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   <news:title>大規模プラズマモデリングにおける逆問題の不安定性（Inverse Problem Instabilities in Large-Scale Plasma Modelling）</news:title>
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   <news:title>医用画像報告のためのハイブリッド検索・生成強化エージェント（Hybrid Retrieval-Generation Reinforced Agent for Medical Image Report Generation）</news:title>
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   <news:title>高性能計算のための高位合成コード変換（Transformations of High-Level Synthesis Codes for High-Performance Computing）</news:title>
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   <news:title>複数処置に対する観測データからの因果推論と潜在交絡（Multiple Causal Inference with Latent Confounding）</news:title>
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   <news:title>有効次元に基づくスケッチ最適化の理論と実践（Optimal Sketching Bounds for Exp-concave Stochastic Minimization）</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>水晶なしクロックでの通信（Communication with Crystal-Free Radios）</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>初期化と活性化関数の選択が深層ニューラルネットワークに与える影響（ON THE SELECTION OF INITIALIZATION AND ACTIVATION FUNCTION FOR DEEP NEURAL NETWORKS）</news:title>
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   <news:title>部分観測領域における何を伝えるべきかを学ぶ（Learning What Information to Give in Partially Observed Domains）</news:title>
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    <news:language>ja</news:language>
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   <news:title>実数値学習器のサンプル圧縮（Sample Compression for Real-Valued Learners）</news:title>
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   <news:title>分類器に依存しないサリエンシーマップ抽出（Classifier-Agnostic Saliency Map Extraction）</news:title>
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   <news:title>文脈依存トークン符号化に基づくメタBiLSTMを用いた形態統語タグ付け（Morphosyntactic Tagging with a Meta-BiLSTM Model over Context Sensitive Token Encodings）</news:title>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>非同期確率的勾配降下の確率修正方程式（Stochastic modified equations for the asynchronous stochastic gradient descent）</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>システム神経科学における教師あり機械学習の役割（The Roles of Supervised Machine Learning in Systems Neuroscience）</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>信頼度推定の偏りを減らす手法（BIAS-REDUCED UNCERTAINTY ESTIMATION FOR DEEP NEURAL CLASSIFIERS）</news:title>
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   <news:genres>Blog</news:genres>
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   <news:title>多項式時間における構造化予測のための最大事後確率摂動モデルの学習 (Learning Maximum-A-Posteriori Perturbation Models for Structured Prediction in Polynomial Time)</news:title>
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    <news:language>ja</news:language>
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   <news:title>Maskingによるノイズ付きラベル処理の新視点 (Masking: A New Perspective of Noisy Supervision)</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>シンプルなキャッシュモデルによる画像認識の精度向上（A Simple Cache Model for Image Recognition）</news:title>
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   <news:genres>Blog</news:genres>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>党派情報を使った投票予測モデルの強化（Party Matters: Enhancing Legislative Embeddings with Author Attributes for Vote Prediction）</news:title>
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   <news:genres>Blog</news:genres>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>サイド情報を用いた制約付きスパース部分空間クラスタリングの強化（Constrained Sparse Subspace Clustering with Side-Information）</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>階層型強化学習によるトピック一貫性のある視覚ストーリー生成（Hierarchically Structured Reinforcement Learning for Topically Coherent Visual Story Generation）</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>言語モデルの数値処理能力（Numeracy for Language Models: Evaluating and Improving their Ability to Predict Numbers）</news:title>
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   <news:genres>Blog</news:genres>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>自然画像における物体の数え上げ学習（Learning To Count Objects In Natural Images For Visual Question Answering）</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>テンソルプログラムの最適化を学習で自動化する（Learning to Optimize Tensor Programs）</news:title>
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   <news:genres>Blog</news:genres>
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  <lastmod>2026-05-07T05:10:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CaptionBotとDrawingBotのターボ学習（Turbo Learning for CaptionBot and DrawingBot）</news:title>
   <news:publication_date>2026-05-07T05:10:45Z</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>ソーシャルメディア検索のための多視点関連性マッチングと階層的ConvNet（Multi-Perspective Relevance Matching with Hierarchical ConvNets for Social Media Search）</news:title>
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   <news:genres>Blog</news:genres>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>光学・赤外全天サーベイにおける機械学習による銀河外天体同定（Machine-learning identification of extragalactic objects in the optical-infrared all-sky surveys）</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>VideoCapsuleNetによる行動検出の単純化（VideoCapsuleNet: A Simplified Network for Action Detection）</news:title>
   <news:publication_date>2026-05-07T05:09:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-05-07T04:16:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Dominant Setsによるスピーカクラスタリングの実務的解説（Speaker Clustering Using Dominant Sets）</news:title>
   <news:publication_date>2026-05-07T04:16:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687373</loc>
  <lastmod>2026-05-07T04:16:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>微分可能な閉形式ソルバーを用いたメタ学習（Meta-Learning with Differentiable Closed-Form Solvers）</news:title>
   <news:publication_date>2026-05-07T04:16:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687371</loc>
  <lastmod>2026-05-07T04:16:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>サンプル圧縮スキームを用いた非仮定学習の新たな下界（A New Lower Bound for Agnostic Learning with Sample Compression Schemes）</news:title>
   <news:publication_date>2026-05-07T04:16:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687369</loc>
  <lastmod>2026-05-07T04:14:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的勾配降下法における適応ステップサイズの収束性（On the Convergence of Stochastic Gradient Descent with Adaptive Stepsizes）</news:title>
   <news:publication_date>2026-05-07T04:14:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687367</loc>
  <lastmod>2026-05-07T04:14:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>過剰な情報と学習の罠（Overabundant Information and Learning Traps）</news:title>
   <news:publication_date>2026-05-07T04:14:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687365</loc>
  <lastmod>2026-05-07T04:14:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>データのためのマーケットプレイス：アルゴリズム的解決策 (A Marketplace for Data: An Algorithmic Solution)</news:title>
   <news:publication_date>2026-05-07T04:14:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687363</loc>
  <lastmod>2026-05-07T04:14:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>頑健な確率オペレータ群による強化学習の改善（A Family of Robust Stochastic Operators for Reinforcement Learning）</news:title>
   <news:publication_date>2026-05-07T04:14:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687361</loc>
  <lastmod>2026-05-07T03:22:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所領域に着目する注意機構で細粒度ゼロショット学習を改善する（Stacked Semantic-Guided Attention Model for Fine-Grained Zero-Shot Learning）</news:title>
   <news:publication_date>2026-05-07T03:22:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687359</loc>
  <lastmod>2026-05-07T03:21:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>地平線・空線検出の自動化比較（Comparison of Semantic Segmentation Approaches for Horizon/Sky Line Detection）</news:title>
   <news:publication_date>2026-05-07T03:21:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687357</loc>
  <lastmod>2026-05-07T03:21:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非定常1次元信号解析のためのカーネル最適化スキーム PiPs（PiPs: a Kernel-based Optimization Scheme for Analyzing Non-Stationary 1D Signals）</news:title>
   <news:publication_date>2026-05-07T03:21:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687355</loc>
  <lastmod>2026-05-07T03:20:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自己段階学習を凹共役理論で読み解く（Understanding Self-Paced Learning under Concave Conjugacy Theory）</news:title>
   <news:publication_date>2026-05-07T03:20:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687353</loc>
  <lastmod>2026-05-07T03:20:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>小さな一歩、大きな飛躍：ディープラーニングのための最小ニュートンソルバー（Small steps and giant leaps: Minimal Newton solvers for Deep Learning）</news:title>
   <news:publication_date>2026-05-07T03:20:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687351</loc>
  <lastmod>2026-05-07T03:19:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>敵対的検閲付きオートエンコーダからの不変表現（Invariant Representations from Adversarially Censored Autoencoders）</news:title>
   <news:publication_date>2026-05-07T03:19:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687349</loc>
  <lastmod>2026-05-07T03:19:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>混合ノイズ除去における変分手法とCNN正則化（Variational based Mixed Noise Removal with CNN）</news:title>
   <news:publication_date>2026-05-07T03:19:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687347</loc>
  <lastmod>2026-05-07T02:27:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>参照表現生成の統合的ニューラル手法（NeuralREG: An end-to-end approach to referring expression generation）</news:title>
   <news:publication_date>2026-05-07T02:27:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687345</loc>
  <lastmod>2026-05-07T02:27:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフカプセル畳み込みニューラルネットワーク（Graph Capsule Convolutional Neural Networks）</news:title>
   <news:publication_date>2026-05-07T02:27:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687343</loc>
  <lastmod>2026-05-07T02:26:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>近似テンソル演算による高速ニューラルネットワーク学習（Faster Neural Network Training with Approximate Tensor Operations）</news:title>
   <news:publication_date>2026-05-07T02:26:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687341</loc>
  <lastmod>2026-05-07T02:26:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>One Monadを使った証明の枠組みを読み解く（One Monad to Prove Them All）</news:title>
   <news:publication_date>2026-05-07T02:26:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687339</loc>
  <lastmod>2026-05-07T02:26:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SAS環境におけるSuper Learnerの導入と実装（Super learning in the SAS system）</news:title>
   <news:publication_date>2026-05-07T02:26:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687337</loc>
  <lastmod>2026-05-07T02:25:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>NEWMAによるスケーラブルなモデルフリーオンライン変化点検出（NEWMA: a new method for scalable model-free online change-point detection）</news:title>
   <news:publication_date>2026-05-07T02:25:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687335</loc>
  <lastmod>2026-05-07T02:25:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>カーネル化されたMDPにおけるオンライン学習の実践的意味（Online Learning in Kernelized Markov Decision Processes）</news:title>
   <news:publication_date>2026-05-07T02:25:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687333</loc>
  <lastmod>2026-05-07T01:34:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>イタリア天文アーカイブの公開基盤：モジュール化と分散（Italian center for Astronomical Archives publishing solution: modular and distributed）</news:title>
   <news:publication_date>2026-05-07T01:34:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687331</loc>
  <lastmod>2026-05-07T01:33:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>楕円混合モデル学習の普遍的枠組み（A universal framework for learning the elliptical mixture model）</news:title>
   <news:publication_date>2026-05-07T01:33:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687329</loc>
  <lastmod>2026-05-07T01:32:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>後ろを見ない学習法――EnKFに基づくバックプロパゲーション不要のニューラルネットワーク訓練（Never look back - A modified EnKF method and its application to the training of neural networks without back propagation）</news:title>
   <news:publication_date>2026-05-07T01:32:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687327</loc>
  <lastmod>2026-05-07T01:32:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DiDA: ドメイン適応のための分離合成（DiDA: Disentangled Synthesis for Domain Adaptation）</news:title>
   <news:publication_date>2026-05-07T01:32:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687325</loc>
  <lastmod>2026-05-07T01:32:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>行き先をどう考えているのか：行動から内部力学信念を推定する手法（Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior）</news:title>
   <news:publication_date>2026-05-07T01:32:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687323</loc>
  <lastmod>2026-05-07T01:32:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DifNet: 拡散によるセマンティックセグメンテーションの実践（DifNet: Semantic Segmentation by Diffusion Networks）</news:title>
   <news:publication_date>2026-05-07T01:32:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687321</loc>
  <lastmod>2026-05-07T01:31:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複雑な物体形態の視覚空間学習（VISUAL SPATIAL LEARNING OF COMPLEX OBJECT MORPHOLOGIES THROUGH INTERACTION WITH VIRTUAL AND REAL-WORLD DATA）</news:title>
   <news:publication_date>2026-05-07T01:31:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687319</loc>
  <lastmod>2026-05-07T00:39:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>アニメ風ポートレイトのスタイル空間探索（Anime Style Space Exploration Using Metric Learning and Generative Adversarial Networks）</news:title>
   <news:publication_date>2026-05-07T00:39:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687317</loc>
  <lastmod>2026-05-07T00:38:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>双方向学習によるニューラルネットワークの堅牢化（Bidirectional Learning for Robust Neural Networks）</news:title>
   <news:publication_date>2026-05-07T00:38:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687315</loc>
  <lastmod>2026-05-07T00:38:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>中間層に入れる「敵対的ノイズ層」が示すCNNの正則化効果（Adversarial Noise Layer: Regularize Neural Network By Adding Noise）</news:title>
   <news:publication_date>2026-05-07T00:38:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687313</loc>
  <lastmod>2026-05-07T00:37:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>原始惑星系円盤の光蒸発と金属量依存性（RADIATION HYDRODYNAMICS SIMULATIONS OF PHOTOEVAPORATION OF PROTOPLANETARY DISKS II: METALLICITY DEPENDENCE OF UV AND X-RAY PHOTOEVAPORATION）</news:title>
   <news:publication_date>2026-05-07T00:37:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687311</loc>
  <lastmod>2026-05-07T00:37:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>テンソルを用いたサブモード座標アルゴリズムによる株価予測（A Tensor-Based Sub-Mode Coordinate Algorithm for Stock Prediction）</news:title>
   <news:publication_date>2026-05-07T00:37:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687309</loc>
  <lastmod>2026-05-07T00:36:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>生の音声（raw speech）からの敵対的学習によるドメイン不変音声認識（ADVERSARIAL LEARNING OF RAW SPEECH FEATURES FOR DOMAIN INVARIANT SPEECH RECOGNITION）</news:title>
   <news:publication_date>2026-05-07T00:36:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687307</loc>
  <lastmod>2026-05-07T00:36:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフデータに対する敵対的攻撃の研究（Adversarial Attacks on Neural Networks for Graph Data）</news:title>
   <news:publication_date>2026-05-07T00:36:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687305</loc>
  <lastmod>2026-05-06T23:45:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ナビゲーション指示を理解するためのデータセットとモデルの提示（A new dataset and model for learning to understand navigational instructions）</news:title>
   <news:publication_date>2026-05-06T23:45:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687303</loc>
  <lastmod>2026-05-06T23:44:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>メモリ拡張ニューラルネットワーク向け省エネ推論アクセラレータ（Energy-Efﬁcient Inference Accelerator for Memory-Augmented Neural Networks on an FPGA）</news:title>
   <news:publication_date>2026-05-06T23:44:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687301</loc>
  <lastmod>2026-05-06T23:44:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多段階グリーディ方策のオンラインおよび近似強化学習における応用（Multiple-Step Greedy Policies in Online and Approximate Reinforcement Learning）</news:title>
   <news:publication_date>2026-05-06T23:44:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687299</loc>
  <lastmod>2026-05-06T23:43:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Transductive Boltzmann Machines（Transductive Boltzmann Machines）</news:title>
