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   <news:title>整数データに特化したスコアリング手法SUSTainの概観（SUSTain: Scalable Unsupervised Scoring for Tensors and its Application to Phenotyping）</news:title>
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   <news:genres>Blog</news:genres>
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   <news:title>材料探索の自律効率的実験設計（Autonomous Efficient Experiment Design for Materials Discovery with Bayesian Model Averaging）</news:title>
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   <news:title>GOODS-N深部20cm帯JVLAイメージングの成果（DEEP JVLA IMAGING OF GOODS-N AT 20CM）</news:title>
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   <news:title>重みの平均化がもたらす広い最適解と汎化改善（Averaging Weights Leads to Wider Optima and Better Generalization）</news:title>
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   <news:title>新たな古典新星の殻の発見（Discovery of a new classical nova shell around a nova-like cataclysmic variable）</news:title>
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   <news:title>二層ライドバーグ原子・極性分子系の有効スピン相互作用（Effective spin-spin interactions in bilayers of Rydberg atoms and polar molecules）</news:title>
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   <news:title>自己相似エポック：データ配列が学習効率を変える（Self-Similar Epochs: Value in Arrangement）</news:title>
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   <news:genres>Blog</news:genres>
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   <news:title>長時間文脈を捉える感情データセットの提案（The OMG-Emotion Behavior Dataset）</news:title>
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   <news:genres>Blog</news:genres>
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   <news:title>測定に基づく適応プロトコルと量子強化学習（Measurement-based adaptation protocol with quantum reinforcement learning）</news:title>
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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>経口崩壊錠の処方をニューラルネットワークで予測する（Predicting Oral Disintegrating Tablet Formulations by Neural Network Techniques）</news:title>
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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>変化点は“スパースな説明”の導入──Variational Bayesianを用いた行列分解/補完の近似手法（Approximate Method of Variational Bayesian Matrix Factorization/Completion with Sparse Prior）</news:title>
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   <news:genres>Blog</news:genres>
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    <news:language>ja</news:language>
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   <news:title>代数的機械学習の概観（Algebraic Machine Learning）</news:title>
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   <news:genres>Blog</news:genres>
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    <news:name>AI Benchmark Research</news:name>
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   <news:title>グラフ上で学ぶドメイン適応（Domain Adaptation on Graphs by Learning Aligned Graph Bases）</news:title>
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    <news:language>ja</news:language>
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   <news:title>デザインによる透明性：視覚的推論における性能と解釈性のギャップを埋める（Transparency by Design: Closing the Gap Between Performance and Interpretability in Visual Reasoning）</news:title>
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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>
   </news:publication>
   <news:title>クロモスフェアのMg IIラインにおける青側増強の観測と解釈（Blue wing enhancement of the chromospheric Mg II h and k lines in a solar flare）</news:title>
   <news:publication_date>2026-04-16T23:04:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680153</loc>
  <lastmod>2026-04-16T23:03:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>木構造受容野を用いたスパースな深層フィードフォワードネットワークの構築（Building Sparse Deep Feedforward Networks using Tree Receptive Fields）</news:title>
   <news:publication_date>2026-04-16T23:03:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680151</loc>
  <lastmod>2026-04-16T23:03:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層畳み込み残差ノイズ除去ネットワークによる画像デモザイキングとノイズ除去の統合（Deep Image Demosaicking using a Cascade of Convolutional Residual Denoising Networks）</news:title>
   <news:publication_date>2026-04-16T23:03:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680149</loc>
  <lastmod>2026-04-16T23:02:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>潜在ツリー型変分オートエンコーダによる多面クラスタリングの提案（LEARNING LATENT SUPERSTRUCTURES IN VARIATIONAL AUTOENCODERS FOR DEEP MULTIDIMENSIONAL CLUSTERING）</news:title>
   <news:publication_date>2026-04-16T23:02:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680147</loc>
  <lastmod>2026-04-16T23:02:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>リアルタイム心血管MRIにおける時空間アーティファクト抑制の深層学習（Real-time Cardiovascular MR with Spatio-temporal Artifact Suppression using Deep Learning）</news:title>
   <news:publication_date>2026-04-16T23:02:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680145</loc>
  <lastmod>2026-04-16T23:01:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層ニューラルネットワークによる量子制御補正の近似（Approximation of quantum control correction scheme using deep neural networks）</news:title>
   <news:publication_date>2026-04-16T23:01:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680143</loc>
  <lastmod>2026-04-16T23:01:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチレベルの文脈を組み合わせたスーパーピクセルによる自然画像ラベリング（Combining Multi-level Contexts of Superpixel using Convolutional Neural Networks to perform Natural Scene Labeling）</news:title>
   <news:publication_date>2026-04-16T23:01:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680141</loc>
  <lastmod>2026-04-16T22:10:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>xDeepFM：明示的・暗黙的な特徴量相互作用を統合する推薦モデル（xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems）</news:title>
   <news:publication_date>2026-04-16T22:10:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680139</loc>
  <lastmod>2026-04-16T22:10:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>広帯域二層反射防止構造によるシリコン真空窓の実現（A 1.6:1 Bandwidth Two-Layer Antireflection Structure for Silicon Matched to the 190–310 GHz Atmospheric Window）</news:title>
   <news:publication_date>2026-04-16T22:10:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680137</loc>
  <lastmod>2026-04-16T22:09:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>量子制御学習における幾何学的表現と時系列表現の比較（Geometrical versus time-series representation of data in quantum control learning）</news:title>
   <news:publication_date>2026-04-16T22:09:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680135</loc>
  <lastmod>2026-04-16T22:08:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ISISの最盛期におけるアラビア語Twitter議論と示唆（ISIS at its apogee: the Arabic discourse on Twitter and what we can learn from that about ISIS support and Foreign Fighters）</news:title>
   <news:publication_date>2026-04-16T22:08:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680133</loc>
  <lastmod>2026-04-16T22:08:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>アドバーサリアル・データ・プログラミング（Adversarial Data Programming: Using GANs to Relax the Bottleneck of Curated Labeled Data）</news:title>
   <news:publication_date>2026-04-16T22:08:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680131</loc>
  <lastmod>2026-04-16T22:08:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率母関数を用いた感染症モデル入門（A primer on the use of probability generating functions in infectious disease modeling）</news:title>
   <news:publication_date>2026-04-16T22:08:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680129</loc>
  <lastmod>2026-04-16T22:08:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>畳み込み非負値行列因子分解の乗法更新則とβダイバージェンス（Multiplicative Updates for Convolutional NMF Under β-Divergence）</news:title>
   <news:publication_date>2026-04-16T22:08:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680127</loc>
  <lastmod>2026-04-16T21:16:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>C-LSTMによるFPGA上の効率的なLSTM実装（C-LSTM: Enabling Efficient LSTM using Structured Compression Techniques on FPGAs）</news:title>
   <news:publication_date>2026-04-16T21:16:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680125</loc>
  <lastmod>2026-04-16T21:13:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>アナログHTMの学習を伴わない特徴抽出とメムリスタ・CMOS回路設計（Feature extraction without learning in an analog Spatial Pooler）</news:title>
   <news:publication_date>2026-04-16T21:13:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680123</loc>
  <lastmod>2026-04-16T21:13:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>信号処理における分割凸フィッティング（Signal Processing and Piecewise Convex Estimation）</news:title>
   <news:publication_date>2026-04-16T21:13:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680121</loc>
  <lastmod>2026-04-16T21:12:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>検索順位学習データセットにおける特徴選択とモデル比較（Feature Selection and Model Comparison on Microsoft Learning-to-Rank Data Sets）</news:title>
   <news:publication_date>2026-04-16T21:12:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680119</loc>
  <lastmod>2026-04-16T21:12:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>網膜OCTのトポロジー保証付きセグメンテーション（Topology guaranteed segmentation of the human retina from OCT using convolutional neural networks）</news:title>
   <news:publication_date>2026-04-16T21:12:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680117</loc>
  <lastmod>2026-04-16T21:12:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>MT-Spike：時間を使うスパイキングニューラルネットで多層学習を実現する（MT-Spike: A Multilayer Time-based Spiking Neuromorphic Architecture with Temporal Error Backpropagation）</news:title>
   <news:publication_date>2026-04-16T21:12:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680115</loc>
  <lastmod>2026-04-16T21:11:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>協働的マルチタスク訓練による敵対的攻撃への防御（Defending against Adversarial Attack towards Deep Neural Networks via Collaborative Multi-Task Training）</news:title>
   <news:publication_date>2026-04-16T21:11:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680113</loc>
  <lastmod>2026-04-16T20:20:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>特徴蒸留：敵対的例に対するDNN志向JPEG圧縮（Feature Distillation: DNN-Oriented JPEG Compression Against Adversarial Examples）</news:title>
   <news:publication_date>2026-04-16T20:20:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680111</loc>
  <lastmod>2026-04-16T20:20:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>別ラベルからのアップリフトモデリング（Uplift Modeling from Separate Labels）</news:title>
   <news:publication_date>2026-04-16T20:20:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680109</loc>
  <lastmod>2026-04-16T20:19:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>近傍を学習するランキング手法の要点（Ranking with Adaptive Neighbors）</news:title>
   <news:publication_date>2026-04-16T20:19:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680107</loc>
  <lastmod>2026-04-16T20:18:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>安定性に基づくモデル非依存プライベート学習（Model-Agnostic Private Learning via Stability）</news:title>
   <news:publication_date>2026-04-16T20:18:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680105</loc>
  <lastmod>2026-04-16T20:18:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>不正確な事前分布に対するロバスト性（Robustness to Incorrect Priors in Partially Observed Stochastic Control）</news:title>
   <news:publication_date>2026-04-16T20:18:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680103</loc>
  <lastmod>2026-04-16T20:17:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DNNに適したJPEG圧縮の再設計—DeepN-JPEGの要点と実務インパクト（DeepN-JPEG: A Deep Neural Network Favorable JPEG-based Image Compression Framework）</news:title>
   <news:publication_date>2026-04-16T20:17:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680101</loc>
  <lastmod>2026-04-16T20:17:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>バケット縮約による近似推論（Bucket Renormalization for Approximate Inference）</news:title>
   <news:publication_date>2026-04-16T20:17:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680099</loc>
  <lastmod>2026-04-16T19:25:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>顔画像における注目領域を用いた軟生体情報分類（SAF-BAGE: Salient Approach for Facial Soft-Biometric Classification - Age, Gender, and Facial Expression）</news:title>
   <news:publication_date>2026-04-16T19:25:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680097</loc>
  <lastmod>2026-04-16T19:18:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>アルゴリズムによる社会介入の提案（Thesis Proposal: Algorithmic Social Intervention）</news:title>
   <news:publication_date>2026-04-16T19:18:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680095</loc>
  <lastmod>2026-04-16T19:17:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数顕著物体の検出・ランキング・即時数の再考（Revisiting Salient Object Detection: Simultaneous Detection, Ranking, and Subitizing of Multiple Salient Objects）</news:title>
   <news:publication_date>2026-04-16T19:17:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680093</loc>
  <lastmod>2026-04-16T19:16:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>垂直メニュー選択における人間のパフォーマンス予測（Predicting Human Performance in Vertical Menu Selection Using Deep Learning）</news:title>
   <news:publication_date>2026-04-16T19:16:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680091</loc>
  <lastmod>2026-04-16T19:15:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>画像から集団感情を推定するマルチモーダル手法（A Multi-Modal Approach to Infer Image Affect）</news:title>
   <news:publication_date>2026-04-16T19:15:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680089</loc>
  <lastmod>2026-04-16T19:15:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>音楽の長期構造を学習する階層潜在ベクトルモデル（A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music）</news:title>
   <news:publication_date>2026-04-16T19:15:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680087</loc>
  <lastmod>2026-04-16T19:14:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>心理学的知見を応用した実行可能な分析（Applications of Psychological Science for Actionable Analytics）</news:title>
   <news:publication_date>2026-04-16T19:14:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680085</loc>
  <lastmod>2026-04-16T18:23:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模ソーシャルデータの落とし穴：公開Redditコーパスの欠損と研究への影響（Caveat Emptor, Computational Social Science: Large-Scale Missing Data in a Widely-Published Reddit Corpus）</news:title>
   <news:publication_date>2026-04-16T18:23:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680083</loc>
  <lastmod>2026-04-16T18:23:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>探索方策を学習するメタポリシー勾配（Learning to Explore with Meta-Policy Gradient）</news:title>
   <news:publication_date>2026-04-16T18:23:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680081</loc>
  <lastmod>2026-04-16T18:22:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非自律的敵対システムの解析（Analysis of Nonautonomous Adversarial Systems）</news:title>
   <news:publication_date>2026-04-16T18:22:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680079</loc>
  <lastmod>2026-04-16T18:21:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自動化されたデータ駆動型無機材料設計とAFLOW（Autonomous data-driven design of inorganic materials with AFLOW）</news:title>
   <news:publication_date>2026-04-16T18:21:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680077</loc>
