<?xml version="1.0" encoding="UTF-8"?>
<!--generator='jetpack-15.8-a.3'-->
<!--Jetpack_Sitemap_Buffer_News_XMLWriter-->
<?xml-stylesheet type="text/xsl" href="//aibr.jp/news-sitemap.xsl"?>
<urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9" xmlns:news="http://www.google.com/schemas/sitemap-news/0.9" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.sitemaps.org/schemas/sitemap/0.9 http://www.sitemaps.org/schemas/sitemap/0.9/sitemap.xsd">
 <url>
  <loc>https://aibr.jp/archives/680954</loc>
  <lastmod>2026-04-19T03:09:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単一画像の被写界深度にも効く濃霧除去の新潮流（A Cascaded Convolutional Neural Network for Single Image Dehazing）</news:title>
   <news:publication_date>2026-04-19T03:09:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680952</loc>
  <lastmod>2026-04-19T03:09:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>定常ステップサイズ下におけるランダム再シャッフル学習（Stochastic Learning under Random Reshuffling with Constant Step-sizes）</news:title>
   <news:publication_date>2026-04-19T03:09:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680950</loc>
  <lastmod>2026-04-19T03:09:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>堅牢な単調逐次最大化（Resilient Monotone Sequential Maximization）</news:title>
   <news:publication_date>2026-04-19T03:09:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680948</loc>
  <lastmod>2026-04-19T03:09:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>End-to-End Video Captioning with Multitask Reinforcement Learning（End-to-End Video Captioning with Multitask Reinforcement Learning）</news:title>
   <news:publication_date>2026-04-19T03:09:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680946</loc>
  <lastmod>2026-04-19T03:09:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>アイテム選別のためのクラウドと機械の協働（Crowd-Machine Collaboration for Item Screening）</news:title>
   <news:publication_date>2026-04-19T03:09:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680944</loc>
  <lastmod>2026-04-19T03:08:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>故障検出性の高いミューテント選択（Selecting Fault Revealing Mutants）</news:title>
   <news:publication_date>2026-04-19T03:08:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680942</loc>
  <lastmod>2026-04-19T03:08:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>HATSによるイベントベース画像認識の堅牢化（HATS: Histograms of Averaged Time Surfaces for Robust Event-based Object Classification）</news:title>
   <news:publication_date>2026-04-19T03:08:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680940</loc>
  <lastmod>2026-04-19T02:17:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>標本画像から種と形質を読み取る試み（Taxon and trait recognition from digitized herbarium specimens using deep convolutional neural networks）</news:title>
   <news:publication_date>2026-04-19T02:17:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680938</loc>
  <lastmod>2026-04-19T02:17:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>血液検査から創傷部感染を非教師ありで見抜く多変量時系列カーネル法（An Unsupervised Multivariate Time Series Kernel Approach for Identifying Patients with Surgical Site Infection from Blood Samples）</news:title>
   <news:publication_date>2026-04-19T02:17:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680936</loc>
  <lastmod>2026-04-19T02:16:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時期依存のエンティティ要素推薦—イベント中心の複数モデル（Multiple Models for Recommending Temporal Aspects of Entities）</news:title>
   <news:publication_date>2026-04-19T02:16:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680934</loc>
  <lastmod>2026-04-19T02:16:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時系列トピックモデルの拡張とスケーラブル推論（Scalable Generalized Dynamic Topic Models）</news:title>
   <news:publication_date>2026-04-19T02:16:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680932</loc>
  <lastmod>2026-04-19T02:16:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>入院再入院予測におけるデータカテゴリの貢献（Contribution of Data Categories to Readmission Prediction Accuracy）</news:title>
   <news:publication_date>2026-04-19T02:16:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680930</loc>
  <lastmod>2026-04-19T02:15:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ベイズネットワークの効率的なサンプリングと構造学習（Efficient Sampling and Structure Learning of Bayesian Networks）</news:title>
   <news:publication_date>2026-04-19T02:15:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680928</loc>
  <lastmod>2026-04-19T02:14:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多変量時系列の表現と分類を変えるリザバー・モデル空間（Reservoir computing approaches for representation and classification of multivariate time series）</news:title>
   <news:publication_date>2026-04-19T02:14:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680926</loc>
  <lastmod>2026-04-19T01:23:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Expeditious Generation of Knowledge Graph Embeddings（Expeditious Generation of Knowledge Graph Embeddings）</news:title>
   <news:publication_date>2026-04-19T01:23:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680924</loc>
  <lastmod>2026-04-19T01:19:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ベクトル値カーネル空間におけるマルチビュー計量学習 (Multi-view Metric Learning in Vector-valued Kernel Spaces)</news:title>
   <news:publication_date>2026-04-19T01:19:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680922</loc>
  <lastmod>2026-04-19T01:19:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GANの理論的性質（Some Theoretical Properties of GANs）</news:title>
   <news:publication_date>2026-04-19T01:19:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680920</loc>
  <lastmod>2026-04-19T01:19:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>周辺分布の一部に高次相関を導入する手法（Introducing higher order correlations to marginals&amp;#039; subset of multivariate data by means of Archimedean copulas）</news:title>
   <news:publication_date>2026-04-19T01:19:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680918</loc>
  <lastmod>2026-04-19T01:19:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>骨格データとResNetによる行動認識の実装と意義（Learning and Recognizing Human Action from Skeleton Movement with Deep Residual Neural Networks）</news:title>
   <news:publication_date>2026-04-19T01:19:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680916</loc>
  <lastmod>2026-04-19T01:18:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>骨格データからの行動認識における深層残差ネットワークの活用（Exploiting deep residual networks for human action recognition from skeletal data）</news:title>
   <news:publication_date>2026-04-19T01:18:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680914</loc>
  <lastmod>2026-04-19T01:18:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>耳認証のドメイン適応と二段階ファインチューニング（Domain Adaptation for Ear Recognition using Deep CNNs）</news:title>
   <news:publication_date>2026-04-19T01:18:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680912</loc>
  <lastmod>2026-04-19T00:27:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スパース系同定のサンプル複雑性（Sample Complexity of Sparse System Identification Problem）</news:title>
   <news:publication_date>2026-04-19T00:27:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680910</loc>
  <lastmod>2026-04-19T00:27:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>RNNを訓練して空間局在を学ばせると格子状表現が自発的に現れる（EMERGENCE OF GRID-LIKE REPRESENTATIONS BY TRAINING RECURRENT NEURAL NETWORKS TO PERFORM SPATIAL LOCALIZATION）</news:title>
   <news:publication_date>2026-04-19T00:27:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680908</loc>
  <lastmod>2026-04-19T00:27:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ρ-hot辞書埋め込みと二層LSTMによる感情解析の進展（ρ-hot Lexicon Embedding-based Two-level LSTM for Sentiment Analysis）</news:title>
   <news:publication_date>2026-04-19T00:27:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680906</loc>
  <lastmod>2026-04-19T00:26:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>適応型逐次MCMCによる状態とパラメータ同時推定（Adaptive Sequential MCMC for Combined State and Parameter Estimation）</news:title>
   <news:publication_date>2026-04-19T00:26:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680904</loc>
  <lastmod>2026-04-19T00:26:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>先を見てから飛べ（Look Before You Leap: Bridging Model-Free and Model-Based Reinforcement Learning for Planned-Ahead Vision-and-Language Navigation）</news:title>
   <news:publication_date>2026-04-19T00:26:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680902</loc>
  <lastmod>2026-04-19T00:26:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CNNの形状バイアスの検証（Assessing Shape Bias Property of Convolutional Neural Networks）</news:title>
   <news:publication_date>2026-04-19T00:26:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680900</loc>
  <lastmod>2026-04-19T00:26:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>PyramidBoxによる文脈支援型シングルショット顔検出の要点（PyramidBox: A Context-assisted Single Shot Face Detector）</news:title>
   <news:publication_date>2026-04-19T00:26:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680898</loc>
  <lastmod>2026-04-18T23:34:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>回転予測による教師なし表現学習（Unsupervised Representation Learning by Predicting Image Rotations）</news:title>
   <news:publication_date>2026-04-18T23:34:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680896</loc>
  <lastmod>2026-04-18T23:34:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ランダム初期化で解ける位相復元 — Gradient Descent with Random Initialization: Fast Global Convergence for Nonconvex Phase Retrieval（Gradient Descent with Random Initialization: Fast Global Convergence for Nonconvex Phase Retrieval）</news:title>
   <news:publication_date>2026-04-18T23:34:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680894</loc>
  <lastmod>2026-04-18T23:33:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Attention on Attention: Visual Question Answeringの注意機構改良がもたらす実務上の示唆（Attention on Attention: Architectures for Visual Question Answering）</news:title>
   <news:publication_date>2026-04-18T23:33:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680892</loc>
  <lastmod>2026-04-18T23:33:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Generative Adversarial Talking Head（Generative Adversarial Talking Head: Bringing Portraits to Life with a Weakly Supervised Neural Network）</news:title>
   <news:publication_date>2026-04-18T23:33:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680890</loc>
  <lastmod>2026-04-18T23:33:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>データ駆動型計算手法：パラメータとオペレータ推定（Data-Driven Computational Methods: Parameter and Operator Estimations）</news:title>
   <news:publication_date>2026-04-18T23:33:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680888</loc>
  <lastmod>2026-04-18T23:33:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>離散データにおける因果推論の実務的要点（Causal Inference on Discrete Data via Estimating Distance Correlations）</news:title>
   <news:publication_date>2026-04-18T23:33:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680886</loc>
  <lastmod>2026-04-18T23:32:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>カメラ影響のモデリングによる合成データからの視覚学習改善（Modeling Camera Effects to Improve Visual Learning from Synthetic Data）</news:title>
   <news:publication_date>2026-04-18T23:32:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680884</loc>
  <lastmod>2026-04-18T22:41:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的グラフィカルモデルにおける推論をGNNで学習する（Inference in Probabilistic Graphical Models by Graph Neural Networks）</news:title>
   <news:publication_date>2026-04-18T22:41:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680882</loc>
  <lastmod>2026-04-18T22:41:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>弱教師ありによる医用画像診断と局所化の多解像度アプローチ（Weakly Supervised Medical Diagnosis and Localization from Multiple Resolutions）</news:title>
   <news:publication_date>2026-04-18T22:41:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680880</loc>
  <lastmod>2026-04-18T22:41:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自動ブラケット撮影からの頑健な深度推定（Robust Depth Estimation from Auto Bracketed Images）</news:title>
   <news:publication_date>2026-04-18T22:41:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680878</loc>
  <lastmod>2026-04-18T22:40:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>不完全経路観測からの逆最適制御（Inverse Optimal Control from Incomplete Trajectory Observations）</news:title>
   <news:publication_date>2026-04-18T22:40:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680876</loc>
  <lastmod>2026-04-18T22:40:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>パーソナライゼーションに向けた製品特性の記述（Product Characterisation towards Personalisation）</news:title>
   <news:publication_date>2026-04-18T22:40:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680874</loc>
  <lastmod>2026-04-18T22:40:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>視覚と触覚を融合した3D形状推定による把持・操作の向上（Multi-Modal Geometric Learning for Grasping and Manipulation）</news:title>
   <news:publication_date>2026-04-18T22:40:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680872</loc>
  <lastmod>2026-04-18T22:39:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>PaaSクラウドのビジネス視点（PaaS Cloud — The Business Perspective）</news:title>
   <news:publication_date>2026-04-18T22:39:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680870</loc>
  <lastmod>2026-04-18T21:48:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>室温バルク半導体における励起子制御とコヒーレント歪みパルス（Exciton Control in a Room-Temperature Bulk Semiconductor with Coherent Strain Pulses）</news:title>
   <news:publication_date>2026-04-18T21:48:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680868</loc>
  <lastmod>2026-04-18T21:48:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>FPGA上での効率的なRNN実装：ブロック巡回行列による圧縮と加速（EFFICIENT RECURRENT NEURAL NETWORKS USING STRUCTURED MATRICES IN FPGAs）</news:title>
   <news:publication_date>2026-04-18T21:48:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680866</loc>
  <lastmod>2026-04-18T21:48:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高相関設計と整列性を持つ回帰問題のグラフベース正則化（Graph-based regularization for regression problems with alignment and highly-correlated designs）</news:title>
   <news:publication_date>2026-04-18T21:48:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680864</loc>
  <lastmod>2026-04-18T21:47:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CADから学ぶロボット組立（Learning Robotic Assembly from CAD）</news:title>
   <news:publication_date>2026-04-18T21:47:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680862</loc>
  <lastmod>2026-04-18T21:47:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>AllenNLPが切り開く研究プラットフォームの標準化（AllenNLP: A Deep Semantic Natural Language Processing Platform）</news:title>
   <news:publication_date>2026-04-18T21:47:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680860</loc>
  <lastmod>2026-04-18T21:47:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ランダム化EMによるドメイン適応（Domain Adaptation with Randomized Expectation Maximization）</news:title>
   <news:publication_date>2026-04-18T21:47:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680858</loc>
