
   <news:publication_date>2026-04-16T03:25:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679857</loc>
  <lastmod>2026-04-16T03:24:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>特殊相対性理論の双第一公準的基盤（A dual first-postulate basis for special relativity）</news:title>
   <news:publication_date>2026-04-16T03:24:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679855</loc>
  <lastmod>2026-04-16T03:24:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高スループット同時分散確率的勾配降下法（High Throughput Synchronous Distributed Stochastic Gradient Descent）</news:title>
   <news:publication_date>2026-04-16T03:24:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679853</loc>
  <lastmod>2026-04-16T03:24:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>サイバーセキュリティにおけるデータサイエンス手法の体系化（DATA SCIENCE METHODOLOGY FOR CYBERSECURITY PROJECTS）</news:title>
   <news:publication_date>2026-04-16T03:24:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-04-16T03:23:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>半準パラメトリック文脈バンディット（Semiparametric Contextual Bandits）</news:title>
   <news:publication_date>2026-04-16T03:23:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679849</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多カーネル回帰によるグラフ信号処理（MULTI-KERNEL REGRESSION FOR GRAPH SIGNAL PROCESSING）</news:title>
   <news:publication_date>2026-04-16T02:32:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679847</loc>
  <lastmod>2026-04-16T02:32:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフ信号処理における極限学習機（Extreme Learning Machine for Graph Signal Processing）</news:title>
   <news:publication_date>2026-04-16T02:32:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679845</loc>
  <lastmod>2026-04-16T02:31:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Noise2Noise：クリーンデータなしで学ぶ画像復元（Noise2Noise: Learning Image Restoration without Clean Data）</news:title>
   <news:publication_date>2026-04-16T02:31:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679843</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>銀河クラスタ環境で剥ぎ取られたガス中の星形成（A Virgo Environmental Survey Tracing Ionised Gas Emission (VESTIGE).III. Star formation in the stripped gas of NGC 4254）</news:title>
   <news:publication_date>2026-04-16T02:30:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679841</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>クラス単位ハッシュによる意味保持ハッシング（Deep Class-Wise Hashing: Semantics-Preserving Hashing via Class-wise Loss）</news:title>
   <news:publication_date>2026-04-16T02:30:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679839</loc>
  <lastmod>2026-04-16T02:30:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>悪意ある実行ファイルの敵対的バイナリによる検出回避（Adversarial Malware Binaries: Evading Deep Learning for Malware Detection in Executables）</news:title>
   <news:publication_date>2026-04-16T02:30:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679837</loc>
  <lastmod>2026-04-16T02:30:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>R3Net：ランダム重み・ReLU・頑健性を結びつける設計（R3Net: Random Weights, Rectifier Linear Units and Robustness for Artificial Neural Network）</news:title>
   <news:publication_date>2026-04-16T02:30:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679835</loc>
  <lastmod>2026-04-16T01:38:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>距離類似度指標に対するGPUによる自己結合の高速化（GPU Accelerated Self-join for the Distance Similarity Metric）</news:title>
   <news:publication_date>2026-04-16T01:38:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679833</loc>
  <lastmod>2026-04-16T01:38:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>状態重なりの計算法を学習する量子アルゴリズム（Learning the quantum algorithm for state overlap）</news:title>
   <news:publication_date>2026-04-16T01:38:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679831</loc>
  <lastmod>2026-04-16T01:38:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>室内自律走行を「行動」レベルで設計する意味（A Deep Learning Based Behavioral Approach to Indoor Autonomous Navigation）</news:title>
   <news:publication_date>2026-04-16T01:38:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679829</loc>
  <lastmod>2026-04-16T01:36:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自己中心的サンプリングネットワークのリンク予測（Link prediction for egocentrically sampled networks）</news:title>
   <news:publication_date>2026-04-16T01:36:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679827</loc>
  <lastmod>2026-04-16T01:36:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>離散ベイズネットワークの多項式時間での学習とサンプル複雑性（Learning discrete Bayesian networks in polynomial time and sample complexity）</news:title>