  <lastmod>2026-04-16T18:21:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>コロイド・顆粒性ガラスにおけるスピンガラス様エージング（Spin-glass–like aging in colloidal and granular glasses）</news:title>
   <news:publication_date>2026-04-16T18:21:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680075</loc>
  <lastmod>2026-04-16T18:21:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多数の戦略的エージェントを持つシステムにおける分散学習（Decentralised Learning in Systems with Many, Many Strategic Agents）</news:title>
   <news:publication_date>2026-04-16T18:21:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680073</loc>
  <lastmod>2026-04-16T18:21:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ゼロ過剰データに効く疎カーネル付き変分ガウス過程（Variational zero-inflated Gaussian processes with sparse kernels）</news:title>
   <news:publication_date>2026-04-16T18:21:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680071</loc>
  <lastmod>2026-04-16T17:29:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習とソフトウェア定義ネットワークで守るIoTの未来（Securing the Internet of Things in the Age of Machine Learning and Software-defined Networking）</news:title>
   <news:publication_date>2026-04-16T17:29:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680069</loc>
  <lastmod>2026-04-16T17:29:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的疾患進行モデルによる臨床予測の実用化（A Probabilistic Disease Progression Model for Predicting Future Clinical Outcome）</news:title>
   <news:publication_date>2026-04-16T17:29:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680067</loc>
  <lastmod>2026-04-16T17:29:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>テンソル列（Tensor Train）を使った主成分分析による次元削減の再定義（Principal Component Analysis with Tensor Train Subspace）</news:title>
   <news:publication_date>2026-04-16T17:29:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680065</loc>
  <lastmod>2026-04-16T17:28:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>タスク簡易化によるクローズドループ物体把持学習（Comparing Task Simplifications to Learn Closed-Loop Object Picking Using Deep Reinforcement Learning）</news:title>
   <news:publication_date>2026-04-16T17:28:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680063</loc>
  <lastmod>2026-04-16T17:27:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>トランジット系惑星のJWST早期公開科学計画（Transiting Exoplanet Community Early Release Science Program for JWST）</news:title>
   <news:publication_date>2026-04-16T17:27:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680061</loc>
  <lastmod>2026-04-16T17:27:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>条件付き活性化による多様なニューロン表現（Conditional Activation for Diverse Neurons in Heterogeneous Networks）</news:title>
   <news:publication_date>2026-04-16T17:27:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680059</loc>
  <lastmod>2026-04-16T17:27:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>LCANetによるエンドツーエンドのリップリーディング（LCANet: End-to-End Lipreading with Cascaded Attention-CTC）</news:title>
   <news:publication_date>2026-04-16T17:27:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680057</loc>
  <lastmod>2026-04-16T16:27:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>麻酔薬分子は液体無秩序相に入りやすい（Common anesthetic molecules prefer to partition in liquid disorder phase in a composite multicomponent membrane）</news:title>
   <news:publication_date>2026-04-16T16:27:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680055</loc>
  <lastmod>2026-04-16T16:27:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Information-Corrected Estimation（Information-Corrected Estimation: A Generalization Error Reducing Parameter Estimation Method）</news:title>
   <news:publication_date>2026-04-16T16:27:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680053</loc>
  <lastmod>2026-04-16T16:27:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>頭部CTにおける重大所見検出の深層学習アルゴリズムの開発と検証（Development and Validation of Deep Learning Algorithms for Detection of Critical Findings in Head CT Scans）</news:title>
   <news:publication_date>2026-04-16T16:27:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680051</loc>
  <lastmod>2026-04-16T16:25:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>構造非拘束モデリング：因果グラフの敵対的学習（Structural Agnostic Modeling: Adversarial Learning of Causal Graphs）</news:title>
   <news:publication_date>2026-04-16T16:25:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680049</loc>
  <lastmod>2026-04-16T16:25:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>能動的報酬評価を考慮した強化学習とMCTSの接合（Active Reinforcement Learning with Monte-Carlo Tree Search）</news:title>
   <news:publication_date>2026-04-16T16:25:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680047</loc>
  <lastmod>2026-04-16T16:24:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>FCNの量子化が生む過学習抑制と高精度セグメンテーション（Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation）</news:title>
   <news:publication_date>2026-04-16T16:24:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680045</loc>
  <lastmod>2026-04-16T15:33:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>診断駆動型カーネル混合による非定常エミュレータ（DIAGNOSTICS-DRIVEN NONSTATIONARY EMULATORS USING KERNEL MIXTURES）</news:title>
   <news:publication_date>2026-04-16T15:33:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680043</loc>
  <lastmod>2026-04-16T15:31:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CNNがマンモグラム分類で利用する視覚プリミティブを専門家が同定する研究（Expert identification of visual primitives used by CNNs during mammogram classification）</news:title>
   <news:publication_date>2026-04-16T15:31:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680041</loc>
  <lastmod>2026-04-16T15:23:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>音声から音楽ジャンルを認識する学習（Learning to Recognize Musical Genre from Audio）</news:title>
   <news:publication_date>2026-04-16T15:23:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680039</loc>
  <lastmod>2026-04-16T15:23:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数ソースからのドメイン適応におけるターゲットシフトへの最適輸送の適用（Optimal Transport for Multi-source Domain Adaptation under Target Shift）</news:title>
   <news:publication_date>2026-04-16T15:23:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680037</loc>
  <lastmod>2026-04-16T15:21:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習に基づく多変量アウトカムの関連性測定法（A machine learning-based approach for estimating and testing associations with multivariate outcomes）</news:title>
   <news:publication_date>2026-04-16T15:21:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680035</loc>
  <lastmod>2026-04-16T15:21:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>立体3D動画の視線予測を学習で実現するモデル（A Learning-Based Visual Saliency Prediction Model for Stereoscopic 3D Video）</news:title>
   <news:publication_date>2026-04-16T15:21:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680033</loc>
  <lastmod>2026-04-16T15:21:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>データベース内でのエンティティ連携を実現するIDEL（IDEL: In-Database Entity Linking with Neural Embeddings）</news:title>
   <news:publication_date>2026-04-16T15:21:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680031</loc>
  <lastmod>2026-04-16T14:29:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>音声認識のためのリソース配慮型音声視覚結合ネットワーク設計 (Resource Aware Design of a Deep Convolutional-Recurrent Neural Network for Speech Recognition through Audio-Visual Sensor Fusion)</news:title>
   <news:publication_date>2026-04-16T14:29:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680029</loc>
  <lastmod>2026-04-16T14:28:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>独立再帰ニューラルネットワーク（IndRNN）：より長く、より深いRNNの構築（Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN）</news:title>
   <news:publication_date>2026-04-16T14:28:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680027</loc>
  <lastmod>2026-04-16T14:27:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>異種時系列イベントの共同表現学習による臨床エンドポイント予測（Learning the Joint Representation of Heterogeneous Temporal Events for Clinical Endpoint Prediction）</news:title>
   <news:publication_date>2026-04-16T14:27:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680025</loc>
  <lastmod>2026-04-16T14:26:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>整数計画による低ランクブーリアン行列近似（Low-Rank Boolean Matrix Approximation by Integer Programming）</news:title>
   <news:publication_date>2026-04-16T14:26:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680023</loc>
  <lastmod>2026-04-16T14:26:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高ダイナミックレンジ動画の視覚的顕著性融合の学習モデル（A Learning-Based Visual Saliency Fusion Model for High Dynamic Range Video）</news:title>
   <news:publication_date>2026-04-16T14:26:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680021</loc>
  <lastmod>2026-04-16T14:26:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オメガ・ケンタウリの深部X線サーベイ（A Deep X-ray Survey of the Globular Cluster Omega Centauri）</news:title>
   <news:publication_date>2026-04-16T14:26:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680019</loc>
  <lastmod>2026-04-16T14:26:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層学習ツールキットとライブラリの概観（A Survey on Deep Learning Toolkits and Libraries for Intelligent User Interfaces）</news:title>
   <news:publication_date>2026-04-16T14:26:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680017</loc>
  <lastmod>2026-04-16T13:34:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>WISERNet: カラー画像ステガノ分析のための幅広いSeparate-then-Reunionネットワーク (WISERNet: Wider Separate-then-reunion Network for Steganalysis of Color Images)</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680015</loc>
  <lastmod>2026-04-16T13:34:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数並列RRAMによるシナプスモデルが示すSNN学習の現実解（A case for multiple and parallel RRAMs as synaptic model for training SNNs）</news:title>
   <news:publication_date>2026-04-16T13:34:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/680013</loc>
  <lastmod>2026-04-16T13:34:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単眼画像からの3D人体姿勢推定を複数視点学習で強化する（Learning Monocular 3D Human Pose Estimation from Multi-view Images）</news:title>
   <news:publication_date>2026-04-16T13:34:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680011</loc>
  <lastmod>2026-04-16T13:33:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Deep k-Nearest Neighbors（Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning）</news:title>
   <news:publication_date>2026-04-16T13:33:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680009</loc>
  <lastmod>2026-04-16T13:33:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>VERSE：類似度に基づく汎用グラフ埋め込み（VERSE: Versatile Graph Embeddings from Similarity Measures）</news:title>
   <news:publication_date>2026-04-16T13:33:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680007</loc>
  <lastmod>2026-04-16T13:32:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>軽量VRを用いた条件付き自動運転のドライバー訓練（Light Virtual Reality Systems for the Training of Conditionally Automated Vehicle Drivers）</news:title>
   <news:publication_date>2026-04-16T13:32:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680005</loc>
  <lastmod>2026-04-16T13:32:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>小さな物体を見逃さない注意機構付き復帰型ニューラルネット（Feature Selective Small Object Detection via Knowledge-based Recurrent Attentive Neural Network）</news:title>
   <news:publication_date>2026-04-16T13:32:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680003</loc>
  <lastmod>2026-04-16T12:41:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>プログラム間の意味を学ぶ階層的コード埋め込み（Hierarchical Learning of Cross-Language Mappings through Distributed Vector Representations for Code）</news:title>
   <news:publication_date>2026-04-16T12:41:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680001</loc>
  <lastmod>2026-04-16T12:41:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>テキストプロンプト音声認証における深層CNN特徴抽出器（DEEP CNN BASED FEATURE EXTRACTOR FOR TEXT-PROMPTED SPEAKER RECOGNITION）</news:title>
   <news:publication_date>2026-04-16T12:41:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679999</loc>
  <lastmod>2026-04-16T12:40:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非対称系における深部障壁下融合への直接反応チャンネルの影響 (Effect of direct reaction channels on deep sub-barrier fusion in asymmetric systems)</news:title>
   <news:publication_date>2026-04-16T12:40:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679997</loc>
  <lastmod>2026-04-16T12:40:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>圧縮動画の多フレーム品質向上（Multi-Frame Quality Enhancement for Compressed Video）</news:title>
   <news:publication_date>2026-04-16T12:40:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679995</loc>
  <lastmod>2026-04-16T12:39:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グループ化線形予測と加速強化学習によるオンラインコンテンツキャッシュ（Using Grouped Linear Prediction and Accelerated Reinforcement Learning for Online Content Caching）</news:title>
   <news:publication_date>2026-04-16T12:39:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679993</loc>
  <lastmod>2026-04-16T12:39:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>RGB-DマルチモーダルRNNによる屋内シーンラベリング（Multimodal Recurrent Neural Networks with Information Transfer Layers for Indoor Scene Labeling）</news:title>
   <news:publication_date>2026-04-16T12:39:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679991</loc>
  <lastmod>2026-04-16T12:39:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>連続行動空間における方策探索の概説（Policy Search in Continuous Action Domains: an Overview）</news:title>
   <news:publication_date>2026-04-16T12:39:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679989</loc>
  <lastmod>2026-04-16T11:47:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>無限腕バンディットにおける純探索（Pure Exploration in Infinite Bandit Models）</news:title>
   <news:publication_date>2026-04-16T11:47:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679987</loc>
  <lastmod>2026-04-16T11:38:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>解釈可能なシステムログ異常検知のためのRNN注意機構（Recurrent Neural Network Attention Mechanisms for Interpretable System Log Anomaly Detection）</news:title>
   <news:publication_date>2026-04-16T11:38:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679985</loc>
  <lastmod>2026-04-16T11:37:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層型強化学習：局所方策で近似する割引版巡回セールスマン問題（Approximating Optimal Discounted TSP Using Local Policies）</news:title>
   <news:publication_date>2026-04-16T11:37:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679983</loc>
  <lastmod>2026-04-16T11:36:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単一粒子系の量子非平衡熱力学（Quantum thermodynamics of single particle systems）</news:title>
   <news:publication_date>2026-04-16T11:36:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679981</loc>
  <lastmod>2026-04-16T11:36:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>二値化された観測からの行列復元の実践的示唆（Binary Matrix Completion Using Unobserved Entries）</news:title>
   <news:publication_date>2026-04-16T11:36:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679979</loc>
  <lastmod>2026-04-16T11:36:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>完全ベイズ多次元ホークス過程のシミュレーションと較正（Simulation and Calibration of a Fully Bayesian Multidimensional Hawkes Process）</news:title>
   <news:publication_date>2026-04-16T11:36:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679977</loc>
  <lastmod>2026-04-16T11:36:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>タンパク質変異の安定性三値分類（Protein Mutation Stability Ternary Classification using Neural Networks and Rigidity Analysis）</news:title>
