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   <news:title>Gradient-Boosted Treesを組み込んだ混合整数凸非線形最適化（Mixed-Integer Convex Nonlinear Optimization with Gradient-Boosted Trees Embedded）</news:title>
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    <news:name>AI Benchmark Research</news:name>
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   <news:title>全てのサンプルが同じではない：重要度サンプリングによる深層学習（Deep Learning with Importance Sampling）</news:title>
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   <news:title>ベイズ逆問題とモデル検証の「ブラックボックス脱却」（Beyond black-boxes in Bayesian inverse problems and model validation: applications in solid mechanics of elastography）</news:title>
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   <news:title>少ない質問で精度と近似解を両立する半教師ありk-means（Semi-Supervised Algorithms for Approximately Optimal and Accurate Clustering）</news:title>
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   <news:title>分散優先経験再生（Distributed Prioritized Experience Replay）</news:title>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>JPEG画像を敵対的攻撃から守る（Protecting JPEG Images Against Adversarial Attacks）</news:title>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>急変に強いスパース同定による高速モデル復元（Sparse Identification of Nonlinear Dynamics for Rapid Model Recovery）</news:title>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>ビッグデータに潜むバイアスの影響（Impact of Biases in Big Data）</news:title>
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  <lastmod>2026-04-13T13:23:22Z</lastmod>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>フィードバック頂点集合問題のパラメータ化アルゴリズムの実験的評価（Experimental Evaluation of Parameterized Algorithms for Feedback Vertex Set）</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>構文情報を組み込む言語モデルの実践的意義（Syntax-Aware Language Modeling with Recurrent Neural Networks）</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>ニューラルネットの損失地形に関する衝撃的な発見（Essentially No Barriers in Neural Network Energy Landscape）</news:title>
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    <news:language>ja</news:language>
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   <news:title>確率的ニューラルネットワークによるモリブデン系合金設計（Probabilistic design of a molybdenum-base alloy using a neural network）</news:title>
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   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/678902</loc>
  <lastmod>2026-04-13T13:21:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>2Dハイブリッド有機無機ペロブスカイトの欠陥変異に基づく深青色発光ダイオード（Deep-blue light emitting diode based on defect variations of a 2D hybrid organic-inorganic low dimensional perovskite semiconductor）</news:title>
   <news:publication_date>2026-04-13T13:21:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/678899</loc>
  <lastmod>2026-04-13T12:29:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>勾配に基づくサンプリングによる最小二乗解の高速化（Gradient-based Sampling: An Adaptive Importance Sampling for Least-squares）</news:title>
   <news:publication_date>2026-04-13T12:29:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/678897</loc>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>鉛直せん断水平流（VSHF）形成不安定性の解析（Vertically Sheared Horizontal Flow-Forming Instability in Stratified Turbulence）</news:title>
   <news:publication_date>2026-04-13T12:19:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/678895</loc>
  <lastmod>2026-04-13T12:18:49Z</lastmod>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>インターネット上の低品質音声からの声のクローン化の可能性（Can we steal your vocal identity from the Internet?: Initial investigation of cloning Obama’s voice using GAN, WaveNet and low-quality found data）</news:title>
   <news:publication_date>2026-04-13T12:18:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-04-13T12:18:03Z</lastmod>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>非常に質量の大きいウルフ・ライエ星Mk 34の155日X線サイクル（The 155-day X-ray cycle of the very massive Wolf-Rayet star Melnick 34 in the Large Magellanic Cloud）</news:title>
   <news:publication_date>2026-04-13T12:18:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-04-13T12:17:50Z</lastmod>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>Deep Cocktail Networkによるマルチソース非教師ありドメイン適応とカテゴリシフト (Deep Cocktail Network: Multi-source Unsupervised Domain Adaptation with Category Shift)</news:title>
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   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/678889</loc>
  <lastmod>2026-04-13T12:17:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>DREAMによる姿勢頑健な顔認識（Pose-Robust Face Recognition via Deep Residual Equivariant Mapping）</news:title>
   <news:publication_date>2026-04-13T12:17:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/678887</loc>
  <lastmod>2026-04-13T12:17:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>多インスタンス深層ニューラルネットワークによるヒッグスCP測定の要点解説（A multi-instance deep neural network classifier: application to Higgs boson CP measurement）</news:title>
   <news:publication_date>2026-04-13T12:17:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/678885</loc>
  <lastmod>2026-04-13T11:25:43Z</lastmod>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>多変量線形回帰における非因果的アーティファクトの検出（Detecting non-causal artifacts in multivariate linear regression models）</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>ランダムウォークで学ぶグラフ生成の第一歩：NetGAN（NetGAN: Generating Graphs via Random Walks）</news:title>
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   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/678881</loc>
  <lastmod>2026-04-13T11:24:57Z</lastmod>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>照明変化画像系列を用いた教師なし単一画像の内在的分解（Unsupervised Deep Single-Image Intrinsic Decomposition using Illumination-Varying Image Sequences）</news:title>
   <news:publication_date>2026-04-13T11:24:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/678879</loc>
  <lastmod>2026-04-13T11:24:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>21cm線と宇宙マイクロ波背景のクロスパワー解析が示す実務的示唆（Study of systematics effects on the Cross Power Spectrum of 21 cm Line and Cosmic Microwave Background using Murchison Widefield Array Data）</news:title>
   <news:publication_date>2026-04-13T11:24:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <lastmod>2026-04-13T11:24:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>目標空間の無教師学習による自発的目標探索（Unsupervised Learning of Goal Spaces for Intrinsically Motivated Goal Exploration）</news:title>
   <news:publication_date>2026-04-13T11:24:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/678875</loc>
  <lastmod>2026-04-13T11:23:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>デジタルロックを機械学習へ導く—勾配ブースティングと深層ニューラルネットワークによる透過率予測（Driving Digital Rock towards Machine Learning: predicting permeability with Gradient Boosting and Deep Neural Networks）</news:title>
   <news:publication_date>2026-04-13T11:23:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/678873</loc>
  <lastmod>2026-04-13T11:23:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スパース複数カーネル学習：ミラー・ストラティファビリティによるサポート同定（Sparse Multiple Kernel Learning: Support Identification via Mirror Stratiﬁability）</news:title>
   <news:publication_date>2026-04-13T11:23:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
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  <loc>https://aibr.jp/archives/678871</loc>
  <lastmod>2026-04-13T10:32:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>畳み込み幾何行列補完（Convolutional Geometric Matrix Completion）</news:title>
   <news:publication_date>2026-04-13T10:32:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
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 <url>
  <loc>https://aibr.jp/archives/678869</loc>
  <lastmod>2026-04-13T10:31:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>量子回路学習の実務的意義（Quantum Circuit Learning）</news:title>
   <news:publication_date>2026-04-13T10:31:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678867</loc>
  <lastmod>2026-04-13T10:30:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>臨床的に意義ある時系列比較の新手法（Clinically Meaningful Comparisons Over Time: An Approach to Measuring Patient Similarity based on Subsequence Alignment）</news:title>
   <news:publication_date>2026-04-13T10:30:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/678865</loc>
  <lastmod>2026-04-13T10:30:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自然言語から構造化クエリ生成のメタ学習（Natural Language to Structured Query Generation via Meta-Learning）</news:title>
   <news:publication_date>2026-04-13T10:30:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/678863</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>Eコマース検索におけるランキング制御を強化学習で最適化する手法（Reinforcement Learning to Rank in E-Commerce Search Engine: Formalization, Analysis, and Application）</news:title>
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   <news:genres>Blog</news:genres>
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 <url>
  <loc>https://aibr.jp/archives/678861</loc>
  <lastmod>2026-04-13T10:29:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次データ構造へのPCAの拡張（Extension of PCA to Higher Order Data Structures: An Introduction to Tensors, Tensor Decompositions, and Tensor PCA）</news:title>
   <news:publication_date>2026-04-13T10:29:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/678859</loc>
  <lastmod>2026-04-13T09:38:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>生波形での多チャネル音源分離を実現する多解像度畳み込みオートエンコーダ（Raw Multi-Channel Audio Source Separation using Multi-Resolution Convolutional Auto-Encoders）</news:title>
   <news:publication_date>2026-04-13T09:38:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678857</loc>
  <lastmod>2026-04-13T09:37:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>検索ランキングを高速化する文脈的特徴選択（Accelerating E-Commerce Search Engine Ranking by Contextual Factor Selection）</news:title>
   <news:publication_date>2026-04-13T09:37:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678855</loc>
  <lastmod>2026-04-13T09:37:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>疎ビューCTのシノグラム合成に基づく深層ニューラルネットワーク（Deep-neural-network based sinogram synthesis for sparse-view CT image reconstruction）</news:title>
   <news:publication_date>2026-04-13T09:37:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678853</loc>
  <lastmod>2026-04-13T09:36:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Autostackerが切り拓くAutoMLの柔軟性（Autostacker: A Compositional Evolutionary Learning System）</news:title>
   <news:publication_date>2026-04-13T09:36:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678851</loc>
  <lastmod>2026-04-13T09:36:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ガウスノイズを加えた行列の特異部分空間摂動に関する最適推定（MATRICES WITH GAUSSIAN NOISE: OPTIMAL ESTIMATES FOR SINGULAR SUBSPACE PERTURBATION）</news:title>
   <news:publication_date>2026-04-13T09:36:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678849</loc>
  <lastmod>2026-04-13T09:36:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>UAVを用いた無線ネットワークの総合チュートリアル（A Tutorial on UAVs for Wireless Networks: Applications, Challenges, and Open Problems）</news:title>
   <news:publication_date>2026-04-13T09:36:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678847</loc>
  <lastmod>2026-04-13T09:36:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチモーダル検索のための相関を抑えたハッシュ符号学習（Learning Decorrelated Hashing Codes for Multimodal Retrieval）</news:title>
   <news:publication_date>2026-04-13T09:36:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678845</loc>
  <lastmod>2026-04-13T08:44:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>セミ教師ありメタラーニングで少数ショット学習を拡張する（META-LEARNING FOR SEMI-SUPERVISED FEW-SHOT CLASSIFICATION）</news:title>
   <news:publication_date>2026-04-13T08:44:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678843</loc>
  <lastmod>2026-04-13T08:43:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CEDMとFFDMを橋渡しするSD-CNN（Shallow-Deep Convolutional Neural Network）</news:title>
   <news:publication_date>2026-04-13T08:43:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678841</loc>
  <lastmod>2026-04-13T08:42:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>進化的生成対抗ネットワーク（Evolutionary Generative Adversarial Networks）</news:title>
   <news:publication_date>2026-04-13T08:42:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678839</loc>
  <lastmod>2026-04-13T08:42:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>半パラメトリック位相記憶によるナビゲーション（SEMI-PARAMETRIC TOPOLOGICAL MEMORY FOR NAVIGATION）</news:title>
   <news:publication_date>2026-04-13T08:42:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678837</loc>
  <lastmod>2026-04-13T08:42:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>静的および動的ロバストPCAと行列補完のレビュー（Static and Dynamic Robust PCA and Matrix Completion: A Review）</news:title>
   <news:publication_date>2026-04-13T08:42:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678835</loc>
  <lastmod>2026-04-13T08:41:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高レベルドメイン特化言語による量子コンピューティングの拡張（Q#: Enabling scalable quantum computing and development with a high-level domain–specific language）</news:title>
   <news:publication_date>2026-04-13T08:41:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678833</loc>
  <lastmod>2026-04-13T08:41:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>宇宙機クラスタの軌道決定におけるカーネル埋め込み手法（Kernel Embedding Approaches to Orbit Determination of Spacecraft Clusters）</news:title>
   <news:publication_date>2026-04-13T08:41:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678831</loc>
  <lastmod>2026-04-13T07:50:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ビットコイン上のポンジ・スキーム検出のためのデータマイニング（Data mining for detecting Bitcoin Ponzi schemes）</news:title>
   <news:publication_date>2026-04-13T07:50:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678829</loc>
  <lastmod>2026-04-13T07:50:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所的リサンプリング偽造検出（Resampling Forgery Detection Using Deep Learning and A-Contrario Analysis）</news:title>
   <news:publication_date>2026-04-13T07:50:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678827</loc>
  <lastmod>2026-04-13T07:49:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Bregman関数とその発散についての再検討（RE-EXAMINATION OF BREGMAN FUNCTIONS AND NEW PROPERTIES OF THEIR DIVERGENCES）</news:title>
   <news:publication_date>2026-04-13T07:49:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678825</loc>
  <lastmod>2026-04-13T07:48:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層的な模倣学習と強化学習の統合がもたらす実務的インパクト（Hierarchical Imitation and Reinforcement Learning）</news:title>
   <news:publication_date>2026-04-13T07:48:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678823</loc>
  <lastmod>2026-04-13T07:48:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>塵に隠れた超新星を赤外で見つけた意義（SPIRITS 16tn in NGC 3556）</news:title>
   <news:publication_date>2026-04-13T07:48:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678821</loc>
  <lastmod>2026-04-13T07:48:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>リッチ観測下のオラクル効率的PAC強化学習（On Oracle-Efficient PAC RL with Rich Observations）</news:title>
   <news:publication_date>2026-04-13T07:48:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678819</loc>
  <lastmod>2026-04-13T07:48:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Shinyを用いた応答面最適化ゲームの現代的更新（A shiny update to an old experiment game）</news:title>
   <news:publication_date>2026-04-13T07:48:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678817</loc>
  <lastmod>2026-04-13T06:56:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ワイル半金属における光起電効果の第一原理研究（Photogalvanic Effect in Weyl Semimetals from First Principles）</news:title>
   <news:publication_date>2026-04-13T06:56:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678815</loc>
  <lastmod>2026-04-13T06:55:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>光干渉計の遠隔教育ウェブサイト（An Educational Website on Interferometry）</news:title>
   <news:publication_date>2026-04-13T06:55:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678813</loc>
  <lastmod>2026-04-13T06:55:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>半教師ありオンライン構造学習による複合イベント認識の実用化（Semi-Supervised Online Structure Learning for Composite Event Recognition）</news:title>
   <news:publication_date>2026-04-13T06:55:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678811</loc>
  <lastmod>2026-04-13T06:54:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>計算最適輸送の数値的展開（Computational Optimal Transport）</news:title>
   <news:publication_date>2026-04-13T06:54:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678809</loc>
  <lastmod>2026-04-13T06:54:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>再構成可能なマニピュレータ・シミュレータ（Aaria: A Reconfigurable Manipulator Simulator）</news:title>
   <news:publication_date>2026-04-13T06:54:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678807</loc>