   <news:publication_date>2026-05-06T23:43:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687297</loc>
  <lastmod>2026-05-06T23:43:16Z</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量子化による低消費電力・高スループット推論（Quantizing Convolutional Neural Networks for Low-Power High-Throughput Inference Engines）</news:title>
   <news:publication_date>2026-05-06T23:43:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687295</loc>
  <lastmod>2026-05-06T23:43:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>最初の星と21-cmグローバル信号の関係（Stellar mass dependence of the 21-cm signal around the first star and its impact on the global signal）</news:title>
   <news:publication_date>2026-05-06T23:43:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687293</loc>
  <lastmod>2026-05-06T23:42:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>正規化されたクリストッフェル関数を用いたレバレッジスコアと密度の関係（Relating Leverage Scores and Density using Regularized Christoffel Functions）</news:title>
   <news:publication_date>2026-05-06T23:42:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687291</loc>
  <lastmod>2026-05-06T22:51:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ビリニア・アテンション・ネットワークの要点（Bilinear Attention Networks）</news:title>
   <news:publication_date>2026-05-06T22:51:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687289</loc>
  <lastmod>2026-05-06T22:51:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>粗から細への顕著物体検出（Coarse-to-Fine Salient Object Detection with Low-Rank Matrix Recovery）</news:title>
   <news:publication_date>2026-05-06T22:51:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687287</loc>
  <lastmod>2026-05-06T22:51:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Batch-Instance Normalizationによるスタイル不変性の獲得（Batch-Instance Normalization for Adaptively Style-Invariant Neural Networks）</news:title>
   <news:publication_date>2026-05-06T22:51:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687285</loc>
  <lastmod>2026-05-06T22:50:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散プリマル・デュアル時系列差分学習（Primal-Dual Distributed Temporal Difference Learning）</news:title>
   <news:publication_date>2026-05-06T22:50:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687283</loc>
  <lastmod>2026-05-06T22:50:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>観察から潜在ポリシーを模倣する手法（Imitating Latent Policies from Observation）</news:title>
   <news:publication_date>2026-05-06T22:50:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687281</loc>
  <lastmod>2026-05-06T22:49:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>MRPCによる飛行時間検出器技術の現状（Status of technology of MRPC time of flight system）</news:title>
   <news:publication_date>2026-05-06T22:49:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687279</loc>
  <lastmod>2026-05-06T22:49:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>進化に導かれた方策勾配—ERLが示す探索と勾配の融合（Evolution-Guided Policy Gradient in Reinforcement Learning）</news:title>
   <news:publication_date>2026-05-06T22:49:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687277</loc>
  <lastmod>2026-05-06T21:57:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>貪欲粒子最適化によるベイズ事後近似（Bayesian posterior approximation via greedy particle optimization）</news:title>
   <news:publication_date>2026-05-06T21:57:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687275</loc>
  <lastmod>2026-05-06T21:57:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文脈予測による教師なし深層背景推定と前景分割（Unsupervised Deep Context Prediction for Background Estimation and Foreground Segmentation）</news:title>
   <news:publication_date>2026-05-06T21:57:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687273</loc>
  <lastmod>2026-05-06T21:56:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Quickshift++：サンプルベースMean Shiftの理論的に優れた初期化 (Quickshift++: Provably Good Initializations for Sample-Based Mean Shift)</news:title>
   <news:publication_date>2026-05-06T21:56:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687271</loc>
  <lastmod>2026-05-06T21:56:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>生成モデルによる制約のない敵対的例の構築（Constructing Unrestricted Adversarial Examples with Generative Models）</news:title>
   <news:publication_date>2026-05-06T21:56:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687269</loc>
  <lastmod>2026-05-06T21:55:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>低解像度マルチスペクトル顔画像におけるクラス代表オートエンコーダによる性別分類（Class Representative Autoencoder for Low Resolution Multi-Spectral Gender Classification）</news:title>
   <news:publication_date>2026-05-06T21:55:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687267</loc>
  <lastmod>2026-05-06T21:55:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Sharp Minimaを平滑化して汎化性能を高める手法（SmoothOut: Smoothing Out Sharp Minima to Improve Generalization in Deep Learning）</news:title>
   <news:publication_date>2026-05-06T21:55:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687265</loc>
  <lastmod>2026-05-06T21:54:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>対流性嵐による停電予測（PREDICTING ELECTRICITY OUTAGES CAUSED BY CONVECTIVE STORMS）</news:title>
   <news:publication_date>2026-05-06T21:54:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687263</loc>
  <lastmod>2026-05-06T21:02:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>双方向依存木表現によるアスペクト抽出の改善（Improving Aspect Term Extraction with Bidirectional Dependency Tree Representation）</news:title>
   <news:publication_date>2026-05-06T21:02:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687261</loc>
  <lastmod>2026-05-06T21:01:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所化された複数カーネル学習による異常検知（Localized Multiple Kernel Learning for Anomaly Detection: One-class Classification）</news:title>
   <news:publication_date>2026-05-06T21:01:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687259</loc>
  <lastmod>2026-05-06T21:00:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ラックスケール・パラメータサーバによる分散DNN訓練の高速化（Parameter Hub: a Rack-Scale Parameter Server for Distributed Deep Neural Network Training）</news:title>
   <news:publication_date>2026-05-06T21:00:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687257</loc>
  <lastmod>2026-05-06T21:00:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動画から心拍と呼吸を測るDeepPhys（DeepPhys: Video-Based Physiological Measurement Using Convolutional Attention Networks）</news:title>
   <news:publication_date>2026-05-06T21:00:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687255</loc>
  <lastmod>2026-05-06T20:59:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CNNとRNNのサンプル効率はなぜ高いのか（sample-complexity of Estimating Convolutional and Recurrent Neural Networks）</news:title>
   <news:publication_date>2026-05-06T20:59:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687253</loc>
  <lastmod>2026-05-06T20:59:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>弱いメモリモデルの構築手法（Constructing a Weak Memory Model）</news:title>
   <news:publication_date>2026-05-06T20:59:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687251</loc>
  <lastmod>2026-05-06T20:59:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数埋め込みと多層比較による文センテンス表現（Sentence Modeling via Multiple Word Embeddings and Multi-level Comparison for Semantic Textual Similarity）</news:title>
   <news:publication_date>2026-05-06T20:59:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687249</loc>
  <lastmod>2026-05-06T20:07:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非凸トランケート損失での学習（Learning with Non-Convex Truncated Losses by SGD）</news:title>
   <news:publication_date>2026-05-06T20:07:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687247</loc>
  <lastmod>2026-05-06T20:06:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>3D点群のための球面畳み込みニューラルネットワーク（Spherical Convolutional Neural Network for 3D Point Clouds）</news:title>
   <news:publication_date>2026-05-06T20:06:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687245</loc>
  <lastmod>2026-05-06T20:06:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GSAE: 遺伝子セットノードを組み込んだオートエンコーダによる機能的ゲノミクス解析（GSAE: an autoencoder with embedded gene-set nodes for genomics functional characterization）</news:title>
   <news:publication_date>2026-05-06T20:06:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687243</loc>
  <lastmod>2026-05-06T20:05:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ハイブリッド・マクロ／マイクロ逆伝播による深層スパイキングニューラルネットワークの学習（Hybrid Macro/Micro Level Backpropagation for Training Deep Spiking Neural Networks）</news:title>
   <news:publication_date>2026-05-06T20:05:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687241</loc>
  <lastmod>2026-05-06T20:05:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オンライン逆強化学習の枠組みと手法（A Framework and Method for Online Inverse Reinforcement Learning）</news:title>
   <news:publication_date>2026-05-06T20:05:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687239</loc>
  <lastmod>2026-05-06T20:05:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Featurized Bidirectional GANによる敵対的防御の考え方（Featurized Bidirectional GAN: Adversarial Defense via Adversarially Learned Semantic Inference）</news:title>
   <news:publication_date>2026-05-06T20:05:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687237</loc>
  <lastmod>2026-05-06T20:04:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>周辺機器の振る舞いを学習する再帰型ニューラルネットワーク（Learning Device Models with Recurrent Neural Networks）</news:title>
   <news:publication_date>2026-05-06T20:04:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687235</loc>
  <lastmod>2026-05-06T19:13:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ユニバーサル音楽変換ネットワーク（A Universal Music Translation Network）</news:title>
   <news:publication_date>2026-05-06T19:13:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687233</loc>
  <lastmod>2026-05-06T19:13:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>補助データで高速化するベイズ最適化（Accelerated Bayesian Optimization through Weight-Prior Tuning）</news:title>
   <news:publication_date>2026-05-06T19:13:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687231</loc>
  <lastmod>2026-05-06T19:12:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>平行移動畳み込みが変えた点――多様体上で使える畳み込みの実装路（Parallel Transport Convolution: A New Tool for Convolutional Neural Networks on Manifolds）</news:title>
   <news:publication_date>2026-05-06T19:12:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687229</loc>
  <lastmod>2026-05-06T19:12:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>一般化された交差エントロピー損失によるノイズ耐性学習（Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels）</news:title>
   <news:publication_date>2026-05-06T19:12:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687227</loc>
  <lastmod>2026-05-06T19:11:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>投影不要アルゴリズムによる高次元推定の効率化（Projection-Free Algorithms in Statistical Estimation）</news:title>
   <news:publication_date>2026-05-06T19:11:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687225</loc>
  <lastmod>2026-05-06T19:11:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散環境向け投影不要アルゴリズムの通信効率化（Communication-Efficient Projection-Free Algorithm for Distributed Optimization）</news:title>
   <news:publication_date>2026-05-06T19:11:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687223</loc>
  <lastmod>2026-05-06T19:11:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大気中の流星による衝撃波の物理学（Physics of Meteor Generated Shock Waves in the Earth’s Atmosphere – A Review）</news:title>
   <news:publication_date>2026-05-06T19:11:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687221</loc>
  <lastmod>2026-05-06T18:19:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>木構造の確率推定を一般化する手法（Generalizing Tree Probability Estimation via Bayesian Networks）</news:title>
   <news:publication_date>2026-05-06T18:19:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687219</loc>
  <lastmod>2026-05-06T18:19:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スパース多課題回帰におけるワッサースタイン正則化（Wasserstein regularization for sparse multi-task regression）</news:title>
   <news:publication_date>2026-05-06T18:19:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687217</loc>
  <lastmod>2026-05-06T18:18:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>シミュレーション最適化のための雑音耐性構造化探索（Optimizing Simulations with Noise-Tolerant Structured Exploration）</news:title>
   <news:publication_date>2026-05-06T18:18:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687215</loc>
  <lastmod>2026-05-06T18:18:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ブラックボックス音声システムに対する標的型敵対的事例（Targeted Adversarial Examples for Black Box Audio Systems）</news:title>
   <news:publication_date>2026-05-06T18:18:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687213</loc>
  <lastmod>2026-05-06T18:18:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>協調型マルチエージェント強化学習における「教えるを学ぶ」枠組み（Learning to Teach in Cooperative Multiagent Reinforcement Learning）</news:title>
   <news:publication_date>2026-05-06T18:18:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687211</loc>
  <lastmod>2026-05-06T18:17:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>疑似逆行列学習（Pseudoinverse Learning）とVESTの要点整理（A VEST of the Pseudoinverse Learning Algorithm）</news:title>
   <news:publication_date>2026-05-06T18:17:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687209</loc>
  <lastmod>2026-05-06T18:17:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>低コストな畳み込みニューラルネットワークの設計（Low-Cost Parameterizations of Deep Convolutional Neural Networks）</news:title>
   <news:publication_date>2026-05-06T18:17:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687207</loc>
  <lastmod>2026-05-06T17:26:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層構造化自己注意による抽出型文書要約モデル（A Hierarchical Structured Self-Attentive Model for Extractive Document Summarization）</news:title>
   <news:publication_date>2026-05-06T17:26:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687205</loc>
  <lastmod>2026-05-06T17:20:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>夢で学ぶロボット制御：実世界で使える視覚運動ポリシーの獲得（Learning Real-World Robot Policies by Dreaming）</news:title>
   <news:publication_date>2026-05-06T17:20:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687203</loc>
  <lastmod>2026-05-06T17:19:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オンライン構造化ラプラス近似による忘却問題の克服（Online Structured Laplace Approximations For Overcoming Catastrophic Forgetting）</news:title>
   <news:publication_date>2026-05-06T17:19:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687201</loc>
  <lastmod>2026-05-06T17:18:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ピクセル離散化による防御の限界を探る（Towards Understanding Limitations of Pixel Discretization Against Adversarial Attacks）</news:title>
   <news:publication_date>2026-05-06T17:18:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687199</loc>
  <lastmod>2026-05-06T17:17:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>安全で効率的な方策改善法：Rerouted Behavior Improvement（CONSTRAINED POLICY IMPROVEMENT FOR SAFE AND EFFICIENT REINFORCEMENT LEARNING）</news:title>
   <news:publication_date>2026-05-06T17:17:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687197</loc>
  <lastmod>2026-05-06T17:16:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多層カーネルリッジ回帰によるワン・クラス分類の進化（Multi-layer Kernel Ridge Regression for One-class Classification）</news:title>
   <news:publication_date>2026-05-06T17:16:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687195</loc>
  <lastmod>2026-05-06T17:16:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所伝播を用いたネットワーク学習（Network Learning with Local Propagation）</news:title>
   <news:publication_date>2026-05-06T17:16:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687193</loc>
  <lastmod>2026-05-06T16:24:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>重なりを許す畳み込みニューラルネットワークの学習手法の改良（Improved Learning of One-hidden-layer Convolutional Neural Networks with Overlaps）</news:title>
   <news:publication_date>2026-05-06T16:24:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687191</loc>
  <lastmod>2026-05-06T16:24:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>事前学習VAEで任意の条件付き推論を可能にするCross-Coding（Conditional Inference in Pre-trained Variational Autoencoders via Cross-Coding）</news:title>
   <news:publication_date>2026-05-06T16:24:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687189</loc>
  <lastmod>2026-05-06T16:24:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習による美徳倫理の形式化（One Formalization of Virtue Ethics via Learning）</news:title>
   <news:publication_date>2026-05-06T16:24:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687187</loc>
  <lastmod>2026-05-06T16:22:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>注意機構への指導で学ぶ合成性（Learning compositionally through attentive guidance）</news:title>