   <news:publication_date>2026-04-16T11:36:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679975</loc>
  <lastmod>2026-04-16T10:44:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>透明物体マッティングの学習（TOM-Net: Learning Transparent Object Matting from a Single Image）</news:title>
   <news:publication_date>2026-04-16T10:44:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679973</loc>
  <lastmod>2026-04-16T10:44:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模LDAをGPUで高速化するCuLDA_CGSの全体像（CuLDA_CGS: Solving Large-scale LDA Problems on GPUs）</news:title>
   <news:publication_date>2026-04-16T10:44:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679971</loc>
  <lastmod>2026-04-16T10:43:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>組合せセミバンディットに対するトンプソン・サンプリング（Thompson Sampling for Combinatorial Semi-Bandits）</news:title>
   <news:publication_date>2026-04-16T10:43:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679969</loc>
  <lastmod>2026-04-16T10:42:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ϵ-ドミナンスで品質予測を簡潔にする（Building Better Quality Predictors Using “ϵ-Dominance”）</news:title>
   <news:publication_date>2026-04-16T10:42:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679967</loc>
  <lastmod>2026-04-16T10:42:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>超立方体形状かつ不連続なデータに対する深層ベイズ学習（Deep Bayesian Supervised Learning given Hypercuboidally-shaped, Discontinuous Data, using Compound Tensor-Variate &amp;amp; Scalar-Variate Gaussian Processes）</news:title>
   <news:publication_date>2026-04-16T10:42:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679965</loc>
  <lastmod>2026-04-16T10:42:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オンライン・ジハード主義ヘイトスピーチの自動検出（Automatic detection of online jihadist hate speech）</news:title>
   <news:publication_date>2026-04-16T10:42:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679963</loc>
  <lastmod>2026-04-16T10:41:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Goodhart効果の類型化が示すもの（Categorizing Variants of Goodhart’s Law）</news:title>
   <news:publication_date>2026-04-16T10:41:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679961</loc>
  <lastmod>2026-04-16T09:50:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>訓練データの剪定が性能を支えた（It was the training data pruning too!）</news:title>
   <news:publication_date>2026-04-16T09:50:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679959</loc>
  <lastmod>2026-04-16T09:49:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>共相関化された歩行エンベロープ（Coregionalised Locomotion Envelopes – A Qualitative Approach）</news:title>
   <news:publication_date>2026-04-16T09:49:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679957</loc>
  <lastmod>2026-04-16T09:49:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>空間重畳による量子リザバーコンピューティングの計算能力強化 (Boosting computational power through spatial multiplexing in quantum reservoir computing)</news:title>
   <news:publication_date>2026-04-16T09:49:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679955</loc>
  <lastmod>2026-04-16T09:48:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非同期SSVEP分類のためのコンパクト畳み込みニューラルネットワーク（Compact Convolutional Neural Networks for Classification of Asynchronous Steady-state Visual Evoked Potentials）</news:title>
   <news:publication_date>2026-04-16T09:48:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679953</loc>
  <lastmod>2026-04-16T09:48:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>COPA: 欠損・大規模データに対する制約付きPARAFAC2（COPA: Constrained PARAFAC2 for Sparse &amp;amp; Large Datasets）</news:title>
   <news:publication_date>2026-04-16T09:48:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679951</loc>
  <lastmod>2026-04-16T09:47:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>方言認識をエンドツーエンドで実現する技術（Convolutional Neural Networks and Language Embeddings for End-to-End Dialect Recognition）</news:title>
   <news:publication_date>2026-04-16T09:47:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679949</loc>
  <lastmod>2026-04-16T09:46:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>胸部X線の異常検出における位置情報対応Denseネットワークの実用性（Learning to recognize Abnormalities in Chest X-Rays with Location-Aware Dense Networks）</news:title>
   <news:publication_date>2026-04-16T09:46:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679947</loc>
  <lastmod>2026-04-16T08:55:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>セトゥスIIの正体：極めて薄い矮小銀河候補の再評価（ON THE NATURE OF ULTRA-FAINT DWARF GALAXY CANDIDATES II: THE CASE OF CETUS II）</news:title>
   <news:publication_date>2026-04-16T08:55:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679945</loc>
  <lastmod>2026-04-16T08:50:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>NASAのアステロイド・グランドチャレンジの戦略と教訓（NASA’s Asteroid Grand Challenge: Strategy, Results, and Lessons Learned）</news:title>
   <news:publication_date>2026-04-16T08:50:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679943</loc>
  <lastmod>2026-04-16T08:49:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>2次元データにおけるオニオンピーリングによる外れ値検出（Onion-Peeling Outlier Detection in 2-D data Sets）</news:title>
   <news:publication_date>2026-04-16T08:49:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679941</loc>
  <lastmod>2026-04-16T08:49:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>二部グラフ一般設定におけるスペクトルクラスタリングの解析（Analysis of spectral clustering algorithms for community detection: the general bipartite setting）</news:title>
   <news:publication_date>2026-04-16T08:49:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679939</loc>
  <lastmod>2026-04-16T08:49:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Taking Turing by surprise? — 倫理的驚きを設計するデジタルコンピュータ（Taking Turing by surprise? Designing ‘digital computers’ for morally-loaded contexts）</news:title>
   <news:publication_date>2026-04-16T08:49:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679937</loc>
  <lastmod>2026-04-16T08:48:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>トポロジー知識と中継深度が非同期コンセンサスに与える影響（Eﬀects of Topology Knowledge and Relay Depth on Asynchronous Consensus）</news:title>
   <news:publication_date>2026-04-16T08:48:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679935</loc>
  <lastmod>2026-04-16T08:48:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>加重ベイズ・ブートストラップの実務的意義（Weighted Bayesian Bootstrap for Scalable Bayes）</news:title>
   <news:publication_date>2026-04-16T08:48:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679933</loc>
  <lastmod>2026-04-16T07:56:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自己符号化器と意味ハッシュによる勾配拡張情報検索（Gradient Augmented Information Retrieval with Autoencoders and Semantic Hashing）</news:title>
   <news:publication_date>2026-04-16T07:56:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679931</loc>
  <lastmod>2026-04-16T07:56:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的・正則化されたグラフ畳み込みネットワークの検討（Probabilistic and Regularized Graph Convolutional Networks）</news:title>
   <news:publication_date>2026-04-16T07:56:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679929</loc>
  <lastmod>2026-04-16T07:55:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>カメラセンサーで粒子を識別する手法（Particle Identification In Camera Image Sensors Using Computer Vision）</news:title>
   <news:publication_date>2026-04-16T07:55:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679927</loc>
  <lastmod>2026-04-16T07:55:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Accuracy-Reliabilityを用いた経験的分散推定の実務的意義（Accuracy-Reliability Cost Function for Empirical Variance Estimation）</news:title>
   <news:publication_date>2026-04-16T07:55:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679925</loc>
  <lastmod>2026-04-16T07:54:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>概念（オントロジー）埋め込みの品質評価指標（Metrics for Evaluating Quality of Embeddings for Ontological Concepts）</news:title>
   <news:publication_date>2026-04-16T07:54:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679923</loc>
  <lastmod>2026-04-16T07:54:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分子動力学と機械学習が切り拓く新しいµオピオイド化学骨格（Machine Learning Harnesses Molecular Dynamics to Discover New µ Opioid Chemotypes）</news:title>
   <news:publication_date>2026-04-16T07:54:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679921</loc>
  <lastmod>2026-04-16T07:54:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GANの射影による画像ノイズ除去（CORRECTION BY PROJECTION: DENOISING IMAGES WITH GENERATIVE ADVERSARIAL NETWORKS）</news:title>
   <news:publication_date>2026-04-16T07:54:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679919</loc>
  <lastmod>2026-04-16T07:02:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分子特性予測のためのPotentialNet（PotentialNet for Molecular Property Prediction）</news:title>
   <news:publication_date>2026-04-16T07:02:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679917</loc>
  <lastmod>2026-04-16T07:02:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>空間特徴を用いた犯罪予測（Predicting Crime Using Spatial Features）</news:title>
   <news:publication_date>2026-04-16T07:02:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679915</loc>
  <lastmod>2026-04-16T07:02:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>誤検出率（FDR）を抑えつつ変数選択を可能にする手法（False Discovery Rate Control via Debiased Lasso）</news:title>
   <news:publication_date>2026-04-16T07:02:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679913</loc>
  <lastmod>2026-04-16T07:00:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>放射線画像解析における非類似度（ディシミラリティ）表現の提案（Dissimilarity-based representation for radiomics applications）</news:title>
   <news:publication_date>2026-04-16T07:00:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679911</loc>
  <lastmod>2026-04-16T07:00:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ノードからネットワークへ：進化的手法で再帰型ニューラルネットワークを設計する（From Nodes to Networks: Evolving Recurrent Neural Networks）</news:title>
   <news:publication_date>2026-04-16T07:00:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679909</loc>
  <lastmod>2026-04-16T07:00:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>拡張アフィニティ・プロパゲーションがもたらす「局所」と「全体」の両取り（Extended Affinity Propagation: Global Discovery and Local Insights）</news:title>
   <news:publication_date>2026-04-16T07:00:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679907</loc>
  <lastmod>2026-04-16T07:00:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ウェアラブルで外来患者の臨床悪化を予測する（Predicting Clinical Deterioration of Outpatients Using Multimodal Data Collected by Wearables）</news:title>
   <news:publication_date>2026-04-16T07:00:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679905</loc>
  <lastmod>2026-04-16T06:08:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>転移可能な分子電荷割当を深層ニューラルネットワークで実現する（Transferable Molecular Charge Assignment Using Deep Neural Networks）</news:title>
   <news:publication_date>2026-04-16T06:08:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679903</loc>
  <lastmod>2026-04-16T06:07:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Flipoutによるミニバッチ内の擬似独立重み摂動（Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches）</news:title>
   <news:publication_date>2026-04-16T06:07:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679901</loc>
  <lastmod>2026-04-16T06:07:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次元線形混合モデルを大規模に学習するための手法（Scalable Algorithms for Learning High-Dimensional Linear Mixed Models）</news:title>
   <news:publication_date>2026-04-16T06:07:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679899</loc>
  <lastmod>2026-04-16T06:06:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スマートフォンによる運転スタイル分類と離散ウェーブレット変換（Smartphone based Driving Style Classification Using Features Made by Discrete Wavelet Transformation）</news:title>
   <news:publication_date>2026-04-16T06:06:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679897</loc>
  <lastmod>2026-04-16T06:06:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スケッチ化正則化アルゴリズムの最適収束率（Optimal Rates of Sketched-regularized Algorithms for Least-Squares Regression over Hilbert Spaces）</news:title>
   <news:publication_date>2026-04-16T06:06:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679895</loc>
  <lastmod>2026-04-16T06:06:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>潜在分布を学習することで生成モデルの表現力を高める（Learning the Base Distribution in Implicit Generative Models）</news:title>
   <news:publication_date>2026-04-16T06:06:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679893</loc>
  <lastmod>2026-04-16T06:05:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>公平な機械学習の遅延影響（Delayed Impact of Fair Machine Learning）</news:title>
   <news:publication_date>2026-04-16T06:05:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679891</loc>
  <lastmod>2026-04-16T05:14:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模集団における個体追跡の実用化を前進させた技術（idtracker.ai: Tracking all individuals in large collectives of unmarked animals）</news:title>
   <news:publication_date>2026-04-16T05:14:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679889</loc>
  <lastmod>2026-04-16T05:14:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Tinderプロフィール分類にFaceNet顔埋め込みを活用する手法（CLASSIFYING ONLINE DATING PROFILES ON TINDER USING FACENET FACIAL EMBEDDINGS）</news:title>
   <news:publication_date>2026-04-16T05:14:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679887</loc>
  <lastmod>2026-04-16T05:13:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>網膜画像における糖尿病網膜症検出の再現研究（Replication study: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs）</news:title>
   <news:publication_date>2026-04-16T05:13:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679885</loc>
  <lastmod>2026-04-16T05:12:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>永続的検証データベースの設計（The Everlasting Database: Statistical Validity at a Fair Price）</news:title>
   <news:publication_date>2026-04-16T05:12:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679883</loc>
  <lastmod>2026-04-16T05:12:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ReLUモデルにおける表現学習と復元 (Representation Learning and Recovery in the ReLU Model)</news:title>
   <news:publication_date>2026-04-16T05:12:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679881</loc>
  <lastmod>2026-04-16T05:12:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自然言語をSPARQLへ変換するニューラル注意機構（Semantic Parsing Natural Language into SPARQL: Improving Target Language Representation with Neural Attention）</news:title>
   <news:publication_date>2026-04-16T05:12:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679879</loc>
  <lastmod>2026-04-16T05:12:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>モバイル・無線ネットワークにおける深層学習の展望（Deep Learning in Mobile and Wireless Networking: A Survey）</news:title>
   <news:publication_date>2026-04-16T05:12:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679877</loc>
  <lastmod>2026-04-16T04:20:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Neural Conditional Gradients（Neural Conditional Gradients）</news:title>
   <news:publication_date>2026-04-16T04:20:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679875</loc>
  <lastmod>2026-04-16T04:19:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Sentinel-2画像の超解像：グローバルに適用可能な深層ニューラルネットワークの学習（Super-resolution of Sentinel-2 images: Learning a globally applicable deep neural network）</news:title>