  <lastmod>2026-04-18T21:46:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大きなサンプリング領域を持つ動的フィルタリング（Dynamic Filtering with Large Sampling Field for ConvNets）</news:title>
   <news:publication_date>2026-04-18T21:46:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680856</loc>
  <lastmod>2026-04-18T20:55:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>モバイルロボット応用における深層学習技術の総覧（A Survey of Deep Learning Techniques for Mobile Robot Applications）</news:title>
   <news:publication_date>2026-04-18T20:55:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680854</loc>
  <lastmod>2026-04-18T20:48:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オンライン学習における十分統計とBurkholder法（Online Learning: Sufficient Statistics and the Burkholder Method）</news:title>
   <news:publication_date>2026-04-18T20:48:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680852</loc>
  <lastmod>2026-04-18T20:47:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>プログラム的弱い教師あり学習によるマルチエージェント軌跡生成（Generating Multi-Agent Trajectories Using Programmatic Weak Supervision）</news:title>
   <news:publication_date>2026-04-18T20:47:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680850</loc>
  <lastmod>2026-04-18T20:47:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>視覚的直感物理理解のベンチマーク：IntPhys 2019（IntPhys 2019: A Benchmark for Visual Intuitive Physics Understanding）</news:title>
   <news:publication_date>2026-04-18T20:47:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680848</loc>
  <lastmod>2026-04-18T20:46:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複雑語識別にカーネル学習を用いる方法（UnibucKernel: A kernel-based learning method for complex word identification）</news:title>
   <news:publication_date>2026-04-18T20:46:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680846</loc>
  <lastmod>2026-04-18T20:46:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>行列補完のLeave-One-Out解析（Leave-one-out Approach for Matrix Completion: Primal and Dual Analysis）</news:title>
   <news:publication_date>2026-04-18T20:46:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680844</loc>
  <lastmod>2026-04-18T20:46:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>天体重力レンズとしての全天監視データ活用とそのインパクト（Gravitationally Lensed Quasars in Gaia: II. Discovery of 24 Lensed Quasars）</news:title>
   <news:publication_date>2026-04-18T20:46:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680842</loc>
  <lastmod>2026-04-18T19:54:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>潜在変数ガウス過程によるメタ強化学習（Meta Reinforcement Learning with Latent Variable Gaussian Processes）</news:title>
   <news:publication_date>2026-04-18T19:54:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680840</loc>
  <lastmod>2026-04-18T19:53:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>次元学習を探る：罰則付き確率的主成分分析による次元推定（Exploring dimension learning via a penalized probabilistic principal component analysis）</news:title>
   <news:publication_date>2026-04-18T19:53:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680838</loc>
  <lastmod>2026-04-18T19:53:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>カテゴリ固有メッシュ再構築の学習（Learning Category-Specific Mesh Reconstruction from Image Collections）</news:title>
   <news:publication_date>2026-04-18T19:53:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680836</loc>
  <lastmod>2026-04-18T19:52:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>有界次数DAGにおけるブロードキャスト（Broadcasting on Bounded Degree DAGs）</news:title>
   <news:publication_date>2026-04-18T19:52:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680834</loc>
  <lastmod>2026-04-18T19:52:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>p波相互作用フェルミ気体における三体散逸のユニタリ制限挙動（Unitarity-limited behavior of three-body collisions in a p-wave interacting Fermi gas）</news:title>
   <news:publication_date>2026-04-18T19:52:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680832</loc>
  <lastmod>2026-04-18T19:52:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>線毛運動解析のためのスタック型ニューラルネットワーク（STACKED NEURAL NETWORKS FOR END-TO-END CILIARY MOTION ANALYSIS）</news:title>
   <news:publication_date>2026-04-18T19:52:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680830</loc>
  <lastmod>2026-04-18T19:52:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>説明を求める権利からより良い意思決定の権利へ（Enslaving the Algorithm: From a “Right to an Explanation” to a “Right to Better Decisions”?）</news:title>
   <news:publication_date>2026-04-18T19:52:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680828</loc>
  <lastmod>2026-04-18T19:00:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>データベース内学習AC/DCの実務的意義（AC/DC: In-Database Learning Thunderstruck）</news:title>
   <news:publication_date>2026-04-18T19:00:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680826</loc>
  <lastmod>2026-04-18T18:51:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ステレオと単眼深度推定の自己教師学習による融合（Fusion of stereo and still monocular depth estimates）</news:title>
   <news:publication_date>2026-04-18T18:51:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680824</loc>
  <lastmod>2026-04-18T18:51:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動的価格設定と競争環境での学習（Dynamic Pricing and Learning with Competition: Insights from the Dynamic Pricing Challenge）</news:title>
   <news:publication_date>2026-04-18T18:51:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680822</loc>
  <lastmod>2026-04-18T18:51:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DeepGaugeが示す深層学習テストの定量基準（DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems）</news:title>
   <news:publication_date>2026-04-18T18:51:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680820</loc>
  <lastmod>2026-04-18T18:50:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自然勾配を用いた深層Q学習（Natural Gradient Deep Q-learning）</news:title>
   <news:publication_date>2026-04-18T18:50:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680818</loc>
  <lastmod>2026-04-18T18:50:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文と動画から俳優と行動をピクセル単位で分離する手法（Actor and Action Video Segmentation from a Sentence）</news:title>
   <news:publication_date>2026-04-18T18:50:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680816</loc>
  <lastmod>2026-04-18T18:50:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>視覚プロセスの動的変分オートエンコーダ（DYNAMIC VARIATIONAL AUTOENCODERS FOR VISUAL PROCESS MODELING）</news:title>
   <news:publication_date>2026-04-18T18:50:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680814</loc>
  <lastmod>2026-04-18T17:58:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>視覚質問応答に説明を加える手法（VQA-E: Explaining, Elaborating, and Enhancing Your Answers for Visual Questions）</news:title>
   <news:publication_date>2026-04-18T17:58:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680812</loc>
  <lastmod>2026-04-18T17:58:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>光学顕微鏡における半盲目空間可変デコンボリューション（SEMI-BLIND SPATIALLY-VARIANT DECONVOLUTION IN OPTICAL MICROSCOPY）</news:title>
   <news:publication_date>2026-04-18T17:58:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680810</loc>
  <lastmod>2026-04-18T17:58:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>MLtunerによるトレーニング自動チューニングの実務的意義（MLtuner: System Support for Automatic Machine Learning Tuning）</news:title>
   <news:publication_date>2026-04-18T17:58:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680808</loc>
  <lastmod>2026-04-18T17:57:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>パッチベースの画像インペインティング（Patch-Based Image Inpainting with Generative Adversarial Networks）</news:title>
   <news:publication_date>2026-04-18T17:57:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680806</loc>
  <lastmod>2026-04-18T17:57:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>海洋渦の検出と追跡におけるニューラルネットワークの適用（OCEAN EDDY IDENTIFICATION AND TRACKING USING NEURAL NETWORKS）</news:title>
   <news:publication_date>2026-04-18T17:57:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680804</loc>
  <lastmod>2026-04-18T17:57:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>VANDELSサーベイが切り拓いた高赤方偏移銀河の深度分光学（The VANDELS spectroscopic survey）</news:title>
   <news:publication_date>2026-04-18T17:57:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680802</loc>
  <lastmod>2026-04-18T17:56:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>距離志向カーマンフィルタ粒子群最適化法（Distance-Oriented Kalman Filter Particle Swarm Optimizer）</news:title>
   <news:publication_date>2026-04-18T17:56:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680800</loc>
  <lastmod>2026-04-18T17:05:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>顔画像で成人判定を行うクラス特異平均オートエンコーダ（Are you eligible? Predicting adulthood from face images via Class Specific Mean Autoencoder）</news:title>
   <news:publication_date>2026-04-18T17:05:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680798</loc>
  <lastmod>2026-04-18T17:05:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>VANDELS ESO公開スペクトロスコピー調査の観測と最初のデータ公開（The VANDELS ESO public spectroscopic survey: observations and first data release）</news:title>
   <news:publication_date>2026-04-18T17:05:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680796</loc>
  <lastmod>2026-04-18T17:05:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>顔属性予測のためのResidual Codeanオートエンコーダ（Residual Codean Autoencoder for Facial Attribute Analysis）</news:title>
   <news:publication_date>2026-04-18T17:05:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680794</loc>
  <lastmod>2026-04-18T17:04:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>部分サンプリングで加速するFrank–Wolfe法の実用性（Frank-Wolfe with Subsampling Oracle）</news:title>
   <news:publication_date>2026-04-18T17:04:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680792</loc>
  <lastmod>2026-04-18T17:03:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スポンサー広告ランキング最適化の深層強化学習（Optimizing Sponsored Search Ranking Strategy by Deep Reinforcement Learning）</news:title>
   <news:publication_date>2026-04-18T17:03:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680790</loc>
  <lastmod>2026-04-18T17:03:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層畳み込み特徴の適応共重み付けによる物体検索の改良（Adaptive Co-Weighting Deep Convolutional Features for Object Retrieval）</news:title>
   <news:publication_date>2026-04-18T17:03:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680788</loc>
  <lastmod>2026-04-18T17:03:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自動化・機械学習・高性能計算による材料開発の高速化（Accelerating Materials Development via Automation, Machine Learning, and High-Performance Computing）</news:title>
   <news:publication_date>2026-04-18T17:03:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680786</loc>
  <lastmod>2026-04-18T16:11:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ロジスティック回帰のリスクとパラメータ収束（Risk and parameter convergence of logistic regression）</news:title>
   <news:publication_date>2026-04-18T16:11:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680784</loc>
  <lastmod>2026-04-18T16:11:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>組織学画像の自動分割と線維化同定を実現する軽量CNN（Segmentation of histological images and fibrosis identification with a convolutional neural network）</news:title>
   <news:publication_date>2026-04-18T16:11:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680782</loc>
  <lastmod>2026-04-18T16:11:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフ学習におけるゲート付き注意機構の提案（GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs）</news:title>
   <news:publication_date>2026-04-18T16:11:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680780</loc>
  <lastmod>2026-04-18T16:09:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ラベルのない現場カメラ網で機能する人物再識別（Unsupervised Cross-dataset Person Re-identiﬁcation by Transfer Learning of Spatial-Temporal Patterns）</news:title>
   <news:publication_date>2026-04-18T16:09:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680778</loc>
  <lastmod>2026-04-18T16:09:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層学習における交絡因子除去が医療予測を改善する仕組み（Removing Confounding Factors Associated Weights in Deep Neural Networks Improves the Prediction Accuracy for Healthcare Applications）</news:title>
   <news:publication_date>2026-04-18T16:09:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680776</loc>
  <lastmod>2026-04-18T16:09:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自然資源ガバナンスのためのサイバネティクス基盤（Towards a Cybernetic Foundation for Natural Resource Governance）</news:title>
   <news:publication_date>2026-04-18T16:09:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680774</loc>
  <lastmod>2026-04-18T16:08:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Flex-Convolutionによる百万規模点群学習（Flex-Convolution Million-Scale Point-Cloud Learning Beyond Grid-Worlds）</news:title>
   <news:publication_date>2026-04-18T16:08:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680772</loc>
  <lastmod>2026-04-18T15:17:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模合成コーパス eSCAPE（eSCAPE: a Large-scale Synthetic Corpus for Automatic Post-Editing）</news:title>
   <news:publication_date>2026-04-18T15:17:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680770</loc>
  <lastmod>2026-04-18T15:16:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動的メモリネットワークによる物体追跡の学習（Learning Dynamic Memory Networks for Object Tracking）</news:title>
   <news:publication_date>2026-04-18T15:16:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680768</loc>
  <lastmod>2026-04-18T15:15:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>転移学習で顔の美的評価を予測する手法の解説（Transferring Rich Deep Features for Facial Beauty Prediction）</news:title>
   <news:publication_date>2026-04-18T15:15:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680766</loc>
  <lastmod>2026-04-18T15:15:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>UAV群の協調学習によるフィールドカバレッジ最適化（Cooperative and Distributed Reinforcement Learning of Drones for Field Coverage）</news:title>
   <news:publication_date>2026-04-18T15:15:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680764</loc>
  <lastmod>2026-04-18T15:15:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>二相流シミュレーション手法の比較：DIとVOFの実務的選択（Comparison between the diffuse interface and volume of fluid methods for simulating two-phase flows）</news:title>
   <news:publication_date>2026-04-18T15:15:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680762</loc>
  <lastmod>2026-04-18T15:15:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>疎性を持つ縮約ランク回帰と非凸正則化（Sparse Reduced Rank Regression With Nonconvex Regularization）</news:title>
   <news:publication_date>2026-04-18T15:15:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680760</loc>
  <lastmod>2026-04-18T15:14:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>行動依存の因子化ベースラインによる政策勾配の分散削減（VARIANCE REDUCTION FOR POLICY GRADIENT WITH ACTION-DEPENDENT FACTORIZED BASELINES）</news:title>
   <news:publication_date>2026-04-18T15:14:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680758</loc>
  <lastmod>2026-04-18T14:22:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層的計量学習とマッチングによる2D/3D幾何対応（Hierarchical Metric Learning and Matching for 2D and 3D Geometric Correspondences）</news:title>