   <news:publication_date>2026-05-06T16:22:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687185</loc>
  <lastmod>2026-05-06T16:22:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>良好充分モデル空間によるモデル集約（Model Aggregation via Good-Enough Model Spaces）</news:title>
   <news:publication_date>2026-05-06T16:22:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687183</loc>
  <lastmod>2026-05-06T16:22:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層強化学習のための教師なしビデオ物体セグメンテーション（Unsupervised Video Object Segmentation for Deep Reinforcement Learning）</news:title>
   <news:publication_date>2026-05-06T16:22:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687181</loc>
  <lastmod>2026-05-06T16:22:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DLBI: 深層学習とベイズ推論を組み合わせた超解像蛍光顕微鏡の構造再構成（DLBI: Deep learning guided Bayesian inference for structure reconstruction of super-resolution fluorescence microscopy）</news:title>
   <news:publication_date>2026-05-06T16:22:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687179</loc>
  <lastmod>2026-05-06T15:30:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>構造的規則性に基づくネットワーク再構築と制御（Network Reconstruction and Controlling Based on Structural Regularity Analysis）</news:title>
   <news:publication_date>2026-05-06T15:30:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687177</loc>
  <lastmod>2026-05-06T15:30:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Seq2CNNによる要約的テキスト分類（Abstractive Text Classification Using Sequence-to-convolution Neural Networks）</news:title>
   <news:publication_date>2026-05-06T15:30:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687175</loc>
  <lastmod>2026-05-06T15:29:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>5GセルフバックホールドmmWaveネットワークにおける経路選択とレート配分の統合フレームワーク（Joint Path Selection and Rate Allocation Framework for 5G Self-Backhauled mmWave Networks）</news:title>
   <news:publication_date>2026-05-06T15:29:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687173</loc>
  <lastmod>2026-05-06T15:29:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>二重流CNNによる構造化時系列分類の要点（STS Classification with Dual-stream CNN）</news:title>
   <news:publication_date>2026-05-06T15:29:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687171</loc>
  <lastmod>2026-05-06T15:29:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>適切複合損失の指数凸性（Exp-concavity of Proper Composite Losses）</news:title>
   <news:publication_date>2026-05-06T15:29:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687169</loc>
  <lastmod>2026-05-06T15:28:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>注意に基づく通信学習によるマルチエージェント協調（Learning Attentional Communication for Multi-Agent Cooperation）</news:title>
   <news:publication_date>2026-05-06T15:28:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687167</loc>
  <lastmod>2026-05-06T15:28:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>予測ゲームの要素：遷移、損失、再パラメータ化（Transitions, Losses, and Re-parameterizations: Elements of Prediction Games）</news:title>
   <news:publication_date>2026-05-06T15:28:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687165</loc>
  <lastmod>2026-05-06T14:36:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>人間主導のランダム化によるデータ探索（Human-guided Data Exploration Using Randomisation）</news:title>
   <news:publication_date>2026-05-06T14:36:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687163</loc>
  <lastmod>2026-05-06T14:35:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非線形分布勾配時系列差分学習の要点（Nonlinear Distributional Gradient Temporal-Difference Learning）</news:title>
   <news:publication_date>2026-05-06T14:35:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687161</loc>
  <lastmod>2026-05-06T14:35:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>コスト感度分類の難しさを定量化する最小限界（Minimax Lower Bounds for Cost Sensitive Classification）</news:title>
   <news:publication_date>2026-05-06T14:35:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687159</loc>
  <lastmod>2026-05-06T14:34:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ウェーブレット畳み込みニューラルネットワーク（Wavelet Convolutional Neural Networks）</news:title>
   <news:publication_date>2026-05-06T14:34:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687157</loc>
  <lastmod>2026-05-06T14:34:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>タスク非依存のメタラーニングがもたらす少数事例学習の安定化（Task-Agnostic Meta-Learning for Few-shot Learning）</news:title>
   <news:publication_date>2026-05-06T14:34:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687155</loc>
  <lastmod>2026-05-06T14:34:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチレベルGaussian記述子による人物再識別の実務的示唆（Multi-Level Gaussian Descriptor for Person Re-Identification）</news:title>
   <news:publication_date>2026-05-06T14:34:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687153</loc>
  <lastmod>2026-05-06T14:34:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>製造現場でのオンラインRFID位置特定の進化（An Online RFID Localization in the Manufacturing Shopfloor）</news:title>
   <news:publication_date>2026-05-06T14:34:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687143</loc>
  <lastmod>2026-05-06T13:42:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>弱教師ありCapsNetによる物体座標推定（Object Localization with a Weakly Supervised CapsNet）</news:title>
   <news:publication_date>2026-05-06T13:42:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687141</loc>
  <lastmod>2026-05-06T13:33:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Lyapunovに基づく安全な強化学習のアプローチ（A Lyapunov-based Approach to Safe Reinforcement Learning）</news:title>
   <news:publication_date>2026-05-06T13:33:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687139</loc>
  <lastmod>2026-05-06T13:32:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>軽量な教師なし深層ループクロージャー（Lightweight Unsupervised Deep Loop Closure）</news:title>
   <news:publication_date>2026-05-06T13:32:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687137</loc>
  <lastmod>2026-05-06T13:31:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多元ソース適応のアルゴリズムと理論（Algorithms and Theory for Multiple-Source Adaptation）</news:title>
   <news:publication_date>2026-05-06T13:31:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687135</loc>
  <lastmod>2026-05-06T13:31:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>骨格（スケルトン）ベース行動認識のための2ストリーム適応型グラフ畳み込みネットワーク（Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition）</news:title>
   <news:publication_date>2026-05-06T13:31:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687133</loc>
  <lastmod>2026-05-06T13:30:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>計算クラスタのジョブ失敗を予測する機械学習（Machine Learning for Predictive Analytics of Compute Cluster Jobs）</news:title>
   <news:publication_date>2026-05-06T13:30:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687131</loc>
  <lastmod>2026-05-06T13:30:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>腫瘍の薬剤応答をゲノム統合プロファイルから予測する深層ニューラルネットワーク（Predicting drug response of tumors from integrated genomic profiles by deep neural networks）</news:title>
   <news:publication_date>2026-05-06T13:30:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687129</loc>
  <lastmod>2026-05-06T12:38:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械指導による逆強化学習の最小デモ探索（Machine Teaching for Inverse Reinforcement Learning: Algorithms and Applications）</news:title>
   <news:publication_date>2026-05-06T12:38:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687127</loc>
  <lastmod>2026-05-06T12:28:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ラマン分光による混合物中の分析対象定量のベイズモデリングと計算（Bayesian Modeling and Computation for Analyte Quantification in Complex Mixtures Using Raman Spectroscopy）</news:title>
   <news:publication_date>2026-05-06T12:28:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687125</loc>
  <lastmod>2026-05-06T12:28:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ソーシャルメディア上の攻撃的表現を減らす無監督テキストスタイル変換（Fighting Offensive Language on Social Media with Unsupervised Text Style Transfer）</news:title>
   <news:publication_date>2026-05-06T12:28:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687123</loc>
  <lastmod>2026-05-06T12:27:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>モード崩壊を扱う新しいGAN設計（BourGAN: Generative Networks with Metric Embeddings）</news:title>
   <news:publication_date>2026-05-06T12:27:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687121</loc>
  <lastmod>2026-05-06T12:26:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>RNNで学ぶグラフ全体の表現（Learning Graph-Level Representations with Recurrent Neural Networks）</news:title>
   <news:publication_date>2026-05-06T12:26:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687119</loc>
  <lastmod>2026-05-06T12:26:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>露出過剰サンプルにおける傾向スコア関数の一貫推定（Consistent Estimation of Propensity Score Functions with Oversampled Exposed Subjects）</news:title>
   <news:publication_date>2026-05-06T12:26:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687117</loc>
  <lastmod>2026-05-06T12:26:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>歩行者軌跡予測の現状とTrajNetベンチマークの示唆（An Evaluation of Trajectory Prediction Approaches and Notes on the TrajNet Benchmark）</news:title>
   <news:publication_date>2026-05-06T12:26:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687115</loc>
  <lastmod>2026-05-06T11:34:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散逆伝播によるサンプリング不要な変分推論（Sampling-Free Variational Inference of Bayesian Neural Networks by Variance Backpropagation）</news:title>
   <news:publication_date>2026-05-06T11:34:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687113</loc>
  <lastmod>2026-05-06T11:34:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>人の顔の潜在表現を学習して心理実験に応用する（Learning a face space for experiments on human identity）</news:title>
   <news:publication_date>2026-05-06T11:34:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687111</loc>
  <lastmod>2026-05-06T11:34:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ヒューマンアクティビティ認識におけるアテンションモデル（On Attention Models for Human Activity Recognition）</news:title>
   <news:publication_date>2026-05-06T11:34:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687109</loc>
  <lastmod>2026-05-06T11:33:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所的なデータ摂動による正則化損失最小化とデータ不可逆性（Regularized Loss Minimizers with Local Data Perturbation: Consistency and Data Irrecoverability）</news:title>
   <news:publication_date>2026-05-06T11:33:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687107</loc>
  <lastmod>2026-05-06T11:33:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>長期屋外顔追跡における深層学習アプローチ（Long-term face tracking in the wild using deep learning）</news:title>
   <news:publication_date>2026-05-06T11:33:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687105</loc>
  <lastmod>2026-05-06T11:33:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層特徴空間におけるサンプリングで人間のカテゴリ表象を捉える（Capturing human category representations by sampling in deep feature spaces）</news:title>
   <news:publication_date>2026-05-06T11:33:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687103</loc>
  <lastmod>2026-05-06T11:33:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層的言語ラベルを用いた深層ニューラルネットワークの視覚表現学習（Learning Hierarchical Visual Representations in Deep Neural Networks Using Hierarchical Linguistic Labels）</news:title>
   <news:publication_date>2026-05-06T11:33:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687101</loc>
  <lastmod>2026-05-06T10:42:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Connected and Automated Vehiclesのための実走行データに基づくエネルギー効率と排出評価手法（Energy Efficiency and Emission Testing for Connected and Automated Vehicles Using Real-World Driving Data）</news:title>
   <news:publication_date>2026-05-06T10:42:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687099</loc>
  <lastmod>2026-05-06T10:42:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ドメイン適応のためのサンプリング方策学習（Learning Sampling Policies for Domain Adaptation）</news:title>
   <news:publication_date>2026-05-06T10:42:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687097</loc>
  <lastmod>2026-05-06T10:41:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>対話状態追跡を変えたGLADの本質（Global-Locally Self-Attentive Dialogue State Tracker）</news:title>
   <news:publication_date>2026-05-06T10:41:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687095</loc>
  <lastmod>2026-05-06T10:40:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>潜在空間における非線形統計（Latent Space Non-Linear Statistics）</news:title>
   <news:publication_date>2026-05-06T10:40:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687093</loc>
  <lastmod>2026-05-06T10:40:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習に基づくMIMO検出の実務的理解（Learning to Detect）</news:title>
   <news:publication_date>2026-05-06T10:40:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687091</loc>
  <lastmod>2026-05-06T10:40:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>異種出力を扱う多出力ガウス過程予測（Heterogeneous Multi-output Gaussian Process Prediction）</news:title>
   <news:publication_date>2026-05-06T10:40:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687089</loc>
  <lastmod>2026-05-06T10:40:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Momentum fractional LMS の設計上の問題点と妥当性検証（Momentum fractional LMS for power signal parameter estimation）</news:title>
   <news:publication_date>2026-05-06T10:40:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687087</loc>
  <lastmod>2026-05-06T09:47:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CapProNetによるカプセル部分空間投影による深層特徴学習（CapProNet: Deep Feature Learning via Orthogonal Projections onto Capsule Subspaces）</news:title>
   <news:publication_date>2026-05-06T09:47:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687085</loc>
  <lastmod>2026-05-06T09:38:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>話者認証におけるスパースアーキテクチャの効果（Sparse Architectures for Text-Independent Speaker Verification Using Deep Neural Networks）</news:title>
   <news:publication_date>2026-05-06T09:38:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687083</loc>
  <lastmod>2026-05-06T09:37:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所競合を伴う非パラメトリックベイズ深層ネットワーク（Nonparametric Bayesian Deep Networks with Local Competition）</news:title>
   <news:publication_date>2026-05-06T09:37:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687081</loc>
  <lastmod>2026-05-06T09:37:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>生成的マルコフ状態モデルが示す分子ダイナミクスの新展開（Deep Generative Markov State Models）</news:title>
   <news:publication_date>2026-05-06T09:37:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687079</loc>
  <lastmod>2026-05-06T09:36:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>エピソード記憶を活用して学習を高速化する手法（Episodic Memory Deep Q-Networks）</news:title>
   <news:publication_date>2026-05-06T09:36:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687077</loc>
  <lastmod>2026-05-06T09:36:42Z</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クロスモーダル写像は本当にモダリティを橋渡しするか（Do Neural Network Cross-Modal Mappings Really Bridge Modalities?）</news:title>
   <news:publication_date>2026-05-06T09:36:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687075</loc>
  <lastmod>2026-05-06T09:36:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>生物模倣デザインにおける生成的創造性（Generative Creativity: Adversarial Learning for Bionic Design）</news:title>
   <news:publication_date>2026-05-06T09:36:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687073</loc>
  <lastmod>2026-05-06T08:45:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>特徴を可変的に剪定して学習を速める方法（Adaptively Pruning Features for Boosted Decision Trees）</news:title>
   <news:publication_date>2026-05-06T08:45:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687071</loc>
  <lastmod>2026-05-06T08:44:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>楕円分布のWasserstein空間を用いた点埋め込みの一般化 (Generalizing Point Embeddings using the Wasserstein Space of Elliptical Distributions)</news:title>
   <news:publication_date>2026-05-06T08:44:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687069</loc>
  <lastmod>2026-05-06T08:43:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>エンティティ・デュエットで検索を賢くする（Entity-Duet Neural Ranking: Understanding the Role of Knowledge Graph Semantics in Neural Information Retrieval）</news:title>
   <news:publication_date>2026-05-06T08:43:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687067</loc>
  <lastmod>2026-05-06T08:43:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>弱ラベル概念を単一スパイキングニューロンで正確に数える方法（Reliable counting of weakly labeled concepts by a single spiking neuron model）</news:title>