   <news:publication_date>2026-04-16T01:36:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679825</loc>
  <lastmod>2026-04-16T01:36:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スタイル集約型ネットワークによる顔ランドマーク検出の頑健化（Style Aggregated Network for Facial Landmark Detection）</news:title>
   <news:publication_date>2026-04-16T01:36:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679823</loc>
  <lastmod>2026-04-16T01:36:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>末端利用者の測定だけで系統を学ぶ：配電網のトポロジーとパラメータ推定（Learning with End-Users in Distribution Grids: Topology and Parameter Estimation）</news:title>
   <news:publication_date>2026-04-16T01:36:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679813</loc>
  <lastmod>2026-04-16T00:44:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>長期安全学習型MPCのための経験推薦手法（Experience Recommendation for Long Term Safe Learning-based Model Predictive Control in Changing Operating Conditions）</news:title>
   <news:publication_date>2026-04-16T00:44:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679811</loc>
  <lastmod>2026-04-16T00:44:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>二面体拡大の類数公式（CLASS NUMBER FORMULA FOR DIHEDRAL EXTENSIONS）</news:title>
   <news:publication_date>2026-04-16T00:44:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679809</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>擬似タスク増強：深層マルチタスク学習からイントラタスク共有へ（Pseudo-task Augmentation: From Deep Multitask Learning to Intratask Sharing—and Back）</news:title>
   <news:publication_date>2026-04-16T00:44:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679807</loc>
  <lastmod>2026-04-16T00:43:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>乳がん組織画像分類の二段階畳み込みニューラルネットワーク（Two-Stage Convolutional Neural Network for Breast Cancer Histology Image Classification）</news:title>
   <news:publication_date>2026-04-16T00:43:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動的グラフにおける表現学習（Representation Learning over Dynamic Graphs）</news:title>
   <news:publication_date>2026-04-16T00:43:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679803</loc>
  <lastmod>2026-04-16T00:43:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>画像品質から学習する局所歪み可視性（Learning Local Distortion Visibility from Image Quality）</news:title>
   <news:publication_date>2026-04-16T00:43:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679801</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>海洋ロボット航行のための部分構造化環境動態学習（Learning Partially Structured Environmental Dynamics for Marine Robotic Navigation）</news:title>
   <news:publication_date>2026-04-16T00:42:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679799</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>データの“体積”を最大化するPCAの新解釈（PCA by Determinant Optimization has no Spurious Local Optima）</news:title>
   <news:publication_date>2026-04-15T23:50:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層分類器を『ダークナレッジ』で可視化する方法（Interpreting Deep Classifiers by Visual Distillation of Dark Knowledge）</news:title>
   <news:publication_date>2026-04-15T23:50:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679795</loc>
  <lastmod>2026-04-15T23:49:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>不確かなラベルから学ぶ多インスタンスChoquet積分による分類・回帰（Multiple Instance Choquet Integral Classifier Fusion and Regression for Remote Sensing Applications）</news:title>
   <news:publication_date>2026-04-15T23:49:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-04-15T23:49:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Entity ResolutionとFederated Learningの接点を解く（Entity Resolution and Federated Learning get a Federated Resolution）</news:title>
   <news:publication_date>2026-04-15T23:49:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679791</loc>
  <lastmod>2026-04-15T23:49:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>WaveNetを用いた売上予測（Sales forecasting using WaveNet within the framework of the Kaggle competition）</news:title>
   <news:publication_date>2026-04-15T23:49:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679789</loc>
  <lastmod>2026-04-15T23:48:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層辞書学習による階層合成ネットワークアプローチ（Deep Dictionary Learning: A PARametric NETwork Approach）</news:title>
   <news:publication_date>2026-04-15T23:48:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679787</loc>
  <lastmod>2026-04-15T23:48:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>組合せ多目的マルチアームバンディット問題（Combinatorial Multi-Objective Multi-Armed Bandit Problem）</news:title>
   <news:publication_date>2026-04-15T23:48:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679785</loc>