   <news:publication_date>2026-04-16T04:19:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679873</loc>
  <lastmod>2026-04-16T04:19:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>未知の常微分方程式を学ぶ非パラメトリック手法（Learning unknown ODE models with Gaussian processes）</news:title>
   <news:publication_date>2026-04-16T04:19:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679871</loc>
  <lastmod>2026-04-16T04:18:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SO-Netによる点群解析の革新（SO-Net: Self-Organizing Network for Point Cloud Analysis）</news:title>
   <news:publication_date>2026-04-16T04:18:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679869</loc>
  <lastmod>2026-04-16T04:18:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動画物体セグメンテーションの新展開 — 再識別と注意機構によるマスク伝播（Video Object Segmentation with Joint Re-identification and Attention-Aware Mask Propagation）</news:title>
   <news:publication_date>2026-04-16T04:18:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679867</loc>
  <lastmod>2026-04-16T04:18:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>FeTa: DCAによる高速プルーニングと一般化誤差保証（FeTa: A DCA Pruning Algorithm with Generalization Error Guarantees）</news:title>
   <news:publication_date>2026-04-16T04:18:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679865</loc>
  <lastmod>2026-04-16T04:17:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>共変チャイラル摂動論におけるオクテットバリオン磁気モーメントのNNLO解析 (Octet baryon magnetic moments at next-to-next-to-leading order in covariant chiral perturbation theory)</news:title>
   <news:publication_date>2026-04-16T04:17:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679863</loc>
  <lastmod>2026-04-16T03:26:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>全方位CNNを用いた屋内プレイス認識とナビゲーション（Omnidirectional CNN for Visual Place Recognition and Navigation）</news:title>
   <news:publication_date>2026-04-16T03:26:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679861</loc>
  <lastmod>2026-04-16T03:26:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>パネルカウントデータに対するガウス過程の変分推論（Variational Inference for Gaussian Process with Panel Count Data）</news:title>
   <news:publication_date>2026-04-16T03:26:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679859</loc>
  <lastmod>2026-04-16T03:25:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>クラウドソーシングデータを活用した深層能動学習（Leveraging Crowdsourcing Data For Deep Active Learning）</news:title>
   <news:publication_date>2026-04-16T03:25:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679857</loc>
  <lastmod>2026-04-16T03:24:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>特殊相対性理論の双第一公準的基盤（A dual first-postulate basis for special relativity）</news:title>
   <news:publication_date>2026-04-16T03:24:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679855</loc>
  <lastmod>2026-04-16T03:24:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高スループット同時分散確率的勾配降下法（High Throughput Synchronous Distributed Stochastic Gradient Descent）</news:title>
   <news:publication_date>2026-04-16T03:24:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679853</loc>
  <lastmod>2026-04-16T03:24:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>サイバーセキュリティにおけるデータサイエンス手法の体系化（DATA SCIENCE METHODOLOGY FOR CYBERSECURITY PROJECTS）</news:title>
   <news:publication_date>2026-04-16T03:24:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679851</loc>
  <lastmod>2026-04-16T03:23:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>半準パラメトリック文脈バンディット（Semiparametric Contextual Bandits）</news:title>
   <news:publication_date>2026-04-16T03:23:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679849</loc>
  <lastmod>2026-04-16T02:32:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多カーネル回帰によるグラフ信号処理（MULTI-KERNEL REGRESSION FOR GRAPH SIGNAL PROCESSING）</news:title>
   <news:publication_date>2026-04-16T02:32:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679847</loc>
  <lastmod>2026-04-16T02:32:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフ信号処理における極限学習機（Extreme Learning Machine for Graph Signal Processing）</news:title>
   <news:publication_date>2026-04-16T02:32:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679845</loc>
  <lastmod>2026-04-16T02:31:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Noise2Noise：クリーンデータなしで学ぶ画像復元（Noise2Noise: Learning Image Restoration without Clean Data）</news:title>
   <news:publication_date>2026-04-16T02:31:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679843</loc>
  <lastmod>2026-04-16T02:30:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>銀河クラスタ環境で剥ぎ取られたガス中の星形成（A Virgo Environmental Survey Tracing Ionised Gas Emission (VESTIGE).III. Star formation in the stripped gas of NGC 4254）</news:title>
   <news:publication_date>2026-04-16T02:30:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679841</loc>
  <lastmod>2026-04-16T02:30:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>クラス単位ハッシュによる意味保持ハッシング（Deep Class-Wise Hashing: Semantics-Preserving Hashing via Class-wise Loss）</news:title>
   <news:publication_date>2026-04-16T02:30:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679839</loc>
  <lastmod>2026-04-16T02:30:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>悪意ある実行ファイルの敵対的バイナリによる検出回避（Adversarial Malware Binaries: Evading Deep Learning for Malware Detection in Executables）</news:title>
   <news:publication_date>2026-04-16T02:30:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679837</loc>
  <lastmod>2026-04-16T02:30:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>R3Net：ランダム重み・ReLU・頑健性を結びつける設計（R3Net: Random Weights, Rectifier Linear Units and Robustness for Artificial Neural Network）</news:title>
   <news:publication_date>2026-04-16T02:30:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679835</loc>
  <lastmod>2026-04-16T01:38:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>距離類似度指標に対するGPUによる自己結合の高速化（GPU Accelerated Self-join for the Distance Similarity Metric）</news:title>
   <news:publication_date>2026-04-16T01:38:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679833</loc>
  <lastmod>2026-04-16T01:38:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>状態重なりの計算法を学習する量子アルゴリズム（Learning the quantum algorithm for state overlap）</news:title>
   <news:publication_date>2026-04-16T01:38:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679831</loc>
  <lastmod>2026-04-16T01:38:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>室内自律走行を「行動」レベルで設計する意味（A Deep Learning Based Behavioral Approach to Indoor Autonomous Navigation）</news:title>
   <news:publication_date>2026-04-16T01:38:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679829</loc>
  <lastmod>2026-04-16T01:36:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自己中心的サンプリングネットワークのリンク予測（Link prediction for egocentrically sampled networks）</news:title>
   <news:publication_date>2026-04-16T01:36:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679827</loc>
  <lastmod>2026-04-16T01:36:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>離散ベイズネットワークの多項式時間での学習とサンプル複雑性（Learning discrete Bayesian networks in polynomial time and sample complexity）</news:title>
   <news:publication_date>2026-04-16T01:36:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679825</loc>
  <lastmod>2026-04-16T01:36:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スタイル集約型ネットワークによる顔ランドマーク検出の頑健化（Style Aggregated Network for Facial Landmark Detection）</news:title>
   <news:publication_date>2026-04-16T01:36:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679823</loc>
  <lastmod>2026-04-16T01:36:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>末端利用者の測定だけで系統を学ぶ：配電網のトポロジーとパラメータ推定（Learning with End-Users in Distribution Grids: Topology and Parameter Estimation）</news:title>
   <news:publication_date>2026-04-16T01:36:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679813</loc>
  <lastmod>2026-04-16T00:44:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>長期安全学習型MPCのための経験推薦手法（Experience Recommendation for Long Term Safe Learning-based Model Predictive Control in Changing Operating Conditions）</news:title>
   <news:publication_date>2026-04-16T00:44:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679811</loc>
  <lastmod>2026-04-16T00:44:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>二面体拡大の類数公式（CLASS NUMBER FORMULA FOR DIHEDRAL EXTENSIONS）</news:title>
   <news:publication_date>2026-04-16T00:44:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679809</loc>
  <lastmod>2026-04-16T00:44:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>擬似タスク増強：深層マルチタスク学習からイントラタスク共有へ（Pseudo-task Augmentation: From Deep Multitask Learning to Intratask Sharing—and Back）</news:title>
   <news:publication_date>2026-04-16T00:44:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679807</loc>
  <lastmod>2026-04-16T00:43:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>乳がん組織画像分類の二段階畳み込みニューラルネットワーク（Two-Stage Convolutional Neural Network for Breast Cancer Histology Image Classification）</news:title>
   <news:publication_date>2026-04-16T00:43:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679805</loc>
  <lastmod>2026-04-16T00:43:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動的グラフにおける表現学習（Representation Learning over Dynamic Graphs）</news:title>
   <news:publication_date>2026-04-16T00:43:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679803</loc>
  <lastmod>2026-04-16T00:43:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>画像品質から学習する局所歪み可視性（Learning Local Distortion Visibility from Image Quality）</news:title>
   <news:publication_date>2026-04-16T00:43:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679801</loc>
  <lastmod>2026-04-16T00:42:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>海洋ロボット航行のための部分構造化環境動態学習（Learning Partially Structured Environmental Dynamics for Marine Robotic Navigation）</news:title>
   <news:publication_date>2026-04-16T00:42:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679799</loc>
  <lastmod>2026-04-15T23:50:50Z</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“体積”を最大化するPCAの新解釈（PCA by Determinant Optimization has no Spurious Local Optima）</news:title>
   <news:publication_date>2026-04-15T23:50:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679797</loc>
  <lastmod>2026-04-15T23:50:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層分類器を『ダークナレッジ』で可視化する方法（Interpreting Deep Classifiers by Visual Distillation of Dark Knowledge）</news:title>
   <news:publication_date>2026-04-15T23:50:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679795</loc>
  <lastmod>2026-04-15T23:49:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>不確かなラベルから学ぶ多インスタンスChoquet積分による分類・回帰（Multiple Instance Choquet Integral Classifier Fusion and Regression for Remote Sensing Applications）</news:title>
   <news:publication_date>2026-04-15T23:49:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679793</loc>
  <lastmod>2026-04-15T23:49:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Entity ResolutionとFederated Learningの接点を解く（Entity Resolution and Federated Learning get a Federated Resolution）</news:title>
   <news:publication_date>2026-04-15T23:49:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679791</loc>
  <lastmod>2026-04-15T23:49:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>WaveNetを用いた売上予測（Sales forecasting using WaveNet within the framework of the Kaggle competition）</news:title>
   <news:publication_date>2026-04-15T23:49:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679789</loc>
  <lastmod>2026-04-15T23:48:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層辞書学習による階層合成ネットワークアプローチ（Deep Dictionary Learning: A PARametric NETwork Approach）</news:title>
   <news:publication_date>2026-04-15T23:48:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679787</loc>
  <lastmod>2026-04-15T23:48:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>組合せ多目的マルチアームバンディット問題（Combinatorial Multi-Objective Multi-Armed Bandit Problem）</news:title>
   <news:publication_date>2026-04-15T23:48:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679785</loc>
  <lastmod>2026-04-15T22:57:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>接触モデルをデータで強化する剛体シミュレーション（Data-Augmented Contact Model for Rigid Body Simulation）</news:title>
   <news:publication_date>2026-04-15T22:57:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679783</loc>
  <lastmod>2026-04-15T22:56:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>類似情報を持つマルチ目的文脈バンディット問題（Multi-objective Contextual Bandit Problem with Similarity Information）</news:title>
   <news:publication_date>2026-04-15T22:56:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679781</loc>
  <lastmod>2026-04-15T22:55:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>線形コンパクトベクトル空間上のフローのコランク（The corank of a flow over the category of linearly compact vector spaces）</news:title>
   <news:publication_date>2026-04-15T22:55:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679779</loc>
  <lastmod>2026-04-15T22:55:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>相関するマルコフ環境下のバンディット問題を読み解く（Bandits for Correlated Markovian Environments）</news:title>
   <news:publication_date>2026-04-15T22:55:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679777</loc>
  <lastmod>2026-04-15T22:54:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>弱い相互作用をもつ関数の経験的評価境界（Empirical bounds for functions with weak interactions）</news:title>
   <news:publication_date>2026-04-15T22:54:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679775</loc>
  <lastmod>2026-04-15T22:54:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>IDS向け機械学習への慢性型中毒攻撃（BEBP: An Poisoning Method Against Machine Learning Based IDSs）</news:title>
   <news:publication_date>2026-04-15T22:54:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679773</loc>
  <lastmod>2026-04-15T22:54:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ソフトロバストActor-Criticによる方策勾配の新展開（Soft-Robust Actor-Critic Policy-Gradient）</news:title>
   <news:publication_date>2026-04-15T22:54:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679771</loc>
  <lastmod>2026-04-15T22:02:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>経路ベースのカーネルブースティングによるサンプル分類（A pathway-based kernel boosting method for sample classification using genomic data）</news:title>
   <news:publication_date>2026-04-15T22:02:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679769</loc>
  <lastmod>2026-04-15T21:54:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多変量時系列における非線形因果検出とスパース加法モデル（Detecting Nonlinear Causality in Multivariate Time Series with Sparse Additive Models）</news:title>
   <news:publication_date>2026-04-15T21:54:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679767</loc>
  <lastmod>2026-04-15T21:54:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時系列データのための深層強化学習：理想化されたトレーディングゲームを解く（Deep reinforcement learning for time series: playing idealized trading games）</news:title>
   <news:publication_date>2026-04-15T21:54:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679765</loc>
  <lastmod>2026-04-15T21:54:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>バイリンガルな実用的色参照の生成（Generating Bilingual Pragmatic Color References）</news:title>
   <news:publication_date>2026-04-15T21:54:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679763</loc>