   <news:publication_date>2026-04-18T14:22:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680756</loc>
  <lastmod>2026-04-18T14:22:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械的プログラミングの三本柱（The Three Pillars of Machine Programming）</news:title>
   <news:publication_date>2026-04-18T14:22:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680754</loc>
  <lastmod>2026-04-18T14:22:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スライド検査の自動品質評価を実務に活かす（SlideNet: Fast and Accurate Slide Quality Assessment Based on Deep Neural Networks）</news:title>
   <news:publication_date>2026-04-18T14:22:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680752</loc>
  <lastmod>2026-04-18T14:22:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Cu/Sn/Cu微小接合における界面相成長と空孔進化の解明（On the Interfacial Phase Growth and Vacancy Evolution during Accelerated Electromigration in Cu/Sn/Cu Microjoints）</news:title>
   <news:publication_date>2026-04-18T14:22:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680750</loc>
  <lastmod>2026-04-18T14:22:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>赤外線画像の多視点ATRにおける協調スパース事前分布（COLLABORATIVE SPARSE PRIORS FOR INFRARED IMAGE MULTI-VIEW ATR）</news:title>
   <news:publication_date>2026-04-18T14:22:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680748</loc>
  <lastmod>2026-04-18T14:21:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オブジェクトの階層的な部分を学ぶ深い非滑らか非負行列因子分解（Learning the Hierarchical Parts of Objects by Deep Non-Smooth Nonnegative Matrix Factorization）</news:title>
   <news:publication_date>2026-04-18T14:21:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680746</loc>
  <lastmod>2026-04-18T14:21:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>モンテカルロ情報幾何学（Monte Carlo Information Geometry: The dually flat case）</news:title>
   <news:publication_date>2026-04-18T14:21:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680744</loc>
  <lastmod>2026-04-18T13:30:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時間情報を取り入れたフレーム補完ネットワーク（A Temporally-Aware Interpolation Network for Video Frame Inpainting）</news:title>
   <news:publication_date>2026-04-18T13:30:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680742</loc>
  <lastmod>2026-04-18T13:30:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>運動量空間レナormalization群変換によるベイズ画像モデリングの高速化（Momentum-Space Renormalization Group Transformation in Bayesian Image Modeling by Gaussian Graphical Model）</news:title>
   <news:publication_date>2026-04-18T13:30:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680740</loc>
  <lastmod>2026-04-18T13:29:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>リアルタイムバースト写真のベストショット選択（Real-time Burst Photo Selection Using a Light-Head Adversarial Network）</news:title>
   <news:publication_date>2026-04-18T13:29:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680738</loc>
  <lastmod>2026-04-18T13:29:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SGNMTデコーダの多用途性と応用（Why not be Versatile? Applications of the SGNMT Decoder for Machine Translation）</news:title>
   <news:publication_date>2026-04-18T13:29:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680736</loc>
  <lastmod>2026-04-18T13:29:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>制約充足問題に基づくニューラルネットワーク訓練法（Training Recurrent Neural Networks as a Constraint Satisfaction Problem）</news:title>
   <news:publication_date>2026-04-18T13:29:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680734</loc>
  <lastmod>2026-04-18T13:29:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動的原子に基づくネットワークによる映像予測（DYAN: A Dynamical Atoms-Based Network For Video Prediction）</news:title>
   <news:publication_date>2026-04-18T13:29:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680732</loc>
  <lastmod>2026-04-18T13:28:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所銀河サンプルによる深堀り表面光度解析の知見（The Lyman Alpha Reference Sample IX: Revelations from deep surface photometry）</news:title>
   <news:publication_date>2026-04-18T13:28:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680730</loc>
  <lastmod>2026-04-18T12:37:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>フィボナッチ数を計算する十二の手法（Twelve Simple Algorithms to Compute Fibonacci Numbers）</news:title>
   <news:publication_date>2026-04-18T12:37:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680728</loc>
  <lastmod>2026-04-18T12:37:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>注意重み付き時系列畳み込みニューラルネットワークによる行動認識の実装と示唆（Attention-based Temporal Weighted Convolutional Neural Network for Action Recognition）</news:title>
   <news:publication_date>2026-04-18T12:37:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680726</loc>
  <lastmod>2026-04-18T12:37:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>肺結節の診断分類を変えた3Dニューラルネットワーク（DIAGNOSTIC CLASSIFICATION OF LUNG NODULES USING 3D NEURAL NETWORKS）</news:title>
   <news:publication_date>2026-04-18T12:37:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680724</loc>
  <lastmod>2026-04-18T12:35:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非常に大きな高赤方偏移銀河の発見とその示唆（Discovery of a Very Large Galaxy at z = 3.72）</news:title>
   <news:publication_date>2026-04-18T12:35:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680722</loc>
  <lastmod>2026-04-18T12:35:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>逆行列的GMMを敵対的に学習する手法（Adversarial Generalized Method of Moments）</news:title>
   <news:publication_date>2026-04-18T12:35:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680720</loc>
  <lastmod>2026-04-18T12:35:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>希少イベントに報いる自動カリキュラム学習（Automated Curriculum Learning by Rewarding Temporally Rare Events）</news:title>
   <news:publication_date>2026-04-18T12:35:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680718</loc>
  <lastmod>2026-04-18T12:35:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>レンジベースのボラティリティ推定値の予測可能性とRNNによる解析（Exploring the predictability of range-based volatility estimators using RNNs）</news:title>
   <news:publication_date>2026-04-18T12:35:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680716</loc>
  <lastmod>2026-04-18T11:42:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Box-Cox ガウス過程による非ガウス時系列学習（Learning non-Gaussian Time Series using the Box-Cox Gaussian Process）</news:title>
   <news:publication_date>2026-04-18T11:42:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680714</loc>
  <lastmod>2026-04-18T11:33:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>量子特徴空間における機械学習（Quantum machine learning in feature Hilbert spaces）</news:title>
   <news:publication_date>2026-04-18T11:33:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680712</loc>
  <lastmod>2026-04-18T11:33:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所バイナリパターンネットワーク（Local Binary Pattern Networks）</news:title>
   <news:publication_date>2026-04-18T11:33:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680710</loc>
  <lastmod>2026-04-18T11:32:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時間非依存ハミルトニアンの教師あり学習によるゲート設計（Supervised learning of time-independent Hamiltonians for gate design）</news:title>
   <news:publication_date>2026-04-18T11:32:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680708</loc>
  <lastmod>2026-04-18T11:32:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ゼロショット検出が拓く未学習カテゴリの発見（Zero Shot Detection）</news:title>
   <news:publication_date>2026-04-18T11:32:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680706</loc>
  <lastmod>2026-04-18T11:32:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Wikidataから欠損言語のWikipedia要約を自動生成する手法（Learning to Generate Wikipedia Summaries for Underserved Languages from Wikidata）</news:title>
   <news:publication_date>2026-04-18T11:32:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680704</loc>
  <lastmod>2026-04-18T11:31:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>プライバシー保護のための表情表現学習（VGAN-Based Image Representation Learning for Privacy-Preserving Facial Expression Recognition）</news:title>
   <news:publication_date>2026-04-18T11:31:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680702</loc>
  <lastmod>2026-04-18T10:40:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>実世界ロボットでの強化学習タスクの組み立て（Setting up a Reinforcement Learning Task with a Real-World Robot）</news:title>
   <news:publication_date>2026-04-18T10:40:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680700</loc>
  <lastmod>2026-04-18T10:40:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散データ上の分散型学習（D2: Decentralized Training over Decentralized Data）</news:title>
   <news:publication_date>2026-04-18T10:40:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680698</loc>
  <lastmod>2026-04-18T10:39:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>領域特徴を学習することで変わる物体検出（Learning Region Features for Object Detection）</news:title>
   <news:publication_date>2026-04-18T10:39:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680696</loc>
  <lastmod>2026-04-18T10:39:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>走査型プローブ顕微鏡の先端条件付け自動化（Autonomous Scanning Probe Microscopy in-situ Tip Conditioning through Machine Learning）</news:title>
   <news:publication_date>2026-04-18T10:39:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680694</loc>
  <lastmod>2026-04-18T10:38:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>行列ロジスティック回帰による最適なリンク予測（Optimal Link Prediction with Matrix Logistic Regression）</news:title>
   <news:publication_date>2026-04-18T10:38:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680692</loc>
  <lastmod>2026-04-18T10:38:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単純ランダム探索が強力な強化学習手法となる理由（Simple random search provides a competitive approach to reinforcement learning）</news:title>
   <news:publication_date>2026-04-18T10:38:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680690</loc>
  <lastmod>2026-04-18T10:38:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DNNの予測を説明するための誘導的特徴反転（Towards Explanation of DNN-based Prediction with Guided Feature Inversion）</news:title>
   <news:publication_date>2026-04-18T10:38:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680688</loc>
  <lastmod>2026-04-18T09:47:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>前向きステップによる射影的スプリッティング手法の提案（Projective Splitting with Forward Steps: Asynchronous and Block-Iterative Operator Splitting）</news:title>
   <news:publication_date>2026-04-18T09:47:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680686</loc>
  <lastmod>2026-04-18T09:46:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>剥ぎ取られた外層をもつ超新星SN 2004dkが水素を含む周囲物質と相互作用している証拠（STRIPPED-ENVELOPE SUPERNOVA SN 2004DK IS NOW INTERACTING WITH HYDROGEN-RICH CIRCUMSTELLAR MATERIAL）</news:title>
   <news:publication_date>2026-04-18T09:46:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680684</loc>
  <lastmod>2026-04-18T09:46:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>バッチ型量子状態指数化と量子ヘッブ学習（Batched quantum state exponentiation and quantum Hebbian learning）</news:title>
   <news:publication_date>2026-04-18T09:46:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680682</loc>
  <lastmod>2026-04-18T09:45:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ドローンと深層学習で現場映像を即時解析する（Live Target Detection with Deep Learning Neural Network and Unmanned Aerial Vehicle on Android Mobile Device）</news:title>
   <news:publication_date>2026-04-18T09:45:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680680</loc>
  <lastmod>2026-04-18T09:45:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>データの本質的次元を最小情報で推定する方法（Estimating the intrinsic dimension of datasets by a minimal neighborhood information）</news:title>
   <news:publication_date>2026-04-18T09:45:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680678</loc>
  <lastmod>2026-04-18T09:45:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>要素分解型空間表現学習と心筋半教師ありセグメンテーションへの応用 (Factorised spatial representation learning: application in semi-supervised myocardial segmentation)</news:title>
   <news:publication_date>2026-04-18T09:45:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680676</loc>
  <lastmod>2026-04-18T09:45:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>『Oumuamua型天体の現場探査の実現可能性と利点』（THE FEASIBILITY AND BENEFITS OF IN SITU EXPLORATION OF ‘OUMUAMUA-LIKE OBJECTS）</news:title>
   <news:publication_date>2026-04-18T09:45:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680674</loc>
  <lastmod>2026-04-18T08:53:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>入力多様性による敵対的例の転送性向上（Improving Transferability of Adversarial Examples with Input Diversity）</news:title>
   <news:publication_date>2026-04-18T08:53:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680672</loc>
  <lastmod>2026-04-18T08:52:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフ上の数値積分：どこを標本化し、どのように重み付けするか（NUMERICAL INTEGRATION ON GRAPHS: WHERE TO SAMPLE AND HOW TO WEIGH）</news:title>
   <news:publication_date>2026-04-18T08:52:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680670</loc>
  <lastmod>2026-04-18T08:52:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>量子加算によるコヒーレンスの定量化（Quantifying coherence with quantum addition）</news:title>
   <news:publication_date>2026-04-18T08:52:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680668</loc>
  <lastmod>2026-04-18T08:51:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習はいつ失敗するか（When Does Machine Learning FAIL? Generalized Transferability for Evasion and Poisoning Attacks）</news:title>
   <news:publication_date>2026-04-18T08:51:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680666</loc>
  <lastmod>2026-04-18T08:50:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>速度場のスロープ制限を用いた発散ゼロの不連続Galerkin二相流ソルバー（Slope limiting the velocity field in a discontinuous Galerkin divergence free two-phase flow solver）</news:title>
   <news:publication_date>2026-04-18T08:50:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680664</loc>
  <lastmod>2026-04-18T08:50:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Doubling Trick が多腕バンディットにもたらすものと限界（What Doubling Tricks Can and Can’t Do for Multi-Armed Bandits）</news:title>
   <news:publication_date>2026-04-18T08:50:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680662</loc>
  <lastmod>2026-04-18T08:50:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層ニューラルネットワークの学習ダイナミクスとガラス物質系の比較（Comparing Dynamics: Deep Neural Networks versus Glassy Systems）</news:title>
   <news:publication_date>2026-04-18T08:50:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680660</loc>
  <lastmod>2026-04-18T07:58:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ポリグロットセマンティックパーシングによるAPI翻訳（Polyglot Semantic Parsing in APIs）</news:title>
   <news:publication_date>2026-04-18T07:58:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680658</loc>
  <lastmod>2026-04-18T07:48:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>特徴量を作らずに行動認識を学ぶ（FEATURELESS: BYPASSING FEATURE EXTRACTION IN ACTION CATEGORIZATION）</news:title>