  <lastmod>2026-04-15T22:57:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>接触モデルをデータで強化する剛体シミュレーション（Data-Augmented Contact Model for Rigid Body Simulation）</news:title>
   <news:publication_date>2026-04-15T22:57:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679783</loc>
  <lastmod>2026-04-15T22:56:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>類似情報を持つマルチ目的文脈バンディット問題（Multi-objective Contextual Bandit Problem with Similarity Information）</news:title>
   <news:publication_date>2026-04-15T22:56:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679781</loc>
  <lastmod>2026-04-15T22:55:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>線形コンパクトベクトル空間上のフローのコランク（The corank of a flow over the category of linearly compact vector spaces）</news:title>
   <news:publication_date>2026-04-15T22:55:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679779</loc>
  <lastmod>2026-04-15T22:55:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>相関するマルコフ環境下のバンディット問題を読み解く（Bandits for Correlated Markovian Environments）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>弱い相互作用をもつ関数の経験的評価境界（Empirical bounds for functions with weak interactions）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>IDS向け機械学習への慢性型中毒攻撃（BEBP: An Poisoning Method Against Machine Learning Based IDSs）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679773</loc>
  <lastmod>2026-04-15T22:54:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ソフトロバストActor-Criticによる方策勾配の新展開（Soft-Robust Actor-Critic Policy-Gradient）</news:title>
   <news:publication_date>2026-04-15T22:54:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679771</loc>
  <lastmod>2026-04-15T22:02:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>経路ベースのカーネルブースティングによるサンプル分類（A pathway-based kernel boosting method for sample classification using genomic data）</news:title>
   <news:publication_date>2026-04-15T22:02:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679769</loc>
  <lastmod>2026-04-15T21:54:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多変量時系列における非線形因果検出とスパース加法モデル（Detecting Nonlinear Causality in Multivariate Time Series with Sparse Additive Models）</news:title>
   <news:publication_date>2026-04-15T21:54:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679767</loc>
  <lastmod>2026-04-15T21:54:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時系列データのための深層強化学習：理想化されたトレーディングゲームを解く（Deep reinforcement learning for time series: playing idealized trading games）</news:title>
   <news:publication_date>2026-04-15T21:54:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679765</loc>
  <lastmod>2026-04-15T21:54:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>バイリンガルな実用的色参照の生成（Generating Bilingual Pragmatic Color References）</news:title>
   <news:publication_date>2026-04-15T21:54:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679763</loc>
  <lastmod>2026-04-15T21:53:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>HabEx向けボルテックスコロナグラフの理論性能と望遠鏡要件（Vortex coronagraphs for the Habitable Exoplanet Imaging Mission）</news:title>
   <news:publication_date>2026-04-15T21:53:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679761</loc>
  <lastmod>2026-04-15T21:53:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ベルウェザーを使った転移学習で良い設定を見つける（Transfer Learning with Bellwethers to find Good Configurations）</news:title>
   <news:publication_date>2026-04-15T21:53:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679759</loc>
  <lastmod>2026-04-15T21:52:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>区分的凸関数推定とモデル選択（Piecewise Convex Function Estimation and Model Selection）</news:title>
   <news:publication_date>2026-04-15T21:52:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679757</loc>
  <lastmod>2026-04-15T21:00:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>未知の刺激を含む分数階動力学における最小センシングの同定と選択（Dealing with Unknown Unknowns: Identification and Selection of Minimal Sensing for Fractional Dynamics with Unknown Inputs）</news:title>
   <news:publication_date>2026-04-15T21:00:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679755</loc>
  <lastmod>2026-04-15T20:52:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>知識支援による一貫性で弱教師ありフレーズグラウンディングを強化する（Knowledge Aided Consistency for Weakly Supervised Phrase Grounding）</news:title>
   <news:publication_date>2026-04-15T20:52:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679753</loc>
  <lastmod>2026-04-15T20:52:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層特徴再構築による単眼深度推定と視覚オドメトリの教師なし学習 (Unsupervised Learning of Monocular Depth Estimation and Visual Odometry with Deep Feature Reconstruction)</news:title>