   <news:publication_date>2026-05-06T08:43:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687065</loc>
  <lastmod>2026-05-06T08:42:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数ドメインに対するロバスト最適化（Robust Optimization over Multiple Domains）</news:title>
   <news:publication_date>2026-05-06T08:42:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687063</loc>
  <lastmod>2026-05-06T08:42:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>救急外来記載（chief complaint）分類における再帰型ニューラルネットワークの実用性（Chief Complaint Classification with Recurrent Neural Networks）</news:title>
   <news:publication_date>2026-05-06T08:42:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687061</loc>
  <lastmod>2026-05-06T08:42:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単一スナップショットで到来方向を高精度に復元する手法（Sequential adaptive elastic net approach for single-snapshot source localization）</news:title>
   <news:publication_date>2026-05-06T08:42:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687059</loc>
  <lastmod>2026-05-06T07:50:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>暴力的場面における顔検出と認識（Wildest Faces: Face Detection and Recognition in Violent Settings）</news:title>
   <news:publication_date>2026-05-06T07:50:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687057</loc>
  <lastmod>2026-05-06T07:40:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>過去勾配を重視する最適化法の改良：Nostalgic Adam（Nostalgic Adam: Weighting more of the past gradients when designing the adaptive learning rate）</news:title>
   <news:publication_date>2026-05-06T07:40:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687055</loc>
  <lastmod>2026-05-06T07:40:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>行列補完とラベル同時推定による伝達学習の実務的再考（Transduction with Matrix Completion Using Smoothed Rank Function）</news:title>
   <news:publication_date>2026-05-06T07:40:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687053</loc>
  <lastmod>2026-05-06T07:40:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>極めて中性子豊富な78Niにおける魔法性の持続（PERSISTENCE OF MAGICITY IN NEUTRON RICH EXOTIC 78Ni IN GROUND AS WELL AS EXCITED STATES）</news:title>
   <news:publication_date>2026-05-06T07:40:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687051</loc>
  <lastmod>2026-05-06T07:39:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>インターネットだけで学ぶ画素単位ラベリング（Learning Pixel-wise Labeling from the Internet without Human Interaction）</news:title>
   <news:publication_date>2026-05-06T07:39:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687049</loc>
  <lastmod>2026-05-06T07:39:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DenseImage Networkによる動画の時空間進化の符号化と理解 (DenseImage Network: Video Spatial-Temporal Evolution Encoding and Understanding)</news:title>
   <news:publication_date>2026-05-06T07:39:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687047</loc>
  <lastmod>2026-05-06T07:39:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>眼底画像から緑内障を検出する円盤認識型アンサンブルネットワーク（Disc-aware Ensemble Network for Glaucoma Screening from Fundus Image）</news:title>
   <news:publication_date>2026-05-06T07:39:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687045</loc>
  <lastmod>2026-05-06T06:48:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>目標空間の自律発見によるパラメータ化スキルの学習（Autonomous discovery of the goal space to learn a parameterized skill）</news:title>
   <news:publication_date>2026-05-06T06:48:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687043</loc>
  <lastmod>2026-05-06T06:47:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>条件付きネットワーク埋め込みが示す実務的インパクト（CONDITIONAL NETWORK EMBEDDINGS）</news:title>
   <news:publication_date>2026-05-06T06:47:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687041</loc>
  <lastmod>2026-05-06T06:47:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>角度分岐ネットワークによるエンドツーエンド運転（End-to-end driving simulation via angle branched network）</news:title>
   <news:publication_date>2026-05-06T06:47:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687039</loc>
  <lastmod>2026-05-06T06:47:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層的かつ摂動的な確率シュレーディンガー方程式の体系と有機材料におけるキャリア動力学への応用（The hierarchical and perturbative forms of stochastic Schrödinger equations and their applications to carrier dynamics in organic materials）</news:title>
   <news:publication_date>2026-05-06T06:47:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687037</loc>
  <lastmod>2026-05-06T06:46:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所再帰処理を持つ深層予測符号化ネットワークによる物体認識の革新（Deep Predictive Coding Network with Local Recurrent Processing for Object Recognition）</news:title>
   <news:publication_date>2026-05-06T06:46:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687035</loc>
  <lastmod>2026-05-06T06:46:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチタスク学習の選び方を学ぶ（Learning to Multitask）</news:title>
   <news:publication_date>2026-05-06T06:46:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687033</loc>
  <lastmod>2026-05-06T06:46:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>浅いニューラルネットワークの最適解はリッジレット変換で表現できる（The global optimum of shallow neural network is attained by ridgelet transform）</news:title>
   <news:publication_date>2026-05-06T06:46:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687031</loc>
  <lastmod>2026-05-06T05:55:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多様な少数ショットテキスト分類と複数メトリクス（Diverse Few-Shot Text Classification with Multiple Metrics）</news:title>
   <news:publication_date>2026-05-06T05:55:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687029</loc>
  <lastmod>2026-05-06T05:54:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非正規化混合モデルの推定と深層表現を用いたクラスタリング（Estimation of Non-Normalized Mixture Models and Clustering Using Deep Representation）</news:title>
   <news:publication_date>2026-05-06T05:54:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687027</loc>
  <lastmod>2026-05-06T05:54:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>物理的に推論を最適化する（Physically optimizing inference）</news:title>
   <news:publication_date>2026-05-06T05:54:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687025</loc>
  <lastmod>2026-05-06T05:54:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>調和された多項式機（Reconciled Polynomial Machine: A Unified Representation of Shallow and Deep Learning Models）</news:title>
   <news:publication_date>2026-05-06T05:54:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687023</loc>
  <lastmod>2026-05-06T05:54:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>遺伝的進化の集合学習としてのGENモデル（GEN Model: An Alternative Approach to Deep Neural Network Models）</news:title>
   <news:publication_date>2026-05-06T05:54:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687021</loc>
  <lastmod>2026-05-06T05:54:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>顔属性操作のための疎グループ化マルチタスク生成対抗ネットワーク（Sparsely Grouped Multi-task Generative Adversarial Networks for Facial Attribute Manipulation）</news:title>
   <news:publication_date>2026-05-06T05:54:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687019</loc>
  <lastmod>2026-05-06T05:53:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層ルーピーニューラルネットによるグラフ表現学習（Deep Loopy Neural Network Model for Graph Structured Data Representation Learning）</news:title>
   <news:publication_date>2026-05-06T05:53:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687017</loc>
  <lastmod>2026-05-06T05:02:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層アンサンブル学習の関数近似視点（On Deep Ensemble Learning from a Function Approximation Perspective）</news:title>
   <news:publication_date>2026-05-06T05:02:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687015</loc>
  <lastmod>2026-05-06T05:02:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高速な視差推定を小型ネットワークで実現する手法（Fast Disparity Estimation using Dense Networks）</news:title>
   <news:publication_date>2026-05-06T05:02:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687013</loc>
  <lastmod>2026-05-06T05:02:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GADAM：Adamと遺伝的アルゴリズムを融合した深層学習の最適化手法（GADAM: Genetic-Evolutionary ADAM for Deep Neural Network Optimization）</news:title>
   <news:publication_date>2026-05-06T05:02:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687011</loc>
  <lastmod>2026-05-06T05:01:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>超音波エラストグラフィにCNNを組み合わせたグローバル推定（Global Ultrasound Elastography Using Convolutional Neural Network）</news:title>
   <news:publication_date>2026-05-06T05:01:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687009</loc>
  <lastmod>2026-05-06T05:01:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>等高線探索を情報効率で最適化する手法（Contour location via entropy reduction）</news:title>
   <news:publication_date>2026-05-06T05:01:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687007</loc>
  <lastmod>2026-05-06T05:00:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>数列予測問題で測るニューロンの計算力（Number Sequence Prediction Problems for Evaluating Computational Powers of Neural Networks）</news:title>
   <news:publication_date>2026-05-06T05:00:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687005</loc>
  <lastmod>2026-05-06T05:00:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>トリムドℓ1正則化が拓く高次元推定の精度向上（M-estimation with the Trimmed ℓ1 Penalty）</news:title>
   <news:publication_date>2026-05-06T05:00:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687003</loc>
  <lastmod>2026-05-06T04:08:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非同期並列学習の新枠組み「Tell Me Something New」(Tell Me Something New: A New Framework for Asynchronous Parallel Learning)</news:title>
   <news:publication_date>2026-05-06T04:08:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/687001</loc>
  <lastmod>2026-05-06T04:08:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>最適な連続DR-部分集合最大化と証明可能な平均場近似への応用（Optimal DR-Submodular Maximization and Applications to Provable Mean Field Inference）</news:title>
   <news:publication_date>2026-05-06T04:08:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686999</loc>
  <lastmod>2026-05-06T04:07:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>異種グラフにおける半教師あり学習とFacebook News Feedへの応用（Semisupervised Learning on Heterogeneous Graphs and its Applications to Facebook News Feed）</news:title>
   <news:publication_date>2026-05-06T04:07:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686997</loc>
  <lastmod>2026-05-06T04:06:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Generative Adversarial Networks によるソフトウェア脆弱性の自動修復（Learning to Repair Software Vulnerabilities with Generative Adversarial Networks）</news:title>
   <news:publication_date>2026-05-06T04:06:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686995</loc>
  <lastmod>2026-05-06T04:06:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高速オンライン強化学習のための2つの幾何学的入力変換法（Two Geometric Input Transformation Methods for Fast Online Reinforcement Learning with Neural Nets）</news:title>
   <news:publication_date>2026-05-06T04:06:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686993</loc>
  <lastmod>2026-05-06T04:06:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>残差ネットワークが深くできる理由：ノルム保存の視点から（Norm-Preservation: Why Residual Networks Can Become Extremely Deep?）</news:title>
   <news:publication_date>2026-05-06T04:06:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686991</loc>
  <lastmod>2026-05-06T04:06:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>射影不要なバンディット凸最適化 (Projection-Free Bandit Convex Optimization)</news:title>
   <news:publication_date>2026-05-06T04:06:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686989</loc>
  <lastmod>2026-05-06T03:14:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>進展的アンサンブルネットワークによるゼロショット認識の実務的含意（Progressive Ensemble Networks for Zero-Shot Recognition）</news:title>
   <news:publication_date>2026-05-06T03:14:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686987</loc>
  <lastmod>2026-05-06T03:14:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>円柱周りの非定常流れの深層動的モデリングと制御（Deep Dynamical Modeling and Control of Unsteady Fluid Flows）</news:title>
   <news:publication_date>2026-05-06T03:14:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686985</loc>
  <lastmod>2026-05-06T03:14:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>人手の知識なしでルービックキューブを解く（Solving the Rubik’s Cube Without Human Knowledge）</news:title>
   <news:publication_date>2026-05-06T03:14:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686983</loc>
  <lastmod>2026-05-06T03:13:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>置換検定による機械学習の交絡検出・定量化・補正（Using permutations to detect, quantify and correct for confounding in machine learning predictions）</news:title>
   <news:publication_date>2026-05-06T03:13:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686981</loc>
  <lastmod>2026-05-06T03:13:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>音声とテキストの埋め込み空間を教師なしでつなぐ方法（Unsupervised Cross-Modal Alignment of Speech and Text Embedding Spaces）</news:title>
   <news:publication_date>2026-05-06T03:13:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686979</loc>
  <lastmod>2026-05-06T03:13:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューラルネットワークを説明するための教師なし学習（Unsupervised Learning of Neural Networks to Explain Neural Networks）</news:title>
   <news:publication_date>2026-05-06T03:13:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686977</loc>
  <lastmod>2026-05-06T03:13:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ラテントフォースモデルの高速カーネル近似（Fast Kernel Approximations for Latent Force Models and Convolved Multiple-Output Gaussian processes）</news:title>
   <news:publication_date>2026-05-06T03:13:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686975</loc>
  <lastmod>2026-05-06T02:21:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ラティス上のDR-サブモジュラ最大化による部分空間選択（Subspace Selection via DR-Submodular Maximization on Lattices）</news:title>
   <news:publication_date>2026-05-06T02:21:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686973</loc>
  <lastmod>2026-05-06T02:20:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>PCAに対する対称関数最適化による局所最適解の不存在（PCA by Optimisation of Symmetric Functions has no Spurious Local Optima）</news:title>
   <news:publication_date>2026-05-06T02:20:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686971</loc>
  <lastmod>2026-05-06T02:20:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>離散変分オートエンコーダの実用的学習手法（DVAE#: Discrete Variational Autoencoders with Relaxed Boltzmann Priors）</news:title>
   <news:publication_date>2026-05-06T02:20:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686969</loc>
  <lastmod>2026-05-06T02:20:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチビュー文分散表現学習（Multi-view Sentence Representation Learning）</news:title>
   <news:publication_date>2026-05-06T02:20:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686967</loc>
  <lastmod>2026-05-06T02:19:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>計算不能（intractable）モデルのフィッシャー効率的推定法（Fisher Efficient Inference of Intractable Models）</news:title>
   <news:publication_date>2026-05-06T02:19:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686965</loc>
  <lastmod>2026-05-06T01:28:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>フェンスを消す深層学習（My camera can see through fences: A deep learning approach for image de-fencing）</news:title>
   <news:publication_date>2026-05-06T01:28:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686963</loc>
  <lastmod>2026-05-06T01:27:22Z</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とモンテカルロ木探索を用いたニューラルアーキテクチャ探索（Neural Architecture Search using Deep Neural Networks and Monte Carlo Tree Search）</news:title>
   <news:publication_date>2026-05-06T01:27:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686961</loc>
  <lastmod>2026-05-06T01:26:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DeepLogicに学ぶ：ニューラルネットワークで論理推論を学習させる意義（DeepLogic: Towards End-to-End Differentiable Logical Reasoning）</news:title>
   <news:publication_date>2026-05-06T01:26:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686959</loc>
  <lastmod>2026-05-06T01:26:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>効率的なオンラインポートフォリオと対数的後悔（Efficient Online Portfolio with Logarithmic Regret）</news:title>
   <news:publication_date>2026-05-06T01:26:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686957</loc>
  <lastmod>2026-05-06T01:26:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習は数学の「面白さ」を見つけられるか（Can machine learning identify interesting mathematics? An exploration using empirically observed laws）</news:title>
   <news:publication_date>2026-05-06T01:26:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686955</loc>