  <lastmod>2026-04-13T06:54:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スキルミオンと反スキルミオンの軌道運動と対生成（Trochoidal motion and pair generation in skyrmion and antiskyrmion dynamics under spin-orbit torques）</news:title>
   <news:publication_date>2026-04-13T06:54:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678805</loc>
  <lastmod>2026-04-13T06:53:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Alpha-Beta対称ダイバージェンスと正定値カーネルの意義（The Alpha-Beta-Symmetric Divergence and their Positive Definite Kernels）</news:title>
   <news:publication_date>2026-04-13T06:53:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678803</loc>
  <lastmod>2026-04-13T06:02:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>属性に基づく合成的計画（Composable Planning with Attributes）</news:title>
   <news:publication_date>2026-04-13T06:02:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678801</loc>
  <lastmod>2026-04-13T06:02:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ベクトル埋め込みの機能性と次元性の理解（Understand Functionality and Dimensionality of Vector Embeddings: the Distributional Hypothesis, the Pairwise Inner Product Loss and Its Bias–Variance Trade-off）</news:title>
   <news:publication_date>2026-04-13T06:02:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678799</loc>
  <lastmod>2026-04-13T06:01:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ストリーミング環境におけるオンライン特徴量ランキング（ONLINE FEATURE RANKING FOR INTRUSION DETECTION SYSTEMS）</news:title>
   <news:publication_date>2026-04-13T06:01:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678797</loc>
  <lastmod>2026-04-13T06:01:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多層グラフクラスタリングのためのパワーミーンラプラシアン（The Power Mean Laplacian for Multilayer Graph Clustering）</news:title>
   <news:publication_date>2026-04-13T06:01:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678795</loc>
  <lastmod>2026-04-13T06:00:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>異種な買い手行動下での頑健な反復オークション（Robust Repeated Auctions under Heterogeneous Buyer Behavior）</news:title>
   <news:publication_date>2026-04-13T06:00:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678793</loc>
  <lastmod>2026-04-13T06:00:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時空間スパイクパターンの最適局所・分散符号化（Optimal localist and distributed coding of spatiotemporal spike patterns through STDP and coincidence detection）</news:title>
   <news:publication_date>2026-04-13T06:00:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678791</loc>
  <lastmod>2026-04-13T06:00:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ノイズと疎なWebマークアップにおける欠損カテゴリ情報の推定（Inferring Missing Categorical Information in Noisy and Sparse Web Markup）</news:title>
   <news:publication_date>2026-04-13T06:00:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678789</loc>
  <lastmod>2026-04-13T05:09:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非パラメトリック時空間サブゴールモデルによる逆強化学習 (Inverse Reinforcement Learning via Nonparametric Spatio-Temporal Subgoal Modeling)</news:title>
   <news:publication_date>2026-04-13T05:09:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678787</loc>
  <lastmod>2026-04-13T05:08:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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    <news:name>AI Benchmark Research</news:name>
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   <news:genres>Blog</news:genres>
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 <url>
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    <news:name>AI Benchmark Research</news:name>
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 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
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   <news:genres>Blog</news:genres>
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 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
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   <news:genres>Blog</news:genres>
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 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 <url>
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    <news:name>AI Benchmark Research</news:name>
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   <news:genres>Blog</news:genres>
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 <url>
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    <news:name>AI Benchmark Research</news:name>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-04-13T04:14:18Z</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>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
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   <news:genres>Blog</news:genres>
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 <url>
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    <news:name>AI Benchmark Research</news:name>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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    <news:name>AI Benchmark Research</news:name>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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 </url>
 <url>
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  <news:news>
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 </url>
 <url>
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 </url>
 <url>
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  <news:news>
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 </url>
 <url>
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  <news:news>
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 </url>
 <url>
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 </url>
 <url>
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  <news:news>
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 </url>
 <url>
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  <news:news>
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    <news:name>AI Benchmark Research</news:name>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 </url>
 <url>
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  <news:news>
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 </url>
 <url>
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  <news:news>
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 </url>
 <url>
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  <news:news>
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    <news:name>AI Benchmark Research</news:name>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 </url>
 <url>
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  <news:news>
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    <news:name>AI Benchmark Research</news:name>
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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>
  </news:news>
 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news: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>2026-04-13T00:32:12Z</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>
   </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:title>協調距離学習を用いた映画向け推薦システム（Collaborative Metric Learning Recommendation System: Application to Theatrical Movie Releases）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>セミデフィニット計画の低ランク解に対するスムース分析（Smoothed analysis for low-rank solutions to semidefinite programs in quadratic penalty form）</news:title>
   <news:publication_date>2026-04-12T23:39:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>SG-MCMCとネットワークプルーニングによる疎構造アンサンブル学習（LEARNING SPARSE STRUCTURED ENSEMBLES WITH SG-MCMC AND NETWORK PRUNING）</news:title>
   <news:publication_date>2026-04-12T23:38:46Z</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>
   <news:title>コレントロピーに基づく回帰と重い裾のノイズモデル（Learning with Correntropy-induced Losses for Regression with Mixture of Symmetric Stable Noise）</news:title>
   <news:publication_date>2026-04-12T23:38:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>One-Class SVMに基づくクラス逐次学習法（A Class-Incremental Learning Method Based on One Class Support Vector Machine）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>逆思考をモデル化する機械学習（Modeling reverse thinking for machine learning）</news:title>
   <news:publication_date>2026-04-12T23:36:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>因果推論のための深層学習（Deep Learning for Causal Inference）</news:title>
   <news:publication_date>2026-04-12T22:35:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/678685</loc>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>トップN推薦における精度・新規性・カバレッジのトレードオフを扱う汎用フレームワーク（A Generic Top-N Recommendation Framework For Trading-off Accuracy, Novelty, and Coverage）</news:title>
   <news:publication_date>2026-04-12T22:34:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>RNNの長期依存性学習を助ける補助損失（Learning Longer-term Dependencies in RNNs with Auxiliary Losses）</news:title>
   <news:publication_date>2026-04-12T22:33:41Z</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>構造化光学受信機における誤差補償法（Error Correction in Structured Optical Receivers）</news:title>
   <news:publication_date>2026-04-12T22:33:27Z</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>2026-04-12T21:42:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ボイド群のネットワーク化は敵対的学習に対してより頑健である（Networking the Boids is More Robust Against Adversarial Learning）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>注意を教えるネットワーク: Guided Attention Inference Network（Tell Me Where to Look: Guided Attention Inference Network）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチスケール逆転による半教師あり学習の実装（Semi-Supervised Learning Enabled by Multiscale Deep Neural Network Inversion）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678667</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>脳腫瘍分類に挑むカプセルネットワーク（BRAIN TUMOR TYPE CLASSIFICATION VIA CAPSULE NETWORKS）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678665</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>ADMMベースのネットワーク化確率的変分推論（ADMM-based Networked Stochastic Variational Inference）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678663</loc>
  <lastmod>2026-04-12T20:47:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Twitterにおけるヘイトスピーチ検出──長尾（Long-tail）問題が解決を遠ざける理由（Hate Speech Detection - the Difficult Long-tail Case of Twitter）</news:title>
   <news:publication_date>2026-04-12T20:47:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678661</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>2026-04-12T20:47:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678659</loc>
  <lastmod>2026-04-12T20:46:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>構造変化の同定における教師なし機械学習法（Identifying structural changes with unsupervised machine learning methods）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678657</loc>
  <lastmod>2026-04-12T20:46:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Augmented CycleGANによる多対多写像の学習（Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data）</news:title>
   <news:publication_date>2026-04-12T20:46:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678655</loc>
  <lastmod>2026-04-12T20:46:18Z</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>2026-04-12T20:46:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678653</loc>
  <lastmod>2026-04-12T20:46:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>潜在空間物理学：流体の時間発展を学習する（Latent Space Physics: Towards Learning the Temporal Evolution of Fluid Flow）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <lastmod>2026-04-12T19:54:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>分散学習におけるビザンチン耐性SGDの一般化（Generalized Byzantine-tolerant SGD）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>弾性源イメージングのための深層学習の数学的枠組み (A Mathematical Framework for Deep Learning in Elastic Source Imaging)</news:title>
   <news:publication_date>2026-04-12T19:53:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678641</loc>
  <lastmod>2026-04-12T19:53:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>初期印刷書籍のOCR精度向上：事前学習・投票・能動学習の組合せ（Improving OCR Accuracy on Early Printed Books by combining Pretraining, Voting, and Active Learning）</news:title>
   <news:publication_date>2026-04-12T19:53:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678639</loc>
  <lastmod>2026-04-12T19:52:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>初期印刷本のOCR精度向上（Improving OCR Accuracy on Early Printed Books using Deep Convolutional Networks）</news:title>
   <news:publication_date>2026-04-12T19:52:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678637</loc>
  <lastmod>2026-04-12T19:02:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs（Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs）</news:title>
   <news:publication_date>2026-04-12T19:02:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678635</loc>
  <lastmod>2026-04-12T19:02:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>監視映像における顔認識のための深層学習アーキテクチャ（Deep Learning Architectures for Face Recognition in Video Surveillance）</news:title>
   <news:publication_date>2026-04-12T19:02:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678633</loc>
  <lastmod>2026-04-12T19:02:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>行動依存ベースラインの幻影（The Mirage of Action-Dependent Baselines in Reinforcement Learning）</news:title>
   <news:publication_date>2026-04-12T19:02:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678631</loc>
  <lastmod>2026-04-12T19:00:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>立体視画像のニューラルスタイル転写（Neural Stereoscopic Image Style Transfer）</news:title>
   <news:publication_date>2026-04-12T19:00:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678629</loc>
  <lastmod>2026-04-12T19:00:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高解像度3D物体表現のための多視点シルエットと深度分解（Multi-View Silhouette and Depth Decomposition for High Resolution 3D Object Representation）</news:title>
   <news:publication_date>2026-04-12T19:00:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678627</loc>
  <lastmod>2026-04-12T19:00:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層ネットワークにおけるスペクトル普遍性の出現（The Emergence of Spectral Universality in Deep Networks）</news:title>
   <news:publication_date>2026-04-12T19:00:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678625</loc>
  <lastmod>2026-04-12T19:00:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単眼カメラによる3次元複数物体追跡（Mono-Camera 3D Multi-Object Tracking Using Deep Learning Detections and PMBM Filtering）</news:title>
   <news:publication_date>2026-04-12T19:00:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678623</loc>
  <lastmod>2026-04-12T18:08:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>実世界における反復動作の推定（Real-World Repetition Estimation by Div, Grad and Curl）</news:title>
   <news:publication_date>2026-04-12T18:08:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678621</loc>
  <lastmod>2026-04-12T18:08:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>順列に基づくモデルで1/√nの壁を破る（Breaking the 1/√n Barrier: Faster Rates for Permutation-based Models in Polynomial Time）</news:title>
   <news:publication_date>2026-04-12T18:08:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678619</loc>
  <lastmod>2026-04-12T18:08:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>照明条件を考慮したマルチスペクトル融合で歩行者検出を強化する手法（Fusion of Multispectral Data Through Illumination-aware Deep Neural Networks for Pedestrian Detection）</news:title>
   <news:publication_date>2026-04-12T18:08:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678617</loc>
  <lastmod>2026-04-12T18:06:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>有害コメント分類のための畳み込みニューラルネットワーク（Convolutional Neural Networks for Toxic Comment Classification）</news:title>
   <news:publication_date>2026-04-12T18:06:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678615</loc>
  <lastmod>2026-04-12T18:06:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>走行データからのV2V遭遇シナリオ抽出（Extraction of V2V Encountering Scenarios from Naturalistic Driving Database）</news:title>
   <news:publication_date>2026-04-12T18:06:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678613</loc>
  <lastmod>2026-04-12T18:06:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>インスタンス最適復元と制限等長性（Instance Optimal Decoding and the Restricted Isometry Property）</news:title>
   <news:publication_date>2026-04-12T18:06:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678611</loc>
  <lastmod>2026-04-12T18:06:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>異なるラベル空間を横断する対（ペア）系列分類のマルチタスク学習（Multi-task Learning of Pairwise Sequence Classification Tasks Over Disparate Label Spaces）</news:title>
   <news:publication_date>2026-04-12T18:06:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678609</loc>
  <lastmod>2026-04-12T17:14:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>RDBMSにおける浮動小数点集計の再現性確保（Reproducible Floating-Point Aggregation in RDBMSs）</news:title>