  <lastmod>2026-04-15T21:53:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>HabEx向けボルテックスコロナグラフの理論性能と望遠鏡要件（Vortex coronagraphs for the Habitable Exoplanet Imaging Mission）</news:title>
   <news:publication_date>2026-04-15T21:53:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679761</loc>
  <lastmod>2026-04-15T21:53:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ベルウェザーを使った転移学習で良い設定を見つける（Transfer Learning with Bellwethers to find Good Configurations）</news:title>
   <news:publication_date>2026-04-15T21:53:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679759</loc>
  <lastmod>2026-04-15T21:52:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>区分的凸関数推定とモデル選択（Piecewise Convex Function Estimation and Model Selection）</news:title>
   <news:publication_date>2026-04-15T21:52:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679757</loc>
  <lastmod>2026-04-15T21:00:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>未知の刺激を含む分数階動力学における最小センシングの同定と選択（Dealing with Unknown Unknowns: Identification and Selection of Minimal Sensing for Fractional Dynamics with Unknown Inputs）</news:title>
   <news:publication_date>2026-04-15T21:00:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679755</loc>
  <lastmod>2026-04-15T20:52:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>知識支援による一貫性で弱教師ありフレーズグラウンディングを強化する（Knowledge Aided Consistency for Weakly Supervised Phrase Grounding）</news:title>
   <news:publication_date>2026-04-15T20:52:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679753</loc>
  <lastmod>2026-04-15T20:52:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層特徴再構築による単眼深度推定と視覚オドメトリの教師なし学習 (Unsupervised Learning of Monocular Depth Estimation and Visual Odometry with Deep Feature Reconstruction)</news:title>
   <news:publication_date>2026-04-15T20:52:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679751</loc>
  <lastmod>2026-04-15T20:52:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スパース表現による敵対的攻撃への対抗（COMBATING ADVERSARIAL ATTACKS USING SPARSE REPRESENTATIONS）</news:title>
   <news:publication_date>2026-04-15T20:52:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679749</loc>
  <lastmod>2026-04-15T20:50:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>敵対的サンプル検出のためのNeural Fingerprinting（Detecting Adversarial Examples using Neural Fingerprinting）</news:title>
   <news:publication_date>2026-04-15T20:50:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679747</loc>
  <lastmod>2026-04-15T20:50:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチクラス不均衡学習における動的アンサンブル選択とデータ前処理の実践的検討（On dynamic ensemble selection and data preprocessing for multi-class imbalance learning）</news:title>
   <news:publication_date>2026-04-15T20:50:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679745</loc>
  <lastmod>2026-04-15T20:50:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層ニューラルネットワークのテスト基準と実践的意義（Testing Deep Neural Networks）</news:title>
   <news:publication_date>2026-04-15T20:50:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679743</loc>
  <lastmod>2026-04-15T19:58:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ベンガル語–英語の混在ツイートで単語レベルの言語判別を行う手法（Language Identification of Bengali-English Code-Mixed data using Character &amp;amp; Phonetic based LSTM Models）</news:title>
   <news:publication_date>2026-04-15T19:58:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679741</loc>
  <lastmod>2026-04-15T19:57:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ノイズだらけのウェブ画像から学ぶためのカテゴリレベル監視（Learning from Noisy Web Data with Category-level Supervision）</news:title>
   <news:publication_date>2026-04-15T19:57:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679739</loc>
  <lastmod>2026-04-15T19:57:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>体積OCTデータからの姿勢推定に対する深層学習アプローチ（A Deep Learning Approach for Pose Estimation from Volumetric OCT Data）</news:title>
   <news:publication_date>2026-04-15T19:57:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679737</loc>
  <lastmod>2026-04-15T19:56:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>QCDのハドロン共鳴ガス相の探索（Exploring the hadron resonance gas phase on the QCD phase diagram）</news:title>
   <news:publication_date>2026-04-15T19:56:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679735</loc>
  <lastmod>2026-04-15T19:56:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>キックスターティングで深層強化学習を加速する方法（Kickstarting Deep Reinforcement Learning）</news:title>
   <news:publication_date>2026-04-15T19:56:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679733</loc>
  <lastmod>2026-04-15T19:56:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>視覚場面における音源の局所化を学習する（Learning to Localize Sound Source in Visual Scenes）</news:title>
   <news:publication_date>2026-04-15T19:56:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679731</loc>
  <lastmod>2026-04-15T19:55:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分解可能サブモジュラ関数の最小化とインシデンス関係の再検討（Revisiting Decomposable Submodular Function Minimization with Incidence Relations）</news:title>
   <news:publication_date>2026-04-15T19:55:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679729</loc>
  <lastmod>2026-04-15T19:04:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>シンボリック表現のベクトル化を学習する方法（LEARNING AND ANALYZING VECTOR ENCODING OF SYMBOLIC REPRESENTATIONS）</news:title>
   <news:publication_date>2026-04-15T19:04:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679727</loc>
  <lastmod>2026-04-15T19:03:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>サブモジュラハイパーグラフとp-ラプラシアンによるスペクトラルクラスタリング（Submodular Hypergraphs: p-Laplacians, Cheeger Inequalities and Spectral Clustering）</news:title>
   <news:publication_date>2026-04-15T19:03:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679725</loc>
  <lastmod>2026-04-15T19:03:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>重み付きグラフに対する差分プライバシー下のクラスタリング（Graph-based Clustering under Differential Privacy）</news:title>
   <news:publication_date>2026-04-15T19:03:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679723</loc>
  <lastmod>2026-04-15T19:03:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Bi1−xSbx合金における磁場誘起Weyl半金属状態の拡張観測（Observation of Chiral character deep in the topological insulating regime in Bi1−xSbx）</news:title>
   <news:publication_date>2026-04-15T19:03:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679721</loc>
  <lastmod>2026-04-15T19:02:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散ブロックチェーンを用いたIoT異常検知の協調フレームワーク（CIoTA: Collaborative IoT Anomaly Detection via Blockchain）</news:title>
   <news:publication_date>2026-04-15T19:02:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679719</loc>
  <lastmod>2026-04-15T19:02:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>eコマース需要予測を改善する深層学習モデルの実務解説（AR-MDN: Associative and Recurrent Mixture Density Networks for eRetail Demand Forecasting）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/679717</loc>
  <lastmod>2026-04-15T19:01:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>コードリポジトリから学ぶクイックフィックス（Learning Quick Fixes from Code Repositories）</news:title>
   <news:publication_date>2026-04-15T19:01:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679715</loc>
  <lastmod>2026-04-15T18:10:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模データから小規模データへ知識を移す方法：Deep Cross-media Knowledge Transfer（Deep Cross-media Knowledge Transfer）</news:title>
   <news:publication_date>2026-04-15T18:10:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679713</loc>
  <lastmod>2026-04-15T18:10:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層学習の表現力と汎化性—深層ネットの理論的優位性の解明（Generalization and Expressivity for Deep Nets）</news:title>
   <news:publication_date>2026-04-15T18:10:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679711</loc>
  <lastmod>2026-04-15T18:10:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Driving Scene Perception Network（Driving Scene Perception Network: Real-time Joint Detection, Depth Estimation and Semantic Segmentation）</news:title>
   <news:publication_date>2026-04-15T18:10:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679709</loc>
  <lastmod>2026-04-15T18:09:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散ネットワークの衝撃（VARIANCE NETWORKS: WHEN EXPECTATION DOES NOT MEET YOUR EXPECTATIONS）</news:title>
   <news:publication_date>2026-04-15T18:09:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679707</loc>
  <lastmod>2026-04-15T18:09:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>有限要素法によるMatérn確率場を生成するSPDEの解法（How to solve the stochastic partial differential equation that gives a Matérn random field using the finite element method）</news:title>
   <news:publication_date>2026-04-15T18:09:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679705</loc>
  <lastmod>2026-04-15T18:09:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>音声認識：画像認識を利用したキーワードスポッティング（Speech Recognition: Key Word Spotting through Image Recognition）</news:title>
   <news:publication_date>2026-04-15T18:09:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679703</loc>
  <lastmod>2026-04-15T18:08:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>条件差分推定へのミニマックス代替損失法（A Minimax Surrogate Loss Approach to Conditional Difference Estimation）</news:title>
   <news:publication_date>2026-04-15T18:08:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679701</loc>
  <lastmod>2026-04-15T17:17:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Newton: クロスバー加速の物理限界に迫る設計（Newton: Gravitating Towards the Physical Limits of Crossbar Acceleration）</news:title>
   <news:publication_date>2026-04-15T17:17:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679699</loc>
  <lastmod>2026-04-15T17:16:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>破産予測モデルの識別能力に対する事象比率の影響（Influence of the Event Rate on Discrimination Abilities of Bankruptcy Prediction Models）</news:title>
   <news:publication_date>2026-04-15T17:16:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679697</loc>
  <lastmod>2026-04-15T17:15:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層マルチタスクネットワークの進化的アーキテクチャ探索（Evolutionary Architecture Search For Deep Multitask Networks）</news:title>
   <news:publication_date>2026-04-15T17:15:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679695</loc>
  <lastmod>2026-04-15T17:15:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>人混みに溶け込むロボットの航法学習（DeepMoTIon: Learning to Navigate Like Humans）</news:title>
   <news:publication_date>2026-04-15T17:15:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679693</loc>
  <lastmod>2026-04-15T17:15:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>注意に基づくグラフニューラルネットワーク（Attention-based Graph Neural Network for Semi-supervised Learning）</news:title>
   <news:publication_date>2026-04-15T17:15:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679691</loc>
  <lastmod>2026-04-15T17:14:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>知識ベースモデルと機械学習を組み合わせるハイブリッド予測（Hybrid Forecasting of Chaotic Processes: Using Machine Learning in Conjunction with a Knowledge-Based Model）</news:title>
   <news:publication_date>2026-04-15T17:14:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679689</loc>
  <lastmod>2026-04-15T17:13:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>石英結晶共振器における電極配列と電気的散逸（Electrode configuration and electrical dissipation of mechanical energy in quartz crystal resonators）</news:title>
   <news:publication_date>2026-04-15T17:13:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679687</loc>
  <lastmod>2026-04-15T16:23:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スパイク時刻に含まれる情報：ポリクロノアス群から導かれる神経符号（On the information in spike timing: neural codes derived from polychronous groups）</news:title>
   <news:publication_date>2026-04-15T16:23:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679685</loc>
  <lastmod>2026-04-15T16:22:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数の外的条件を考慮したJoint PLDAのスコアリング手法（Scoring Formulation for Multi-Condition Joint PLDA）</news:title>
   <news:publication_date>2026-04-15T16:22:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679683</loc>
  <lastmod>2026-04-15T16:22:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Bit-Tacticalが示した「無駄な計算」を狙う設計革新（Bit-Tactical: Exploiting Ineffectual Computations in Convolutional Neural Networks: Which, Why, and How）</news:title>
   <news:publication_date>2026-04-15T16:22:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679681</loc>
  <lastmod>2026-04-15T16:21:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>滑らかな関数のモジュロ1サンプルの頑健な推定と位相展開への応用（Provably robust estimation of modulo 1 samples of a smooth function with applications to phase unwrapping）</news:title>
   <news:publication_date>2026-04-15T16:21:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679679</loc>
  <lastmod>2026-04-15T16:21:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>競争的機械学習：理論上の最良予測が競争で最良を意味しない理由（Competitive Machine Learning: Best Theoretical Prediction vs Optimization）</news:title>
   <news:publication_date>2026-04-15T16:21:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679677</loc>
  <lastmod>2026-04-15T16:20:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>持続景観のためのノンパラメトリックリスク評価と密度推定（Nonparametric Risk Assessment and Density Estimation for Persistence Landscapes）</news:title>
   <news:publication_date>2026-04-15T16:20:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679675</loc>
  <lastmod>2026-04-15T16:20:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>逐次データにおける増分決定木に基づく異常検知（Sequential Outlier Detection based on Incremental Decision Trees）</news:title>
   <news:publication_date>2026-04-15T16:20:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679673</loc>
  <lastmod>2026-04-15T15:28:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>格子基底と立ち上がり波による高速ガウス過程近似（Standing Wave Decomposition Gaussian Process）</news:title>
   <news:publication_date>2026-04-15T15:28:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679671</loc>
  <lastmod>2026-04-15T15:27:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ランダム初期化ネットワークに潜む勝ち馬（The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks）</news:title>
   <news:publication_date>2026-04-15T15:27:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679669</loc>
  <lastmod>2026-04-15T15:27:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マグマだまりの力学とCO2フラックシングの影響（Mechanics of magma chamber with implication to the effect of CO2 fluxing）</news:title>
   <news:publication_date>2026-04-15T15:27:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679667</loc>
  <lastmod>2026-04-15T15:26:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時間別類似性に基づく時間毎太陽放射予測（Hourly-Similarity Based Solar Forecasting Using Multi-Model Machine Learning Blending）</news:title>
   <news:publication_date>2026-04-15T15:26:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679665</loc>
  <lastmod>2026-04-15T15:26:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチメディア鑑定から学ぶ敵対的例の検出（Detecting Adversarial Examples – A Lesson from Multimedia Forensics）</news:title>
   <news:publication_date>2026-04-15T15:26:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679663</loc>
  <lastmod>2026-04-15T15:26:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>凸計画を用いた敵対的事例生成について（On Generation of Adversarial Examples using Convex Programming）</news:title>