  <lastmod>2026-05-06T01:25:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>逐次学習による主曲線の自動要約（Sequential Learning of Principal Curves: Summarizing Data Streams on the Fly）</news:title>
   <news:publication_date>2026-05-06T01:25:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686953</loc>
  <lastmod>2026-05-06T00:34:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Wasserstein測度に基づくメジャー・コアセット（Wasserstein Measure Coresets）</news:title>
   <news:publication_date>2026-05-06T00:34:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686951</loc>
  <lastmod>2026-05-06T00:34:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>利用者主導のプライバシーのための協調学習（Learning to Collaborate for User-Controlled Privacy）</news:title>
   <news:publication_date>2026-05-06T00:34:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686949</loc>
  <lastmod>2026-05-06T00:34:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>d次元ヒストグラム上のKantorovich–Wasserstein距離計算を(d+1)-部グラフで効率化（Computing Kantorovich-Wasserstein Distances on d-dimensional histograms using (d + 1)-partite graphs）</news:title>
   <news:publication_date>2026-05-06T00:34:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686947</loc>
  <lastmod>2026-05-06T00:33:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>地球温度変動のボラティリティ推定アルゴリズム（Algorithms for Estimating Trends in Global Temperature Volatility）</news:title>
   <news:publication_date>2026-05-06T00:33:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686945</loc>
  <lastmod>2026-05-06T00:33:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Twitter上の政治議論と有権者の傾向（Political Discussion and Leanings on Twitter: the 2016 Italian Constitutional Referendum）</news:title>
   <news:publication_date>2026-05-06T00:33:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686943</loc>
  <lastmod>2026-05-06T00:33:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>欠損データを確率的に扱うニューラルネットワーク（Processing of missing data by neural networks）</news:title>
   <news:publication_date>2026-05-06T00:33:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686941</loc>
  <lastmod>2026-05-06T00:32:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Type I超新星PS16aqvの光度曲線の複雑性と放射性物質に関する深い限界（THE TYPE I SUPERLUMINOUS SUPERNOVA PS16AQV: LIGHTCURVE COMPLEXITY AND DEEP LIMITS ON RADIOACTIVE EJECTA IN A FAST EVENT）</news:title>
   <news:publication_date>2026-05-06T00:32:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686939</loc>
  <lastmod>2026-05-05T23:41:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>重力波イベントに対する強いレンズ銀河団の迅速光学追観測（Rapid optical follow-up of strong-lensing galaxy clusters in LIGO–Virgo GW sky localizations）</news:title>
   <news:publication_date>2026-05-05T23:41:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686937</loc>
  <lastmod>2026-05-05T23:31:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GumBoltが切り開く離散潜在空間の新展開（GumBolt: Extending Gumbel trick to Boltzmann priors）</news:title>
   <news:publication_date>2026-05-05T23:31:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686935</loc>
  <lastmod>2026-05-05T23:31:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>線形ガウス・マルコフ過程の効率的カーネル学習（Accurate Kernel Learning for Linear Gaussian Markov Processes using a Scalable Likelihood Computation）</news:title>
   <news:publication_date>2026-05-05T23:31:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686933</loc>
  <lastmod>2026-05-05T23:29:59Z</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>2026-05-05T23:29:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686931</loc>
  <lastmod>2026-05-05T23:29:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>接尾辞両方向LSTMによる文表現改善（Improved Sentence Modeling using Suffix Bidirectional LSTM）</news:title>
   <news:publication_date>2026-05-05T23:29:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686929</loc>
  <lastmod>2026-05-05T23:29:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>損失関数から学習データを復元する理論（Reconstruction of training samples from loss functions）</news:title>
   <news:publication_date>2026-05-05T23:29:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686927</loc>
  <lastmod>2026-05-05T23:29:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ポジティブと非ラベルデータから学ぶ手法の実務的意義（Positive and Unlabeled Learning through Negative Selection and Imbalance-aware Classification）</news:title>
   <news:publication_date>2026-05-05T23:29:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686925</loc>
  <lastmod>2026-05-05T22:37:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>勾配空間の効率的探索によるオンライン学習ランキングの高速化（Efficient Exploration of Gradient Space for Online Learning to Rank）</news:title>
   <news:publication_date>2026-05-05T22:37:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686923</loc>
  <lastmod>2026-05-05T22:37:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>力場に着想を得た記述子による材料機械学習（Machine learning with force-field inspired descriptors for materials: fast screening and mapping energy landscape）</news:title>
   <news:publication_date>2026-05-05T22:37:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686921</loc>
  <lastmod>2026-05-05T22:37:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GANE: 生成的敵対ネットワークを用いたネットワーク埋め込み（GANE: A Generative Adversarial Network Embedding）</news:title>
   <news:publication_date>2026-05-05T22:37:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686919</loc>
  <lastmod>2026-05-05T22:36:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチテーパーとHDP-HMMによる睡眠EEGのスペクトル推定（Multitaper Spectral Estimation HDP-HMMs for EEG Sleep Inference）</news:title>
   <news:publication_date>2026-05-05T22:36:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686917</loc>
  <lastmod>2026-05-05T22:36:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>データ依存の正則化で「覚え込み」を止める――GRSVNetによる本質パターン学習の提案 (Stop memorizing: A data-dependent regularization framework for intrinsic pattern learning)</news:title>
   <news:publication_date>2026-05-05T22:36:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686915</loc>
  <lastmod>2026-05-05T22:36:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>弱い教師ありで学ぶ3D形状補完（Learning 3D Shape Completion under Weak Supervision）</news:title>
   <news:publication_date>2026-05-05T22:36:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686913</loc>
  <lastmod>2026-05-05T22:35:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ブレンデッド条件付き勾配法の本質と応用可能性（Blended Conditional Gradients: the unconditioning of conditional gradients）</news:title>
   <news:publication_date>2026-05-05T22:35:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686911</loc>
  <lastmod>2026-05-05T21:43:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>生成モデルで逆問題を教師なしに解く（An Unsupervised Approach to Solving Inverse Problems using Generative Adversarial Networks）</news:title>
   <news:publication_date>2026-05-05T21:43:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686909</loc>
  <lastmod>2026-05-05T21:43:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>空間環境における近似ベイズ推論（Approximate Bayesian inference in spatial environments）</news:title>
   <news:publication_date>2026-05-05T21:43:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686907</loc>
  <lastmod>2026-05-05T21:43:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>コンテキストを考慮した動作学習と推論（Learning and Inferring Movement with Deep Generative Model）</news:title>
   <news:publication_date>2026-05-05T21:43:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686905</loc>
  <lastmod>2026-05-05T21:43:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>XOGANによる一対多の教師なし画像翻訳（XOGAN: One-to-Many Unsupervised Image-to-Image Translation）</news:title>
   <news:publication_date>2026-05-05T21:43:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686903</loc>
  <lastmod>2026-05-05T21:41:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>相互情報量に基づく動的学習率（Dynamic learning rate using Mutual Information）</news:title>
   <news:publication_date>2026-05-05T21:41:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686901</loc>
  <lastmod>2026-05-05T21:40:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>逐次ニューラル尤度（Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows）</news:title>
   <news:publication_date>2026-05-05T21:40:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686899</loc>
  <lastmod>2026-05-05T21:40:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Siamese Capsule Networksによる少量学習の探求（Siamese Capsule Networks）</news:title>
   <news:publication_date>2026-05-05T21:40:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686897</loc>
  <lastmod>2026-05-05T20:49:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分布的ロバスト逆共分散推定：ワッサースタイン・シュリンク推定器 (Distributionally Robust Inverse Covariance Estimation: The Wasserstein Shrinkage Estimator)</news:title>
   <news:publication_date>2026-05-05T20:49:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686895</loc>
  <lastmod>2026-05-05T20:48:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-05-05T20:48:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686893</loc>
  <lastmod>2026-05-05T20:48:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>再帰的知識蒸留によるモデル圧縮（RECURRENT KNOWLEDGE DISTILLATION）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686891</loc>
  <lastmod>2026-05-05T20:48:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Markov Chain Importance Sampling：MCMCの計算効率を大幅に改善する再利用型推定法（Markov Chain Importance Sampling – a highly efficient estimator for MCMC）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/686889</loc>
  <lastmod>2026-05-05T20:47:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>尤度を用いないコスモロジー推論のためのベイズ最適化（Bayesian optimisation for likelihood-free cosmological inference）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686887</loc>
  <lastmod>2026-05-05T20:47:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ハッブル・タランチュラ計画が示した機械学習による前主系列星同定の刷新（Hubble Tarantula Treasury Project – VI. Identification of Pre–Main-Sequence Stars using Machine Learning techniques）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686885</loc>
  <lastmod>2026-05-05T20:46:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>低コストなRNN性能予測手法の実務的意義（Low-Cost Recurrent Neural Network Expected Performance Evaluation）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/686883</loc>
  <lastmod>2026-05-05T19:55:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>層状ニューラルネットワークからの知識発見：非負タスク分解による可視化（Knowledge Discovery from Layered Neural Networks based on Non-negative Task Decomposition）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/686881</loc>
  <lastmod>2026-05-05T19:55:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>木編集距離の埋め込み学習が拓く計量学習の実用化（Tree Edit Distance Learning via Adaptive Symbol Embeddings）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <lastmod>2026-05-05T19:55:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/686877</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>条件付き生成対抗ネットワークを用いた画像キャプショニングの改善（Improving Image Captioning with Conditional Generative Adversarial Nets）</news:title>
   <news:publication_date>2026-05-05T19:54:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686875</loc>
  <lastmod>2026-05-05T19:53:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>強化模倣学習で学習時間を5倍短縮する地図不要ナビ（Reinforced Imitation: Sample Efficient Deep Reinforcement Learning for Map-less Navigation by Leveraging Prior Demonstrations）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686873</loc>
  <lastmod>2026-05-05T19:53:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ベイズモデル削減が変えるモデル選択の速度と実務応用（Bayesian Model Reduction）</news:title>
   <news:publication_date>2026-05-05T19:53:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686871</loc>
  <lastmod>2026-05-05T19:53:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-05-05T19:01:37Z</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トロピカル幾何学（Tropical Geometry of Deep Neural Networks）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686867</loc>
  <lastmod>2026-05-05T19:01:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686865</loc>
  <lastmod>2026-05-05T18:59:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>安全制約を組み込むTrusted Neural Networks（Trusted Neural Networks for Safety-Constrained Autonomous Control）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/686863</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>
   <news:title>多機能認知レーダのタスクスケジューリングにおけるMCTSと政策ネットワークの統合（Multifunction Cognitive Radar Task Scheduling Using Monte Carlo Tree Search and Policy Networks）</news:title>
   <news:publication_date>2026-05-05T18:59:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686859</loc>
  <lastmod>2026-05-05T18:59:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>クロスバリデーショングラディエントによる汎化最適化（Optimizing for Generalization in Machine Learning with Cross-Validation Gradients）</news:title>
   <news:publication_date>2026-05-05T18:59:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686857</loc>
  <lastmod>2026-05-05T18:58:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ユーザーに寄り添うAndroidアプリの個人化セキュリティ説明（Catering to Your Concerns: Automatic Generation of Personalised Security-Centric Descriptions for Android Apps）</news:title>
   <news:publication_date>2026-05-05T18:58:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686855</loc>
  <lastmod>2026-05-05T18:07:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文のエンコーダと文脈化ベクトルによる議論推論理解（SNU IDS at SemEval-2018 Task 12: Sentence Encoder with Contextualized Vectors for Argument Reasoning Comprehension）</news:title>
   <news:publication_date>2026-05-05T18:07:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686853</loc>
  <lastmod>2026-05-05T18:06:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>実世界デモから人間可読な計画を学ぶ合成データ学習（Synthetically Trained Neural Networks for Learning Human-Readable Plans from Real-World Demonstrations）</news:title>
   <news:publication_date>2026-05-05T18:06:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686851</loc>
  <lastmod>2026-05-05T18:05:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Around View Monitoringに基づく自動運転のシーン理解ネットワーク（Scene Understanding Networks for Autonomous Driving based on Around View Monitoring System）</news:title>
   <news:publication_date>2026-05-05T18:05:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686849</loc>
  <lastmod>2026-05-05T18:04:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>リンパ節転移予測のための多目的ラジオミクスと3D畳み込みニューラルネットワークの統合（Predicting Lymph Node Metastasis in Head and Neck Cancer by Combining Many-objective Radiomics and 3-dimensional Convolutional Neural Network through Evidential Reasoning）</news:title>
   <news:publication_date>2026-05-05T18:04:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686847</loc>
  <lastmod>2026-05-05T18:04:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スタイライズされた画像キャプション生成の分離学習（SemStyle: Learning to Generate Stylised Image Captions using Unaligned Text）</news:title>
   <news:publication_date>2026-05-05T18:04:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686845</loc>
  <lastmod>2026-05-05T18:04:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>MARS—メモリ注意機構による適応的推薦（MARS: Memory Attention-Aware Recommender System）</news:title>
   <news:publication_date>2026-05-05T18:04:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686843</loc>
  <lastmod>2026-05-05T17:13:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>順列学習のためのSinkhorn方策勾配（Learning Permutations with Sinkhorn Policy Gradient）</news:title>
   <news:publication_date>2026-05-05T17:13:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686841</loc>
  <lastmod>2026-05-05T17:11:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>センサーデータに対する深層ニューラルネットワークの理解と改善（Understanding and Improving Deep Neural Network for Activity Recognition）</news:title>
   <news:publication_date>2026-05-05T17:11:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686839</loc>
  <lastmod>2026-05-05T17:09:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Deep Nested Agent による階層強化学習の効率化（Hierarchical Reinforcement Learning with Deep Nested Agents）</news:title>
   <news:publication_date>2026-05-05T17:09:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686837</loc>
  <lastmod>2026-05-05T17:08:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>コーパスベースの対話ポリシー最適化のためのニューラルユーザシミュレータ（Neural User Simulation for Corpus-based Policy Optimisation for Spoken Dialogue Systems）</news:title>
   <news:publication_date>2026-05-05T17:08:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686835</loc>
  <lastmod>2026-05-05T17:08:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>反例に導かれるデータ拡張（Counterexample-Guided Data Augmentation）</news:title>
   <news:publication_date>2026-05-05T17:08:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686833</loc>