   <news:publication_date>2026-04-12T17:14:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678607</loc>
  <lastmod>2026-04-12T17:13:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューラルネットワークによる相と相転移学習のパラメータ診断 (Parameter diagnostics of phases and phase transition learning by neural networks)</news:title>
   <news:publication_date>2026-04-12T17:13:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678605</loc>
  <lastmod>2026-04-12T17:13:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ENEM結果から見る物理学習の難点（Physics learning difficulties from the perspective of ENEM results）</news:title>
   <news:publication_date>2026-04-12T17:13:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678603</loc>
  <lastmod>2026-04-12T17:12:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>音声信号の深層因子分解（DEEP FACTORIZATION FOR SPEECH SIGNAL）</news:title>
   <news:publication_date>2026-04-12T17:12:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678601</loc>
  <lastmod>2026-04-12T17:12:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層ピクセルレベル事前分布を用いた逆問題解法（Solving Inverse Computational Imaging Problems using Deep Pixel-level Prior）</news:title>
   <news:publication_date>2026-04-12T17:12:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678599</loc>
  <lastmod>2026-04-12T17:11:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>少ない方が良い：標準コンパイラ最適化レベルを活用して性能とエネルギーを改善する（Less is More: Exploiting the Standard Compiler Optimization Levels for Better Performance and Energy Consumption）</news:title>
   <news:publication_date>2026-04-12T17:11:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678597</loc>
  <lastmod>2026-04-12T17:11:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>敵対的サンプルを学習に活かす能動学習（Adversarial Active Learning for Deep Networks: a Margin Based Approach）</news:title>
   <news:publication_date>2026-04-12T17:11:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678595</loc>
  <lastmod>2026-04-12T16:20:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スケルトンに基づく行動認識の時空間グラフ畳み込み（Spatio-Temporal Graph Convolution for Skeleton Based Action Recognition）</news:title>
   <news:publication_date>2026-04-12T16:20:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678593</loc>
  <lastmod>2026-04-12T16:19:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>粗から細への非剛体レジストレーション（Coarse to fine non-rigid registration: a chain of scale-specific neural networks for multimodal image alignment with application to remote sensing）</news:title>
   <news:publication_date>2026-04-12T16:19:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678591</loc>
  <lastmod>2026-04-12T16:19:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数タスクを同時学習する差分方策勾配（DiGrad: Multi-Task Reinforcement Learning with Shared Actions）</news:title>
   <news:publication_date>2026-04-12T16:19:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678589</loc>
  <lastmod>2026-04-12T16:18:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>バイオインフォマティクスと医療における深層学習の役割（Bioinformatics and Medicine in the Era of Deep Learning）</news:title>
   <news:publication_date>2026-04-12T16:18:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678587</loc>
  <lastmod>2026-04-12T16:18:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時系列重視の顧客離反予測とPU学習の実務的意義（Time-sensitive Customer Churn Prediction based on PU Learning）</news:title>
   <news:publication_date>2026-04-12T16:18:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678585</loc>
  <lastmod>2026-04-12T16:18:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>軌道に沿った線形時変モデルの同定法——滑らかな変化と不連続な変化を扱う凸最適化手法（Identification of LTV Dynamical Models with Smooth or Discontinuous Time Evolution by means of Convex Optimization）</news:title>
   <news:publication_date>2026-04-12T16:18:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678583</loc>
  <lastmod>2026-04-12T16:18:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>事前情報を必要としないマッチング畳み込みニューラルネットワーク (Matching Convolutional Neural Networks without Priors about Data)</news:title>
   <news:publication_date>2026-04-12T16:18:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678581</loc>
  <lastmod>2026-04-12T15:26:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>堅牢なオブジェクトネス転移による物体検出の改良（Robust Objectness Transfer for Object Detection）</news:title>
   <news:publication_date>2026-04-12T15:26:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678579</loc>
  <lastmod>2026-04-12T15:26:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時間変動チャネル追跡と空間時系列基底展開（Time Varying Channel Tracking with Spatial and Temporal BEM for Massive MIMO Systems）</news:title>
   <news:publication_date>2026-04-12T15:26:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678577</loc>
  <lastmod>2026-04-12T15:25:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ガウス・メタ埋め込みによる高次裾野PLDAモデルの効率的スコアリング（Gaussian meta-embeddings for efficient scoring of a heavy-tailed PLDA model）</news:title>
   <news:publication_date>2026-04-12T15:25:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678575</loc>
  <lastmod>2026-04-12T15:25:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューラルネットワークの表現学習と情報ボトルネック原理（Learning Representations for Neural Network-Based Classification Using the Information Bottleneck Principle）</news:title>
   <news:publication_date>2026-04-12T15:25:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678573</loc>
  <lastmod>2026-04-12T15:24:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>効率的な深層ニューラルネットワーク学習のためのL1ノルムバッチ正規化（L1-Norm Batch Normalization for Efficient Training of Deep Neural Networks）</news:title>
   <news:publication_date>2026-04-12T15:24:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678571</loc>
  <lastmod>2026-04-12T15:24:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>再帰地平線制御のためのマルチステップ予測モデル（On multi-step prediction models for receding horizon control）</news:title>
   <news:publication_date>2026-04-12T15:24:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678569</loc>
  <lastmod>2026-04-12T14:33:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>層ごとの適応学習率によるフィードフォワードニューラルネットワークの訓練（Train Feedfoward Neural Network with Layer-wise Adaptive Rate via Approximating Back-matching Propagation）</news:title>
   <news:publication_date>2026-04-12T14:33:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678567</loc>
  <lastmod>2026-04-12T14:32:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>遅延勾配を加速するモメンタム補償（Accelerating Asynchronous Algorithms for Convex Optimization by Momentum Compensation）</news:title>
   <news:publication_date>2026-04-12T14:32:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678565</loc>
  <lastmod>2026-04-12T14:31:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>認知レーダのアンテナ選択を深層学習で行う方法（Cognitive Radar Antenna Selection via Deep Learning）</news:title>
   <news:publication_date>2026-04-12T14:31:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678563</loc>
  <lastmod>2026-04-12T14:31:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Recurrent Residual Moduleによる動画推論の高速化（Recurrent Residual Module for Fast Inference in Videos）</news:title>
   <news:publication_date>2026-04-12T14:31:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678561</loc>
  <lastmod>2026-04-12T14:30:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>カーネル損失に基づくオンライン学習（Online learning with kernel losses）</news:title>
   <news:publication_date>2026-04-12T14:30:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678559</loc>
  <lastmod>2026-04-12T14:30:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ネットワーククラスタ型マルチモーダルバグ局所化 (Network-Clustered Multi-Modal Bug Localization)</news:title>
   <news:publication_date>2026-04-12T14:30:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678557</loc>
  <lastmod>2026-04-12T14:30:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次元のApproximate Bayesian Computation（High-dimensional ABC）</news:title>
   <news:publication_date>2026-04-12T14:30:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678555</loc>
  <lastmod>2026-04-12T13:38:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>近似ベイズ計算（Overview of Approximate Bayesian Computation）</news:title>
   <news:publication_date>2026-04-12T13:38:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678553</loc>
  <lastmod>2026-04-12T13:38:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>敵対的事例の転移可能性の理解と強化（UNDERSTANDING AND ENHANCING THE TRANSFERABILITY OF ADVERSARIAL EXAMPLES）</news:title>
   <news:publication_date>2026-04-12T13:38:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678551</loc>
  <lastmod>2026-04-12T13:37:52Z</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Actor‑Critic型文脈バンディットのmHealth応用（Robust Actor‑Critic Contextual Bandit for Mobile Health）</news:title>
   <news:publication_date>2026-04-12T13:37:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678549</loc>
  <lastmod>2026-04-12T13:37:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>表現型に基づく自己学習型睡眠時無呼吸スクリーニング（Phenotype-based and Self-learning Inter-individual Sleep Apnea Screening with a Level IV Monitoring System）</news:title>
   <news:publication_date>2026-04-12T13:37:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678547</loc>
  <lastmod>2026-04-12T13:37:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>音楽ジャンル分類で人間と同等の精度を達成したCNN（Convolutional Neural Network Achieves Human-level Accuracy in Music Genre Classification）</news:title>
   <news:publication_date>2026-04-12T13:37:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678545</loc>
  <lastmod>2026-04-12T13:36:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>3次指数和の最大値の分布（ON THE DISTRIBUTION OF THE MAXIMUM OF CUBIC EXPONENTIAL SUMS）</news:title>
   <news:publication_date>2026-04-12T13:36:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678543</loc>
  <lastmod>2026-04-12T12:46:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>拡張LSTMと依存型双方向再帰ニューラルネットワークの要点整理（On Extended Long Short-term Memory and Dependent Bidirectional Recurrent Neural Network）</news:title>
   <news:publication_date>2026-04-12T12:46:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678541</loc>
  <lastmod>2026-04-12T12:45:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所部分グラフから学ぶリンク予測（Link Prediction Based on Graph Neural Networks）</news:title>
   <news:publication_date>2026-04-12T12:45:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678539</loc>
  <lastmod>2026-04-12T12:44:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>二重峰の電波光度曲線を持つ超新星の観測（THE DOUBLE-PEAKED RADIO LIGHT CURVE OF PTF11QCJ）</news:title>
   <news:publication_date>2026-04-12T12:44:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678537</loc>
  <lastmod>2026-04-12T12:44:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単一視点画像から食事のカロリーを推定する手法（SINGLE-VIEW FOOD PORTION ESTIMATION: LEARNING IMAGE-TO-ENERGY MAPPINGS USING GENERATIVE ADVERSARIAL NETWORKS）</news:title>
   <news:publication_date>2026-04-12T12:44:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678535</loc>
  <lastmod>2026-04-12T12:44:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多観測回帰（Multi-Observation Regression）</news:title>
   <news:publication_date>2026-04-12T12:44:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678533</loc>
  <lastmod>2026-04-12T12:43:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ソーシャルメディアによる先住文化保存の実践（Preservation of Indigenous Culture among Indigenous Migrants through Social Media: the Igorot Peoples）</news:title>
   <news:publication_date>2026-04-12T12:43:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678531</loc>
  <lastmod>2026-04-12T11:52:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>低輝度GPS/CSS電波源の高解像度観測が示すもの（High-resolution Observations of Low-luminosity Gigahertz-Peaked Spectrum and Compact Steep Spectrum Sources）</news:title>
   <news:publication_date>2026-04-12T11:52:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678529</loc>
  <lastmod>2026-04-12T11:52:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>方向統計に基づく深層距離学習による画像分類と検索（Directional Statistics-based Deep Metric Learning for Image Classification and Retrieval）</news:title>
   <news:publication_date>2026-04-12T11:52:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678527</loc>
  <lastmod>2026-04-12T11:52:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>布操作におけるランダムフォレストベースの模倣学習（Cloth Manipulation Using Random-Forest-Based Imitation Learning）</news:title>
   <news:publication_date>2026-04-12T11:52:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678525</loc>
  <lastmod>2026-04-12T11:51:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>軌跡のセマンティック分割（Semantic segmentation of trajectories with agent models）</news:title>
   <news:publication_date>2026-04-12T11:51:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678523</loc>
  <lastmod>2026-04-12T11:51:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>二値潜在変数モデルの学習：テンソル固有対手法（Learning Binary Latent Variable Models: A Tensor Eigenpair Approach）</news:title>
   <news:publication_date>2026-04-12T11:51:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678521</loc>
  <lastmod>2026-04-12T11:51:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Lpノルムが示す限界と経営への示唆（On the Suitability of Lp-norms for Creating and Preventing Adversarial Examples）</news:title>
   <news:publication_date>2026-04-12T11:51:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678519</loc>
  <lastmod>2026-04-12T11:50:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>数独を用いた計算レッドチーミングにおけるスキル表現と獲得（Computational Red Teaming in a Sudoku Solving Context: Neural Network Based Skill Representation and Acquisition）</news:title>
   <news:publication_date>2026-04-12T11:50:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678517</loc>
  <lastmod>2026-04-12T10:59:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>近似ベイズ計算のサンプリング技法（ABC Samplers）</news:title>
   <news:publication_date>2026-04-12T10:59:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678515</loc>
  <lastmod>2026-04-12T10:58:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>限定された方策クラスでの最適化（Optimizing over a Restricted Policy Class in Markov Decision Processes）</news:title>
   <news:publication_date>2026-04-12T10:58:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678513</loc>
  <lastmod>2026-04-12T10:58:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自己を基に他者をモデル化する多者強化学習（Modeling Others using Oneself in Multi-Agent Reinforcement Learning）</news:title>
   <news:publication_date>2026-04-12T10:58:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678511</loc>
  <lastmod>2026-04-12T10:57:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>バイオ医療情報検索における高速テキスト関連性モデル（A Fast Deep Learning Model for Textual Relevance in Biomedical Information Retrieval）</news:title>
   <news:publication_date>2026-04-12T10:57:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678509</loc>
  <lastmod>2026-04-12T10:57:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模グラフ埋め込みを実現する多層フレームワーク（MILE: A Multi-Level Framework for Scalable Graph Embedding）</news:title>
   <news:publication_date>2026-04-12T10:57:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678507</loc>
  <lastmod>2026-04-12T10:57:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>地層断面のベイズ形状モデリング（Bayesian shape modelling of cross-sectional geological data）</news:title>
   <news:publication_date>2026-04-12T10:57:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678505</loc>
  <lastmod>2026-04-12T10:57:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>制約付き最適化を学習する――最適なアクティブ制約集合の同定（Learning for Constrained Optimization: Identifying Optimal Active Constraint Sets）</news:title>
   <news:publication_date>2026-04-12T10:57:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678503</loc>
  <lastmod>2026-04-12T10:06:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ハイパーパラメータの調整可能性（Tunability: Importance of Hyperparameters of Machine Learning Algorithms）</news:title>
   <news:publication_date>2026-04-12T10:06:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678501</loc>
  <lastmod>2026-04-12T10:06:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多斉次（multiaffine）制約を持つ最適化に対するADMMの収束解析（ADMM for Multiaffine Constrained Optimization）</news:title>
   <news:publication_date>2026-04-12T10:06:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678499</loc>
  <lastmod>2026-04-12T10:05:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチキャリア無線ネットワークにおける低計算量のエネルギー効率最適化学習手法（A Learning Approach for Low-Complexity Optimization of Energy Efficiency in Multi-Carrier Wireless Networks）</news:title>
   <news:publication_date>2026-04-12T10:05:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678497</loc>
  <lastmod>2026-04-12T10:04:57Z</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>2026-04-12T10:04:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678495</loc>