   <news:publication_date>2026-04-18T07:48:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680656</loc>
  <lastmod>2026-04-18T07:48:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次元ロジスティック回帰のための現代的最尤理論（A Modern Maximum-Likelihood Theory for High-dimensional Logistic Regression）</news:title>
   <news:publication_date>2026-04-18T07:48:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680654</loc>
  <lastmod>2026-04-18T07:47:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>hs-to-coqを用いた実運用Haskellコードの検証（Ready, Set, Verify! Applying hs-to-coq to real-world Haskell code）</news:title>
   <news:publication_date>2026-04-18T07:47:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680652</loc>
  <lastmod>2026-04-18T07:47:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単一方向の重要性と汎化性能（On the Importance of Single Directions for Generalization）</news:title>
   <news:publication_date>2026-04-18T07:47:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680650</loc>
  <lastmod>2026-04-18T07:47:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>静止画像における動き予測（D´ej`a Vu: Motion Prediction in Static Images）</news:title>
   <news:publication_date>2026-04-18T07:47:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680648</loc>
  <lastmod>2026-04-18T07:46:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ガウス過程における非対称カーネルによるターゲット分散学習（Asymmetric kernel in Gaussian Processes for learning target variance）</news:title>
   <news:publication_date>2026-04-18T07:46:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680646</loc>
  <lastmod>2026-04-18T06:55:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>放射線損傷シリコンにおける点欠陥とクラスタ欠陥の研究 (Study of point- and cluster-defects in radiation-damaged silicon)</news:title>
   <news:publication_date>2026-04-18T06:55:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680644</loc>
  <lastmod>2026-04-18T06:54:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>PyGOMによる常微分方程式モデルの実務的簡素化（PyGOM — A Python Package for Simplifying Modelling with Systems of Ordinary Differential Equations）</news:title>
   <news:publication_date>2026-04-18T06:54:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680642</loc>
  <lastmod>2026-04-18T06:54:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>価格形成の普遍性を示す深層学習の視点（Universal features of price formation in financial markets: perspectives from Deep Learning）</news:title>
   <news:publication_date>2026-04-18T06:54:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680640</loc>
  <lastmod>2026-04-18T06:53:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数視点混合ネットワークによる乳房微細石灰化の分類（A Mixture of Views Network with Applications to the Classification of Breast Microcalcifications）</news:title>
   <news:publication_date>2026-04-18T06:53:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680638</loc>
  <lastmod>2026-04-18T06:53:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>空撮画像から車線だけを正確に抜き出す技術の衝撃（Aerial LaneNet: Lane Marking Semantic Segmentation in Aerial Imagery using Wavelet-Enhanced Cost-sensitive Symmetric Fully Convolutional Neural Networks）</news:title>
   <news:publication_date>2026-04-18T06:53:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680636</loc>
  <lastmod>2026-04-18T06:52:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>意味的ローカリティを用いたニューラルネットワークメモリプリフェッチ (A neural network memory prefetcher using semantic locality)</news:title>
   <news:publication_date>2026-04-18T06:52:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680634</loc>
  <lastmod>2026-04-18T06:52:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>教師なしセマンティック深層ハッシュ（UNSUPERVISED SEMANTIC DEEP HASHING）</news:title>
   <news:publication_date>2026-04-18T06:52:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680632</loc>
  <lastmod>2026-04-18T06:01:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ランダム演算子のスペクトル多重度と局所統計の結びつき（Global multiplicity bounds and Spectral Statistics for Random Operators）</news:title>
   <news:publication_date>2026-04-18T06:01:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680630</loc>
  <lastmod>2026-04-18T06:01:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>生物学的活性化関数による深層学習の改善（Deep learning improved by biological activation functions）</news:title>
   <news:publication_date>2026-04-18T06:01:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680628</loc>
  <lastmod>2026-04-18T06:00:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次元線形モデルにおける交絡因子検出（Confounder Detection in High Dimensional Linear Models using First Moments of Spectral Measures）</news:title>
   <news:publication_date>2026-04-18T06:00:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680626</loc>
  <lastmod>2026-04-18T05:59:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散認知無線ネットワークにおけるジャミング下での協調学習（Learning to Coordinate in a Decentralized Cognitive Radio Network in Presence of Jammers）</news:title>
   <news:publication_date>2026-04-18T05:59:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680624</loc>
  <lastmod>2026-04-18T05:59:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>音声の表現学習における異ドメイン構音データ活用（ACOUSTIC FEATURE LEARNING USING CROSS-DOMAIN ARTICULATORY MEASUREMENTS）</news:title>
   <news:publication_date>2026-04-18T05:59:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680622</loc>
  <lastmod>2026-04-18T05:59:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習可能な画像暗号化（Learnable Image Encryption）</news:title>
   <news:publication_date>2026-04-18T05:59:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680620</loc>
  <lastmod>2026-04-18T05:59:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>音楽スタイル変換の位置づけと課題（Music Style Transfer: A Position Paper）</news:title>
   <news:publication_date>2026-04-18T05:59:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680618</loc>
  <lastmod>2026-04-18T05:06:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スマートフォンデータから行動ルールを抽出する方法（Mining User Behavioral Rules from Smartphone Data through Association Analysis）</news:title>
   <news:publication_date>2026-04-18T05:06:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680616</loc>
  <lastmod>2026-04-18T05:06:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Attention-GANによる野生画像での物体変換（Attention-GAN for Object Transfiguration in Wild Images）</news:title>
   <news:publication_date>2026-04-18T05:06:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680614</loc>
  <lastmod>2026-04-18T05:05:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Faster R-CNNの分類力を目覚めさせる（Revisiting RCNN: On Awakening the Classification Power of Faster RCNN）</news:title>
   <news:publication_date>2026-04-18T05:05:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680612</loc>
  <lastmod>2026-04-18T05:04:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ロボット操作のための合成可能な深層強化学習（Composable Deep Reinforcement Learning for Robotic Manipulation）</news:title>
   <news:publication_date>2026-04-18T05:04:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680610</loc>
  <lastmod>2026-04-18T05:04:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチモーダル感情分析の基礎とベンチマーク構築（Multimodal Sentiment Analysis: Addressing Key Issues and Setting up the Baselines）</news:title>
   <news:publication_date>2026-04-18T05:04:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680608</loc>
  <lastmod>2026-04-18T05:04:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>TOMAAT: 体積医用画像解析のクラウドサービス化（TOMAAT: volumetric medical image analysis as a cloud service）</news:title>
   <news:publication_date>2026-04-18T05:04:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680606</loc>
  <lastmod>2026-04-18T05:04:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非局所低ランクテンソル因子解析による画像復元（Nonlocal Low-Rank Tensor Factor Analysis for Image Restoration）</news:title>
   <news:publication_date>2026-04-18T05:04:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680604</loc>
  <lastmod>2026-04-18T04:11:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ソフトウェア定義ネットワーク向けの効率的な異常検知法（Towards an Efficient Anomaly-Based Intrusion Detection for Software-Defined Networks）</news:title>
   <news:publication_date>2026-04-18T04:11:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680602</loc>
  <lastmod>2026-04-18T04:11:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>EnvelopeNetsによる高速ニューラルアーキテクチャ構築（Fast Neural Architecture Construction using EnvelopeNets）</news:title>
   <news:publication_date>2026-04-18T04:11:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680600</loc>
  <lastmod>2026-04-18T04:11:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>密集フェムトセル環境におけるQoSを考慮した出力割当の機械学習的アプローチ（A Machine Learning Approach for Power Allocation in HetNets Considering QoS）</news:title>
   <news:publication_date>2026-04-18T04:11:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680598</loc>
  <lastmod>2026-04-18T04:10:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ワンビット通信で分散検出を実現する（Detection under One-Bit Messaging over Adaptive Networks）</news:title>
   <news:publication_date>2026-04-18T04:10:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680596</loc>
  <lastmod>2026-04-18T04:09:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模動的予測回帰の分解・再結合戦略（Large-Scale Dynamic Predictive Regressions）</news:title>
   <news:publication_date>2026-04-18T04:09:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680594</loc>
  <lastmod>2026-04-18T04:09:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>長期予測のための集約戦略（Aggregating Strategies for Long-term Forecasting）</news:title>
   <news:publication_date>2026-04-18T04:09:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680592</loc>
  <lastmod>2026-04-18T04:09:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>視覚と意味を同時に識別可能にするゼロショット学習（Discriminative Learning of Latent Features for Zero-Shot Recognition）</news:title>
   <news:publication_date>2026-04-18T04:09:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680590</loc>
  <lastmod>2026-04-18T03:17:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>プログラムを言葉にして学ぶ――抽象化された象徴的トレースからのコードベクトル（Code Vectors: Understanding Programs Through Embedded Abstracted Symbolic Traces）</news:title>
   <news:publication_date>2026-04-18T03:17:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680588</loc>
  <lastmod>2026-04-18T03:17:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>格子基底縮約を用いた高次元線形回帰（High Dimensional Linear Regression using Lattice Basis Reduction）</news:title>
   <news:publication_date>2026-04-18T03:17:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680586</loc>
  <lastmod>2026-04-18T03:17:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>監督学習によるスキルミオン相認識の実用的意義（Supervised-learning approach for recognizing magnetic skyrmion phases）</news:title>
   <news:publication_date>2026-04-18T03:17:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680584</loc>
  <lastmod>2026-04-18T03:16:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>異なる深度マップを賢く融合する半教師ありマルチスケール敵対ネットワーク（SDF-MAN: SEMI-SUPERVISED DISPARITY FUSION WITH MULTI-SCALE ADVERSARIAL NETWORKS）</news:title>
   <news:publication_date>2026-04-18T03:16:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680582</loc>
  <lastmod>2026-04-18T03:15:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オンライン鎖分割とスケジューリング（On-line Chain Partitioning Approach to Scheduling）</news:title>
   <news:publication_date>2026-04-18T03:15:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680580</loc>
  <lastmod>2026-04-18T03:15:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>三峡ダム流域の土地利用マッピング（Land use mapping in the Three Gorges Reservoir Area based on semantic segmentation deep learning method）</news:title>
   <news:publication_date>2026-04-18T03:15:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680578</loc>
  <lastmod>2026-04-18T03:15:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次元データにおける希少特徴選択の再考（Rare Feature Selection in High Dimensions）</news:title>
   <news:publication_date>2026-04-18T03:15:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680576</loc>
  <lastmod>2026-04-18T02:23:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>LINE ARTIST：マルチスタイル スケッチから絵画生成スキーム（LINE ARTIST: A Multi-style Sketch to Painting Synthesis Scheme）</news:title>
   <news:publication_date>2026-04-18T02:23:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680574</loc>
  <lastmod>2026-04-18T02:23:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ウェブを知識ベースとして複雑な質問に答える方法（The Web as a Knowledge-base for Answering Complex Questions）</news:title>
   <news:publication_date>2026-04-18T02:23:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680572</loc>
  <lastmod>2026-04-18T02:22:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Zoom and Learn: 新領域に適応する深層ステレオマッチング（Zoom and Learn: Generalizing Deep Stereo Matching to Novel Domains）</news:title>
   <news:publication_date>2026-04-18T02:22:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680570</loc>
  <lastmod>2026-04-18T02:21:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>生態写真から蝶を自動検出・種同定する技術の実務的意義（Faster R-CNN based butterfly automatic identification）</news:title>
   <news:publication_date>2026-04-18T02:21:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680568</loc>
  <lastmod>2026-04-18T02:21:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>エントロピー最小化による適応的意思決定（Adaptive Decision Making via Entropy Minimization）</news:title>
   <news:publication_date>2026-04-18T02:21:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680566</loc>
  <lastmod>2026-04-18T02:21:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DAMPEデータを用いた電子・陽子分離の機械学習手法（A machine learning method to separate cosmic ray electrons from protons from 10 to 100 GeV using DAMPE data）</news:title>
   <news:publication_date>2026-04-18T02:21:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680564</loc>
  <lastmod>2026-04-18T02:21:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>半触覚インターフェースによる楽器学習の再定義（ShIFT: A Semi-haptic Interface for Flute Tutoring）</news:title>
   <news:publication_date>2026-04-18T02:21:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680562</loc>
  <lastmod>2026-04-18T01:29:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>盲目的量子計算は常に検証可能にできる（Blind quantum computing can always be made verifiable）</news:title>
   <news:publication_date>2026-04-18T01:29:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680560</loc>
  <lastmod>2026-04-18T01:29:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習によるスパイキングネットワークの反復動態（Learning recurrent dynamics in spiking networks）</news:title>
   <news:publication_date>2026-04-18T01:29:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680558</loc>
  <lastmod>2026-04-18T01:28:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大学卒業後の収入を決める特徴量の選択（Feature Selection of Post-Graduation Income of College Students in the United States）</news:title>
   <news:publication_date>2026-04-18T01:28:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680556</loc>