   <news:publication_date>2026-04-15T20:52:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679751</loc>
  <lastmod>2026-04-15T20:52:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スパース表現による敵対的攻撃への対抗（COMBATING ADVERSARIAL ATTACKS USING SPARSE REPRESENTATIONS）</news:title>
   <news:publication_date>2026-04-15T20:52:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679749</loc>
  <lastmod>2026-04-15T20:50:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>敵対的サンプル検出のためのNeural Fingerprinting（Detecting Adversarial Examples using Neural Fingerprinting）</news:title>
   <news:publication_date>2026-04-15T20:50:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679747</loc>
  <lastmod>2026-04-15T20:50:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチクラス不均衡学習における動的アンサンブル選択とデータ前処理の実践的検討（On dynamic ensemble selection and data preprocessing for multi-class imbalance learning）</news:title>
   <news:publication_date>2026-04-15T20:50:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679745</loc>
  <lastmod>2026-04-15T20:50:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層ニューラルネットワークのテスト基準と実践的意義（Testing Deep Neural Networks）</news:title>
   <news:publication_date>2026-04-15T20:50:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679743</loc>
  <lastmod>2026-04-15T19:58:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ベンガル語–英語の混在ツイートで単語レベルの言語判別を行う手法（Language Identification of Bengali-English Code-Mixed data using Character &amp;amp; Phonetic based LSTM Models）</news:title>
   <news:publication_date>2026-04-15T19:58:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679741</loc>
  <lastmod>2026-04-15T19:57:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ノイズだらけのウェブ画像から学ぶためのカテゴリレベル監視（Learning from Noisy Web Data with Category-level Supervision）</news:title>
   <news:publication_date>2026-04-15T19:57:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679739</loc>
  <lastmod>2026-04-15T19:57:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>体積OCTデータからの姿勢推定に対する深層学習アプローチ（A Deep Learning Approach for Pose Estimation from Volumetric OCT Data）</news:title>
   <news:publication_date>2026-04-15T19:57:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679737</loc>
  <lastmod>2026-04-15T19:56:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>QCDのハドロン共鳴ガス相の探索（Exploring the hadron resonance gas phase on the QCD phase diagram）</news:title>
   <news:publication_date>2026-04-15T19:56:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679735</loc>
  <lastmod>2026-04-15T19:56:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>キックスターティングで深層強化学習を加速する方法（Kickstarting Deep Reinforcement Learning）</news:title>
   <news:publication_date>2026-04-15T19:56:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679733</loc>
  <lastmod>2026-04-15T19:56:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>視覚場面における音源の局所化を学習する（Learning to Localize Sound Source in Visual Scenes）</news:title>
   <news:publication_date>2026-04-15T19:56:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679731</loc>
  <lastmod>2026-04-15T19:55:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分解可能サブモジュラ関数の最小化とインシデンス関係の再検討（Revisiting Decomposable Submodular Function Minimization with Incidence Relations）</news:title>
   <news:publication_date>2026-04-15T19:55:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679729</loc>
  <lastmod>2026-04-15T19:04:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>シンボリック表現のベクトル化を学習する方法（LEARNING AND ANALYZING VECTOR ENCODING OF SYMBOLIC REPRESENTATIONS）</news:title>
   <news:publication_date>2026-04-15T19:04:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679727</loc>
  <lastmod>2026-04-15T19:03:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>サブモジュラハイパーグラフとp-ラプラシアンによるスペクトラルクラスタリング（Submodular Hypergraphs: p-Laplacians, Cheeger Inequalities and Spectral Clustering）</news:title>
   <news:publication_date>2026-04-15T19:03:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679725</loc>
  <lastmod>2026-04-15T19:03:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>重み付きグラフに対する差分プライバシー下のクラスタリング（Graph-based Clustering under Differential Privacy）</news:title>
   <news:publication_date>2026-04-15T19:03:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679723</loc>
  <lastmod>2026-04-15T19:03:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Bi1−xSbx合金における磁場誘起Weyl半金属状態の拡張観測（Observation of Chiral character deep in the topological insulating regime in Bi1−xSbx）</news:title>
   <news:publication_date>2026-04-15T19:03:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679721</loc>