  <lastmod>2026-05-05T17:08:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スペクトル特徴スケーリング法による教師あり次元削減 (Spectral Feature Scaling Method for Supervised Dimensionality Reduction)</news:title>
   <news:publication_date>2026-05-05T17:08:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686831</loc>
  <lastmod>2026-05-05T17:06:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>病理画像でのテラバイト規模深層Multiple Instance Learning（Terabyte-scale Deep Multiple Instance Learning for Classification and Localization in Pathology）</news:title>
   <news:publication_date>2026-05-05T17:06:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686829</loc>
  <lastmod>2026-05-05T16:15:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>料理画像における物体の状態識別（Identifying Object States in Cooking-Related Images）</news:title>
   <news:publication_date>2026-05-05T16:15:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686827</loc>
  <lastmod>2026-05-05T16:12:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>フォレスト混合下界によるブロック不要の並列推論（A Forest Mixture Bound for Block-Free Parallel Inference）</news:title>
   <news:publication_date>2026-05-05T16:12:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686825</loc>
  <lastmod>2026-05-05T16:11:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多染色免疫組織化学組織セグメンテーションの一般化（Generalizing multistain immunohistochemistry tissue segmentation using one-shot color deconvolution deep neural networks）</news:title>
   <news:publication_date>2026-05-05T16:11:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686823</loc>
  <lastmod>2026-05-05T16:11:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>EU域内越境インターネット購入の供給側データ駆動測定法（A Data-Driven Supply-Side Approach for Measuring Cross-Border Internet Purchases）</news:title>
   <news:publication_date>2026-05-05T16:11:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686821</loc>
  <lastmod>2026-05-05T16:11:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散型MAC最適化のための高速強化学習（Fast reinforcement learning for decentralized MAC optimization）</news:title>
   <news:publication_date>2026-05-05T16:11:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686819</loc>
  <lastmod>2026-05-05T16:10:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>思考の言語を柔軟にする：概念露出ごとのベイジアン文法更新 (Towards a more flexible Language of Thought: Bayesian grammar updates after each concept exposure)</news:title>
   <news:publication_date>2026-05-05T16:10:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686817</loc>
  <lastmod>2026-05-05T16:09:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>カテゴリ変数の符号化と標準化に関する一考察 (A Note on Coding and Standardization of Categorical Variables in (Sparse) Group Lasso Regression)</news:title>
   <news:publication_date>2026-05-05T16:09:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686815</loc>
  <lastmod>2026-05-05T15:17:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>乳房X線画像の高密度可変ビット長圧縮を可能にする全畳み込みモデル（Fully Convolutional Model for Variable Bit Length and Lossy High Density Compression of Mammograms）</news:title>
   <news:publication_date>2026-05-05T15:17:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686813</loc>
  <lastmod>2026-05-05T15:16:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単眼画像から弱教師あり学習で得る3次元人体姿勢推定（It’s all Relative: Monocular 3D Human Pose Estimation from Weakly Supervised Data）</news:title>
   <news:publication_date>2026-05-05T15:16:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686811</loc>
  <lastmod>2026-05-05T15:15:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自然言語表現が科学研究の結果を予測する（Neural language representations predict outcomes of scientific research）</news:title>
   <news:publication_date>2026-05-05T15:15:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686809</loc>
  <lastmod>2026-05-05T15:13:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非滑らかモデルの学習：IV分位回帰と関連問題（Learning non-smooth models: instrumental variable quantile regressions and related problems）</news:title>
   <news:publication_date>2026-05-05T15:13:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686807</loc>
  <lastmod>2026-05-05T15:13:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>弱教師付き動画学習のためのNeuralNetwork-Viterbi（NeuralNetwork-Viterbi: A Framework for Weakly Supervised Video Learning）</news:title>
   <news:publication_date>2026-05-05T15:13:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686805</loc>
  <lastmod>2026-05-05T15:11:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ツリー編集距離の入門と実装ガイド（Revisiting the tree edit distance and its backtracing: A tutorial）</news:title>
   <news:publication_date>2026-05-05T15:11:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686803</loc>
  <lastmod>2026-05-05T15:11:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>曲線文様の設計同定（Design Identification of Curve Patterns on Cultural Heritage Objects: Combining Template Matching and CNN-based Re-Ranking）</news:title>
   <news:publication_date>2026-05-05T15:11:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686801</loc>
  <lastmod>2026-05-05T14:18:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ScaffoldNetによる生体工学用ポリマースキャフォールドの検出と分類（ScaffoldNet: Detecting and Classifying Biomedical Polymer-Based Scaffolds via a Convolutional Neural Network）</news:title>
   <news:publication_date>2026-05-05T14:18:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686799</loc>
  <lastmod>2026-05-05T14:17:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>回転に強い畳み込みフィルタ分解による表現の安定化（RotDCF: Decomposition of Convolutional Filters for Rotation-Equivariant Deep Networks）</news:title>
   <news:publication_date>2026-05-05T14:17:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686797</loc>
  <lastmod>2026-05-05T14:15:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>未知のトピック数に対する最小最大（ミニマックス）保証を持つ高速アルゴリズム（A fast algorithm with minimax optimal guarantees for topic models with an unknown number of topics）</news:title>
   <news:publication_date>2026-05-05T14:15:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686795</loc>
  <lastmod>2026-05-05T14:14:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>不完全観測からのサブスペース推定（Subspace Estimation from Incomplete Observations: A High-Dimensional Analysis）</news:title>
   <news:publication_date>2026-05-05T14:14:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686793</loc>
  <lastmod>2026-05-05T14:14:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>状態数に依存しない決定論的MDPの厳密解法（Memoryless Exact Solutions for Deterministic MDPs with Sparse Rewards）</news:title>
   <news:publication_date>2026-05-05T14:14:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686791</loc>
  <lastmod>2026-05-05T14:13:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数の原因がもたらす恩恵 — Deconfounderによる観察データの因果推論の刷新 (The Blessings of Multiple Causes)</news:title>
   <news:publication_date>2026-05-05T14:13:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686789</loc>
  <lastmod>2026-05-05T14:13:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深度を活かしたスライディングウィンドウによる物体候補生成（Disparity Sliding Window: Object Proposals From Disparity Images）</news:title>
   <news:publication_date>2026-05-05T14:13:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686787</loc>
  <lastmod>2026-05-05T13:19:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DNNとk-NN：記憶と汎化の共存を解く（DNN or k-NN: That is the Generalize vs. Memorize Question）</news:title>
   <news:publication_date>2026-05-05T13:19:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686785</loc>
  <lastmod>2026-05-05T13:19:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチテナント・マルチフレームワーク環境におけるDLaaSの信頼性（Dependability in a Multi-tenant Multi-framework Deep Learning as-a-Service Platform）</news:title>
   <news:publication_date>2026-05-05T13:19:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686783</loc>
  <lastmod>2026-05-05T13:18:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>凸凹ゲームにおける加速収束の技術と意味（Faster Rates for Convex-Concave Games）</news:title>
   <news:publication_date>2026-05-05T13:18:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686781</loc>
  <lastmod>2026-05-05T13:18:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>電子カルテを問答で注釈する手法（Annotating Electronic Medical Records for Question Answering）</news:title>
   <news:publication_date>2026-05-05T13:18:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686779</loc>
  <lastmod>2026-05-05T13:18:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>クロスドメイン属性表現のCNNによる枠組み（Cross-domain attribute representation based on convolutional neural network）</news:title>
   <news:publication_date>2026-05-05T13:18:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686777</loc>
  <lastmod>2026-05-05T13:17:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>レーンチェンジの状況評価：再帰モデルと予測の結合（Situation Assessment for Planning Lane Changes: Combining Recurrent Models and Prediction）</news:title>
   <news:publication_date>2026-05-05T13:17:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686775</loc>
  <lastmod>2026-05-05T13:16:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>空間不均一な個体群動態の数値解析手法（Spatially inhomogeneous population dynamics: beyond the mean field approximation）</news:title>
   <news:publication_date>2026-05-05T13:16:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686764</loc>
  <lastmod>2026-05-05T12:23:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>実数階の等方性全変動による画像再構成（REAL ORDER (AN)-ISOTROPIC TOTAL VARIATION IN IMAGE PROCESSING - PART I: ANALYTICAL ANALYSIS AND FUNCTIONAL PROPERTIES）</news:title>
   <news:publication_date>2026-05-05T12:23:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686762</loc>
  <lastmod>2026-05-05T12:23:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Interpolatron：深層ニューラルネットワークの最適化を加速する補間・外挿手法 (Interpolatron: Interpolation or Extrapolation Schemes to Accelerate Optimization for Deep Neural Networks)</news:title>
   <news:publication_date>2026-05-05T12:23:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686760</loc>
  <lastmod>2026-05-05T12:22:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>最小マージン損失による顔認識の識別力強化（Minimum Margin Loss for Deep Face Recognition）</news:title>
   <news:publication_date>2026-05-05T12:22:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686758</loc>
  <lastmod>2026-05-05T12:21:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>言語拡張によるテキストベースゲーム学習の単一エージェント化（Language Expansion In Text-Based Games）</news:title>
   <news:publication_date>2026-05-05T12:21:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686756</loc>
  <lastmod>2026-05-05T12:20:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GANomalyによる半教師あり異常検知（GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training）</news:title>
   <news:publication_date>2026-05-05T12:20:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686754</loc>
  <lastmod>2026-05-05T12:20:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>臨床文書における医療概念間の関係分類を改善するCNNとマルチプーリング手法（Classifying medical relations in clinical text via convolutional neural networks）</news:title>
   <news:publication_date>2026-05-05T12:20:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686752</loc>
  <lastmod>2026-05-05T12:20:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>カスカディア沈黙を破る（Breaking Cascadia’s Silence: Machine Learning Reveals the Constant Chatter of the Megathrust）</news:title>
   <news:publication_date>2026-05-05T12:20:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686750</loc>
  <lastmod>2026-05-05T11:27:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>コンテナ積み込みのための進化的強化学習（Evolutionary RL for Container Loading）</news:title>
   <news:publication_date>2026-05-05T11:27:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686748</loc>
  <lastmod>2026-05-05T11:24:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大電網の高速判定を可能にした図（グラフ）畳み込み深層学習の適用（Fast Transient Stability Assessment of Large Power Grids Based on Massive Online Historical Data and Graph Convolutional Deep Learning）</news:title>
   <news:publication_date>2026-05-05T11:24:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686746</loc>
  <lastmod>2026-05-05T11:23:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>疑似アノテータを用いたシングルショット能動学習（Single Shot Active Learning using Pseudo Annotators）</news:title>
   <news:publication_date>2026-05-05T11:23:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686744</loc>
  <lastmod>2026-05-05T11:23:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>UAVを使ったセルラー通信の対ジャミング強化（UAV-Aided Cellular Communications with Deep Reinforcement Learning Against Jamming）</news:title>
   <news:publication_date>2026-05-05T11:23:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686742</loc>
  <lastmod>2026-05-05T11:22:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次元構造を用いた翌日電力価格予測：単変量対多変量フレームワーク (Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworks)</news:title>
   <news:publication_date>2026-05-05T11:22:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686740</loc>
  <lastmod>2026-05-05T11:22:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>独立成分分析を相互依存指標で拡張する手法（Independent Component Analysis via Energy-based and Kernel-based Mutual Dependence Measures）</news:title>
   <news:publication_date>2026-05-05T11:22:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686738</loc>
  <lastmod>2026-05-05T11:22:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>共変量を考慮した条件付き平均独立の検定（Testing for Conditional Mean Independence with Covariates through Martingale Difference Divergence）</news:title>
   <news:publication_date>2026-05-05T11:22:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686736</loc>
  <lastmod>2026-05-05T10:30:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スキャッタリング変換を用いた生成ネットワークの逆問題としての定式化（Generative Networks as Inverse Problems with Scattering Transforms）</news:title>
   <news:publication_date>2026-05-05T10:30:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686734</loc>
  <lastmod>2026-05-05T10:22:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>網膜画像の構造を守るデクラウド処理（Structure-preserving Guided Retinal Image Filtering and Its Application for Optic Disc Analysis）</news:title>
   <news:publication_date>2026-05-05T10:22:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686732</loc>
  <lastmod>2026-05-05T10:20:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ボックス格子による確率的知識グラフ埋め込み（Probabilistic Embedding of Knowledge Graphs with Box Lattice Measures）</news:title>
   <news:publication_date>2026-05-05T10:20:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686730</loc>
  <lastmod>2026-05-05T10:19:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Androidマルウェア検出ツールDroidMark（DroidMark – A Tool for Android Malware Detection using Taint Analysis and Bayesian Network）</news:title>
   <news:publication_date>2026-05-05T10:19:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686728</loc>
  <lastmod>2026-05-05T10:18:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Defense-GANによる敵対的攻撃からの保護（DEFENSE-GAN: PROTECTING CLASSIFIERS AGAINST ADVERSARIAL ATTACKS USING GENERATIVE MODELS）</news:title>
   <news:publication_date>2026-05-05T10:18:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686726</loc>
  <lastmod>2026-05-05T10:18:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>モバイル向け転移学習による手話認識の最適化（Optimization of Transfer Learning for Sign Language Recognition Targeting Mobile Platform）</news:title>
   <news:publication_date>2026-05-05T10:18:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686724</loc>
  <lastmod>2026-05-05T10:18:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>タクシー需要予測におけるHEDGEベースの空間分割戦略（Taxi demand forecasting: A HEDGE based tessellation strategy for improved accuracy）</news:title>
   <news:publication_date>2026-05-05T10:18:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686722</loc>
  <lastmod>2026-05-05T09:25:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>外挿を用いた非負値行列因子分解アルゴリズムの高速化（Accelerating Nonnegative Matrix Factorization Algorithms using Extrapolation）</news:title>
   <news:publication_date>2026-05-05T09:25:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686720</loc>
  <lastmod>2026-05-05T09:24:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多層接続マップを用いた車載センサのコンテキスト予測型クラウド通信（Machine Learning based Context-predictive Car-to-cloud Communication Using Multi-layer Connectivity Maps for Upcoming 5G Networks）</news:title>
   <news:publication_date>2026-05-05T09:24:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686718</loc>
  <lastmod>2026-05-05T09:23:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>共分散を活用する特徴選択の新手法——Covariance-Insured Screening（Covariance-Insured Screening）</news:title>
   <news:publication_date>2026-05-05T09:23:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686716</loc>