  <lastmod>2026-04-18T01:27:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>陽性と未ラベルの分類で頑健なAUC最大化と外れ値検出・特徴選択の統合（A Robust AUC Maximization Framework with Simultaneous Outlier Detection and Feature Selection for Positive-Unlabeled Classification）</news:title>
   <news:publication_date>2026-04-18T01:27:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680554</loc>
  <lastmod>2026-04-18T01:26:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>軌跡に基づくシーン理解と非パラメトリック混合モデル（Trajectory-based Scene Understanding using Dirichlet Process Mixture Model）</news:title>
   <news:publication_date>2026-04-18T01:26:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680552</loc>
  <lastmod>2026-04-18T01:26:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自己反復回帰による顔ランドマーク検出とランドマーク注意ネットワーク（Facial Landmarks Detection by Self-Iterative Regression based Landmarks-Attention Network）</news:title>
   <news:publication_date>2026-04-18T01:26:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680550</loc>
  <lastmod>2026-04-18T01:25:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>情報理論的セキュリティと隠密通信（Information-Theoretic Security or Covert Communication）</news:title>
   <news:publication_date>2026-04-18T01:25:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680547</loc>
  <lastmod>2026-04-18T00:33:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>構造的Query‑by‑Committeeの統一的枠組み（Structural query-by-committee）</news:title>
   <news:publication_date>2026-04-18T00:33:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680545</loc>
  <lastmod>2026-04-18T00:33:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多重散乱の高速かつ高精度な反転を行う深層学習（Efficient and accurate inversion of multiple scattering with deep learning）</news:title>
   <news:publication_date>2026-04-18T00:33:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680543</loc>
  <lastmod>2026-04-18T00:33:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>集中治療室における早期院内死亡予測（Early Hospital Mortality Prediction using Vital Signals）</news:title>
   <news:publication_date>2026-04-18T00:33:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680541</loc>
  <lastmod>2026-04-18T00:32:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層ニューラルネットワークのスケーラブル検証に向けた二重アプローチ（A Dual Approach to Scalable Verification of Deep Networks）</news:title>
   <news:publication_date>2026-04-18T00:32:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680539</loc>
  <lastmod>2026-04-18T00:32:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>有限時間スループット最大化とセンシング最適化（Finite Horizon Throughput Maximization and Sensing Optimization in Wireless Powered Devices over Fading Channels）</news:title>
   <news:publication_date>2026-04-18T00:32:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680537</loc>
  <lastmod>2026-04-18T00:32:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>サービス提供者視点のAutoML：多デバイス・多テナント下でのGP‑EIによるモデル選択（AutoML from Service Provider’s Perspective: Multi-device, Multi-tenant Model Selection with GP-EI）</news:title>
   <news:publication_date>2026-04-18T00:32:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680535</loc>
  <lastmod>2026-04-18T00:31:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>長期依存性学習のためのフーリエ回帰ユニット（Learning Long Term Dependencies via Fourier Recurrent Units）</news:title>
   <news:publication_date>2026-04-18T00:31:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680533</loc>
  <lastmod>2026-04-17T23:39:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>なぜそうなのかを教えてください？知識グラフ関係の説明文抽出（Tell Me Why Is It So? Explaining Knowledge Graph Relationships by Finding Descriptive Support Passages）</news:title>
   <news:publication_date>2026-04-17T23:39:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680531</loc>
  <lastmod>2026-04-17T23:39:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>アクティブラーニングで脆弱性検査を効率化する発想（Improving Vulnerability Inspection Efficiency Using Active Learning）</news:title>
   <news:publication_date>2026-04-17T23:39:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680529</loc>
  <lastmod>2026-04-17T23:39:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多相配電網におけるトポロジー推定（Topology Estimation using Graphical Models in Multi-Phase Power Distribution Grids）</news:title>
   <news:publication_date>2026-04-17T23:39:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680527</loc>
  <lastmod>2026-04-17T23:38:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>サブキューブ混合モデルの学習を高速化する理論的進展（Beyond the Low-Degree Algorithm: Mixtures of Subcubes and Their Applications）</news:title>
   <news:publication_date>2026-04-17T23:38:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680525</loc>
  <lastmod>2026-04-17T23:38:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>生物シグナルネットワークにおける三頂点モチーフが示す微細トポロジーと機能の関係 (Analysis of Triplet Motifs in Biological Signed Oriented Graphs Suggests a Relationship Between Fine Topology and Function)</news:title>
   <news:publication_date>2026-04-17T23:38:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680523</loc>
  <lastmod>2026-04-17T23:38:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>弱凸関数の確率的モデルベース最小化（Stochastic Model-Based Minimization of Weakly Convex Functions）</news:title>
   <news:publication_date>2026-04-17T23:38:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680521</loc>
  <lastmod>2026-04-17T23:38:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SeqFace: 連続情報を活用した顔認識（SeqFace: Make full use of sequence information for face recognition）</news:title>
   <news:publication_date>2026-04-17T23:38:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680519</loc>
  <lastmod>2026-04-17T22:46:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>テンソルの凸ココクラスタリングによる可証的分割法（Provable Convex Co-clustering of Tensors）</news:title>
   <news:publication_date>2026-04-17T22:46:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680517</loc>
  <lastmod>2026-04-17T22:46:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SDP緩和の隠れた積分性と半ランダム堅牢性（Hidden Integrality and Semi-random Robustness of SDP Relaxation for Sub-Gaussian Mixture Model）</news:title>
   <news:publication_date>2026-04-17T22:46:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680515</loc>
  <lastmod>2026-04-17T22:45:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>MergeNetによる小さな路上障害物検出の革新（MergeNet: A Deep Net Architecture for Small Obstacle Discovery）</news:title>
   <news:publication_date>2026-04-17T22:45:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680513</loc>
  <lastmod>2026-04-17T22:45:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>概念学習による無監督視覚グラウンディングの学習（Learning Unsupervised Visual Grounding Through Semantic Self-Supervision）</news:title>
   <news:publication_date>2026-04-17T22:45:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680511</loc>
  <lastmod>2026-04-17T22:45:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>イベントベース視覚データのロバスト追跡（Robust event-stream pattern tracking based on correlative filter）</news:title>
   <news:publication_date>2026-04-17T22:45:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680509</loc>
  <lastmod>2026-04-17T22:44:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>可変長粒子群最適化による深層畳み込みニューラルネットワークの進化（Evolving Deep Convolutional Neural Networks by Variable-length Particle Swarm Optimization for Image Classification）</news:title>
   <news:publication_date>2026-04-17T22:44:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680507</loc>
  <lastmod>2026-04-17T22:44:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>画像ラベルのみで注目領域を検出する弱教師あり手法（Weakly Supervised Salient Object Detection Using Image Labels）</news:title>
   <news:publication_date>2026-04-17T22:44:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680505</loc>
  <lastmod>2026-04-17T21:53:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>平均回帰ポートフォリオの設計と推定（Mean Reverting Portfolios via Penalized OU-Likelihood Estimation）</news:title>
   <news:publication_date>2026-04-17T21:53:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680503</loc>
  <lastmod>2026-04-17T21:53:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>映像解析による待ち行列理論に基づくインテリジェント交通信号スケジューリング（Queuing Theory Guided Intelligent Traffic Scheduling through Video Analysis using Dirichlet Process Mixture Model）</news:title>
   <news:publication_date>2026-04-17T21:53:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680501</loc>
  <lastmod>2026-04-17T21:53:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>粗い微分方程式の解の定義について（On the definition of a solution to a rough differential equation）</news:title>
   <news:publication_date>2026-04-17T21:53:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680499</loc>
  <lastmod>2026-04-17T21:52:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>小規模・大規模の著者認証に関するニューラルネットワークの実験（Experiments with Neural Networks for Small and Large Scale Authorship Verification）</news:title>
   <news:publication_date>2026-04-17T21:52:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680497</loc>
  <lastmod>2026-04-17T21:52:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>潜在自己相関の最大化がタンパク質動力学の変分符号化に与える利点（Variational Encoding of Protein Dynamics Benefits from Maximizing Latent Autocorrelation）</news:title>
   <news:publication_date>2026-04-17T21:52:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680495</loc>
  <lastmod>2026-04-17T21:52:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ピクセル単位クラスタ学習による提案不要のインスタンスセグメンテーション（Learning to Cluster for Proposal-Free Instance Segmentation）</news:title>
   <news:publication_date>2026-04-17T21:52:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680493</loc>
  <lastmod>2026-04-17T21:52:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>制約付き深層学習と条件付き勾配法の応用（Constrained Deep Learning using Conditional Gradient and Applications in Computer Vision）</news:title>
   <news:publication_date>2026-04-17T21:52:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680491</loc>
  <lastmod>2026-04-17T21:01:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>二部型スピンガラスとニューラルネットにおけるレプリカ対称性の破れ（Replica Symmetry Breaking in Bipartite Spin Glasses and Neural Networks）</news:title>
   <news:publication_date>2026-04-17T21:01:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680489</loc>
  <lastmod>2026-04-17T21:00:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>通信圧縮と分散学習の両立で変わる分散学習の実務適用（Communication Compression for Decentralized Training）</news:title>
   <news:publication_date>2026-04-17T21:00:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680487</loc>
  <lastmod>2026-04-17T21:00:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次元・モデルミススペシフィケーション下の大規模モデル選択（Large-Scale Model Selection with Misspecification）</news:title>
   <news:publication_date>2026-04-17T21:00:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680485</loc>
  <lastmod>2026-04-17T20:59:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Blaschke unwinding AFDに基づく心電図圧縮法の実用化可能性（A Novel Blaschke Unwinding Adaptive Fourier Decomposition Based Signal Compression Algorithm With Application on ECG Signals）</news:title>
   <news:publication_date>2026-04-17T20:59:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680483</loc>
  <lastmod>2026-04-17T20:59:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>人工マイクロスイマーの強化学習による自律化（Reinforcement Learning of Artificial Microswimmers）</news:title>
   <news:publication_date>2026-04-17T20:59:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680481</loc>
  <lastmod>2026-04-17T20:59:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>物体インスタンスの弱教師ありセグメンテーションを切り貼りで学ぶ（Learning to Segment by Cut and Paste）</news:title>
   <news:publication_date>2026-04-17T20:59:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680479</loc>
  <lastmod>2026-04-17T20:59:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>成長するデータベースの差分プライバシー（Differential Privacy for Growing Databases）</news:title>
   <news:publication_date>2026-04-17T20:59:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680477</loc>
  <lastmod>2026-04-17T20:07:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層成分解析と交互方向ニューラルネットワーク（Deep Component Analysis via Alternating Direction Neural Networks）</news:title>
   <news:publication_date>2026-04-17T20:07:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680475</loc>
  <lastmod>2026-04-17T20:07:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>意思決定支援におけるテキスト感情認識のための深層学習（Deep learning for affective computing: text-based emotion recognition in decision support）</news:title>
   <news:publication_date>2026-04-17T20:07:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680473</loc>
  <lastmod>2026-04-17T20:07:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習で条件付き現金給付（CCT）政策をどう評価するか（Evaluating Conditional Cash Transfer Policies with Machine Learning Methods）</news:title>
   <news:publication_date>2026-04-17T20:07:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680471</loc>
  <lastmod>2026-04-17T20:06:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オンラインデータのテキスト分類におけるコーパス統計（Corpus Statistics in Text Classification of Online Data）</news:title>
   <news:publication_date>2026-04-17T20:06:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680469</loc>
  <lastmod>2026-04-17T20:06:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DUNE 35トン試作検出器の光検出器システムの時間性能（Photon detector system timing performance in the DUNE 35-ton prototype）</news:title>
   <news:publication_date>2026-04-17T20:06:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680467</loc>
  <lastmod>2026-04-17T20:05:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>経済・金融時系列予測：ARIMA vs. LSTM（Forecasting Economic and Financial Time Series: ARIMA vs. LSTM）</news:title>
   <news:publication_date>2026-04-17T20:05:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680465</loc>
  <lastmod>2026-04-17T20:05:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>再帰型逆伝播の復活と改良（Reviving and Improving Recurrent Back-Propagation）</news:title>
   <news:publication_date>2026-04-17T20:05:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680463</loc>
  <lastmod>2026-04-17T19:13:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>経験的ゲーム理論解析の一般化手法（A Generalised Method for Empirical Game Theoretic Analysis）</news:title>
   <news:publication_date>2026-04-17T19:13:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680461</loc>
  <lastmod>2026-04-17T19:13:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>対数出力の整合で堅牢化する手法（Adversarial Logit Pairing）</news:title>
   <news:publication_date>2026-04-17T19:13:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680459</loc>
  <lastmod>2026-04-17T19:13:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューラル機械翻訳のためのTensor2Tensor（Tensor2Tensor for Neural Machine Translation）</news:title>
   <news:publication_date>2026-04-17T19:13:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680457</loc>
  <lastmod>2026-04-17T19:11:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>セマンティック敵対的事例とその示唆（Semantic Adversarial Examples）</news:title>
   <news:publication_date>2026-04-17T19:11:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680455</loc>