  <lastmod>2026-04-12T10:04:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ボックスカーネルを用いた近線形時間の局所多項式非パラメトリック推定（Near-Linear Time Local Polynomial Nonparametric Estimation with Box Kernels）</news:title>
   <news:publication_date>2026-04-12T10:04:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678493</loc>
  <lastmod>2026-04-12T10:04:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>i3PosNet：頭蓋側手術におけるX線からの器具姿勢推定（i3PosNet: Instrument Pose Estimation from X-Ray in temporal bone surgery）</news:title>
   <news:publication_date>2026-04-12T10:04:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678491</loc>
  <lastmod>2026-04-12T10:04:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>データ依存PAC-Bayes事前分布と差分プライバシー（Data-dependent PAC-Bayes priors via differential privacy）</news:title>
   <news:publication_date>2026-04-12T10:04:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678489</loc>
  <lastmod>2026-04-12T09:12:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>行列値クエリに対する差分プライバシーメカニズム設計（A Differential Privacy Mechanism Design Under Matrix-Valued Query）</news:title>
   <news:publication_date>2026-04-12T09:12:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678487</loc>
  <lastmod>2026-04-12T09:12:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Shampoo: Preconditioned Stochastic Tensor Optimization（Shampoo: Preconditioned Stochastic Tensor Optimization）</news:title>
   <news:publication_date>2026-04-12T09:12:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678485</loc>
  <lastmod>2026-04-12T09:11:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>デモを活用する強化学習で多様な視覚運動スキルを学ぶ（Reinforcement and Imitation Learning for Diverse Visuomotor Skills）</news:title>
   <news:publication_date>2026-04-12T09:11:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678483</loc>
  <lastmod>2026-04-12T09:11:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>汚染されたバンディットにおける最良腕同定（Best Arm Identification for Contaminated Bandits）</news:title>
   <news:publication_date>2026-04-12T09:11:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678481</loc>
  <lastmod>2026-04-12T09:11:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>アルゴリズム判断の「公平性」を人はどう感じるか（Human Perceptions of Fairness in Algorithmic Decision Making: A Case Study of Criminal Risk Prediction）</news:title>
   <news:publication_date>2026-04-12T09:11:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678479</loc>
  <lastmod>2026-04-12T09:11:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>欠測データ下のスパース遷移行列推定（Missing Data in Sparse Transition Matrix Estimation for Sub-Gaussian Vector Autoregressive Processes）</news:title>
   <news:publication_date>2026-04-12T09:11:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678477</loc>
  <lastmod>2026-04-12T09:10:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層ボルツマン機械による量子多体系の正確表現（Constructing exact representations of quantum many-body systems with deep neural networks）</news:title>
   <news:publication_date>2026-04-12T09:10:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678475</loc>
  <lastmod>2026-04-12T08:19:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>検索を組み合わせて敵対的攻撃に強くする手法の要点（Retrieval-Augmented Convolutional Neural Networks for Improved Robustness against Adversarial Examples）</news:title>
   <news:publication_date>2026-04-12T08:19:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678473</loc>
  <lastmod>2026-04-12T08:18:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>相互作用によって明らかになる独立に制御可能な変動要因の分解（Disentangling the independently controllable factors of variation by interacting with the world）</news:title>
   <news:publication_date>2026-04-12T08:18:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678471</loc>
  <lastmod>2026-04-12T08:18:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Actor-Critic法における関数近似誤差への対処（Addressing Function Approximation Error in Actor-Critic Methods）</news:title>
   <news:publication_date>2026-04-12T08:18:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678469</loc>
  <lastmod>2026-04-12T08:17:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自己組織化マップによるカテゴリ一般化の説明（Self-organizing maps and generalization: an algorithmic description of Numerosity and Variability effects）</news:title>
   <news:publication_date>2026-04-12T08:17:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678467</loc>
  <lastmod>2026-04-12T08:17:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>遷移流の縮約モデルのための人工ニューラルネットワーク枠組み（An artificial neural network framework for reduced order modeling of transient flows）</news:title>
   <news:publication_date>2026-04-12T08:17:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678465</loc>
  <lastmod>2026-04-12T08:17:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多帯層状超伝導体における軌道量子化とFFLO不安定性の相互作用（Interplay between orbital-quantization effects and the Fulde–Ferrell–Larkin–Ovchinnikov instability in multiple-band layered superconductors）</news:title>
   <news:publication_date>2026-04-12T08:17:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678463</loc>
  <lastmod>2026-04-12T08:16:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数目標強化学習とロボット制御の挑戦（Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research）</news:title>
   <news:publication_date>2026-04-12T08:16:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678461</loc>
  <lastmod>2026-04-12T07:25:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>磁気共鳴画像の自己超解像（Self Super-Resolution for Magnetic Resonance Images Using Deep Networks）</news:title>
   <news:publication_date>2026-04-12T07:25:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678459</loc>
  <lastmod>2026-04-12T07:25:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>乳腺組織画像の転移学習による分類（Classification of breast cancer histology images using transfer learning）</news:title>
   <news:publication_date>2026-04-12T07:25:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678457</loc>
  <lastmod>2026-04-12T07:24:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>サンプルから代数多様体を学ぶ手法（Learning Algebraic Varieties from Samples）</news:title>
   <news:publication_date>2026-04-12T07:24:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678455</loc>
  <lastmod>2026-04-12T07:24:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>敵対的ニューラルネットワークによる匿名化表現学習（Learning Anonymized Representations with Adversarial Neural Networks）</news:title>
   <news:publication_date>2026-04-12T07:24:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678453</loc>
  <lastmod>2026-04-12T07:24:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非パラメトリック活性化関数によるグラフ畳み込みネットワークの改善（Improving Graph Convolutional Networks with Non-Parametric Activation Functions）</news:title>
   <news:publication_date>2026-04-12T07:24:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678451</loc>
  <lastmod>2026-04-12T07:23:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ハイパーネットワークによる確率的ハイパーパラメータ最適化（Stochastic Hyperparameter Optimization through Hypernetworks）</news:title>
   <news:publication_date>2026-04-12T07:23:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678449</loc>
  <lastmod>2026-04-12T07:23:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DropLasso：単一細胞RNA-seqデータに強いLassoの変種（DropLasso: A robust variant of Lasso for single cell RNA-seq data）</news:title>
   <news:publication_date>2026-04-12T07:23:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678447</loc>
  <lastmod>2026-04-12T06:31:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>密度カーネル推定量の平均化手法（Averaging of density kernel estimators）</news:title>
   <news:publication_date>2026-04-12T06:31:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678445</loc>
  <lastmod>2026-04-12T06:31:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>切り込み車両に耐える確率的MPCを用いた協調適応巡航制御の学習ベース設計（A Learning-based Stochastic MPC Design for Cooperative Adaptive Cruise Control to Handle Interfering Vehicles）</news:title>
   <news:publication_date>2026-04-12T06:31:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678443</loc>
  <lastmod>2026-04-12T06:30:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複雑ネットワークにおける自己学習による知識獲得のダイナミクス (The Dynamics of Knowledge Acquisition via Self-Learning in Complex Networks)</news:title>
   <news:publication_date>2026-04-12T06:30:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678441</loc>
  <lastmod>2026-04-12T06:30:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Max‑Mahalanobis線形判別分析ネットワークの要点（Max‑Mahalanobis Linear Discriminant Analysis Networks）</news:title>
   <news:publication_date>2026-04-12T06:30:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678439</loc>
  <lastmod>2026-04-12T06:30:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Yedroudj-Netによる空間ステガノアリシスの革新（YEDROUDJ-NET: AN EFFICIENT CNN FOR SPATIAL STEGANALYSIS）</news:title>
   <news:publication_date>2026-04-12T06:30:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678437</loc>
  <lastmod>2026-04-12T06:30:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スパース一般化固有値問題を分解で解く手法（A Decomposition Algorithm for the Sparse Generalized Eigenvalue Problem）</news:title>
   <news:publication_date>2026-04-12T06:30:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678435</loc>
  <lastmod>2026-04-12T06:29:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>指数凸性による次元非依存の情報集中（Dimension-free Information Concentration via Exp-Concavity）</news:title>
   <news:publication_date>2026-04-12T06:29:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678433</loc>
  <lastmod>2026-04-12T05:38:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>カテゴリ単位モデルによる単眼物体SLAMの構築（Constructing Category-Specific Models for Monocular Object-SLAM）</news:title>
   <news:publication_date>2026-04-12T05:38:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678431</loc>
  <lastmod>2026-04-12T05:38:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>広帯域電波スペクトルエネルギー分布の新しい特徴付け法（A novel approach for characterising broadband radio spectral energy distributions）</news:title>
   <news:publication_date>2026-04-12T05:38:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678429</loc>
  <lastmod>2026-04-12T05:37:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多言語セマンティックパースによるリンクドデータ問答（AMUSE: Multilingual Semantic Parsing for Question Answering over Linked Data）</news:title>
   <news:publication_date>2026-04-12T05:37:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678427</loc>
  <lastmod>2026-04-12T05:36:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
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   <news:genres>Blog</news:genres>
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    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news: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: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>2026-04-12T04:44:02Z</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>2026-04-12T04:43:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-04-12T04:43:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>2D/3Dポーズ推定と行動認識を同時に行うマルチタスク深層学習（2D/3D Pose Estimation and Action Recognition using Multitask Deep Learning）</news:title>
   <news:publication_date>2026-04-12T04:42:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-04-12T04:42:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:publication_date>2026-04-12T04:42:24Z</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>
   </news:publication>
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   <news:publication_date>2026-04-12T03:50:33Z</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: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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 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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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>
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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>
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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>
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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>
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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>
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   <news:publication_date>2026-04-12T02:54:51Z</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>
  </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>非スパース構造を持つ高次元線形モデルの検定可能性（Testability of High-Dimensional Linear Models with Non-Sparse Structures）</news:title>
   <news:publication_date>2026-04-12T02:03:06Z</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>
   </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>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>敵対的学習によるワンクラス分類器で実現する異常検知（Adversarially Learned One-Class Classifier for Novelty Detection）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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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:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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 <url>
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    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:title>注目領域を導くGAN訓練法（Attention-Aware Generative Adversarial Networks (ATA-GANs)）</news:title>
   <news:publication_date>2026-04-12T01:05:50Z</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>
   </news:publication>
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   <news:publication_date>2026-04-12T01:05:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 <url>
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  <lastmod>2026-04-12T01:04:40Z</lastmod>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ログデータを活用する能動学習の新地平（Active Learning with Logged Data）</news:title>
   <news:publication_date>2026-04-12T01:04:40Z</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>
   </news:publication>
   <news:title>テンソルリングによる大幅圧縮（Wide Compression: Tensor Ring Nets）</news:title>
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   <news:genres>Blog</news:genres>
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 <url>
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   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オンチップ学習と低消費電力SNN分類器の提案（Neuromorphic Spike Sorting with Memristive Crossbar SNN）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/678341</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多クラス共通空間パターンに基づくEEG—適応学習分類器を組み合わせたBCIの改善（Multiclass Common Spatial Pattern for EEG based Brain Computer Interface with Adaptive Learning Classifier）</news:title>
   <news:publication_date>2026-04-12T00:11:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
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  <loc>https://aibr.jp/archives/678339</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>銀河タグ付け：フォトメトリック赤方偏移の精練と群れリッチネス向上（Galaxy Tagging: photometric redshift refinement and group richness enhancement）</news:title>
   <news:publication_date>2026-04-12T00:11:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678337</loc>
  <lastmod>2026-04-12T00:10:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Cakewalkサンプリングの実務的解説（Cakewalk Sampling）</news:title>
   <news:publication_date>2026-04-12T00:10:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/678335</loc>
  <lastmod>2026-04-11T23:19:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習による文書ランキングの深層ニューラルネットワーク（Deep Neural Network for Learning to Rank）</news:title>
   <news:publication_date>2026-04-11T23:19:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678333</loc>
  <lastmod>2026-04-11T23:19:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>n-fold整数計画法の評価と調整（Evaluating and Tuning n-fold Integer Programming）</news:title>
   <news:publication_date>2026-04-11T23:19:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678331</loc>
  <lastmod>2026-04-11T23:19:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動的時空間配列モデルのスパースネットワーク推定（Sparse Network Estimation for Dynamical Spatio-temporal Array Models）</news:title>
   <news:publication_date>2026-04-11T23:19:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678329</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>2026-04-11T23:17:55Z</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>2026-04-11T23:17:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>トラック輸送の動的入札を学習する強化学習（Reinforcement Learning for Dynamic Bidding in Truckload Markets）</news:title>