  <lastmod>2026-05-05T09:22:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ネットワークスライシングにおける資源管理のための深層強化学習（Deep Reinforcement Learning for Resource Management in Network Slicing）</news:title>
   <news:publication_date>2026-05-05T09:22:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686714</loc>
  <lastmod>2026-05-05T09:21:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>解釈可能な並列リカレントニューラルネットワークと畳み込み注意によるマルチモーダル活動モデリング (Interpretable Parallel Recurrent Neural Networks with Convolutional Attentions for Multi-Modality Activity Modeling)</news:title>
   <news:publication_date>2026-05-05T09:21:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686712</loc>
  <lastmod>2026-05-05T09:21:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分割構造の不連続を一般化ひずみとして扱う手法と離散・連続系のフーリエ変換（Structural discontinuity as generalized strain and Fourier transform for discrete-continuous systems）</news:title>
   <news:publication_date>2026-05-05T09:21:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686710</loc>
  <lastmod>2026-05-05T09:20:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>クロスターゲット・スタンス分類と自己注意ネットワーク（Cross-Target Stance Classification with Self-Attention Networks）</news:title>
   <news:publication_date>2026-05-05T09:20:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686708</loc>
  <lastmod>2026-05-05T08:28:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ADMMと加速ADMMを連続力学系として見る（ADMM and Accelerated ADMM as Continuous Dynamical Systems）</news:title>
   <news:publication_date>2026-05-05T08:28:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686706</loc>
  <lastmod>2026-05-05T08:28:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>未知の純粋量子状態の学習手法（Learning unknown pure quantum states）</news:title>
   <news:publication_date>2026-05-05T08:28:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686704</loc>
  <lastmod>2026-05-05T08:27:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>アフィンスプラインで解き明かす深層学習の仕組み（Mad Max: Affine Spline Insights into Deep Learning）</news:title>
   <news:publication_date>2026-05-05T08:27:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686702</loc>
  <lastmod>2026-05-05T08:27:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>協調フィルタリングのためのニューラル・パーソナライズド・エンベディング（Neural Personalized Embedding for Collaborative Filtering）</news:title>
   <news:publication_date>2026-05-05T08:27:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686700</loc>
  <lastmod>2026-05-05T08:26:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オンライン請願の内容に基づく人気度予測（Content-based Popularity Prediction of Online Petitions Using a Deep Regression Model）</news:title>
   <news:publication_date>2026-05-05T08:26:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686698</loc>
  <lastmod>2026-05-05T08:26:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時間変動する人気プロファイルを扱うキャッシングの学習理論的視点（Caching With Time-Varying Popularity Profiles: A Learning-Theoretic Perspective）</news:title>
   <news:publication_date>2026-05-05T08:26:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686696</loc>
  <lastmod>2026-05-05T08:26:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>浸透性を持つ帯水層の潮汐応答とその応用（Tidal Response of Groundwater in a Leaky Aquifer – Application to Oklahoma）</news:title>
   <news:publication_date>2026-05-05T08:26:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686694</loc>
  <lastmod>2026-05-05T07:34:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スロット整列を組み込んだ深層アンサンブルによる逐次自然言語生成（A Deep Ensemble Model with Slot Alignment for Sequence-to-Sequence Natural Language Generation）</news:title>
   <news:publication_date>2026-05-05T07:34:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686692</loc>
  <lastmod>2026-05-05T07:34:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動画からの高密度深度と自己運動を学習するリカレントニューラルネットワーク（Recurrent Neural Network for Learning Dense Depth and Ego-Motion from Video）</news:title>
   <news:publication_date>2026-05-05T07:34:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686690</loc>
  <lastmod>2026-05-05T07:33:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>睡眠ステージ分類における同時分類・予測のCNNフレームワーク（Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification）</news:title>
   <news:publication_date>2026-05-05T07:33:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686688</loc>
  <lastmod>2026-05-05T07:33:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>平衡状態の因果を捕らえる新枠組み―Causal Constraints Modelsの紹介 (Beyond Structural Causal Models: Causal Constraints Models)</news:title>
   <news:publication_date>2026-05-05T07:33:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686686</loc>
  <lastmod>2026-05-05T07:32:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層テンソル分解による畳み込みニューラルネットワークの終端学習（End-to-end Learning of a Convolutional Neural Network via Deep Tensor Decomposition）</news:title>
   <news:publication_date>2026-05-05T07:32:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686684</loc>
  <lastmod>2026-05-05T07:32:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>BLEUスコアと意味表現は対立するか（Are BLEU and Meaning Representation in Opposition?）</news:title>
   <news:publication_date>2026-05-05T07:32:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686682</loc>
  <lastmod>2026-05-05T07:31:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ガウス型ノイズを最適化して差分プライバシーを強化する（Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising）</news:title>
   <news:publication_date>2026-05-05T07:31:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686680</loc>
  <lastmod>2026-05-05T06:40:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>表形式データ向け正則化学習ネットワーク（Regularization Learning Networks: Deep Learning for Tabular Datasets）</news:title>
   <news:publication_date>2026-05-05T06:40:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686678</loc>
  <lastmod>2026-05-05T06:32:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複合意味関係分類（Composite Semantic Relation Classification）</news:title>
   <news:publication_date>2026-05-05T06:32:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686676</loc>
  <lastmod>2026-05-05T06:32:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>V2V通信における深層強化学習による資源割当の分散化（Deep Reinforcement Learning based Resource Allocation for V2V Communications）</news:title>
   <news:publication_date>2026-05-05T06:32:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686674</loc>
  <lastmod>2026-05-05T06:32:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>QuaterNet: 四元数ベースの反復モデルによる人間の動作予測（QuaterNet: A Quaternion-based Recurrent Model for Human Motion）</news:title>
   <news:publication_date>2026-05-05T06:32:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686672</loc>
  <lastmod>2026-05-05T06:30:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>正則化された有限次元カーネルSobolev差異（Regularized Finite Dimensional Kernel Sobolev Discrepancy）</news:title>
   <news:publication_date>2026-05-05T06:30:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686670</loc>
  <lastmod>2026-05-05T06:29:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大きなデータ変動への耐性を高める自己内変換ネットワーク（Resisting Large Data Variations via Introspective Transformation Network）</news:title>
   <news:publication_date>2026-05-05T06:29:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686668</loc>
  <lastmod>2026-05-05T06:29:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>予測ルールの再形成（Prediction Rule Reshaping）</news:title>
   <news:publication_date>2026-05-05T06:29:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686666</loc>
  <lastmod>2026-05-05T05:37:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>X線血管造影ビデオにおける深層セグメンテーションと登録（Deep Segmentation and Registration in X-Ray Angiography Video）</news:title>
   <news:publication_date>2026-05-05T05:37:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686664</loc>
  <lastmod>2026-05-05T05:37:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>汚れた出力上でのタスク不可知な頑健学習（Task Agnostic Robust Learning on Corrupt Outputs）</news:title>
   <news:publication_date>2026-05-05T05:37:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686662</loc>
  <lastmod>2026-05-05T05:36:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>MOABB: 信頼できるBCIアルゴリズムベンチマーク（MOABB: Trustworthy algorithm benchmarking for BCIs）</news:title>
   <news:publication_date>2026-05-05T05:36:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686660</loc>
  <lastmod>2026-05-05T05:35:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューラル多重スケール画像圧縮（Neural Multi-scale Image Compression）</news:title>
   <news:publication_date>2026-05-05T05:35:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686658</loc>
  <lastmod>2026-05-05T05:35:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>回帰と多様体学習の統合による物体認識と姿勢推定（When Regression Meets Manifold Learning for Object Recognition and Pose Estimation）</news:title>
   <news:publication_date>2026-05-05T05:35:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686656</loc>
  <lastmod>2026-05-05T05:35:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>睡眠中の脳波から推定する「脳年齢」とその示唆（Brain Age from the Electroencephalogram of Sleep）</news:title>
   <news:publication_date>2026-05-05T05:35:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686654</loc>
  <lastmod>2026-05-05T05:35:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>薬物服用発言の検出に向けたディープラーニング研究（phramacovigilance - Exploring Deep Learning Techniques for Identifying Mentions of Medication Intake from Twitter）</news:title>
   <news:publication_date>2026-05-05T05:35:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686652</loc>
  <lastmod>2026-05-05T04:43:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>メモリ節約を重視した厳格Very Fast Decision Tree（Strict Very Fast Decision Tree）</news:title>
   <news:publication_date>2026-05-05T04:43:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686650</loc>
  <lastmod>2026-05-05T04:43:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高速レチノモルフィックイベント駆動表現による映像認識と強化学習（Fast Retinomorphic Event-Driven Representations for Video Recognition and Reinforcement Learning）</news:title>
   <news:publication_date>2026-05-05T04:43:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686648</loc>
  <lastmod>2026-05-05T04:33:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>継続学習のためのProgress &amp;amp; Compress（Progress &amp;amp; Compress: A scalable framework for continual learning）</news:title>
   <news:publication_date>2026-05-05T04:33:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686646</loc>
  <lastmod>2026-05-05T04:32:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>観測から確率的チャネルモデルを学習する（Approximating the Void: Learning Stochastic Channel Models from Observation with Variational Generative Adversarial Networks）</news:title>
   <news:publication_date>2026-05-05T04:32:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686644</loc>
  <lastmod>2026-05-05T04:31:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>銀河の星形成率を機械学習で推定する手法（Stellar formation rates in galaxies using Machine Learning models）</news:title>
   <news:publication_date>2026-05-05T04:31:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686642</loc>
  <lastmod>2026-05-05T04:31:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>補助タスクを用いたマルチタスク学習の強化（Auxiliary Tasks in Multi-task Learning）</news:title>
   <news:publication_date>2026-05-05T04:31:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686640</loc>
  <lastmod>2026-05-05T04:31:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチタスク学習による細胞内巨視分子の分類・セグメンテーション・粗構造復元（Multi-task Learning for Macromolecule Classification, Segmentation and Coarse Structural Recovery in Cryo-Tomography）</news:title>
   <news:publication_date>2026-05-05T04:31:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686638</loc>
  <lastmod>2026-05-05T03:39:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文脈を取り込むことで対話理解が一段進化する（A Context-based Approach for Dialogue Act Recognition using Simple Recurrent Neural Networks）</news:title>
   <news:publication_date>2026-05-05T03:39:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686636</loc>
  <lastmod>2026-05-05T03:31:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>完全教師なしでの単語埋め込みクロスリンガル写像に対する頑健な自己学習法（A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings）</news:title>
   <news:publication_date>2026-05-05T03:31:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686634</loc>
  <lastmod>2026-05-05T03:31:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>定曲率多様体上のグラフ埋め込みによるグラフストリームの変化検出 (Change Detection in Graph Streams by Learning Graph Embeddings on Constant-Curvature Manifolds)</news:title>
   <news:publication_date>2026-05-05T03:31:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686632</loc>
  <lastmod>2026-05-05T03:29:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>構造化非線形変数選択（Structured nonlinear variable selection）</news:title>
   <news:publication_date>2026-05-05T03:29:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686630</loc>
  <lastmod>2026-05-05T03:29:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>発話単位注意を用いた双方向RNNによる会話分析（Conversational Analysis using Utterance-level Attention-based Bidirectional Recurrent Neural Networks）</news:title>
   <news:publication_date>2026-05-05T03:29:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686628</loc>
  <lastmod>2026-05-05T03:29:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>移動の表現学習（Learning Representations of Spatial Displacement through Sensorimotor Prediction）</news:title>
   <news:publication_date>2026-05-05T03:29:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686626</loc>
  <lastmod>2026-05-05T03:28:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>量子K近傍法に基づく画像分類（Image Classification Based on Quantum KNN Algorithm）</news:title>
   <news:publication_date>2026-05-05T03:28:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686624</loc>
  <lastmod>2026-05-05T02:36:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>異常検知の説明に向けて（Towards Explaining Anomalies: A Deep Taylor Decomposition of One-Class Models）</news:title>
   <news:publication_date>2026-05-05T02:36:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686622</loc>
  <lastmod>2026-05-05T02:36:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次元時系列データ解析におけるカーネル転移作用素の固有関数利用法（Analyzing high-dimensional time-series data using kernel transfer operator eigenfunctions）</news:title>
   <news:publication_date>2026-05-05T02:36:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686620</loc>
  <lastmod>2026-05-05T02:35:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>患者依存性を排した特徴学習のための敵対的訓練（Adversarial Training for Patient-Independent Feature Learning with IVOCT Data for Plaque Classification）</news:title>
   <news:publication_date>2026-05-05T02:35:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686618</loc>
  <lastmod>2026-05-05T02:34:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>合成非類似度測度とフィンガープリント測位への応用（CDM: Compound dissimilarity measure and an application to fingerprinting-based positioning）</news:title>
   <news:publication_date>2026-05-05T02:34:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686616</loc>
  <lastmod>2026-05-05T02:34:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>軽量ピラミッドネットワークによる単一画像の雨除去（Lightweight Pyramid Networks for Image Deraining）</news:title>
   <news:publication_date>2026-05-05T02:34:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686614</loc>
  <lastmod>2026-05-05T02:33:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文脈的拡張によるテキストデータ増強（Contextual Augmentation: Data Augmentation by Words with Paradigmatic Relations）</news:title>
   <news:publication_date>2026-05-05T02:33:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686612</loc>
  <lastmod>2026-05-05T02:32:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多関係ネットワークの構造表現学習（A Structural Representation Learning for Multi-relational Networks）</news:title>
   <news:publication_date>2026-05-05T02:32:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686610</loc>
  <lastmod>2026-05-05T01:41:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DNSグラフマイニングにおけるアジリティバイアスの検証（Investigating the Agility Bias in DNS Graph Mining）</news:title>
   <news:publication_date>2026-05-05T01:41:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686608</loc>
  <lastmod>2026-05-05T01:41:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単眼カメラとコンパクトセマンティックマップによる車両自己位置推定（Monocular Vehicle Self-localization method based on Compact Semantic Map）</news:title>
   <news:publication_date>2026-05-05T01:41:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686606</loc>
  <lastmod>2026-05-05T01:40:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>FollowNet：自然言語指示に従うロボット航行（FollowNet: Robot Navigation by Following Natural Language Directions with Deep Reinforcement Learning）</news:title>