  <lastmod>2026-04-17T19:11:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>電弱ゲージボソンのパートン分布関数（Electroweak Gauge Boson Parton Distribution Functions）</news:title>
   <news:publication_date>2026-04-17T19:11:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680453</loc>
  <lastmod>2026-04-17T19:11:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Snap MLによる階層的機械学習フレームワークの要点解説（Snap ML: A Hierarchical Framework for Machine Learning）</news:title>
   <news:publication_date>2026-04-17T19:11:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680451</loc>
  <lastmod>2026-04-17T19:10:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>顔を照明プローブとして使う（Faces as Lighting Probes via Unsupervised Deep Highlight Extraction）</news:title>
   <news:publication_date>2026-04-17T19:10:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680449</loc>
  <lastmod>2026-04-17T18:18:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率プログラムのネスト化がもたらす統計的影響（Nesting Probabilistic Programs）</news:title>
   <news:publication_date>2026-04-17T18:18:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680447</loc>
  <lastmod>2026-04-17T18:18:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Deep Structured Active Contoursによる建物境界の精密化（Learning deep structured active contours end-to-end）</news:title>
   <news:publication_date>2026-04-17T18:18:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680445</loc>
  <lastmod>2026-04-17T18:18:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>粒子ベースの変分的BNMFアプローチ（A particle-based variational approach to BNMF）</news:title>
   <news:publication_date>2026-04-17T18:18:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680443</loc>
  <lastmod>2026-04-17T18:17:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>EVA2によるライブコンピュータビジョンの省力化（EVA2: Exploiting Temporal Redundancy in Live Computer Vision）</news:title>
   <news:publication_date>2026-04-17T18:17:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680441</loc>
  <lastmod>2026-04-17T18:17:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>部分的なマルチマッチングの同期化（Synchronisation of Partial Multi-Matchings via Non-negative Factorisations）</news:title>
   <news:publication_date>2026-04-17T18:17:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680439</loc>
  <lastmod>2026-04-17T18:16:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>超拡散銀河の星成分の分光学的特徴づけ（Spectroscopic characterisation of the stellar content of ultra diffuse galaxies）</news:title>
   <news:publication_date>2026-04-17T18:16:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680437</loc>
  <lastmod>2026-04-17T18:16:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>合成画像の現実性最適化によるパーツ分割性能の改善（Improved Part Segmentation Performance by Optimising Realism of Synthetic Images using Cycle Generative Adversarial Networks）</news:title>
   <news:publication_date>2026-04-17T18:16:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680435</loc>
  <lastmod>2026-04-17T17:24:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次元確率反転に対する随伴モデルと機械学習の統合（High-dimensional Stochastic Inversion via Adjoint Models and Machine Learning）</news:title>
   <news:publication_date>2026-04-17T17:24:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680433</loc>
  <lastmod>2026-04-17T17:24:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>二層最適化によるハイブリッドシステムの反証探索（Two-Layered Falsification of Hybrid Systems Guided by Monte Carlo Tree Search）</news:title>
   <news:publication_date>2026-04-17T17:24:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680431</loc>
  <lastmod>2026-04-17T17:24:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフ分割ニューラルネットワークによる半教師付き分類（Graph Partition Neural Networks for Semi-Supervised Classification）</news:title>
   <news:publication_date>2026-04-17T17:24:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680429</loc>
  <lastmod>2026-04-17T17:22:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>手書き文書におけるテキストと固有表現の同時認識（Joint Recognition of Handwritten Text and Named Entities with a Neural End-to-end Model）</news:title>
   <news:publication_date>2026-04-17T17:22:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680427</loc>
  <lastmod>2026-04-17T17:22:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>シオンガン新区における将来都市成長のシミュレーション（Simulating the future urban growth in Xiongan New Area: a upcoming big city in China）</news:title>
   <news:publication_date>2026-04-17T17:22:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680425</loc>
  <lastmod>2026-04-17T17:22:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>回転等変性を組み込んだ高解像度土地被覆マッピング（Land cover mapping at very high resolution with rotation equivariant CNNs）</news:title>
   <news:publication_date>2026-04-17T17:22:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680423</loc>
  <lastmod>2026-04-17T17:22:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>共有施設利用者の調整をデータ駆動予測で支援する（Coordinating users of shared facilities via data-driven predictive assistants and game theory）</news:title>
   <news:publication_date>2026-04-17T17:22:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680421</loc>
  <lastmod>2026-04-17T16:30:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチビュー3D物体検索に効くトリプレットセンター損失（Triplet-Center Loss for Multi-View 3D Object Retrieval）</news:title>
   <news:publication_date>2026-04-17T16:30:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680419</loc>
  <lastmod>2026-04-17T16:22:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分子グラフ畳み込みによる薬物物性予測の新展開（Chemi-Net: A molecular graph convolutional network for accurate drug property prediction）</news:title>
   <news:publication_date>2026-04-17T16:22:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680417</loc>
  <lastmod>2026-04-17T16:22:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>協力と競争で重みを決める発電予測アンサンブル（A Multi-Scheme Ensemble Using Coopetitive Soft-Gating With Application to Power Forecasting for Renewable Energy Generation）</news:title>
   <news:publication_date>2026-04-17T16:22:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680415</loc>
  <lastmod>2026-04-17T16:21:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>顔写真ベースの年齢推定データベースの公開がもたらすインパクト（The AgeGuess database: an open online resource on chronological and perceived ages of people aged 3-100）</news:title>
   <news:publication_date>2026-04-17T16:21:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680413</loc>
  <lastmod>2026-04-17T16:20:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>UVITによるラム圧剥離の観測：Abell 85のGASPジェリーフィッシュ銀河JO201の剥離ガスにおける星形成（UVIT view of ram-pressure stripping in action: Star formation in the stripped gas of the GASP jellyfish galaxy JO201 in Abell 85）</news:title>
   <news:publication_date>2026-04-17T16:20:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680411</loc>
  <lastmod>2026-04-17T16:20:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>第三世代クォークに結合する重いBSM粒子の探索（Search for heavy BSM particles coupling to third generation quarks at CMS）</news:title>
   <news:publication_date>2026-04-17T16:20:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680409</loc>
  <lastmod>2026-04-17T16:19:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模データの信頼性応用における複雑性の次元（Big Data and Reliability Applications: The Complexity Dimension）</news:title>
   <news:publication_date>2026-04-17T16:19:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680407</loc>
  <lastmod>2026-04-17T15:27:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ApolloScapeによる自動運転用大規模データセットの価値（The ApolloScape Open Dataset for Autonomous Driving and its Application）</news:title>
   <news:publication_date>2026-04-17T15:27:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680405</loc>
  <lastmod>2026-04-17T15:27:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ARMスケーラブルベクター拡張（The ARM Scalable Vector Extension）</news:title>
   <news:publication_date>2026-04-17T15:27:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680403</loc>
  <lastmod>2026-04-17T15:27:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GDPR時代におけるHCIの優先課題（Some HCI Priorities for GDPR-Compliant Machine Learning）</news:title>
   <news:publication_date>2026-04-17T15:27:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680401</loc>
  <lastmod>2026-04-17T15:26:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>巨大星周囲の塵円盤に見つかった偏光の意味（A polarized dusty disk around a massive star）</news:title>
   <news:publication_date>2026-04-17T15:26:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680399</loc>
  <lastmod>2026-04-17T15:25:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>物体の言語記述による検出と検索の統合（Object Captioning and Retrieval with Natural Language）</news:title>
   <news:publication_date>2026-04-17T15:25:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680397</loc>
  <lastmod>2026-04-17T15:25:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>エネルギー自律型モバイルネットワークの実現（Energy Sustainable Mobile Networks via Energy Routing, Learning and Foresighted Optimization）</news:title>
   <news:publication_date>2026-04-17T15:25:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680395</loc>
  <lastmod>2026-04-17T15:25:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>間質性肺疾患の病理組織セグメンテーション（Semantic Segmentation of Pathological Lung Tissue with Dilated Fully Convolutional Networks）</news:title>
   <news:publication_date>2026-04-17T15:25:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680393</loc>
  <lastmod>2026-04-17T14:33:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>現代のデータ拡張のカーネル理論（A Kernel Theory of Modern Data Augmentation）</news:title>
   <news:publication_date>2026-04-17T14:33:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680391</loc>
  <lastmod>2026-04-17T14:25:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層圧縮表現からの画像理解（TOWARDS IMAGE UNDERSTANDING FROM DEEP COMPRESSION WITHOUT DECODING）</news:title>
   <news:publication_date>2026-04-17T14:25:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680389</loc>
  <lastmod>2026-04-17T14:24:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>対称群上のガウス場：予測と学習（Gaussian field on the symmetric group: prediction and learning）</news:title>
   <news:publication_date>2026-04-17T14:24:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680387</loc>
  <lastmod>2026-04-17T14:23:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>木構造から学ぶスパース深層ネットワークの設計（Learning Sparse Deep Feedforward Networks via Tree Skeleton Expansion）</news:title>
   <news:publication_date>2026-04-17T14:23:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680385</loc>
  <lastmod>2026-04-17T14:23:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層学習の脆弱性 (Vulnerability of Deep Learning)</news:title>
   <news:publication_date>2026-04-17T14:23:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680383</loc>
  <lastmod>2026-04-17T14:22:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>作業記憶を用いた視覚推論のためのデータセットとアーキテクチャ（A Dataset and Architecture for Visual Reasoning with a Working Memory）</news:title>
   <news:publication_date>2026-04-17T14:22:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680381</loc>
  <lastmod>2026-04-17T14:22:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>線形的にサブクリティカルなプラズマにおける電子ホール不安定性（Electron hole instability in linearly sub-critical plasmas）</news:title>
   <news:publication_date>2026-04-17T14:22:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680379</loc>
  <lastmod>2026-04-17T13:31:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>周辺ビューによる実時間移動物体検知・追跡・分類（Real-time Detection, Tracking, and Classification of Moving and Stationary Objects using Multiple Fisheye Images）</news:title>
   <news:publication_date>2026-04-17T13:31:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680377</loc>
  <lastmod>2026-04-17T13:31:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>汚れたデータが示す業務リスクと選択指針（Impacts of Dirty Data: an Experimental Evaluation）</news:title>
   <news:publication_date>2026-04-17T13:31:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680375</loc>
  <lastmod>2026-04-17T13:30:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DNNの学習を評価するためのベンチマーク設計（TBD: Benchmarking and Analyzing Deep Neural Network Training）</news:title>
   <news:publication_date>2026-04-17T13:30:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680373</loc>
  <lastmod>2026-04-17T13:29:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>さまようブラックホールの速度と周囲媒質の特性を推定する手法（Characterizing the velocity of a wandering black hole and properties of the surrounding medium using convolutional neural networks）</news:title>
   <news:publication_date>2026-04-17T13:29:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680371</loc>
  <lastmod>2026-04-17T13:29:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時間的相互作用データにおける疎性・不均一性・相互応答性・コミュニティ構造のモデル化（Modelling sparsity, heterogeneity, reciprocity and community structure in temporal interaction data）</news:title>
   <news:publication_date>2026-04-17T13:29:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680369</loc>
  <lastmod>2026-04-17T13:29:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散キャッシュ対応V2Xネットワークの提案と課題（Distributed Cache Enabled V2X Network, Proposals, Research Trends and Challenging Issues）</news:title>
   <news:publication_date>2026-04-17T13:29:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680367</loc>
  <lastmod>2026-04-17T13:29:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動的構造を持つ意味伝播ネットワークの要点（Dynamic-structured Semantic Propagation Network）</news:title>
   <news:publication_date>2026-04-17T13:29:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680365</loc>
  <lastmod>2026-04-17T12:36:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>誤ったシステムモデルに対するロバスト性（ROBUSTNESS TO INCORRECT SYSTEM MODELS IN STOCHASTIC CONTROL）</news:title>
   <news:publication_date>2026-04-17T12:36:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680363</loc>
  <lastmod>2026-04-17T12:28:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>無線ネットワークにおけるRF異常検知のための深層予測符号化ニューラルネットワーク（Deep Predictive Coding Neural Network for RF Anomaly Detection in Wireless Networks）</news:title>
   <news:publication_date>2026-04-17T12:28:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680361</loc>
  <lastmod>2026-04-17T12:28:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ガウス過程における定数時予測分布の実現（Constant-Time Predictive Distributions for Gaussian Processes）</news:title>
   <news:publication_date>2026-04-17T12:28:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680359</loc>
  <lastmod>2026-04-17T12:27:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Deep Multiple Instance Learningによるゼロショット画像タグ付け（Deep Multiple Instance Learning for Zero-shot Image Tagging）</news:title>
   <news:publication_date>2026-04-17T12:27:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680357</loc>
  <lastmod>2026-04-17T12:26:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ゼロショット物体検出（Zero-Shot Object Detection: Learning to Simultaneously Recognize and Localize Novel Concepts）</news:title>
   <news:publication_date>2026-04-17T12:26:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680355</loc>