   <news:publication_date>2026-04-11T23:17:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678323</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>2026-04-11T23:17:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/678321</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-04-11T22:25:36Z</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>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-04-11T22:24:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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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>Muon Hunter：Zooniverseプラットフォームによる市民科学と機械学習の融合（Muon Hunter: a Zooniverse project）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678303</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>ゲートレベル回路の逆解析に対するSATベース手法と故障注入・プロービングの活用（SAT-based Reverse Engineering of Gate-Level Schematics using Fault Injection and Probing）</news:title>
   <news:publication_date>2026-04-11T21:30:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678301</loc>
  <lastmod>2026-04-11T21:30:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Product Kernel Interpolationによるスケーラブルなガウス過程（Product Kernel Interpolation for Scalable Gaussian Processes）</news:title>
   <news:publication_date>2026-04-11T21:30:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678299</loc>
  <lastmod>2026-04-11T21:30:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次元非凸スパース学習のための半スムース・ニュートン法（A Semi-Smooth Newton Algorithm for High-Dimensional Nonconvex Sparse Learning）</news:title>
   <news:publication_date>2026-04-11T21:30:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/678297</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>二次精度ハミルトニアンモンテカルロの次元的タイト境界（Dimensionally Tight Bounds for Second-Order Hamiltonian Monte Carlo）</news:title>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>空間遺伝子発現解析の見落としを減らす方法（Improving Recall of In Situ Sequencing by Self-Learned Features and a Graphical Model）</news:title>
   <news:publication_date>2026-04-11T21:29:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678293</loc>
  <lastmod>2026-04-11T20:37:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678291</loc>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678289</loc>
  <lastmod>2026-04-11T20:37:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多段階スケールのGCNによる半教師付きノード分類（N-GCN: Multi-scale Graph Convolution for Semi-supervised Node Classification）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>個々の樹木を表す航空LiDAR 3D点群に対する針葉樹/広葉樹分類のための深層学習（Deep learning for conifer/deciduous classification of airborne LiDAR 3D point clouds representing individual trees）</news:title>
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   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
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   <news:genres>Blog</news:genres>
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 <url>
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  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SP理論による知能の統合的理解（INTRODUCTION TO THE SP THEORY OF INTELLIGENCE）</news:title>
   <news:publication_date>2026-04-11T20:36:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678281</loc>
  <lastmod>2026-04-11T20:36:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>物理教育に計算的視点を統合する（Integrating a Computational Perspective in Physics Courses）</news:title>
   <news:publication_date>2026-04-11T20:36:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678279</loc>
  <lastmod>2026-04-11T19:44:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>強相関電子系の表面プローブ画像を機械学習で分類する（Classifying surface probe images in strongly correlated electronic systems via machine learning）</news:title>
   <news:publication_date>2026-04-11T19:44:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678277</loc>
  <lastmod>2026-04-11T19:44:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>一つの大きなネットワークで何でも学ばせる（One Big Net For Everything）</news:title>
   <news:publication_date>2026-04-11T19:44:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678275</loc>
  <lastmod>2026-04-11T19:42:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>カメラ再局在化のためのオイラー角ベース損失関数（Euler angles based loss function for camera relocalization with Deep learning）</news:title>
   <news:publication_date>2026-04-11T19:42:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678273</loc>
  <lastmod>2026-04-11T19:42:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>畳み込みニューラルネットワークとルンゲ＝クッタ手法の融合（Convolutional Neural Networks combined with Runge–Kutta Methods）</news:title>
   <news:publication_date>2026-04-11T19:42:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678271</loc>
  <lastmod>2026-04-11T19:42:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>基本に立ち返る：AtariでのCanonical Evolution Strategiesのベンチマーク（Back to Basics: Benchmarking Canonical Evolution Strategies for Playing Atari）</news:title>
   <news:publication_date>2026-04-11T19:42:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678269</loc>
  <lastmod>2026-04-11T19:42:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Wasserstein距離における分布推定のミニマックス率（Minimax Rates of Distribution Estimation in Wasserstein Distance）</news:title>
   <news:publication_date>2026-04-11T19:42:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678267</loc>
  <lastmod>2026-04-11T18:50:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>実世界屋内ロボット視覚ナビゲーションのためのデータセット構築（The AdobeIndoorNav Dataset: Towards Deep Reinforcement Learning based Real-world Indoor Robot Visual Navigation）</news:title>
   <news:publication_date>2026-04-11T18:50:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678265</loc>
  <lastmod>2026-04-11T18:49:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>PSOベースのFuzzy Markup Languageによる学習パフォーマンス評価と教育応用（PSO-based Fuzzy Markup Language for Student Learning Performance Evaluation and Educational Application）</news:title>
   <news:publication_date>2026-04-11T18:49:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678263</loc>
  <lastmod>2026-04-11T18:49:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>実時間物体追跡のための二重Siameseネットワーク（A Twofold Siamese Network for Real-Time Object Tracking）</news:title>
   <news:publication_date>2026-04-11T18:49:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678261</loc>
  <lastmod>2026-04-11T18:48:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高並列アーキテクチャ上の確率的勾配降下法（Stochastic Gradient Descent on Highly-Parallel Architectures）</news:title>
   <news:publication_date>2026-04-11T18:48:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678259</loc>
  <lastmod>2026-04-11T18:48:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>カスケード型マルチスケールクロスネットワークによる単一画像超解像（Single Image Super-Resolution via Cascaded Multi-Scale Cross Network）</news:title>
   <news:publication_date>2026-04-11T18:48:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678257</loc>
  <lastmod>2026-04-11T18:47:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Webインターフェースで学習する強化学習：ワークフローによる探索制約（Reinforcement Learning on Web Interfaces Using Workflow-Guided Exploration）</news:title>
   <news:publication_date>2026-04-11T18:47:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678255</loc>
  <lastmod>2026-04-11T18:47:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>カーネル平滑固有直交分解による流体動態の高速エミュレーション（Kernel-smoothed proper orthogonal decomposition (KSPOD)-based emulation for prediction of spatiotemporally evolving flow dynamics）</news:title>
   <news:publication_date>2026-04-11T18:47:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678253</loc>
  <lastmod>2026-04-11T17:55:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Residual Dense Networkによる画像超解像の革新（Residual Dense Network for Image Super-Resolution）</news:title>
   <news:publication_date>2026-04-11T17:55:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678251</loc>
  <lastmod>2026-04-11T17:55:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>構文指向変分オートエンコーダによる構造化データ生成（SYNTAX-DIRECTED VARIATIONAL AUTOENCODER FOR STRUCTURED DATA）</news:title>
   <news:publication_date>2026-04-11T17:55:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678249</loc>
  <lastmod>2026-04-11T17:54:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>極めて高速な決定木（Extremely Fast Decision Tree）</news:title>
   <news:publication_date>2026-04-11T17:54:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678247</loc>
  <lastmod>2026-04-11T17:54:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>現実的なグラフ生成を実現する深層自己回帰モデル（GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models）</news:title>
   <news:publication_date>2026-04-11T17:54:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678245</loc>
  <lastmod>2026-04-11T17:54:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SGDの歩み：訓練軌跡から見る学習率とバッチサイズの役割（A Walk with SGD）</news:title>
   <news:publication_date>2026-04-11T17:54:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678243</loc>
  <lastmod>2026-04-11T17:54:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>生成器のヤコビアン条件数とGAN性能の因果関係（Is Generator Conditioning Causally Related to GAN Performance?）</news:title>
   <news:publication_date>2026-04-11T17:54:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678241</loc>
  <lastmod>2026-04-11T17:53:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>モデルツリーによるNHLドラフト選手評価（Model Trees for Identifying Exceptional Players in the NHL Draft）</news:title>
   <news:publication_date>2026-04-11T17:53:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678239</loc>
  <lastmod>2026-04-11T17:02:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>拡散マップとNyström法の統合（Diffusion Maps Meet Nyström）</news:title>
   <news:publication_date>2026-04-11T17:02:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678237</loc>
  <lastmod>2026-04-11T17:01:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>行動―臨床フェノタイピングによる糖尿病自己観察データ解析（Behavioral-clinical phenotyping with type 2 diabetes self-monitoring data）</news:title>
   <news:publication_date>2026-04-11T17:01:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678235</loc>
  <lastmod>2026-04-11T17:01:37Z</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>2026-04-11T17:01:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678233</loc>
  <lastmod>2026-04-11T17:00:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>古典クライアントによるブラインド量子計算の可能性（On the possibility of classical client blind quantum computing）</news:title>
   <news:publication_date>2026-04-11T17:00:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678231</loc>
  <lastmod>2026-04-11T17:00:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>フォーラム議論の生産性を数で見る（Talking by the numbers: Networks identify productive forum discussions）</news:title>
   <news:publication_date>2026-04-11T17:00:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678229</loc>
  <lastmod>2026-04-11T16:59:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ネットワーク接続エージェントによる完全分散型マルチエージェント強化学習（Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents）</news:title>
   <news:publication_date>2026-04-11T16:59:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678227</loc>
  <lastmod>2026-04-11T16:59:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的エキスパートを用いたコンテクスチュアル・バンディット（Contextual Bandits with Stochastic Experts）</news:title>
   <news:publication_date>2026-04-11T16:59:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678225</loc>
  <lastmod>2026-04-11T16:08:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非ラベル下でのドメイン適応を洗練する手法：DIRT-TとVADAの要点（A DIRT-T Approach to Unsupervised Domain Adaptation）</news:title>
   <news:publication_date>2026-04-11T16:08:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678223</loc>
  <lastmod>2026-04-11T16:08:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフレット統計を大規模グラフで効率推定する手法の意義（Estimating Graphlet Statistics via Lifting）</news:title>
   <news:publication_date>2026-04-11T16:08:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678221</loc>
  <lastmod>2026-04-11T16:07:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>野外における長期的顔老化の深層学習アプローチ（Longitudinal Face Aging in the Wild - Recent Deep Learning Approaches）</news:title>
   <news:publication_date>2026-04-11T16:07:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678219</loc>
  <lastmod>2026-04-11T16:06:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>人間と機械を統合する銀河形態分類の枠組み（INTEGRATING HUMAN AND MACHINE INTELLIGENCE IN GALAXY MORPHOLOGY CLASSIFICATION TASKS）</news:title>
   <news:publication_date>2026-04-11T16:06:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678217</loc>
  <lastmod>2026-04-11T16:06:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ハイパースペクトル画像解析における機械学習の総説（Machine learning based hyperspectral image analysis: A survey）</news:title>
   <news:publication_date>2026-04-11T16:06:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678215</loc>
  <lastmod>2026-04-11T16:05:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>離脱を伴う学習の理論と実務的示唆（Learning with Abandonment）</news:title>
   <news:publication_date>2026-04-11T16:05:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678213</loc>
  <lastmod>2026-04-11T16:04:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>タクシー需要予測における深層マルチビュー時空間ネットワーク（Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction）</news:title>
   <news:publication_date>2026-04-11T16:04:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678211</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>万能なニューラルネットワークデコーダが拓くトポロジカル符号の実用性（Advantages of versatile neural-network decoding for topological codes）</news:title>
   <news:publication_date>2026-04-11T15:13:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678209</loc>
  <lastmod>2026-04-11T15:11:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Riesz表現子を用いたデバイアス機械学習によるグローバル・ローカル推定（De-Biased Machine Learning of Global and Local Parameters Using Regularized Riesz Representers）</news:title>
   <news:publication_date>2026-04-11T15:11:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678207</loc>
  <lastmod>2026-04-11T15:11:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>観察データから最適方針を学ぶ（Learning Optimal Policies from Observational Data）</news:title>
   <news:publication_date>2026-04-11T15:11:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678205</loc>
  <lastmod>2026-04-11T15:11:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>シミュレーションで制御器の安全性を能動的に検証する（Verifying Controllers Against Adversarial Examples with Bayesian Optimization）</news:title>
   <news:publication_date>2026-04-11T15:11:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678203</loc>
  <lastmod>2026-04-11T15:11:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>バンディットベースのパーソナライズにおけるバイアス制御のアルゴリズム枠組み（An Algorithmic Framework to Control Bias in Bandit-based Personalization）</news:title>
   <news:publication_date>2026-04-11T15:11:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678201</loc>
  <lastmod>2026-04-11T15:10:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>潜在的順列を学習するGumbel-Sinkhornネットワーク（LEARNING LATENT PERMUTATIONS WITH GUMBEL-SINKHORN NETWORKS）</news:title>
   <news:publication_date>2026-04-11T15:10:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678199</loc>
  <lastmod>2026-04-11T14:19:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>超伝導前駆相SmFeAsOにおける負の熱膨張の証拠（Evidence for negative thermal expansion in the superconducting precursor phase SmFeAsO）</news:title>
   <news:publication_date>2026-04-11T14:19:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678197</loc>
  <lastmod>2026-04-11T14:18:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>損失を意識した重み量子化（LOSS-AWARE WEIGHT QUANTIZATION OF DEEP NETWORKS）</news:title>
   <news:publication_date>2026-04-11T14:18:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678195</loc>
  <lastmod>2026-04-11T14:17:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文章抽出型要約のための文ランキングと強化学習（Ranking Sentences for Extractive Summarization with Reinforcement Learning）</news:title>
   <news:publication_date>2026-04-11T14:17:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678193</loc>
  <lastmod>2026-04-11T14:17:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>習熟学習の実践的検証：CTAI試験のログ分析が示す導入上の要点（Mastery Learning in Practice: A (Mostly) Descriptive Analysis of Log Data from the Cognitive Tutor Algebra I Effectiveness Trial）</news:title>
   <news:publication_date>2026-04-11T14:17:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678191</loc>
  <lastmod>2026-04-11T14:17:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>公平性制約下の経験的リスク最小化（Empirical Risk Minimization Under Fairness Constraints）</news:title>
   <news:publication_date>2026-04-11T14:17:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678189</loc>
  <lastmod>2026-04-11T14:17:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>任意の分類器に対する敵対的脆弱性（Adversarial vulnerability for any classifier）</news:title>