   <news:publication_date>2026-04-15T15:26:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679661</loc>
  <lastmod>2026-04-15T15:25:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Ehrhart理論によるプライバシーと忠実度のトレードオフ (The Trade-off between Privacy and Fidelity via Ehrhart Theory)</news:title>
   <news:publication_date>2026-04-15T15:25:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679659</loc>
  <lastmod>2026-04-15T14:34:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>クォークとグルーオンのジェット断面による重イオン衝突の解明（Probing heavy ion collisions using quark and gluon jet substructure）</news:title>
   <news:publication_date>2026-04-15T14:34:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679657</loc>
  <lastmod>2026-04-15T14:33:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ヒルベルト第6問題：厳密性への果てしない道（Hilbert’s Sixth Problem: the endless road to rigour）</news:title>
   <news:publication_date>2026-04-15T14:33:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679655</loc>
  <lastmod>2026-04-15T14:33:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所的な冷凍障害（quenched disorder）を学習する手法とその意義（Learning local, quenched disorder in plasticity and other crackling noise phenomena）</news:title>
   <news:publication_date>2026-04-15T14:33:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679653</loc>
  <lastmod>2026-04-15T14:32:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>逆空間スパース表現を用いた統合的腫瘍分類フレームワーク（An Integrated Inverse Space Sparse Representation Framework for Tumor Classification）</news:title>
   <news:publication_date>2026-04-15T14:32:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679651</loc>
  <lastmod>2026-04-15T14:32:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>都市規模の電子カルテが示す薬物相互作用の年齢・性差バイアス（City-wide Electronic Health Records Reveal Gender and Age Biases in Administration of Known Drug-Drug Interactions）</news:title>
   <news:publication_date>2026-04-15T14:32:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679649</loc>
  <lastmod>2026-04-15T14:32:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>チェレンコフ望遠鏡アレイとKM3ニュートリノ望遠鏡が示す広がった源の検出可能性（On the potential of Cherenkov Telescope Arrays and KM3 Neutrino Telescopes for the detection of extended sources）</news:title>
   <news:publication_date>2026-04-15T14:32:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679647</loc>
  <lastmod>2026-04-15T14:32:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>脆弱道路利用者の意図検出と予測（Intentions of Vulnerable Road Users – Detection and Forecasting by Means of Machine Learning）</news:title>
   <news:publication_date>2026-04-15T14:32:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679645</loc>
  <lastmod>2026-04-15T13:40:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>音声認証における同型暗号によるテンプレート保護（Homomorphic Encryption for Speaker Recognition: Protection of Biometric Templates and Vendor Model Parameters）</news:title>
   <news:publication_date>2026-04-15T13:40:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679643</loc>
  <lastmod>2026-04-15T13:39:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ブラックボックスAndroidマルウェア検出の説明（Explaining Black-box Android Malware Detection）</news:title>
   <news:publication_date>2026-04-15T13:39:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679641</loc>
  <lastmod>2026-04-15T13:38:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所化された深層学習を実現するニューラルネットの構築（Construction of neural networks for realization of localized deep learning）</news:title>
   <news:publication_date>2026-04-15T13:38:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679639</loc>
  <lastmod>2026-04-15T13:38:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自転車の発進動作協調検出（Cooperative Starting Movement Detection of Cyclists Using Convolutional Neural Networks and a Boosted Stacking Ensemble）</news:title>
   <news:publication_date>2026-04-15T13:38:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679637</loc>
  <lastmod>2026-04-15T13:38:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Graph-based Implicit Feedbackを用いた協調フィルタリング（Collaborative Filtering with Graph-based Implicit Feedback）</news:title>
   <news:publication_date>2026-04-15T13:38:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679635</loc>
  <lastmod>2026-04-15T13:38:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>空間概念と語彙モデルの改善されたスケーラブルなオンライン学習（Improved and Scalable Online Learning of Spatial Concepts and Language Models with Mapping）</news:title>
   <news:publication_date>2026-04-15T13:38:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679633</loc>
  <lastmod>2026-04-15T13:37:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>知識・情報・主体性を評価するマルチエージェント強化学習：スマートビルディングの事例研究（Valuing knowledge, information and agency in Multi-agent Reinforcement Learning: a case study in smart buildings）</news:title>
   <news:publication_date>2026-04-15T13:37:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679631</loc>
  <lastmod>2026-04-15T12:46:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高度に自律化された学習による歩行者等の安全性改善（Highly Automated Learning for Improved Active Safety of Vulnerable Road Users）</news:title>
   <news:publication_date>2026-04-15T12:46:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679629</loc>
  <lastmod>2026-04-15T12:46:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>視覚ベースの自己位置推定とオドメトリを同時に学習する（Deep Auxiliary Learning for Visual Localization and Odometry）</news:title>
   <news:publication_date>2026-04-15T12:46:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679627</loc>
  <lastmod>2026-04-15T12:45:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>物体に対する把持動作の自動分類（Automated Classification of Hand-grip action on Objects using Machine Learning）</news:title>
   <news:publication_date>2026-04-15T12:45:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679625</loc>
  <lastmod>2026-04-15T12:44:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的セミスムース・ニュートン法による非滑らか非凸最適化（A Stochastic Semismooth Newton Method for Nonsmooth Nonconvex Optimization）</news:title>
   <news:publication_date>2026-04-15T12:44:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679623</loc>
  <lastmod>2026-04-15T12:44:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>注意型オートエンコーダを用いた教師なし質問検索モデル（An Unsupervised Model with Attention Autoencoders for Question Retrieval）</news:title>
   <news:publication_date>2026-04-15T12:44:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679621</loc>
  <lastmod>2026-04-15T12:44:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>RippleNetによる推薦の革新（RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems）</news:title>
   <news:publication_date>2026-04-15T12:44:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679619</loc>
  <lastmod>2026-04-15T12:44:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Malytics: マルウェア検出スキームの要点と実務的意義 (Malytics: A Malware Detection Scheme)</news:title>
   <news:publication_date>2026-04-15T12:44:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679617</loc>
  <lastmod>2026-04-15T11:52:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>偽情報の初期拡散は本物とどう違うか（Fake news propagate differently from real news even at early stages of spreading）</news:title>
   <news:publication_date>2026-04-15T11:52:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679615</loc>
  <lastmod>2026-04-15T11:52:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自然発想の適応型フォグアーキテクチャ（Adaptive Nature-inspired Fog Architecture）</news:title>
   <news:publication_date>2026-04-15T11:52:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679613</loc>
  <lastmod>2026-04-15T11:51:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動的問題のためのベイズ最適化（Bayesian Optimization for Dynamic Problems）</news:title>
   <news:publication_date>2026-04-15T11:51:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679611</loc>
  <lastmod>2026-04-15T11:51:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>視覚から触感を推定する深層学習（Deep Visuo-Tactile Learning: Estimation of Tactile Properties from Images）</news:title>
   <news:publication_date>2026-04-15T11:51:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679609</loc>
  <lastmod>2026-04-15T11:50:24Z</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で解くフーリエパイティグラフィー（Solving Fourier ptychographic imaging problems via neural network modeling and TensorFlow）</news:title>
   <news:publication_date>2026-04-15T11:50:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679607</loc>
  <lastmod>2026-04-15T11:50:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>地上PM2.5のリアルタイムかつシームレスな監視（REAL-TIME AND SEAMLESS MONITORING OF GROUND-LEVEL PM2.5 USING SATELLITE REMOTE SENSING）</news:title>
   <news:publication_date>2026-04-15T11:50:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679605</loc>
  <lastmod>2026-04-15T11:49:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層的畳み込み特徴の融合による人体セグメンテーションとファッション分類（Fusing Hierarchical Convolutional Features for Human Body Segmentation and Clothing Fashion Classification）</news:title>
   <news:publication_date>2026-04-15T11:49:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679603</loc>
  <lastmod>2026-04-15T10:58:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>低精度で高精度を実現する学習法（High-Accuracy Low-Precision Training）</news:title>
   <news:publication_date>2026-04-15T10:58:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679601</loc>
  <lastmod>2026-04-15T10:58:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>クロスビュー画像合成の条件付きGANによるアプローチ（Cross-View Image Synthesis using Conditional GANs）</news:title>
   <news:publication_date>2026-04-15T10:58:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679599</loc>
  <lastmod>2026-04-15T10:57:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>離散潜在変数を用いた系列モデルの高速デコーディング（Fast Decoding in Sequence Models Using Discrete Latent Variables）</news:title>
   <news:publication_date>2026-04-15T10:57:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679597</loc>
  <lastmod>2026-04-15T10:56:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>近似推論ネットワークによる学習（Learning Approximate Inference Networks for Structured Prediction）</news:title>
   <news:publication_date>2026-04-15T10:56:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679595</loc>
  <lastmod>2026-04-15T10:56:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>判別的事前分布を学習してブラインド画像復元を強化する（Learning a Discriminative Prior for Blind Image Deblurring）</news:title>
   <news:publication_date>2026-04-15T10:56:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679593</loc>
  <lastmod>2026-04-15T10:56:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所学習埋め込みと異種書誌ネットワークによるエキスパート検索の革新（Expert Finding in Heterogeneous Bibliographic Networks with Locally-trained Embeddings）</news:title>
   <news:publication_date>2026-04-15T10:56:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679591</loc>
  <lastmod>2026-04-15T10:55:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層認識損失を用いたニューラル細粒度エンティティ型分類（Neural Fine-Grained Entity Type Classification with Hierarchy-Aware Loss）</news:title>
   <news:publication_date>2026-04-15T10:55:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679589</loc>
  <lastmod>2026-04-15T10:03:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>タスク特化視覚注目予測のためのメモリ拡張条件付き生成対抗ネットワーク（Task Specific Visual Saliency Prediction with Memory Augmented Conditional Generative Adversarial Networks）</news:title>
   <news:publication_date>2026-04-15T10:03:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679587</loc>
  <lastmod>2026-04-15T10:03:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>予測による追跡：複数人の局在化と追跡のための深層生成モデル（Tracking by Prediction: A Deep Generative Model for Multi-Person localisation and Tracking）</news:title>
   <news:publication_date>2026-04-15T10:03:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679585</loc>
  <lastmod>2026-04-15T10:03:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数層かつ複数条件のデータ統合を扱う確率モデルの設計（Joint Multiple Multi-layered Gaussian Graphical Models）</news:title>
   <news:publication_date>2026-04-15T10:03:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679583</loc>
  <lastmod>2026-04-15T10:02:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>外観変換による困難条件下でのロバストな計測位置特定（Adversarial Training for Adverse Conditions: Robust Metric Localisation using Appearance Transfer）</news:title>
   <news:publication_date>2026-04-15T10:02:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679581</loc>
  <lastmod>2026-04-15T10:02:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層意味情報を用いた顔画像の手ぶれ除去（Deep Semantic Face Deblurring）</news:title>
   <news:publication_date>2026-04-15T10:02:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679579</loc>
  <lastmod>2026-04-15T10:01:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>顧客満足度評価におけるポジティビティバイアスの検出（Positivity Bias in Customer Satisfaction Ratings）</news:title>
   <news:publication_date>2026-04-15T10:01:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679577</loc>
  <lastmod>2026-04-15T10:01:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>次元ロバストなMCMCによるベイズ逆問題の効率化（Dimension-Robust MCMC in Bayesian Inverse Problems）</news:title>
   <news:publication_date>2026-04-15T10:01:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679575</loc>
  <lastmod>2026-04-15T09:10:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>MVRチェーン図の性質（On the Properties of MVR Chain Graphs）</news:title>
   <news:publication_date>2026-04-15T09:10:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679573</loc>
  <lastmod>2026-04-15T09:10:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>BOCANetによるハードウェアオブフュスケーション攻撃の変革（Deep RNN-Oriented Paradigm Shift through BOCANet: Broken Obfuscated Circuit Attack）</news:title>
   <news:publication_date>2026-04-15T09:10:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679571</loc>
  <lastmod>2026-04-15T09:09:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SVDDを用いたハイパースペクトルデータ解析のための新しいカーネル帯域幅選定基準（A New Bandwidth Selection Criterion for Using SVDD to Analyze Hyperspectral Data）</news:title>
   <news:publication_date>2026-04-15T09:09:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679569</loc>
  <lastmod>2026-04-15T09:08:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>異常バイオマーカー濃縮のための統合機械学習パイプライン（Integrated machine learning pipeline for aberrant biomarker enrichment）</news:title>
   <news:publication_date>2026-04-15T09:08:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679567</loc>
  <lastmod>2026-04-15T09:08:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>極端な分類問題におけるグラフ上の効率的な損失基準デコーディング（Efficient Loss-Based Decoding on Graphs for Extreme Classification）</news:title>
   <news:publication_date>2026-04-15T09:08:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679565</loc>
  <lastmod>2026-04-15T09:08:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフの深層生成モデルの学習 (Learning Deep Generative Models of Graphs)</news:title>
   <news:publication_date>2026-04-15T09:08:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679563</loc>
  <lastmod>2026-04-15T09:07:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時系列異常検知における精度・再現率の再定義（Precision and Recall for Time Series）</news:title>
   <news:publication_date>2026-04-15T09:07:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679561</loc>