  <lastmod>2026-04-17T12:26:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ハライド・ペロブスカイトの安定性を機械学習で設計する（Stability Engineering of Halide Perovskite via Machine Learning）</news:title>
   <news:publication_date>2026-04-17T12:26:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680353</loc>
  <lastmod>2026-04-17T12:26:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数学校や病院で測る因果効果の読み解き方（Identifying and Estimating Principal Causal Effects in Multi-site Trials）</news:title>
   <news:publication_date>2026-04-17T12:26:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680351</loc>
  <lastmod>2026-04-17T11:35:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>交差点における転移可能な歩行者軌跡予測モデル（Transferable Pedestrian Motion Prediction Models at Intersections）</news:title>
   <news:publication_date>2026-04-17T11:35:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680349</loc>
  <lastmod>2026-04-17T11:34:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>二部ネットワークの最適クラスタリング（Optimal Bipartite Network Clustering）</news:title>
   <news:publication_date>2026-04-17T11:34:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680347</loc>
  <lastmod>2026-04-17T11:34:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>レクリエーショナルランナーの乳酸閾値推定に機械学習を使う意義（Estimation of lactate threshold with machine learning techniques in recreational runners）</news:title>
   <news:publication_date>2026-04-17T11:34:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680345</loc>
  <lastmod>2026-04-17T11:33:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的勾配で鞍点を脱出する仕組み（Escaping Saddles with Stochastic Gradients）</news:title>
   <news:publication_date>2026-04-17T11:33:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680343</loc>
  <lastmod>2026-04-17T11:33:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Deep Learningによる超短パルス再構成（Deep Learning Reconstruction of Ultra-Short Pulses）</news:title>
   <news:publication_date>2026-04-17T11:33:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680341</loc>
  <lastmod>2026-04-17T11:33:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>畳み込みニューラルネットワークのハードウェア実装効率化（Efficient Hardware Realization of Convolutional Neural Networks using Intra-Kernel Regular Pruning）</news:title>
   <news:publication_date>2026-04-17T11:33:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680339</loc>
  <lastmod>2026-04-17T11:32:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>リッジ回帰と可証的な決定論的リッジレバレッジスコアサンプリング（Ridge Regression and Provable Deterministic Ridge Leverage Score Sampling）</news:title>
   <news:publication_date>2026-04-17T11:32:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680337</loc>
  <lastmod>2026-04-17T10:41:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Higuchiのフラクタル次元とサンプルエントロピーを特徴量としたEEG機械学習によるうつ病検出（EEG machine learning with Higuchi’s fractal dimension and Sample Entropy as features for successful detection of depression）</news:title>
   <news:publication_date>2026-04-17T10:41:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680335</loc>
  <lastmod>2026-04-17T10:41:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Deep Co-Training による半教師あり画像認識の実務的意義（Deep Co-Training for Semi-Supervised Image Recognition）</news:title>
   <news:publication_date>2026-04-17T10:41:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680333</loc>
  <lastmod>2026-04-17T10:41:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Real-time Deep Pose Estimation with Geodesic Loss for Image-to-Template Rigid Registration（Real-time Deep Pose Estimation with Geodesic Loss for Image-to-Template Rigid Registration）</news:title>
   <news:publication_date>2026-04-17T10:41:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680331</loc>
  <lastmod>2026-04-17T10:40:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Chironによるプライバシー保護されたML-as-a-Serviceの実現（Chiron: Privacy-preserving Machine Learning as a Service）</news:title>
   <news:publication_date>2026-04-17T10:40:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680329</loc>
  <lastmod>2026-04-17T10:39:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>光学的電荷変換による高周波エレクトロメトリ（Electrometry by Optical Charge Conversion of Deep Defects in 4H-SiC）</news:title>
   <news:publication_date>2026-04-17T10:39:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680327</loc>
  <lastmod>2026-04-17T10:39:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>航空旅程選択予測のためのPointer Networksを用いたDeep Choice Model（Deep Choice Model Using Pointer Networks for Airline Itinerary Prediction）</news:title>
   <news:publication_date>2026-04-17T10:39:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680325</loc>
  <lastmod>2026-04-17T10:38:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習済み畳み込みニューラルネットワークの不変性の研究（Studying Invariances of Trained Convolutional Neural Networks）</news:title>
   <news:publication_date>2026-04-17T10:38:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680323</loc>
  <lastmod>2026-04-17T09:48:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習と評価の接点（Learning meets Assessment: On the relation between Item Response Theory and Bayesian Knowledge Tracing）</news:title>
   <news:publication_date>2026-04-17T09:48:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680321</loc>
  <lastmod>2026-04-17T09:47:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文脈的変調と算術的相互作用の情報理論的分解（Contrasting information theoretic decompositions of modulatory and arithmetic interactions in neural information processing systems）</news:title>
   <news:publication_date>2026-04-17T09:47:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680319</loc>
  <lastmod>2026-04-17T09:47:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CNNをFPGAへ写像するためのツールフロー調査（Toolflows for Mapping Convolutional Neural Networks on FPGAs）</news:title>
   <news:publication_date>2026-04-17T09:47:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680317</loc>
  <lastmod>2026-04-17T09:47:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>クーロンガス電気統計がKPZ方程式の大ゆらぎを制御する（Coulomb-gas electrostatics controls large fluctuations of the KPZ equation）</news:title>
   <news:publication_date>2026-04-17T09:47:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680315</loc>
  <lastmod>2026-04-17T09:47:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GossipGraDによる通信効率化で大規模学習を現実に（GossipGraD: Scalable Deep Learning using Gossip Communication based Asynchronous Gradient Descent）</news:title>
   <news:publication_date>2026-04-17T09:47:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680313</loc>
  <lastmod>2026-04-17T09:47:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ブロックチェーン上の分散型データ販売（Distributed Data Vending on Blockchain）</news:title>
   <news:publication_date>2026-04-17T09:47:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680311</loc>
  <lastmod>2026-04-17T09:46:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層構造推論ネットワークによる顔表情の部分ユニット認識（Deep Structure Inference Network for Facial Action Unit Recognition）</news:title>
   <news:publication_date>2026-04-17T09:46:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680309</loc>
  <lastmod>2026-04-17T08:55:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習による反復デコーディングでロスのある画像圧縮を改善する手法（Learned Neural Iterative Decoding for Lossy Image Compression Systems）</news:title>
   <news:publication_date>2026-04-17T08:55:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680307</loc>
  <lastmod>2026-04-17T08:55:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューラルネットワークの自己複製の実装と訓練手法（Neural Network Quine）</news:title>
   <news:publication_date>2026-04-17T08:55:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680305</loc>
  <lastmod>2026-04-17T08:54:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>限られた時系列データから構造を暗黙的に捉える手法（Capturing Structure Implicitly from Time-Series having Limited Data）</news:title>
   <news:publication_date>2026-04-17T08:54:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680303</loc>
  <lastmod>2026-04-17T08:54:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>擬似マスクを用いた物体検出の拡張（Pseudo Mask Augmented Object Detection）</news:title>
   <news:publication_date>2026-04-17T08:54:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680301</loc>
  <lastmod>2026-04-17T08:54:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習を伴う実物オプションにおける技術的不確実性（Technical Uncertainty in Real Options with Learning）</news:title>
   <news:publication_date>2026-04-17T08:54:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680299</loc>
  <lastmod>2026-04-17T08:54:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所スペクトルグラフ畳み込みを用いた点群特徴学習（Local Spectral Graph Convolution for Point Set Feature Learning）</news:title>
   <news:publication_date>2026-04-17T08:54:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680297</loc>
  <lastmod>2026-04-17T08:53:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ユーザデータ注釈の課題と実務的な道筋（CHALLENGES IN ANNOTATION OF USER DATA FOR UBIQUITOUS SYSTEMS: RESULTS FROM THE 1ST ARDUOUS WORKSHOP）</news:title>
   <news:publication_date>2026-04-17T08:53:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680295</loc>
  <lastmod>2026-04-17T08:02:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>OFDMオートエンコーダによる通信システムのエンドツーエンド学習（OFDM-Autoencoder for End-to-End Learning of Communications Systems）</news:title>
   <news:publication_date>2026-04-17T08:02:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680293</loc>
  <lastmod>2026-04-17T08:01:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非定常・非混合時系列予測の理論とアルゴリズム（Theory and Algorithms for Forecasting Time Series）</news:title>
   <news:publication_date>2026-04-17T08:01:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680291</loc>
  <lastmod>2026-04-17T08:00:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文脈依存ランキング関数を学習する深層アーキテクチャ（Deep Architectures for Learning Context-dependent Ranking Functions）</news:title>
   <news:publication_date>2026-04-17T08:00:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680289</loc>
  <lastmod>2026-04-17T07:59:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>受容野プロファイルからV1の距離モデルへ（From Receptive Profiles to a Metric Model of V1）</news:title>
   <news:publication_date>2026-04-17T07:59:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680287</loc>
  <lastmod>2026-04-17T07:59:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>モンドリアン木と森のミニマックス最適率（Minimax Optimal Rates for Mondrian Trees and Forests）</news:title>
   <news:publication_date>2026-04-17T07:59:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680285</loc>
  <lastmod>2026-04-17T07:59:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフ上のガウス過程（Gaussian Processes Over Graphs）</news:title>
   <news:publication_date>2026-04-17T07:59:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680283</loc>
  <lastmod>2026-04-17T07:59:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>関係データ領域におけるPAC推論（PAC-Reasoning in Relational Domains）</news:title>
   <news:publication_date>2026-04-17T07:59:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680281</loc>
  <lastmod>2026-04-17T07:07:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>注目領域分割によるサリエンシー予測の再定式化（Salient Region Segmentation）</news:title>
   <news:publication_date>2026-04-17T07:07:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680279</loc>
  <lastmod>2026-04-17T07:07:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>カナリア諸島観測でのPlanck PSZ1光学的検証と特性付け（Optical validation and characterization of Planck PSZ1 sources at the Canary Islands observatories）</news:title>
   <news:publication_date>2026-04-17T07:07:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680277</loc>
  <lastmod>2026-04-17T07:06:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>FDD方式のMassive MIMOにおけるUL/DL共分散外挿と能動的チャネル希薄化（FDD Massive MIMO via UL/DL Channel Covariance Extrapolation and Active Channel Sparsification）</news:title>
   <news:publication_date>2026-04-17T07:06:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680275</loc>
  <lastmod>2026-04-17T07:05:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>何が目を引くか？視覚的サリエンシーモデルの可視化と理解 (What Catches the Eye? Visualizing and Understanding Deep Saliency Models)</news:title>
   <news:publication_date>2026-04-17T07:05:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680273</loc>
  <lastmod>2026-04-17T07:05:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非把持操作による物体再配置を学習する研究（Rearrangement with Nonprehensile Manipulation Using Deep Reinforcement Learning）</news:title>
   <news:publication_date>2026-04-17T07:05:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680271</loc>
  <lastmod>2026-04-17T07:05:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>溝状キャピラリー内の毛管凝縮に対する修正版ケルビン方程式（Modified Kelvin equations for capillary condensation in narrow and wide grooves）</news:title>
   <news:publication_date>2026-04-17T07:05:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680269</loc>
  <lastmod>2026-04-17T07:04:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>特徴マップ部分空間における線形関係を利用したConvNets圧縮（Exploring Linear Relationship in Feature Map Subspace for ConvNets Compression）</news:title>
   <news:publication_date>2026-04-17T07:04:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680267</loc>
  <lastmod>2026-04-17T06:13:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>FRB170827のリアルタイム検出が明かすマイクロ構造（FRB microstructure revealed by the real-time detection of FRB170827）</news:title>
   <news:publication_date>2026-04-17T06:13:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680265</loc>
  <lastmod>2026-04-17T06:13:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数異種データセットでの畳み込みネットワーク訓練（Training of Convolutional Networks on Multiple Heterogeneous Datasets for Street Scene Semantic Segmentation）</news:title>
   <news:publication_date>2026-04-17T06:13:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680263</loc>
  <lastmod>2026-04-17T06:12:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>フェルミオン崩壊最終状態におけるヒッグス粒子の測定と拡張スカラー部門の探索 (Higgs boson measurements and extended scalar sector searches in fermionic final states)</news:title>
   <news:publication_date>2026-04-17T06:12:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680261</loc>
  <lastmod>2026-04-17T06:11:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>情報抽出に関する最近の貢献の研究（A Study of Recent Contributions on Information Extraction）</news:title>
   <news:publication_date>2026-04-17T06:11:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680259</loc>
  <lastmod>2026-04-17T06:11:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>構造正則化を用いた関係分類の新展開（Structure Regularized Neural Network for Entity Relation Classification for Chinese Literature Text）</news:title>
   <news:publication_date>2026-04-17T06:11:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680257</loc>
  <lastmod>2026-04-17T06:11:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>量子化された単語ベクトル（Word2Bits - Quantized Word Vectors）</news:title>
   <news:publication_date>2026-04-17T06:11:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680255</loc>