   <news:publication_date>2026-04-11T14:17:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678187</loc>
  <lastmod>2026-04-11T14:16:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次元ベクトル意味論の実務的理解（High-Dimensional Vector Semantics）</news:title>
   <news:publication_date>2026-04-11T14:16:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678185</loc>
  <lastmod>2026-04-11T13:25:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GAtor: 分子結晶構造予測のための第一原理遺伝的アルゴリズム（GAtor: A First Principles Genetic Algorithm for Molecular Crystal Structure Prediction）</news:title>
   <news:publication_date>2026-04-11T13:25:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678183</loc>
  <lastmod>2026-04-11T13:25:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単純な振動子によるリザバーコンピューティング：仮想ネットワークと実ネットワークの接点（Reservoir computing with simple oscillators: Virtual and real networks）</news:title>
   <news:publication_date>2026-04-11T13:25:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678181</loc>
  <lastmod>2026-04-11T13:23:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>意味ベクトル空間による副作用考慮の拡張（Semantic Vector Spaces for Broadening Consideration of Consequences）</news:title>
   <news:publication_date>2026-04-11T13:23:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678179</loc>
  <lastmod>2026-04-11T13:23:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>粗視化ポテンシャルを深層学習で構築する手法（DeePCG: constructing coarse-grained models via deep neural networks）</news:title>
   <news:publication_date>2026-04-11T13:23:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678177</loc>
  <lastmod>2026-04-11T13:23:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ペナルティ化とニューラルネットワークによる最適輸送とヘッジ問題の計算（Computation of optimal transport and related hedging problems via penalization and neural networks）</news:title>
   <news:publication_date>2026-04-11T13:23:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678175</loc>
  <lastmod>2026-04-11T13:23:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>インド手書き文字のスクリプト識別（Indic Handwritten Script Identification using Offline-Online multi-modal Deep Network）</news:title>
   <news:publication_date>2026-04-11T13:23:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678173</loc>
  <lastmod>2026-04-11T12:31:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>1ビット重みで動くワイド残差ネットワークの実用化（TRAINING WIDE RESIDUAL NETWORKS FOR DEPLOYMENT USING A SINGLE BIT FOR EACH WEIGHT）</news:title>
   <news:publication_date>2026-04-11T12:31:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678171</loc>
  <lastmod>2026-04-11T12:31:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的協調環境における重み付き二重深層マルチエージェント強化学習（Weighted Double Deep Multiagent Reinforcement Learning in Stochastic Cooperative Environments）</news:title>
   <news:publication_date>2026-04-11T12:31:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678169</loc>
  <lastmod>2026-04-11T12:30:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>順列のための重み付きKendallと高次カーネル（The Weighted Kendall and High-order Kernels for Permutations）</news:title>
   <news:publication_date>2026-04-11T12:30:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678167</loc>
  <lastmod>2026-04-11T12:29:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多次元ヒストグラムの高速かつ標本近似最適学習法（Fast and Sample Near-Optimal Algorithms for Learning Multidimensional Histograms）</news:title>
   <news:publication_date>2026-04-11T12:29:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678165</loc>
  <lastmod>2026-04-11T12:29:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>裁判文の自動生成による可解性向上（Interpretable Charge Predictions for Criminal Cases: Learning to Generate Court Views from Fact Descriptions）</news:title>
   <news:publication_date>2026-04-11T12:29:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678163</loc>
  <lastmod>2026-04-11T12:29:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>超新星残骸のX線偏光撮像の実践的側面（Practical aspects of X-ray imaging polarimetry of supernova remnants and other extended sources）</news:title>
   <news:publication_date>2026-04-11T12:29:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678161</loc>
  <lastmod>2026-04-11T12:28:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフ類似性と近似同型性（Graph Similarity and Approximate Isomorphism）</news:title>
   <news:publication_date>2026-04-11T12:28:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678159</loc>
  <lastmod>2026-04-11T11:37:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>語音から統語へ：言語埋め込みによる無監督言語類型論(From Phonology to Syntax: Unsupervised Linguistic Typology at Different Levels with Language Embeddings)</news:title>
   <news:publication_date>2026-04-11T11:37:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678157</loc>
  <lastmod>2026-04-11T11:36:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューラルネットワークの判断を可視化する方法（Coloring black boxes: visualization of neural network decisions）</news:title>
   <news:publication_date>2026-04-11T11:36:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678155</loc>
  <lastmod>2026-04-11T11:35:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>AEkNN: AutoEncoderとkNNを組み合わせた次元削減内蔵型分類器（AEkNN: An AutoEncoder kNN-based classiﬁer with built-in dimensionality reduction）</news:title>
   <news:publication_date>2026-04-11T11:35:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678153</loc>
  <lastmod>2026-04-11T11:35:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Autoencoderを用いた画像圧縮：学習は量子化に依存しないか？ (AUTOENCODER BASED IMAGE COMPRESSION: CAN THE LEARNING BE QUANTIZATION INDEPENDENT?)</news:title>
   <news:publication_date>2026-04-11T11:35:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678151</loc>
  <lastmod>2026-04-11T11:35:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>順序情報を重視した推薦の新潮流 — Sequence-Aware Recommender Systems（Sequence-Aware Recommender Systems）</news:title>
   <news:publication_date>2026-04-11T11:35:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678149</loc>
  <lastmod>2026-04-11T11:34:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>効率的なニューラル音声合成の革新（Efficient Neural Audio Synthesis）</news:title>
   <news:publication_date>2026-04-11T11:34:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678147</loc>
  <lastmod>2026-04-11T10:42:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>固有値分解を不要にするDPPの厳密サンプリング手法（Exact Sampling of Determinantal Point Processes without Eigendecomposition）</news:title>
   <news:publication_date>2026-04-11T10:42:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678145</loc>
  <lastmod>2026-04-11T10:42:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>太陽重力レンズを用いた系外惑星の直接多画素撮像と分光（Direct Multipixel Imaging and Spectroscopy of an exoplanet with a Solar Gravity Lens Mission）</news:title>
   <news:publication_date>2026-04-11T10:42:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678143</loc>
  <lastmod>2026-04-11T10:42:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>カーネル二標本検定の指数的一貫性（Exponentially Consistent Kernel Two-Sample Tests）</news:title>
   <news:publication_date>2026-04-11T10:42:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678141</loc>
  <lastmod>2026-04-11T10:40:57Z</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>2026-04-11T10:40:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
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  <lastmod>2026-04-11T10:40:44Z</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>2026-04-11T10:40:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678137</loc>
  <lastmod>2026-04-11T10:40:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-04-11T10:40:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678135</loc>
  <lastmod>2026-04-11T10:40:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>カーネル再帰的ABC：近似尤度問題を解く点推定の新手法（Kernel Recursive ABC: Point Estimation with Intractable Likelihood）</news:title>
   <news:publication_date>2026-04-11T10:40:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678133</loc>
  <lastmod>2026-04-11T09:48:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>音声から直接意味を理解するエンドツーエンドSLU（Towards End-to-End Spoken Language Understanding）</news:title>
   <news:publication_date>2026-04-11T09:48:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678131</loc>
  <lastmod>2026-04-11T09:48:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模データの構造を活かす低ランク行列推定（Harnessing Structures in Big Data via Guaranteed Low-Rank Matrix Estimation）</news:title>
   <news:publication_date>2026-04-11T09:48:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678129</loc>
  <lastmod>2026-04-11T09:47:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Deep Defense: 攻撃に強いDNNを育てる訓練法（Deep Defense: Training DNNs with Improved Adversarial Robustness）</news:title>
   <news:publication_date>2026-04-11T09:47:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678127</loc>
  <lastmod>2026-04-11T09:47:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所適応学習損失によるセマンティック画像セグメンテーションの改善（Locally Adaptive Learning Loss for Semantic Image Segmentation）</news:title>
   <news:publication_date>2026-04-11T09:47:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678125</loc>
  <lastmod>2026-04-11T09:47:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>サブワード対応ニューラル言語モデルにおける重みの再利用（Reusing Weights in Subword-aware Neural Language Models）</news:title>
   <news:publication_date>2026-04-11T09:47:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678123</loc>
  <lastmod>2026-04-11T09:46:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>D最適設計と近似アルゴリズムの実用性を高める進展（Approximation Algorithms for D-optimal Design）</news:title>
   <news:publication_date>2026-04-11T09:46:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678121</loc>
  <lastmod>2026-04-11T08:55:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>リモートセンシングにおける欠損データ再構成の統一的アプローチ（Missing Data Reconstruction in Remote Sensing image with a Unified Spatial-Temporal-Spectral Deep Convolutional Neural Network）</news:title>
   <news:publication_date>2026-04-11T08:55:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678119</loc>
  <lastmod>2026-04-11T08:54:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>WaveNetは音声をどう理解しているか（Do WaveNets Dream of Acoustic Waves?）</news:title>
   <news:publication_date>2026-04-11T08:54:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678117</loc>
  <lastmod>2026-04-11T08:54:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>予算制約下の入札を強化学習で解く（Budget Constrained Bidding by Model-free Reinforcement Learning in Display Advertising）</news:title>
   <news:publication_date>2026-04-11T08:54:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678115</loc>
  <lastmod>2026-04-11T08:53:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフ上の予測を自動エンコーダで学習する（Learning to Make Predictions on Graphs with Autoencoders）</news:title>
   <news:publication_date>2026-04-11T08:53:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678113</loc>
  <lastmod>2026-04-11T08:53:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>欠搥値を含むデータ解析のための効率的なk-means型アルゴリズム（An efficient k-means-type algorithm for clustering datasets with incomplete records）</news:title>
   <news:publication_date>2026-04-11T08:53:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678111</loc>
  <lastmod>2026-04-11T08:53:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時系列線形システム同定に関する新しい解析視点（Learning Without Mixing: Towards A Sharp Analysis of Linear System Identification）</news:title>
   <news:publication_date>2026-04-11T08:53:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678109</loc>
  <lastmod>2026-04-11T08:52:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>IRASデータを用いたH II領域選別基準の改良（Improved selection criteria for H ii regions, based on IRAS sources）</news:title>
   <news:publication_date>2026-04-11T08:52:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678107</loc>
  <lastmod>2026-04-11T08:01:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>音声ベースの感情認識を深層マルチモーダルで高精度化する（DEEP MULTIMODAL LEARNING FOR EMOTION RECOGNITION IN SPOKEN LANGUAGE）</news:title>
   <news:publication_date>2026-04-11T08:01:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678105</loc>
  <lastmod>2026-04-11T08:01:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多様な探索による迅速かつ安全な方策改善（Diverse Exploration for Fast and Safe Policy Improvement）</news:title>
   <news:publication_date>2026-04-11T08:01:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678103</loc>
  <lastmod>2026-04-11T08:00:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>有限要素法と有限差分法を組み合わせた等方性弾性波のエネルギー保存的シミュレーション手法（Combining finite element and finite difference methods for isotropic elastic wave simulations in an energy-conserving manner）</news:title>
   <news:publication_date>2026-04-11T08:00:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678101</loc>
  <lastmod>2026-04-11T07:59:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>比例ボリュームサンプリングによるA最適設計の近似アルゴリズム（Proportional Volume Sampling and Approximation Algorithms for A-Optimal Design）</news:title>
   <news:publication_date>2026-04-11T07:59:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678099</loc>
  <lastmod>2026-04-11T07:59:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>対麻痺者用外骨格のフィードバック制御によるハンズフリー歩行（Feedback Control of an Exoskeleton for Paraplegics: Toward Robustly Stable Hands-free Dynamic Walking）</news:title>
   <news:publication_date>2026-04-11T07:59:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678097</loc>
  <lastmod>2026-04-11T07:59:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次再帰ニューラルネットワークによる音声音響モデリング（HIGH ORDER RECURRENT NEURAL NETWORKS FOR ACOUSTIC MODELLING）</news:title>
   <news:publication_date>2026-04-11T07:59:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678095</loc>
  <lastmod>2026-04-11T07:59:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>データ駆動の確率的動力学システムの深層学習アルゴリズム（Deep learning algorithm for data-driven simulation of noisy dynamical system）</news:title>
   <news:publication_date>2026-04-11T07:59:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678093</loc>
  <lastmod>2026-04-11T07:07:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層学習が示すホログラフィーの可視化（Deep Learning and AdS/CFT）</news:title>
   <news:publication_date>2026-04-11T07:07:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678091</loc>
  <lastmod>2026-04-11T07:07:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>腫瘍病理画像のベイズ的マーク相互作用モデル（A Bayesian Mark Interaction Model for Analysis of Tumor Pathology Images）</news:title>
   <news:publication_date>2026-04-11T07:07:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678089</loc>
  <lastmod>2026-04-11T07:07:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>構造化制御ネットワークによる方策設計の簡潔化（Structured Control Nets for Deep Reinforcement Learning）</news:title>
   <news:publication_date>2026-04-11T07:07:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678087</loc>
  <lastmod>2026-04-11T07:05:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SPLATNetによる点群処理の効率化（SPLATNet: Sparse Lattice Networks for Point Cloud Processing）</news:title>
   <news:publication_date>2026-04-11T07:05:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678085</loc>
  <lastmod>2026-04-11T07:05:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>継続学習の普遍エージェント「Unicorn」(Unicorn: Continual learning with a universal, off-policy agent)</news:title>
   <news:publication_date>2026-04-11T07:05:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678083</loc>
  <lastmod>2026-04-11T07:04:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動的ネットワークにおける対立と慣習（Conﬂict and Convention in Dynamic Networks）</news:title>
   <news:publication_date>2026-04-11T07:04:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678081</loc>
  <lastmod>2026-04-11T07:04:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機密性を保ったブースティング学習の実装（Confidential Boosting with Random Linear Classifiers）</news:title>
   <news:publication_date>2026-04-11T07:04:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678079</loc>
  <lastmod>2026-04-11T06:12:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複素数を情報損失なく実数化する表現法（Arbitrarily Substantial Number Representation for Complex Number）</news:title>
   <news:publication_date>2026-04-11T06:12:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678077</loc>
  <lastmod>2026-04-11T06:12:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>最適化幾何で示す暗黙的バイアスの特性（Characterizing Implicit Bias in Terms of Optimization Geometry）</news:title>
   <news:publication_date>2026-04-11T06:12:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678075</loc>
  <lastmod>2026-04-11T06:11:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時系列解析における構造付き低ランク行列補完（Structured low-rank matrix completion for forecasting in time series analysis）</news:title>