  <lastmod>2026-04-15T08:15:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>報酬で促進される運動学習モデル――皮質と基底核の並列経路による学習（A model of reward-modulated motor learning with parallel cortical and basal ganglia pathways）</news:title>
   <news:publication_date>2026-04-15T08:15:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679559</loc>
  <lastmod>2026-04-15T08:15:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Kepler-78 と超短周期惑星の発見が示すもの（Kepler-78 and the Ultra-Short-Period Planets）</news:title>
   <news:publication_date>2026-04-15T08:15:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679557</loc>
  <lastmod>2026-04-15T08:14:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>埋め込み層は本当に最適解か——Deep Metric Learningにおける一般化の再検討 (Generalization in Metric Learning: Should the Embedding Layer be Embedding Layer?)</news:title>
   <news:publication_date>2026-04-15T08:14:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679555</loc>
  <lastmod>2026-04-15T08:14:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>近傍の矮小銀河における周囲銀河塵の検出と意味（Cold Circumgalactic Dust in Nearby Dwarf Galaxies）</news:title>
   <news:publication_date>2026-04-15T08:14:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679553</loc>
  <lastmod>2026-04-15T08:14:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GONetによるトラバーサビリティ推定の半教師あり学習（GONet: A Semi-Supervised Deep Learning Approach For Traversability Estimation）</news:title>
   <news:publication_date>2026-04-15T08:14:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679551</loc>
  <lastmod>2026-04-15T08:13:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>アクティブラーニングによる効率的な相図サンプリング（Efficient Phase Diagram Sampling by Active Learning）</news:title>
   <news:publication_date>2026-04-15T08:13:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679549</loc>
  <lastmod>2026-04-15T08:13:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>通信スケジューリングで分散深層学習を加速する（TicTac: Accelerating Distributed Deep Learning with Communication Scheduling）</news:title>
   <news:publication_date>2026-04-15T08:13:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679547</loc>
  <lastmod>2026-04-15T07:21:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Domain Adaptive Faster R-CNNによる現場適応型物体検出（Domain Adaptive Faster R-CNN for Object Detection in the Wild）</news:title>
   <news:publication_date>2026-04-15T07:21:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679545</loc>
  <lastmod>2026-04-15T07:21:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>近似的メトリック公平性とPACF学習の要点（Probably Approximately Correct and Fair Learning）</news:title>
   <news:publication_date>2026-04-15T07:21:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679543</loc>
  <lastmod>2026-04-15T07:20:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>外れ値に強い回帰の効率的アルゴリズム（Efficient Algorithms for Outlier-Robust Regression）</news:title>
   <news:publication_date>2026-04-15T07:20:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679541</loc>
  <lastmod>2026-04-15T07:19:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>近似到達可能性の分類器ベース手法（A Classification-based Approach for Approximate Reachability）</news:title>
   <news:publication_date>2026-04-15T07:19:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679539</loc>
  <lastmod>2026-04-15T07:19:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>連続的対称性の破れを持つモデルの最適化改善（Improving Optimization for Models With Continuous Symmetry Breaking）</news:title>
   <news:publication_date>2026-04-15T07:19:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679537</loc>
  <lastmod>2026-04-15T07:19:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>計算資源制約下の意識を通した公平性（Fairness Through Computationally-Bounded Awareness）</news:title>
   <news:publication_date>2026-04-15T07:19:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679535</loc>
  <lastmod>2026-04-15T07:18:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模領域における対話管理のための封建的強化学習（Feudal Reinforcement Learning for Dialogue Management in Large Domains）</news:title>
   <news:publication_date>2026-04-15T07:18:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679533</loc>
  <lastmod>2026-04-15T06:27:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SVMによるソフトウェア欠陥予測——コードスメルを使った実証的アプローチ（Predicting Software Defects Through SVM: An Empirical Approach）</news:title>
   <news:publication_date>2026-04-15T06:27:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679531</loc>
  <lastmod>2026-04-15T06:26:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>計算核物理における深層学習の応用（Deep Learning: A Tool for Computational Nuclear Physics）</news:title>
   <news:publication_date>2026-04-15T06:26:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679529</loc>
  <lastmod>2026-04-15T06:26:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層学習で地震位相を自動検出する革新（PhaseNet: A Deep-Neural-Network-Based Seismic Arrival Time Picking Method）</news:title>
   <news:publication_date>2026-04-15T06:26:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679527</loc>
  <lastmod>2026-04-15T06:25:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>安全な多剤併用（ポリファーマシー）への薬剤推薦手法（Drug Recommendation toward Safe Polypharmacy）</news:title>
   <news:publication_date>2026-04-15T06:25:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679525</loc>
  <lastmod>2026-04-15T06:25:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>入力‑出力のトレードオフを用いたアグリゲーション（Aggregation using input-output trade-off）</news:title>
   <news:publication_date>2026-04-15T06:25:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679523</loc>
  <lastmod>2026-04-15T06:25:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>IM-ROのための影響モデルパラメータ推定に対するベイズと機械学習アプローチ (A Bayesian and Machine Learning approach to estimating Influence Model parameters for IM-RO)</news:title>
   <news:publication_date>2026-04-15T06:25:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679521</loc>
  <lastmod>2026-04-15T06:24:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>規則優先分類器の学習（Learning Rules-First Classifiers）</news:title>
   <news:publication_date>2026-04-15T06:24:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679519</loc>
  <lastmod>2026-04-15T05:32:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>プライベート深層学習のための人工データ生成（Generating Artificial Data for Private Deep Learning）</news:title>
   <news:publication_date>2026-04-15T05:32:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679517</loc>
  <lastmod>2026-04-15T05:32:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>敵対的生成で実現する物理層の自動設計（Physical Layer Communications System Design Over-the-Air Using Adversarial Networks）</news:title>
   <news:publication_date>2026-04-15T05:32:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679515</loc>
  <lastmod>2026-04-15T05:32:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SentRNA：人間の設計戦略の事前知識を組み込んだ計算的RNA設計の改善 (SentRNA: Improving computational RNA design by incorporating a prior of human design strategies)</news:title>
   <news:publication_date>2026-04-15T05:32:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679513</loc>
  <lastmod>2026-04-15T05:31:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>決定論的重み付きケプストラム距離の適用性と解釈（Applicability and interpretation of the deterministic weighted cepstral distance）</news:title>
   <news:publication_date>2026-04-15T05:31:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679511</loc>
  <lastmod>2026-04-15T05:31:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ラベルなしデータを活用した群衆計数の学習（Leveraging Unlabeled Data for Crowd Counting by Learning to Rank）</news:title>
   <news:publication_date>2026-04-15T05:31:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679509</loc>
  <lastmod>2026-04-15T05:30:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>MACネットワークによる機械推論（COMPOSITIONAL ATTENTION NETWORKS FOR MACHINE REASONING）</news:title>
   <news:publication_date>2026-04-15T05:30:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679507</loc>
  <lastmod>2026-04-15T05:30:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習モデルによる強磁性相の組成最適化（Compositional optimization of hard-magnetic phases with machine-learning models）</news:title>
   <news:publication_date>2026-04-15T05:30:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679505</loc>
  <lastmod>2026-04-15T04:38:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチ仮説視覚慣性フローの提案（Multi-Hypothesis Visual-Inertial Flow）</news:title>
   <news:publication_date>2026-04-15T04:38:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679503</loc>
  <lastmod>2026-04-15T04:38:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>RGB-DとIMUを統合してスケール付き絶対軌跡を自己教師学習で推定する手法（Vision-Aided Absolute Trajectory Estimation Using an Unsupervised Deep Network with Online Error Correction）</news:title>
   <news:publication_date>2026-04-15T04:38:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679501</loc>
  <lastmod>2026-04-15T04:38:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層的ヒューリスティック学習による効率的な規範形成（Hierarchical Heuristic Learning towards Efficient Norm Emergence）</news:title>
   <news:publication_date>2026-04-15T04:38:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679499</loc>
  <lastmod>2026-04-15T04:37:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニッケル基超合金の設計を変えた機械学習の一手（Design of a nickel-base superalloy using a neural network）</news:title>
   <news:publication_date>2026-04-15T04:37:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679497</loc>
  <lastmod>2026-04-15T04:36:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>情報フローで結ばれる能動粒子（Active Particles Bound by Information Flows）</news:title>
   <news:publication_date>2026-04-15T04:36:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679495</loc>
  <lastmod>2026-04-15T04:36:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>意味関係を保つゼロショット学習の設計（Preserving Semantic Relations for Zero-Shot Learning）</news:title>
   <news:publication_date>2026-04-15T04:36:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679493</loc>
  <lastmod>2026-04-15T04:36:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>落入領域における銀河の星形成停止（The quench of the star formation in galaxies in the infall region of Abell 85）</news:title>
   <news:publication_date>2026-04-15T04:36:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679491</loc>
  <lastmod>2026-04-15T03:45:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>詐欺ICO識別のための深層学習システム（IcoRating: A Deep-Learning System for Scam ICO Identification）</news:title>
   <news:publication_date>2026-04-15T03:45:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679489</loc>
  <lastmod>2026-04-15T03:44:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SA-IGAによる社会的最適化を目指す強化学習（SA-IGA: A Multiagent Reinforcement Learning Method Towards Socially Optimal Outcomes）</news:title>
   <news:publication_date>2026-04-15T03:44:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679487</loc>
  <lastmod>2026-04-15T03:44:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>クロスドメイン推薦と深層ドメイン適応（Cross-domain Recommendation via Deep Domain Adaptation）</news:title>
   <news:publication_date>2026-04-15T03:44:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679485</loc>
  <lastmod>2026-04-15T03:43:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>制御認識スケジューリングのための深層強化学習（DEEPCAS: A Deep Reinforcement Learning Algorithm for Control-Aware Scheduling）</news:title>
   <news:publication_date>2026-04-15T03:43:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679483</loc>
  <lastmod>2026-04-15T03:43:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深いネットワークで学ぶ効果的な二値視覚表現（Learning Effective Binary Visual Representations with Deep Networks）</news:title>
   <news:publication_date>2026-04-15T03:43:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679481</loc>
  <lastmod>2026-04-15T03:43:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>一次導出のメタ学習アルゴリズム（On First-Order Meta-Learning Algorithms）</news:title>
   <news:publication_date>2026-04-15T03:43:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679479</loc>
  <lastmod>2026-04-15T03:43:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時系列データの潜在因子を切り分ける：Disentangled Sequential Autoencoder（Disentangled Sequential Autoencoder）</news:title>
   <news:publication_date>2026-04-15T03:43:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679477</loc>
  <lastmod>2026-04-15T02:51:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ソフトなペアワイズ類似度を用いた改良深層ハッシュ法（Improved Deep Hashing with Soft Pairwise Similarity for Multi-label Image Retrieval）</news:title>
   <news:publication_date>2026-04-15T02:51:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679475</loc>
  <lastmod>2026-04-15T02:50:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>損失関数における特徴分布の再考（Rethinking Feature Distribution for Loss Functions in Image Classification）</news:title>
   <news:publication_date>2026-04-15T02:50:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679473</loc>
  <lastmod>2026-04-15T02:50:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SLADS-Netによる動的サンプリングの実務的意義（SLADS-Net: Supervised Learning Approach for Dynamic Sampling using Deep Neural Networks）</news:title>
   <news:publication_date>2026-04-15T02:50:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679471</loc>
  <lastmod>2026-04-15T02:49:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層ネットワークの近似境界に関するいくつかの考察 (Some Approximation Bounds for Deep Networks)</news:title>
   <news:publication_date>2026-04-15T02:49:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679469</loc>
  <lastmod>2026-04-15T02:49:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多目的深層強化学習フレームワーク（A Multi-Objective Deep Reinforcement Learning Framework）</news:title>
   <news:publication_date>2026-04-15T02:49:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679467</loc>
  <lastmod>2026-04-15T02:49:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層モデルの学習：臨界点と局所開放性（Learning Deep Models: Critical Points and Local Openness）</news:title>
   <news:publication_date>2026-04-15T02:49:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679465</loc>
  <lastmod>2026-04-15T02:48:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>社会から個へ：クラウドソーシング評価の多層モデルによる簡潔な分解（From Social to Individuals: a Parsimonious Path of Multi-level Models for Crowdsourced Preference Aggregation）</news:title>
   <news:publication_date>2026-04-15T02:48:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679461</loc>
  <lastmod>2026-04-15T01:56:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ダブルチーム戦略を学ぶ深層強化学習（The Advantage of Doubling: A Deep Reinforcement Learning Approach to Studying the Double Team in the NBA）</news:title>
   <news:publication_date>2026-04-15T01:56:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679459</loc>
  <lastmod>2026-04-15T01:56:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフ上の区分的に滑らかな信号の多重解像度表現（Multiresolution Representations for Piecewise-Smooth Signals on Graphs）</news:title>
   <news:publication_date>2026-04-15T01:56:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679457</loc>
  <lastmod>2026-04-15T01:55:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>顧客対応に“心”を届けるトーン対応チャットボット（Touch Your Heart: A Tone-aware Chatbot for Customer Care on Social Media）</news:title>
   <news:publication_date>2026-04-15T01:55:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679455</loc>
  <lastmod>2026-04-15T01:55:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DBpedia n-triplesによる質問から回答への翻訳（Translating Questions into Answers using DBPedia n-triples）</news:title>