  <lastmod>2026-04-15T19:02:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散ブロックチェーンを用いたIoT異常検知の協調フレームワーク（CIoTA: Collaborative IoT Anomaly Detection via Blockchain）</news:title>
   <news:publication_date>2026-04-15T19:02:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679719</loc>
  <lastmod>2026-04-15T19:02:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>eコマース需要予測を改善する深層学習モデルの実務解説（AR-MDN: Associative and Recurrent Mixture Density Networks for eRetail Demand Forecasting）</news:title>
   <news:publication_date>2026-04-15T19:02:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679717</loc>
  <lastmod>2026-04-15T19:01:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>コードリポジトリから学ぶクイックフィックス（Learning Quick Fixes from Code Repositories）</news:title>
   <news:publication_date>2026-04-15T19:01:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679715</loc>
  <lastmod>2026-04-15T18:10:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模データから小規模データへ知識を移す方法：Deep Cross-media Knowledge Transfer（Deep Cross-media Knowledge Transfer）</news:title>
   <news:publication_date>2026-04-15T18:10:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679713</loc>
  <lastmod>2026-04-15T18:10:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層学習の表現力と汎化性—深層ネットの理論的優位性の解明（Generalization and Expressivity for Deep Nets）</news:title>
   <news:publication_date>2026-04-15T18:10:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679711</loc>
  <lastmod>2026-04-15T18:10:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Driving Scene Perception Network（Driving Scene Perception Network: Real-time Joint Detection, Depth Estimation and Semantic Segmentation）</news:title>
   <news:publication_date>2026-04-15T18:10:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679709</loc>
  <lastmod>2026-04-15T18:09:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散ネットワークの衝撃（VARIANCE NETWORKS: WHEN EXPECTATION DOES NOT MEET YOUR EXPECTATIONS）</news:title>
   <news:publication_date>2026-04-15T18:09:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679707</loc>
  <lastmod>2026-04-15T18:09:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>有限要素法によるMatérn確率場を生成するSPDEの解法（How to solve the stochastic partial differential equation that gives a Matérn random field using the finite element method）</news:title>
   <news:publication_date>2026-04-15T18:09:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679705</loc>
  <lastmod>2026-04-15T18:09:05Z</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：画像認識を利用したキーワードスポッティング（Speech Recognition: Key Word Spotting through Image Recognition）</news:title>
   <news:publication_date>2026-04-15T18:09:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679703</loc>
  <lastmod>2026-04-15T18:08:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>条件差分推定へのミニマックス代替損失法（A Minimax Surrogate Loss Approach to Conditional Difference Estimation）</news:title>
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   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/679701</loc>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 <url>
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  <lastmod>2026-04-15T17:16:00Z</lastmod>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-04-15T17:16:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-04-15T17:15:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/679695</loc>
  <lastmod>2026-04-15T17:15:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-04-15T17:15:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/679693</loc>
  <lastmod>2026-04-15T17:15:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>注意に基づくグラフニューラルネットワーク（Attention-based Graph Neural Network for Semi-supervised Learning）</news:title>
   <news:publication_date>2026-04-15T17:15:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-04-15T17:14:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/679689</loc>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-04-15T17:13:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-04-15T16:23:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
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  <lastmod>2026-04-15T16:22:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-04-15T16:22:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-04-15T16:22:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-04-15T16:21:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <lastmod>2026-04-15T16:21:09Z</lastmod>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
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   <news:genres>Blog</news:genres>
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