   <news:publication_date>2026-04-11T06:11:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678073</loc>
  <lastmod>2026-04-11T06:10:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>テンソル場ニューラルネットワーク：3D点群に対する回転・並進等変性ニューラルネットワーク（Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds）</news:title>
   <news:publication_date>2026-04-11T06:10:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678071</loc>
  <lastmod>2026-04-11T06:10:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模バッチ学習と敵対的耐性のヘシアン解析（Hessian-based Analysis of Large Batch Training and Robustness to Adversaries）</news:title>
   <news:publication_date>2026-04-11T06:10:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678069</loc>
  <lastmod>2026-04-11T06:10:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
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   <news:publication_date>2026-04-11T06:10:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
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 </url>
 <url>
  <loc>https://aibr.jp/archives/678067</loc>
  <lastmod>2026-04-11T06:10:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ベクトル場を用いるニューラルネットワーク（Vector Field Based Neural Networks）</news:title>
   <news:publication_date>2026-04-11T06:10:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678065</loc>
  <lastmod>2026-04-11T05:18:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>重なり合う信号を用いた触覚センサの概念と実装（Touch Sensors with Overlapping Signals: Concept Investigation on Planar Sensors with Resistive or Optical Transduction）</news:title>
   <news:publication_date>2026-04-11T05:18:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678063</loc>
  <lastmod>2026-04-11T05:18:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>小脳計算の新しいモデル：学習と忘却が誤差で結びつく（A new model for Cerebellar computation）</news:title>
   <news:publication_date>2026-04-11T05:18:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678061</loc>
  <lastmod>2026-04-11T05:17:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>人間の視覚にも効く敵対的入力の発見（Adversarial Examples that Fool both Computer Vision and Time-Limited Humans）</news:title>
   <news:publication_date>2026-04-11T05:17:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678059</loc>
  <lastmod>2026-04-11T05:16:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>カテゴリカル分布強化学習の解析（An Analysis of Categorical Distributional Reinforcement Learning）</news:title>
   <news:publication_date>2026-04-11T05:16:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678057</loc>
  <lastmod>2026-04-11T05:16:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>投影不要なオンライン最適化と確率的勾配：凸性からサブモジュラまで (Projection-Free Online Optimization with Stochastic Gradient: From Convexity to Submodularity)</news:title>
   <news:publication_date>2026-04-11T05:16:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678055</loc>
  <lastmod>2026-04-11T05:16:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>因果的に生成される定常時系列の学習（Learning Causally-Generated Stationary Time Series）</news:title>
   <news:publication_date>2026-04-11T05:16:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678053</loc>
  <lastmod>2026-04-11T05:15:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>空間的Lambda–Fleming–Viot過程と変動する選択（The spatial Lambda-Fleming-Viot process with fluctuating selection）</news:title>
   <news:publication_date>2026-04-11T05:15:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678051</loc>
  <lastmod>2026-04-11T04:24:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>有限メモリを持つ集団での協調的最良選択学習（Collaboratively Learning the Best Option, Using Bounded Memory）</news:title>
   <news:publication_date>2026-04-11T04:24:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678049</loc>
  <lastmod>2026-04-11T04:23:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>経路特異的反事実的公平性（Path-Specific Counterfactual Fairness）</news:title>
   <news:publication_date>2026-04-11T04:23:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678047</loc>
  <lastmod>2026-04-11T04:23:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>人物再識別のための調和的注意ネットワーク（Harmonious Attention Network for Person Re-Identification）</news:title>
   <news:publication_date>2026-04-11T04:23:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678045</loc>
  <lastmod>2026-04-11T04:23:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層オンライン動画手振れ補正（Deep Online Video Stabilization）</news:title>
   <news:publication_date>2026-04-11T04:23:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678043</loc>
  <lastmod>2026-04-11T04:23:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>支援環境におけるプライバシー保護の深層学習アプローチ（A Deep Learning Approach for Privacy Preservation in Assisted Living）</news:title>
   <news:publication_date>2026-04-11T04:23:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678041</loc>
  <lastmod>2026-04-11T04:22:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>測度空間における最適化としてのサンプリング（Sampling as optimization in the space of measures: The Langevin dynamics as a composite optimization problem）</news:title>
   <news:publication_date>2026-04-11T04:22:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678039</loc>
  <lastmod>2026-04-11T04:22:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>乳癌組織像のディープラーニングによる分類（Classification of Breast Cancer Histology using Deep Learning）</news:title>
   <news:publication_date>2026-04-11T04:22:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678037</loc>
  <lastmod>2026-04-11T03:30:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単語ネットワークとして捉えるトピックモデルの再設計（Learning Topic Models by Neighborhood Aggregation）</news:title>
   <news:publication_date>2026-04-11T03:30:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678035</loc>
  <lastmod>2026-04-11T03:23:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>iisignatureによる反復積分シグネチャとログシグネチャの効率的計算（The iisignature library: efficient calculation of iterated-integral signatures and log signatures）</news:title>
   <news:publication_date>2026-04-11T03:23:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678033</loc>
  <lastmod>2026-04-11T03:22:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>低解像度顔画像の識別を助ける合成手法：MagnifyMe（MagnifyMe: Aiding Cross Resolution Face Recognition via Identity Aware Synthesis）</news:title>
   <news:publication_date>2026-04-11T03:22:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678031</loc>
  <lastmod>2026-04-11T03:21:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層アンサンブルにおける多様性正則化によるキャリブレーション改善（DIVERSITY REGULARIZATION IN DEEP ENSEMBLES）</news:title>
   <news:publication_date>2026-04-11T03:21:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678029</loc>
  <lastmod>2026-04-11T03:21:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>内発的動機とメンタルリプレイによる確率的再帰ネットワークの効率的オンライン適応（Intrinsic Motivation and Mental Replay enable Efficient Online Adaptation in Stochastic Recurrent Networks）</news:title>
   <news:publication_date>2026-04-11T03:21:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678027</loc>
  <lastmod>2026-04-11T03:20:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>低強度LiDARと光子数分解検出器を用いた圧縮センシング（Low Intensity LiDAR using Compressed Sensing and a Photon Number Resolving Detector）</news:title>
   <news:publication_date>2026-04-11T03:20:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678025</loc>
  <lastmod>2026-04-11T03:20:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高性能スパース通信による機械学習の効率化（SPARCML: High-Performance Sparse Communication for Machine Learning）</news:title>
   <news:publication_date>2026-04-11T03:20:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678023</loc>
  <lastmod>2026-04-11T02:29:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スパースな軌跡集合から学ぶルーティング（Learning to Route with Sparse Trajectory Sets）</news:title>
   <news:publication_date>2026-04-11T02:29:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678021</loc>
  <lastmod>2026-04-11T02:29:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>反復平均化によるSGDの正則化効果（Iterate averaging as regularization for stochastic gradient descent）</news:title>
   <news:publication_date>2026-04-11T02:29:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678019</loc>
  <lastmod>2026-04-11T02:28:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Sounderfeit：物理モデルをクローンする条件付き敵対的オートエンコーダ（Sounderfeit: Cloning a Physical Model with Conditional Adversarial Autoencoders）</news:title>
   <news:publication_date>2026-04-11T02:28:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678017</loc>
  <lastmod>2026-04-11T02:28:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SeNA-CNNによる継続学習の実用化（SeNA-CNN: Overcoming Catastrophic Forgetting in Convolutional Neural Networks by Selective Network Augmentation）</news:title>
   <news:publication_date>2026-04-11T02:28:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678015</loc>
  <lastmod>2026-04-11T02:28:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分類器のランダムノイズに対する頑健性（Robustness of classifiers to uniform ℓp and Gaussian noise）</news:title>
   <news:publication_date>2026-04-11T02:28:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678013</loc>
  <lastmod>2026-04-11T02:28:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>水波分散関係の準解析解とHCMTへの応用 (Semi-explicit solutions to the water-wave dispersion relation and their role in the nonlinear Hamiltonian Coupled-Mode theory)</news:title>
   <news:publication_date>2026-04-11T02:28:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678011</loc>
  <lastmod>2026-04-11T02:27:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非剛体物体追跡のための深層マルチスケール時空間識別的サリエンシーマップ（Non-rigid Object Tracking via Deep Multi-scale Spatial-Temporal Discriminative Saliency Maps）</news:title>
   <news:publication_date>2026-04-11T02:27:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678009</loc>
  <lastmod>2026-04-11T01:36:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Visual Analyticsに機械学習を統合する最先端動向（The State of the Art in Integrating Machine Learning into Visual Analytics）</news:title>
   <news:publication_date>2026-04-11T01:36:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678007</loc>
  <lastmod>2026-04-11T01:36:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>アスペクト認識潜在因子モデルによる評価予測の刷新（Aspect-Aware Latent Factor Model: Rating Prediction with Ratings and Reviews）</news:title>
   <news:publication_date>2026-04-11T01:36:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678005</loc>
  <lastmod>2026-04-11T01:35:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>アクチグラフィによる睡眠/覚醒パターン検出（Actigraphy-based Sleep/Wake Pattern Detection using Convolutional Neural Networks）</news:title>
   <news:publication_date>2026-04-11T01:35:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678003</loc>
  <lastmod>2026-04-11T01:35:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層学習顔認識の敵対的攻撃に対する堅牢性の解明（Unravelling Robustness of Deep Learning based Face Recognition Against Adversarial Attacks）</news:title>
   <news:publication_date>2026-04-11T01:35:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/678001</loc>
  <lastmod>2026-04-11T01:35:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>半教師ありセグメンテーションに対する敵対的学習の実践（Adversarial Learning for Semi-Supervised Semantic Segmentation）</news:title>
   <news:publication_date>2026-04-11T01:35:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677999</loc>
  <lastmod>2026-04-11T01:34:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非同期環境におけるビザンチン耐性機械学習（Asynchronous Byzantine Machine Learning）</news:title>
   <news:publication_date>2026-04-11T01:34:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677997</loc>
  <lastmod>2026-04-11T01:34:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非同期確率近似における漸近的バイアス誤差と深層マルチエージェント学習（Asynchronous stochastic approximations with asymptotically biased errors and deep multi-agent learning）</news:title>
   <news:publication_date>2026-04-11T01:34:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677995</loc>
  <lastmod>2026-04-11T00:42:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動画における人物再識別の時間的残差学習（Video Person Re-identification by Temporal Residual Learning）</news:title>
   <news:publication_date>2026-04-11T00:42:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677993</loc>
  <lastmod>2026-04-11T00:32:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散学習に潜む隠れた脆弱性（The Hidden Vulnerability of Distributed Learning in Byzantium）</news:title>
   <news:publication_date>2026-04-11T00:32:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677991</loc>
  <lastmod>2026-04-11T00:31:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>地域化されたマルチアームドバンディットの実用意義（Regional Multi-Armed Bandits）</news:title>
   <news:publication_date>2026-04-11T00:31:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677989</loc>
  <lastmod>2026-04-11T00:31:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ラベルなし画像から類似性を学ぶ深層無監督学習（Deep Unsupervised Learning of Visual Similarities）</news:title>
   <news:publication_date>2026-04-11T00:31:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677987</loc>
  <lastmod>2026-04-11T00:30:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的スパイキングニューラルネットワークに対する敵対的訓練（Adversarial Training for Probabilistic Spiking Neural Networks）</news:title>
   <news:publication_date>2026-04-11T00:30:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677985</loc>
  <lastmod>2026-04-11T00:30:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層学習を用いた1H磁気共鳴スペクトロスコピー画像の超解像（Super-Resolution 1H Magnetic Resonance Spectroscopic Imaging utilizing Deep Learning）</news:title>
   <news:publication_date>2026-04-11T00:30:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677983</loc>
  <lastmod>2026-04-10T23:38:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>視点の雲で行動を読む――ポーズ不要で高精度な行動認識（Glimpse Clouds: Human Activity Recognition from Unstructured Feature Points）</news:title>
   <news:publication_date>2026-04-10T23:38:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677981</loc>
  <lastmod>2026-04-10T23:38:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>L2非拡張ニューラルネットワーク（L2-Nonexpansive Neural Networks）</news:title>
   <news:publication_date>2026-04-10T23:38:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677979</loc>
  <lastmod>2026-04-10T23:37:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>線形回帰混合モデルの学習とほぼ最適な複雑度（Learning Mixtures of Linear Regressions with Nearly Optimal Complexity）</news:title>
   <news:publication_date>2026-04-10T23:37:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677977</loc>
  <lastmod>2026-04-10T23:37:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>フェデレーテッド・メタラーニングによる高速収束と通信効率化（Federated Meta-Learning with Fast Convergence and Efficient Communication）</news:title>
   <news:publication_date>2026-04-10T23:37:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677975</loc>
  <lastmod>2026-04-10T23:36:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>弱教師あり物体局所化を変えたデータ強化の工夫（Improved Techniques for the Weakly-Supervised Object Localization）</news:title>
   <news:publication_date>2026-04-10T23:36:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677973</loc>
  <lastmod>2026-04-10T23:36:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模状態空間を持つマルコフ連鎖のエントロピー率推定（Entropy Rate Estimation for Markov Chains with Large State Space）</news:title>
   <news:publication_date>2026-04-10T23:36:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677971</loc>
  <lastmod>2026-04-10T23:35:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>姿勢不変な3Dマッチングのためのキーポイント検出器と記述子のエンドツーエンド学習（End-to-end learning of keypoint detector and descriptor for pose invariant 3D matching）</news:title>
   <news:publication_date>2026-04-10T23:35:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677969</loc>
  <lastmod>2026-04-10T22:44:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>個別化ガウス過程によるADAS‑Cog13予測（Personalized Gaussian Processes for Forecasting of ADAS‑Cog13）</news:title>
   <news:publication_date>2026-04-10T22:44:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677967</loc>
  <lastmod>2026-04-10T22:44:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>短文ソーシャルメディアのマルチモーダル固有表現認識（Multimodal Named Entity Recognition for Short Social Media Posts）</news:title>
   <news:publication_date>2026-04-10T22:44:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677965</loc>
  <lastmod>2026-04-10T22:43:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューラル予測符号化による話者特性の教師なし学習（Neural Predictive Coding using Convolutional Neural Networks towards Unsupervised Learning of Speaker Characteristics）</news:title>
   <news:publication_date>2026-04-10T22:43:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677963</loc>
  <lastmod>2026-04-10T22:43:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>収束するアクター・クリティックアルゴリズム（Convergent Actor-Critic Algorithms Under Off-Policy Training and Function Approximation）</news:title>