   <news:publication_date>2026-04-15T01:55:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679453</loc>
  <lastmod>2026-04-15T01:55:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>補間限界における確率的・分散的勾配降下法の高速収束（Fast Convergence for Stochastic and Distributed Gradient Descent in the Interpolation Limit）</news:title>
   <news:publication_date>2026-04-15T01:55:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679451</loc>
  <lastmod>2026-04-15T01:54:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>N-ヘテロ環カルベン配位子を持つイリジウム(III)錯体の非放射減衰と安定性（Non-radiative decay and stability of N-heterocyclic carbene iridium(III) complexes）</news:title>
   <news:publication_date>2026-04-15T01:54:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679449</loc>
  <lastmod>2026-04-15T01:54:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ネットワーク上で分布の平均（Wassersteinバリセンター）を合意的に求める手法（Distributed Computation of Wasserstein Barycenters over Networks）</news:title>
   <news:publication_date>2026-04-15T01:54:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679447</loc>
  <lastmod>2026-04-15T01:03:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>正方格子上の双極子ハイゼンベルグ模型に対する繰り込み群解析（Renormalization group analysis of dipolar Heisenberg model on square lattice）</news:title>
   <news:publication_date>2026-04-15T01:03:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679445</loc>
  <lastmod>2026-04-15T01:03:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>量子支援クラスタリング分析（Quantum-assisted cluster analysis）</news:title>
   <news:publication_date>2026-04-15T01:03:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679443</loc>
  <lastmod>2026-04-15T01:03:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文の表現学習を効率化する枠組み（AN EFFICIENT FRAMEWORK FOR LEARNING SENTENCE REPRESENTATIONS）</news:title>
   <news:publication_date>2026-04-15T01:03:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679433</loc>
  <lastmod>2026-04-15T01:01:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>集合間相互作用の深層モデル（Deep Models of Interactions Across Sets）</news:title>
   <news:publication_date>2026-04-15T01:01:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679431</loc>
  <lastmod>2026-04-15T01:01:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>WNGradによる学習率自動調整がもたらす実務的インパクト（WNGrad: Learn the Learning Rate in Gradient Descent）</news:title>
   <news:publication_date>2026-04-15T01:01:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679429</loc>
  <lastmod>2026-04-15T01:01:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時間重視のバンディット学習における満足解探索（Satisﬁcing in Time-Sensitive Bandit Learning）</news:title>
   <news:publication_date>2026-04-15T01:01:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679427</loc>
  <lastmod>2026-04-15T01:01:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>トポロジカルフォトニクスの逆問題を機械学習で解く（Machine Learning Inverse Problem for Topological Photonics）</news:title>
   <news:publication_date>2026-04-15T01:01:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679425</loc>
  <lastmod>2026-04-15T00:09:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>RNNと単語埋め込みに現れる代数構造の発見（The emergent algebraic structure of RNNs and embeddings in NLP）</news:title>
   <news:publication_date>2026-04-15T00:09:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679423</loc>
  <lastmod>2026-04-15T00:09:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>巨大な中心銀河のサイズと星の殻は環境（ダークマターハロー）に依存する（A Detection of the Environmental Dependence of the Sizes and Stellar Haloes of Massive Central Galaxies）</news:title>
   <news:publication_date>2026-04-15T00:09:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679421</loc>
  <lastmod>2026-04-15T00:08:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>模倣か革新か：空間的社会ジレンマゲームにおける戦略更新態度の競合（Imitate or innovate: competition of strategy updating attitudes in spatial social dilemma games）</news:title>
   <news:publication_date>2026-04-15T00:08:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679419</loc>
  <lastmod>2026-04-15T00:07:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-04-15T00:07:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
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 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層強化学習の高速化手法（Accelerated Methods for Deep Reinforcement Learning）</news:title>
   <news:publication_date>2026-04-15T00:07:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-04-14T23:15:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-04-14T23:15:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-04-14T23:14:15Z</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>Transfer Neural AutoMLの本質と経営への示唆（Transfer Learning with Neural AutoML）</news:title>
   <news:publication_date>2026-04-14T23:13:22Z</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>深層バックプロジェクションネットワークによる超解像（Deep Back-Projection Networks For Super-Resolution）</news:title>
   <news:publication_date>2026-04-14T23:13:14Z</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>望み水準に基づく摂動学習オートマタ（Aspiration-based Perturbed Learning Automata）</news:title>
   <news:publication_date>2026-04-14T23:13:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679401</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-04-14T23:12:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-04-14T22:21:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-04-14T22:20:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-04-14T22:20:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-04-14T22:19:07Z</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>
   </news:publication>
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   <news:publication_date>2026-04-14T22:18:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-04-14T22:18:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-04-14T21:27:06Z</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>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-04-14T21:24:39Z</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>目標指向の視覚運動計画を学習に基づき生成する手法（Generating Goal-Directed Visuomotor Plans Based on Learning Using a Predictive Coding-type Deep Visuomotor Recurrent Neural Network Model）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-04-14T20:33:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-04-14T20:32:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-04-14T20:32:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679363</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>ピラミッド人物照合ネットワークによる再識別の革新（Pyramid Person Matching Network for Person Re-identification）</news:title>
   <news:publication_date>2026-04-14T20:32:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複雑な意味経路を利用したメタグラフ埋め込み（MetaGraph2Vec: Complex Semantic Path Augmented Heterogeneous Network Embedding）</news:title>
   <news:publication_date>2026-04-14T19:29:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/679345</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>オンラインピア評価にHodgeRankを使う意義（An Application of HodgeRank to Online Peer Assessment）</news:title>
   <news:publication_date>2026-04-14T18:37:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/679343</loc>
  <lastmod>2026-04-14T18:36:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>有向グラフ上の線形最適化アルゴリズムと幾何学的収束（A linear algorithm for optimization over directed graphs with geometric convergence）</news:title>
   <news:publication_date>2026-04-14T18:36:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-04-14T18:36:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>公平な分類への削減アプローチ（A Reductions Approach to Fair Classification）</news:title>
   <news:publication_date>2026-04-14T18:36:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/679339</loc>
  <lastmod>2026-04-14T18:36:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>視線計測を用いた認知負荷理論の検証（Use of Eye-Tracking Technology to Investigate Cognitive Load Theory）</news:title>
   <news:publication_date>2026-04-14T18:36:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/679337</loc>
  <lastmod>2026-04-14T18:36:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>敵対的摂動に強い複数カーネルk平均クラスタリング（Robust Multiple Kernel k-means Clustering using Min-Max Optimization）</news:title>
   <news:publication_date>2026-04-14T18:36:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
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 <url>
  <loc>https://aibr.jp/archives/679335</loc>
  <lastmod>2026-04-14T18:35:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>不連続性に敏感な最適制御学習（Discontinuity-Sensitive Optimal Control Learning by Mixture of Experts）</news:title>
   <news:publication_date>2026-04-14T18:35:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679333</loc>
  <lastmod>2026-04-14T17:44:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非線形次元削減と線形スムージングの統一理論（On Nonlinear Dimensionality Reduction, Linear Smoothing and Autoencoding）</news:title>
   <news:publication_date>2026-04-14T17:44:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
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  <loc>https://aibr.jp/archives/679331</loc>
  <lastmod>2026-04-14T17:43:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ノイズ下での誘導部分グラフ検出のためのマッチドフィルタ（Matched Filters for Noisy Induced Subgraph Detection）</news:title>
   <news:publication_date>2026-04-14T17:43:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679329</loc>
  <lastmod>2026-04-14T17:43:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>畳み込み特徴量のカテゴリカル混合モデルによる画像トピック発見（Categorical Mixture Models on VGGNet activations）</news:title>
   <news:publication_date>2026-04-14T17:43:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679327</loc>
  <lastmod>2026-04-14T17:43:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>音声分類のためのマスク付き条件付きニューラルネットワーク (Masked Conditional Neural Networks for Audio Classification)</news:title>
   <news:publication_date>2026-04-14T17:43:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679325</loc>
  <lastmod>2026-04-14T17:42:57Z</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によるタンパク質–リガンド評価の可視化（Visualizing Convolutional Neural Network Protein-Ligand Scoring）</news:title>
   <news:publication_date>2026-04-14T17:42:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679323</loc>
  <lastmod>2026-04-14T17:42:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>任意離散系列における異常検知のゼロ境界LSTM（Arbitrary Discrete Sequence Anomaly Detection with Zero Boundary LSTM）</news:title>
   <news:publication_date>2026-04-14T17:42:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679321</loc>
  <lastmod>2026-04-14T17:42:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>セントーラス銀河群の矮小銀河距離測定に関するTRGB法の適用（Tip of the red giant branch distances to the dwarf galaxies dw1335-29 and dw1340-30 in the Centaurus group）</news:title>
   <news:publication_date>2026-04-14T17:42:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679319</loc>
  <lastmod>2026-04-14T16:51:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>定常時系列の次元削減のための確率的非凸最適化（Dimensionality Reduction for Stationary Time Series via Stochastic Nonconvex Optimization）</news:title>
   <news:publication_date>2026-04-14T16:51:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679317</loc>
  <lastmod>2026-04-14T16:42:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>小さく速い予測器を学ぶ：SMaLLの考え方と実務的意義（Learning SMaLL Predictors）</news:title>
   <news:publication_date>2026-04-14T16:42:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679315</loc>
  <lastmod>2026-04-14T16:42:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>フロンティアフィールド銀河のKormendy関係が示すもの（The Kormendy Relation of Galaxies in the Frontier Fields Clusters: Abell S1063 and MACS J1149.5+2223）</news:title>
   <news:publication_date>2026-04-14T16:42:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679313</loc>
  <lastmod>2026-04-14T16:41:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>メモリアクセスパターンの学習による高効率プリフェッチ（Learning Memory Access Patterns）</news:title>
   <news:publication_date>2026-04-14T16:41:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679311</loc>
  <lastmod>2026-04-14T16:41:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>近接サーマルイメージングによる材料認識（Deep Thermal Imaging: Proximate Material Type Recognition in the Wild through Deep Learning of Spatial Surface Temperature Patterns）</news:title>
   <news:publication_date>2026-04-14T16:41:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679309</loc>
  <lastmod>2026-04-14T16:40:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>胸部X線画像のマルチラベル分類手法の比較（Comparison of Deep Learning Approaches for Multi-Label Chest X-Ray Classification）</news:title>
   <news:publication_date>2026-04-14T16:40:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679307</loc>
  <lastmod>2026-04-14T16:40:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Deep Super Learnerが示す現実的な「深層化」戦略（Deep Super Learner: A Deep Ensemble for Classification Problems）</news:title>
   <news:publication_date>2026-04-14T16:40:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679305</loc>
  <lastmod>2026-04-14T15:49:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的モデルベース強化学習によるニューラルネットワーク制御器の合成 (Synthesizing Neural Network Controllers with Probabilistic Model-Based Reinforcement Learning)</news:title>
   <news:publication_date>2026-04-14T15:49:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679303</loc>
  <lastmod>2026-04-14T15:49:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単眼カメラだけで動きを学ぶ――密な3Dフローからの視覚オドメトリと密3Dマッピング（Learning monocular visual odometry with dense 3D mapping from dense 3D flow）</news:title>
   <news:publication_date>2026-04-14T15:49:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679301</loc>
  <lastmod>2026-04-14T15:48:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ゼロショット図式-画像ハッシング（Zero-Shot Sketch-Image Hashing）</news:title>
   <news:publication_date>2026-04-14T15:48:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679299</loc>
  <lastmod>2026-04-14T15:47:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GeoNet: 動画から深度・オプティカルフロー・カメラ姿勢を共同で学習する手法（GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose）</news:title>
   <news:publication_date>2026-04-14T15:47:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679297</loc>
  <lastmod>2026-04-14T15:47:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>低ダイナミックレンジ画像から高ダイナミックレンジを再構築するExpandNet（ExpandNet: A Deep Convolutional Neural Network for High Dynamic Range Expansion from Low Dynamic Range Content）</news:title>
   <news:publication_date>2026-04-14T15:47:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679295</loc>
  <lastmod>2026-04-14T15:47:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>目標志向のエンドツーエンド対話システムと生成型応答（An End-to-End Goal-Oriented Dialog System with a Generative Natural Language Response Generation）</news:title>
   <news:publication_date>2026-04-14T15:47:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/679293</loc>
  <lastmod>2026-04-14T15:46:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>個人化された露出制御（Personalized Exposure Control Using Adaptive Metering and Reinforcement Learning）</news:title>
   <news:publication_date>2026-04-14T15:46:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
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