   <news:publication_date>2026-05-05T01:40:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686604</loc>
  <lastmod>2026-05-05T01:40:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>候補抽出と解答選択の共同学習による読解強化（Joint Training of Candidate Extraction and Answer Selection for Reading Comprehension）</news:title>
   <news:publication_date>2026-05-05T01:40:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686602</loc>
  <lastmod>2026-05-05T01:40:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>仮想エッジコンピューティングにおける最適化された計算オフロード性能（Optimized Computation Offloading Performance in Virtual Edge Computing Systems via Deep Reinforcement Learning）</news:title>
   <news:publication_date>2026-05-05T01:40:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686600</loc>
  <lastmod>2026-05-05T01:40:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>市場の自己学習とインビジブルハンド推論（Market Self-Learning of Signals, Impact and Optimal Trading: Invisible Hand Inference with Free Energy）</news:title>
   <news:publication_date>2026-05-05T01:40:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686598</loc>
  <lastmod>2026-05-05T01:40:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>可変計量過緩和ハイブリッド近接外勾配法のアルゴリズム枠組み（An Algorithmic Framework of Variable Metric Over-Relaxed Hybrid Proximal Extra-Gradient Method）</news:title>
   <news:publication_date>2026-05-05T01:40:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686596</loc>
  <lastmod>2026-05-05T00:39:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>特徴アフィニティに基づく疑似ラベリングによる半教師あり人物再識別（Feature Affinity based Pseudo-Labeling for Semi-supervised Person Re-identification）</news:title>
   <news:publication_date>2026-05-05T00:39:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686594</loc>
  <lastmod>2026-05-05T00:37:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>画像ピラミッドでスケールを適応的に融合する群衆カウント法（Crowd Counting by Adaptively Fusing Predictions from an Image Pyramid）</news:title>
   <news:publication_date>2026-05-05T00:37:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686592</loc>
  <lastmod>2026-05-05T00:37:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非負テンソル分解に基づく教師なし機械学習による反応性混合解析（Unsupervised Machine Learning Based on Non-Negative Tensor Factorization for Analyzing Reactive-Mixing）</news:title>
   <news:publication_date>2026-05-05T00:37:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686590</loc>
  <lastmod>2026-05-05T00:37:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>堅牢でプライバシー保護されたテキスト表現の学習（Towards Robust and Privacy-preserving Text Representations）</news:title>
   <news:publication_date>2026-05-05T00:37:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686588</loc>
  <lastmod>2026-05-05T00:35:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ドメインに含まれる要素とは（What’s in a Domain? Learning Domain-Robust Text Representations using Adversarial Training）</news:title>
   <news:publication_date>2026-05-05T00:35:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686586</loc>
  <lastmod>2026-05-04T23:44:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>協調的判別器による長文生成の改善（Learning to Write with Cooperative Discriminators）</news:title>
   <news:publication_date>2026-05-04T23:44:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686584</loc>
  <lastmod>2026-05-04T23:44:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>パラメータ化クリッピング活性化による量子化ニューラルネットワーク（PACT: PARAMETERIZED CLIPPING ACTIVATION FOR QUANTIZED NEURAL NETWORKS）</news:title>
   <news:publication_date>2026-05-04T23:44:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686582</loc>
  <lastmod>2026-05-04T23:44:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>シーン非依存な人物再識別の評価（An Evaluation of Deep CNN Baselines for Scene-Independent Person Re-Identiﬁcation）</news:title>
   <news:publication_date>2026-05-04T23:44:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686580</loc>
  <lastmod>2026-05-04T23:42:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SoPaによるCNN・RNN・WFSAの橋渡し（SoPa: Bridging CNNs, RNNs, and Weighted Finite-State Machines）</news:title>
   <news:publication_date>2026-05-04T23:42:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686578</loc>
  <lastmod>2026-05-04T23:42:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ケック・ライマン連続スペクトル調査（KLCS）が示す高赤方偏移銀河のイオン化放射（THE KECK LYMAN CONTINUUM SPECTROSCOPIC SURVEY (KLCS): THE EMERGENT IONIZING SPECTRUM OF GALAXIES AT Z ∼3）</news:title>
   <news:publication_date>2026-05-04T23:42:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686576</loc>
  <lastmod>2026-05-04T23:42:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>意味ターゲット駆動ナビゲーションのための視覚表現（Visual Representations for Semantic Target Driven Navigation）</news:title>
   <news:publication_date>2026-05-04T23:42:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686574</loc>
  <lastmod>2026-05-04T23:41:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Spark-MPIが拓く第5のパラダイム（Spark-MPI: Approaching the Fifth Paradigm of Cognitive Applications）</news:title>
   <news:publication_date>2026-05-04T23:41:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686572</loc>
  <lastmod>2026-05-04T22:49:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>交絡因子の因果効果共変（Causal-effect Covariability of Confounders）</news:title>
   <news:publication_date>2026-05-04T22:49:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686570</loc>
  <lastmod>2026-05-04T22:48:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層強化学習エージェントは意図をモデル化するか（Do deep reinforcement learning agents model intentions?）</news:title>
   <news:publication_date>2026-05-04T22:48:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686568</loc>
  <lastmod>2026-05-04T22:48:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高赤方偏移銀河のフィードバックを探る：拡張された紫外線光度関数による検証（Probing feedback in high-z galaxies using extended UV luminosity functions）</news:title>
   <news:publication_date>2026-05-04T22:48:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686566</loc>
  <lastmod>2026-05-04T22:47:01Z</lastmod>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-05-04T22:46:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-05-04T21:53:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-05-04T21:53:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-05-04T21:52:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-05-04T21:51:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>推論問題における「ハードフェーズ」のガラス性の解明（On the glassy nature of the hard phase in inference problems）</news:title>
   <news:publication_date>2026-05-04T21:51:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686548</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>信念の濃淡を扱う社会的学習モデル（Naive Bayesian Learning in Social Networks）</news:title>
   <news:publication_date>2026-05-04T21:51:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686546</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>地球を「系外惑星」として観測する実証実験（Using Deep Space Climate Observatory Measurements to Study the Earth as An Exoplanet）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686544</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフ信号のサンプリングを強化学習として解く（Graph Signal Sampling via Reinforcement Learning）</news:title>
   <news:publication_date>2026-05-04T20:58:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686542</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>混合音声から直接読み取る 完全エンドツーエンド多人数音声認識（A Purely End-to-end System for Multi-speaker Speech Recognition）</news:title>
   <news:publication_date>2026-05-04T20:56:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>量子化可能な埋め込み表現の効率的なエンドツーエンド学習（Efficient end-to-end learning for quantizable representations）</news:title>
   <news:publication_date>2026-05-04T20:56:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層的マルチスケールニューラルネットワークにおける継続学習（Continuous Learning in a Hierarchical Multiscale Neural Network）</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>
   <news:title>表形式強化学習における人間知の活用（Leveraging human knowledge in tabular reinforcement learning）</news:title>
   <news:publication_date>2026-05-04T20:55:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
  </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>
   </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>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686522</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>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686520</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>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-05-04T19:06:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686516</loc>
  <lastmod>2026-05-04T19:06:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-05-04T19:06:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>歪みに強い注目領域分割を実現するメトリック表現ネットワーク（Ro-SOS: Metric Expression Network for Robust Salient Object Segmentation）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-05-04T19:04:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>マイクロストラクチャ雑音下における非パラメトリックベイズ的ボラティリティ学習（Nonparametric Bayesian volatility learning under microstructure noise）</news:title>
   <news:publication_date>2026-05-04T19:04:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686510</loc>
  <lastmod>2026-05-04T19:04:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-05-04T19:04:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-05-04T19:04:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686506</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文体に敏感な単語ベクトルの教師なし学習（Unsupervised Learning of Style-sensitive Word Vectors）</news:title>
   <news:publication_date>2026-05-04T18:13:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686504</loc>
  <lastmod>2026-05-04T18:02:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>正規表現とニューラルネットワークの結婚（Marrying Up Regular Expressions with Neural Networks）</news:title>
   <news:publication_date>2026-05-04T18:02:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686502</loc>
  <lastmod>2026-05-04T18:00:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686500</loc>
  <lastmod>2026-05-04T18:00:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <lastmod>2026-05-04T18:00:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>世代学習で育てる深層ニューラルネットワーク（Training Deep Neural Networks in Generations: A More Tolerant Teacher Educates Better Students）</news:title>
   <news:publication_date>2026-05-04T17:08:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/686492</loc>
  <lastmod>2026-05-04T17:00:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>軌道フェッシュバッハ共鳴を用いた希土類フェルミ気体の単粒子励起と強結合効果（Single-particle Excitations and Strong Coupling Effects in the BCS-BEC Crossover Regime of a Rare-Earth Fermi Gas with an Orbital Feshbach Resonance）</news:title>
   <news:publication_date>2026-05-04T17:00:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686490</loc>
  <lastmod>2026-05-04T17:00:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文章の簡素化を行うSeq2Seqモデル（Simplifying Sentences with Sequence to Sequence Models）</news:title>
   <news:publication_date>2026-05-04T17:00:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
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 <url>
  <loc>https://aibr.jp/archives/686488</loc>
  <lastmod>2026-05-04T16:59:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>核内EMC効果をΛ/¯Λ生成で探る—半包含型深部非弾性散乱による差別化の提案 (Nuclear EMC effect through ¯Λ/Λ production in semi-inclusive deep-inelastic scattering processes)</news:title>
   <news:publication_date>2026-05-04T16:59:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686486</loc>
  <lastmod>2026-05-04T16:58:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>顔と声の対応を学習する方法（On Learning Associations of Faces and Voices）</news:title>
   <news:publication_date>2026-05-04T16:58:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686484</loc>
  <lastmod>2026-05-04T16:58:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動的ニューラルネットワークによるロボットの新奇動作生成（A Dynamic Neural Network Approach to Generating Robot’s Novel Actions: A Simulation Experiment）</news:title>
   <news:publication_date>2026-05-04T16:58:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686482</loc>
  <lastmod>2026-05-04T16:57:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Radio Galaxy Zoo におけるラジオ源の銀河ホスト同定と機械学習による自動化（Radio Galaxy Zoo: Machine learning for radio source host galaxy cross-identification）</news:title>
   <news:publication_date>2026-05-04T16:57:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686480</loc>
  <lastmod>2026-05-04T16:06:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>決定境界を支持する敵対的サンプルを用いた知識蒸留（Knowledge Distillation with Adversarial Samples Supporting Decision Boundary）</news:title>
   <news:publication_date>2026-05-04T16:06:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686478</loc>
  <lastmod>2026-05-04T16:06:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>経験再生の進歩（Advances in Experience Replay）</news:title>
   <news:publication_date>2026-05-04T16:06:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686476</loc>
  <lastmod>2026-05-04T16:06:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>顔認識におけるパッチ間相関を学習する完全結合型1対Nマッチング手法（Fully Associative Patch-based 1-to-N Matcher for Face Recognition）</news:title>
   <news:publication_date>2026-05-04T16:06:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686474</loc>
  <lastmod>2026-05-04T16:05:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>車両軌跡予測における畳み込みソーシャルプーリングの意義（Convolutional Social Pooling for Vehicle Trajectory Prediction）</news:title>
   <news:publication_date>2026-05-04T16:05:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686472</loc>
  <lastmod>2026-05-04T16:04:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非線形次元削減による複数データセットの判別分析（Nonlinear Dimensionality Reduction for Discriminative Analytics of Multiple Datasets）</news:title>
   <news:publication_date>2026-05-04T16:04:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686470</loc>
  <lastmod>2026-05-04T16:04:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オンラインメトリック学習の多層フレームワーク（A Multilayer Framework for Online Metric Learning）</news:title>
   <news:publication_date>2026-05-04T16:04:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686468</loc>
  <lastmod>2026-05-04T16:04:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>顔画像のブレ除去を学ぶ（Learning to Deblur Images with Exemplars）</news:title>
   <news:publication_date>2026-05-04T16:04:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686466</loc>
  <lastmod>2026-05-04T15:12:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>社会的多様性が情報カスケードを抑える仕組み（Social diversity for reducing the impact of information cascades on social learning）</news:title>
   <news:publication_date>2026-05-04T15:12:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686464</loc>
  <lastmod>2026-05-04T15:11:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>モデルは本当に「質問」を理解しているか（Did the Model Understand the Question?）</news:title>
   <news:publication_date>2026-05-04T15:11:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686462</loc>
  <lastmod>2026-05-04T15:09:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Crowdbreaksによる公衆衛生トレンド追跡（Crowdbreaks: Tracking Health Trends using Public Social Media Data and Crowdsourcing）</news:title>
   <news:publication_date>2026-05-04T15:09:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686460</loc>
  <lastmod>2026-05-04T15:09:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複雑高次元データと限られたシミュレーション下の近似ベイズ計算に向けて（ABC-CDE: Towards Approximate Bayesian Computation with Complex High-Dimensional Data and Limited Simulations）</news:title>
   <news:publication_date>2026-05-04T15:09:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686458</loc>
  <lastmod>2026-05-04T15:09:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>同質的リーマン多様体上のCNNとその神経画像への応用（A CNN for homogeneous Riemannian manifolds with applications to Neuroimaging）</news:title>
   <news:publication_date>2026-05-04T15:09:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686456</loc>
  <lastmod>2026-05-04T15:07:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SkyLensによる重力レンズ像シミュレーションの実務的意義（Image simulations for gravitational lensing with SkyLens）</news:title>
   <news:publication_date>2026-05-04T15:07:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/686454</loc>
  <lastmod>2026-05-04T15:07:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Schema.org Actionsによる機械可読Web API（Machine Readable Web APIs with Schema.org Action Annotations）</news:title>
   <news:publication_date>2026-05-04T15:07:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
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