   <news:publication_date>2026-04-10T22:43:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677961</loc>
  <lastmod>2026-04-10T22:43:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ドライバーの手の検出と把持解析（Driver Hand Localization and Grasp Analysis）</news:title>
   <news:publication_date>2026-04-10T22:43:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677959</loc>
  <lastmod>2026-04-10T22:43:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CoVeRによる共変量別単語埋め込みの学習（CoVeR: Learning Covariate-Specific Vector Representations with Tensor Decompositions）</news:title>
   <news:publication_date>2026-04-10T22:43:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677957</loc>
  <lastmod>2026-04-10T22:42:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>通信なしで群れを学習する（Learning to Gather without Communication）</news:title>
   <news:publication_date>2026-04-10T22:42:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677955</loc>
  <lastmod>2026-04-10T21:51:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>方策勾配のための変分推論（Variational Inference for Policy Gradient）</news:title>
   <news:publication_date>2026-04-10T21:51:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677953</loc>
  <lastmod>2026-04-10T21:51:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ESO 428-G014の深いChandra観測が示す大規模X線拡がりのスペクトルと形態（Deep Chandra Observations of ESO 428-G014: II. Spectral Properties and Morphology of the Large-Scale Extended X-ray Emission）</news:title>
   <news:publication_date>2026-04-10T21:51:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677951</loc>
  <lastmod>2026-04-10T21:50:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>説明を学ぶ：モデル解釈に対する情報理論的視点（Learning to Explain: An Information-Theoretic Perspective on Model Interpretation）</news:title>
   <news:publication_date>2026-04-10T21:50:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677949</loc>
  <lastmod>2026-04-10T21:49:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>モデル解釈性の操作と測定（Manipulating and Measuring Model Interpretability）</news:title>
   <news:publication_date>2026-04-10T21:49:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677947</loc>
  <lastmod>2026-04-10T21:49:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>セマンティックセグメンテーションの境界精緻化（Semantic Segmentation Refinement by Monte Carlo Region Growing of High Confidence Detections）</news:title>
   <news:publication_date>2026-04-10T21:49:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677945</loc>
  <lastmod>2026-04-10T21:49:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>感覚データを敏感な推論から守る（Protecting Sensory Data against Sensitive Inferences）</news:title>
   <news:publication_date>2026-04-10T21:49:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677943</loc>
  <lastmod>2026-04-10T21:49:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>連続緩和によるMAP推論の非凸的再考（Continuous Relaxation of MAP Inference: A Nonconvex Perspective）</news:title>
   <news:publication_date>2026-04-10T21:49:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677941</loc>
  <lastmod>2026-04-10T20:57:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習と記憶を見分ける検証法（Detecting Learning vs Memorization in Deep Neural Networks using Shared Structure Validation Sets）</news:title>
   <news:publication_date>2026-04-10T20:57:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677939</loc>
  <lastmod>2026-04-10T20:49:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多端子メムトランジスタによるモノレイヤーMoS2デバイスの実装（Multi-Terminal Memtransistors from Polycrystalline Monolayer MoS2）</news:title>
   <news:publication_date>2026-04-10T20:49:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677937</loc>
  <lastmod>2026-04-10T20:49:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフサンプリングによるモチーフ数の推定（Counting Motifs with Graph Sampling）</news:title>
   <news:publication_date>2026-04-10T20:49:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677935</loc>
  <lastmod>2026-04-10T20:49:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>視覚的顕著性を用いた静止画における人検出の改善（ViS-HuD: Using Visual Saliency to Improve Human Detection with Convolutional Neural Networks）</news:title>
   <news:publication_date>2026-04-10T20:49:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677933</loc>
  <lastmod>2026-04-10T20:48:06Z</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共謀の脅威と実験的証拠（Algorithmic Collusion in Cournot Duopoly Market: Evidence from Experimental Economics）</news:title>
   <news:publication_date>2026-04-10T20:48:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677931</loc>
  <lastmod>2026-04-10T20:47:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>血液ドナーの二値予測に最適な分類器の見極め（Determining the best classifier for predicting the value of a Boolean field on a blood donor database）</news:title>
   <news:publication_date>2026-04-10T20:47:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677929</loc>
  <lastmod>2026-04-10T20:47:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>最も明るい銀河（BCG）の恒星速度分散プロファイルの多様性（Diversity in the stellar velocity dispersion profiles of a large sample of Brightest Cluster Galaxies z ≤0.3）</news:title>
   <news:publication_date>2026-04-10T20:47:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677927</loc>
  <lastmod>2026-04-10T19:56:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューラルガスを用いた球状星団の分類（Neural Gas based classification of Globular Clusters）</news:title>
   <news:publication_date>2026-04-10T19:56:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677925</loc>
  <lastmod>2026-04-10T19:55:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>カスケード予測モデルの近似アルゴリズム（Approximation Algorithms for Cascading Prediction Models）</news:title>
   <news:publication_date>2026-04-10T19:55:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677923</loc>
  <lastmod>2026-04-10T19:54:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数カテゴリ学習における深層畳み込みネットワークの強さ（Learning Multiple Categories on Deep Convolution Networks）</news:title>
   <news:publication_date>2026-04-10T19:54:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677921</loc>
  <lastmod>2026-04-10T19:54:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>天文学におけるデータ氾濫とフォトメトリック赤方偏移の扱い（Data Deluge in Astrophysics: Photometric Redshifts as a Template Use Case）</news:title>
   <news:publication_date>2026-04-10T19:54:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677919</loc>
  <lastmod>2026-04-10T19:54:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>不確実性を学習する確率的ビデオ生成（Stochastic Video Generation with a Learned Prior）</news:title>
   <news:publication_date>2026-04-10T19:54:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677917</loc>
  <lastmod>2026-04-10T19:03:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スケーラブルで頑健な疎部分空間クラスタリング（Scalable and Robust Sparse Subspace Clustering Using Randomized Clustering and Multilayer Graphs）</news:title>
   <news:publication_date>2026-04-10T19:03:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677915</loc>
  <lastmod>2026-04-10T19:02:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>欠如に基づく説明：対照的説明と重要な不在（Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives）</news:title>
   <news:publication_date>2026-04-10T19:02:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677913</loc>
  <lastmod>2026-04-10T19:01:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>順序付き選好探索による意思決定支援（Ordered Preference Elicitation Strategies for Supporting Multi-Objective Decision Making）</news:title>
   <news:publication_date>2026-04-10T19:01:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677911</loc>
  <lastmod>2026-04-10T19:01:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>プログラム推定のための学習的合成（Learning to Synthesize）</news:title>
   <news:publication_date>2026-04-10T19:01:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677909</loc>
  <lastmod>2026-04-10T19:01:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>トップ-k分類に適した滑らかな損失関数（SMOOTH LOSS FUNCTIONS FOR DEEP TOP-K CLASSIFICATION）</news:title>
   <news:publication_date>2026-04-10T19:01:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677907</loc>
  <lastmod>2026-04-10T19:01:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習ポテンシャルと能動学習による結晶構造予測の高速化（Accelerating crystal structure prediction by machine-learning interatomic potentials with active learning）</news:title>
   <news:publication_date>2026-04-10T19:01:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677905</loc>
  <lastmod>2026-04-10T18:10:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>バッチ正規化とバッチ構造が深層畳み込みネットワークの挙動に与える影響（Batch Normalization and the impact of batch structure on the behavior of deep convolution networks）</news:title>
   <news:publication_date>2026-04-10T18:10:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677903</loc>
  <lastmod>2026-04-10T18:09:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DeepASLによる非侵襲・常時利用可能な手話翻訳の実現（DeepASL: Enabling Ubiquitous and Non-Intrusive Word and Sentence-Level Sign Language Translation）</news:title>
   <news:publication_date>2026-04-10T18:09:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677901</loc>
  <lastmod>2026-04-10T18:09:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DeepCWCによる深層特徴と古典表現の協働重み付けによる画像分類（Collaboratively Weighting Deep and Classic Representation）</news:title>
   <news:publication_date>2026-04-10T18:09:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677899</loc>
  <lastmod>2026-04-10T18:08:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動的非線形シミュレータの模擬におけるガウス過程の応用（Emulating dynamic non-linear simulators using Gaussian processes）</news:title>
   <news:publication_date>2026-04-10T18:08:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677897</loc>
  <lastmod>2026-04-10T18:08:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>レコメンドを「アルゴリズムだけ」で考えない発想（Improving Recommender Systems Beyond the Algorithm）</news:title>
   <news:publication_date>2026-04-10T18:08:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677895</loc>
  <lastmod>2026-04-10T18:08:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>行列の対数行列式を速く安定に計算する手法（Variational Bayesian Approximation of Log Determinants）</news:title>
   <news:publication_date>2026-04-10T18:08:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677893</loc>
  <lastmod>2026-04-10T18:07:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>カーネルを用いた普遍的仮説検定の最適性（Universal Hypothesis Testing with Kernels）</news:title>
   <news:publication_date>2026-04-10T18:07:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677891</loc>
  <lastmod>2026-04-10T17:16:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>継続的な生涯学習とニューラルネットワークの挑戦（Continual Lifelong Learning with Neural Networks: A Review）</news:title>
   <news:publication_date>2026-04-10T17:16:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677889</loc>
  <lastmod>2026-04-10T17:16:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>情報理論的コートレーニング（Information Theoretic Co-Training）</news:title>
   <news:publication_date>2026-04-10T17:16:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677887</loc>
  <lastmod>2026-04-10T17:15:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>クリップされた行動に対する方策勾配（Clipped Action Policy Gradient）</news:title>
   <news:publication_date>2026-04-10T17:15:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677885</loc>
  <lastmod>2026-04-10T17:15:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ホモジニアスなモデル群から作るヘテロジニアスアンサンブル（Building heterogeneous ensembles by pooling homogeneous ones）</news:title>
   <news:publication_date>2026-04-10T17:15:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677883</loc>
  <lastmod>2026-04-10T17:15:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>相対論的光曲げモデルはSeyfert銀河のX線変動を説明できるか（Can the relativistic light bending model explain X-ray spectral variations of Seyfert galaxies?）</news:title>
   <news:publication_date>2026-04-10T17:15:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677881</loc>
  <lastmod>2026-04-10T17:14:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>識別的ラベル一貫ドメイン適応（Discriminative Label Consistent Domain Adaptation）</news:title>
   <news:publication_date>2026-04-10T17:14:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677879</loc>
  <lastmod>2026-04-10T17:14:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>指数重みづけの多面性とオンライン学習への応用（The Many Faces of Exponential Weights in Online Learning）</news:title>
   <news:publication_date>2026-04-10T17:14:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677877</loc>
  <lastmod>2026-04-10T16:23:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>積分表現に基づくガウス過程の学習（Learning Integral Representations of Gaussian Processes）</news:title>
   <news:publication_date>2026-04-10T16:23:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677875</loc>
  <lastmod>2026-04-10T16:22:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>BRUNO: 交換可能データのための深い再帰モデル（BRUNO: A Deep Recurrent Model for Exchangeable Data）</news:title>
   <news:publication_date>2026-04-10T16:22:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677873</loc>
  <lastmod>2026-04-10T16:22:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>作用素空間におけるスムーズポイントとBishop‑Phelps‑Bollobás型定理（Smooth Points in Operator Spaces and Some Bishop-Phelps-Bollobás Type Theorems in Banach Spaces）</news:title>
   <news:publication_date>2026-04-10T16:22:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677871</loc>
  <lastmod>2026-04-10T16:21:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次元カオス系のデータ駆動予測（Data-Driven Forecasting of High-Dimensional Chaotic Systems with Long-Short Term Memory Networks）</news:title>
   <news:publication_date>2026-04-10T16:21:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677869</loc>
  <lastmod>2026-04-10T16:21:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>敵対的分類に対するリスク分析的アプローチ（Adversarial classification: An adversarial risk analysis approach）</news:title>
   <news:publication_date>2026-04-10T16:21:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677867</loc>
  <lastmod>2026-04-10T16:21:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>視覚と触覚の特徴共有による布地のテクスチャ認識（ViTac: Feature Sharing between Vision and Tactile Sensing for Cloth Texture Recognition）</news:title>
   <news:publication_date>2026-04-10T16:21:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677865</loc>
  <lastmod>2026-04-10T16:21:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模グラフのスペクトル近似とコースニングの理論的保証（Spectrally approximating large graphs with smaller graphs）</news:title>
   <news:publication_date>2026-04-10T16:21:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677863</loc>
  <lastmod>2026-04-10T15:29:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>混雑した細胞のインスタンス分割のための多クラス重み付き損失（MULTICLASS WEIGHTED LOSS FOR INSTANCE SEGMENTATION OF CLUTTERED CELLS）</news:title>
   <news:publication_date>2026-04-10T15:29:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677861</loc>
  <lastmod>2026-04-10T15:20:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Lassoの高速ソルバー Celer（Celer: a Fast Solver for the Lasso with Dual Extrapolation）</news:title>
   <news:publication_date>2026-04-10T15:20:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677859</loc>
  <lastmod>2026-04-10T15:19:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>インターネット広告学習システムによるがんスクリーニング（Screening for cancer using a learning Internet advertising system）</news:title>
   <news:publication_date>2026-04-10T15:19:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677857</loc>
  <lastmod>2026-04-10T15:18:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>規模拡張された分割・結合MCMCと局所性敏感サンプリング（Scaling-up Split-Merge MCMC with Locality Sensitive Sampling (LSS)）</news:title>
   <news:publication_date>2026-04-10T15:18:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677855</loc>
  <lastmod>2026-04-10T15:18:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>内発的動機と自己認識で「遊ぶ」エージェントの学習（Learning to Play With Intrinsically-Motivated, Self-Aware Agents）</news:title>
   <news:publication_date>2026-04-10T15:18:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677853</loc>
  <lastmod>2026-04-10T15:18:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>好奇心駆動の内発的動機から生じる構造化行動（Emergence of Structured Behaviors from Curiosity-Based Intrinsic Motivation）</news:title>
   <news:publication_date>2026-04-10T15:18:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/677851</loc>
  <lastmod>2026-04-10T15:17:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>画像依存ラベル空間の学習によるマルチラベル分類（Learning Image Conditioned Label Space for Multilabel Classification）</news:title>
   <news:publication_date>2026-04-10T15:17:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
</urlset>