  <lastmod>2026-04-17T06:11:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>クロンネッカー積に基づく高速サブスペースクラスタリング (Fast Subspace Clustering Based on the Kronecker Product)</news:title>
   <news:publication_date>2026-04-17T06:11:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680253</loc>
  <lastmod>2026-04-17T05:19:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Sylvester正規化フローによる変分推論の強化（Sylvester Normalizing Flows for Variational Inference）</news:title>
   <news:publication_date>2026-04-17T05:19:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680251</loc>
  <lastmod>2026-04-17T05:19:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>映像から境界と3D形状を同時学習する手法の解説 — LEGO: Learning Edge with Geometry all at Once by Watching Videos (LEGO: Learning Edge with Geometry all at Once by Watching Videos)</news:title>
   <news:publication_date>2026-04-17T05:19:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680249</loc>
  <lastmod>2026-04-17T05:18:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層学習による定量磁化率マッピング：QSMnet（Quantitative Susceptibility Mapping using Deep Neural Network: QSMnet）</news:title>
   <news:publication_date>2026-04-17T05:18:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680247</loc>
  <lastmod>2026-04-17T05:17:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>空間サンプリングによるガウス源の再構成（Reconstructing Gaussian sources by spatial sampling）</news:title>
   <news:publication_date>2026-04-17T05:17:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680245</loc>
  <lastmod>2026-04-17T05:17:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高解像度ガイダンスで高速に復元する学習可能ガイドフィルタ（Fast End-to-End Trainable Guided Filter）</news:title>
   <news:publication_date>2026-04-17T05:17:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680243</loc>
  <lastmod>2026-04-17T05:17:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散スパース学習におけるデータ分割最適化が収束を加速する仕組み（Proximal SCOPE for distributed sparse learning: Better data partition implies faster convergence rate）</news:title>
   <news:publication_date>2026-04-17T05:17:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680241</loc>
  <lastmod>2026-04-17T05:16:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューラルテキスト生成の過去・現在・未来（Neural Text Generation: Past, Present and Beyond）</news:title>
   <news:publication_date>2026-04-17T05:16:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680239</loc>
  <lastmod>2026-04-17T04:25:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大きなマージンを持つ深層ネットワークの設計（Large Margin Deep Networks for Classification）</news:title>
   <news:publication_date>2026-04-17T04:25:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680237</loc>
  <lastmod>2026-04-17T04:24:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>既存モメンタム法の限界と確率的最適化における示唆（On the insufﬁciency of existing momentum schemes for Stochastic Optimization）</news:title>
   <news:publication_date>2026-04-17T04:24:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680235</loc>
  <lastmod>2026-04-17T04:23:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高速ポートレート編集のためのFacelet-Bank（Facelet-Bank for Fast Portrait Manipulation）</news:title>
   <news:publication_date>2026-04-17T04:23:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680233</loc>
  <lastmod>2026-04-17T04:23:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>構造化推論ネットワークによる変分メッセージパッシング（VARIATIONAL MESSAGE PASSING WITH STRUCTURED INFERENCE NETWORKS）</news:title>
   <news:publication_date>2026-04-17T04:23:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680231</loc>
  <lastmod>2026-04-17T04:22:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>顔の行動単位検出と顔アライメントのための深層適応注意機構（Deep Adaptive Attention for Joint Facial Action Unit Detection and Face Alignment）</news:title>
   <news:publication_date>2026-04-17T04:22:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680229</loc>
  <lastmod>2026-04-17T04:22:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Cassieに深層強化学習で歩行を学ばせる（Feedback Control For Cassie With Deep Reinforcement Learning）</news:title>
   <news:publication_date>2026-04-17T04:22:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680227</loc>
  <lastmod>2026-04-17T04:21:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>最適輸送を用いたGANの改良（IMPROVING GANS USING OPTIMAL TRANSPORT）</news:title>
   <news:publication_date>2026-04-17T04:21:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680225</loc>
  <lastmod>2026-04-17T03:30:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>トランジット法による系外惑星検出（Detection of Exoplanets Using the Transit Method）</news:title>
   <news:publication_date>2026-04-17T03:30:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680223</loc>
  <lastmod>2026-04-17T03:30:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>最小I-MAP MCMCによる因果DAG構造発見のスケーラブル手法（Minimal I-MAP MCMC for Scalable Structure Discovery in Causal DAG Models）</news:title>
   <news:publication_date>2026-04-17T03:30:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680221</loc>
  <lastmod>2026-04-17T03:29:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>映像中の物体検出を強化する時空間サンプリングネットワーク（Spatiotemporal Sampling Networks）</news:title>
   <news:publication_date>2026-04-17T03:29:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680219</loc>
  <lastmod>2026-04-17T03:29:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文脈認識型ミックスドリアリティ：環境の意味を理解するMRフレームワーク（Context-Aware Mixed Reality: A Framework for Ubiquitous Interaction）</news:title>
   <news:publication_date>2026-04-17T03:29:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680217</loc>
  <lastmod>2026-04-17T03:29:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Webアプリの機械学習による仮想パッチ適用（Machine learning-assisted virtual patching of web applications）</news:title>
   <news:publication_date>2026-04-17T03:29:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680215</loc>
  <lastmod>2026-04-17T03:28:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単眼画像から深さと信頼度を同時に学ぶ方法（Self-Supervised Monocular Image Depth Learning and Confidence Estimation）</news:title>
   <news:publication_date>2026-04-17T03:28:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680213</loc>
  <lastmod>2026-04-17T03:28:25Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>アルゼンチンにおける深宇宙アンテナ（Las antenas de espacio profundo en la Argentina）</news:title>
   <news:publication_date>2026-04-17T03:28:25Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680211</loc>
  <lastmod>2026-04-17T02:37:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ヒートマップ規制による物体カウント精度向上（Improving Object Counting with Heatmap Regulation）</news:title>
   <news:publication_date>2026-04-17T02:37:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680209</loc>
  <lastmod>2026-04-17T02:37:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>海底ケーブルの増強で重要なのは電力を分散する設計（Importance of Amplifier Physics in Maximizing the Capacity of Submarine Links）</news:title>
   <news:publication_date>2026-04-17T02:37:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680207</loc>
  <lastmod>2026-04-17T02:36:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>言語ピボットによる非対訳画像キャプション生成（Unpaired Image Captioning by Language Pivoting）</news:title>
   <news:publication_date>2026-04-17T02:36:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680205</loc>
  <lastmod>2026-04-17T02:36:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ヒッグスとトップの共演：ttHの多レプトン最終状態での証拠 (Evidence for associated production of a Higgs boson with a top quark pair in final states with electrons, muons, and hadronically decaying τ leptons at √s = 13 TeV)</news:title>
   <news:publication_date>2026-04-17T02:36:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680203</loc>
  <lastmod>2026-04-17T02:36:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ターゲット変化検出と衛星画像解析（Targeted change detection in remote sensing images）</news:title>
   <news:publication_date>2026-04-17T02:36:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680201</loc>
  <lastmod>2026-04-17T02:36:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>肺癌の深層学習による診断支援（Computer-aided diagnosis of lung carcinoma using deep learning – a pilot study）</news:title>
   <news:publication_date>2026-04-17T02:36:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680199</loc>
  <lastmod>2026-04-17T02:35:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>整数データに特化したスコアリング手法SUSTainの概観（SUSTain: Scalable Unsupervised Scoring for Tensors and its Application to Phenotyping）</news:title>
   <news:publication_date>2026-04-17T02:35:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680197</loc>
  <lastmod>2026-04-17T01:44:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>材料探索の自律効率的実験設計（Autonomous Efficient Experiment Design for Materials Discovery with Bayesian Model Averaging）</news:title>
   <news:publication_date>2026-04-17T01:44:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680195</loc>
  <lastmod>2026-04-17T01:44:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自動車の速度と車線変更の意思決定を自動で学ぶ手法（Automated Speed and Lane Change Decision Making using Deep Reinforcement Learning）</news:title>
   <news:publication_date>2026-04-17T01:44:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680193</loc>
  <lastmod>2026-04-17T01:44:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GOODS-N深部20cm帯JVLAイメージングの成果（DEEP JVLA IMAGING OF GOODS-N AT 20CM）</news:title>
   <news:publication_date>2026-04-17T01:44:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680191</loc>
  <lastmod>2026-04-17T01:43:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>重みの平均化がもたらす広い最適解と汎化改善（Averaging Weights Leads to Wider Optima and Better Generalization）</news:title>
   <news:publication_date>2026-04-17T01:43:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680189</loc>
  <lastmod>2026-04-17T01:43:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>一般化構造CNN（Generalised Structural CNNs for time series data with arbitrary graph topology）</news:title>
   <news:publication_date>2026-04-17T01:43:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680187</loc>
  <lastmod>2026-04-17T01:42:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>3Dゲームにおける同時複数行動の模倣学習（Imitation Learning with Concurrent Actions in 3D Games）</news:title>
   <news:publication_date>2026-04-17T01:42:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680185</loc>
  <lastmod>2026-04-17T01:42:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>新たな古典新星の殻の発見（Discovery of a new classical nova shell around a nova-like cataclysmic variable）</news:title>
   <news:publication_date>2026-04-17T01:42:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680183</loc>
  <lastmod>2026-04-17T00:51:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>画像の自動彩色を変えた生成的敵対ネットワークの応用（Image Colorization using Generative Adversarial Networks）</news:title>
   <news:publication_date>2026-04-17T00:51:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680181</loc>
  <lastmod>2026-04-17T00:50:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>冗長性技術による分散最適化とストラッグラー緩和（Redundancy Techniques for Distributed Optimization）</news:title>
   <news:publication_date>2026-04-17T00:50:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680179</loc>
  <lastmod>2026-04-17T00:50:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率計算ベースのニューラルネットと2値ニューラルネットの普遍近似性と等価性（On the Universal Approximation Property and Equivalence of Stochastic Computing-based Neural Networks and Binary Neural Networks）</news:title>
   <news:publication_date>2026-04-17T00:50:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680177</loc>
  <lastmod>2026-04-17T00:49:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>電子線照射下のWS2における欠陥と相の進化の深層学習解析（Deep Learning Analysis of Defect and Phase Evolution During Electron Beam Induced Transformations in WS2）</news:title>
   <news:publication_date>2026-04-17T00:49:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680175</loc>
  <lastmod>2026-04-17T00:49:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単眼SAR画像からの地形推定に向けたCNNアプローチ（Towards Monocular Digital Elevation Model (DEM) Estimation by Convolutional Neural Networks）</news:title>
   <news:publication_date>2026-04-17T00:49:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680173</loc>
  <lastmod>2026-04-17T00:48:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>二層ライドバーグ原子・極性分子系の有効スピン相互作用（Effective spin-spin interactions in bilayers of Rydberg atoms and polar molecules）</news:title>
   <news:publication_date>2026-04-17T00:48:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680171</loc>
  <lastmod>2026-04-17T00:48:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自己相似エポック：データ配列が学習効率を変える（Self-Similar Epochs: Value in Arrangement）</news:title>
   <news:publication_date>2026-04-17T00:48:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680169</loc>
  <lastmod>2026-04-16T23:57:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>長時間文脈を捉える感情データセットの提案（The OMG-Emotion Behavior Dataset）</news:title>
   <news:publication_date>2026-04-16T23:57:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680167</loc>
  <lastmod>2026-04-16T23:57:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>測定に基づく適応プロトコルと量子強化学習（Measurement-based adaptation protocol with quantum reinforcement learning）</news:title>
   <news:publication_date>2026-04-16T23:57:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680165</loc>
  <lastmod>2026-04-16T23:56:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>経口崩壊錠の処方をニューラルネットワークで予測する（Predicting Oral Disintegrating Tablet Formulations by Neural Network Techniques）</news:title>
   <news:publication_date>2026-04-16T23:56:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680163</loc>
  <lastmod>2026-04-16T23:56:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>変化点は“スパースな説明”の導入──Variational Bayesianを用いた行列分解/補完の近似手法（Approximate Method of Variational Bayesian Matrix Factorization/Completion with Sparse Prior）</news:title>
   <news:publication_date>2026-04-16T23:56:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680161</loc>
  <lastmod>2026-04-16T23:56:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>代数的機械学習の概観（Algebraic Machine Learning）</news:title>
   <news:publication_date>2026-04-16T23:56:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680159</loc>
  <lastmod>2026-04-16T23:55:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフ上で学ぶドメイン適応（Domain Adaptation on Graphs by Learning Aligned Graph Bases）</news:title>
   <news:publication_date>2026-04-16T23:55:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680157</loc>
  <lastmod>2026-04-16T23:55:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>デザインによる透明性：視覚的推論における性能と解釈性のギャップを埋める（Transparency by Design: Closing the Gap Between Performance and Interpretability in Visual Reasoning）</news:title>
   <news:publication_date>2026-04-16T23:55:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680155</loc>
  <lastmod>2026-04-16T23:04:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>クロモスフェアのMg IIラインにおける青側増強の観測と解釈（Blue wing enhancement of the chromospheric Mg II h and k lines in a solar flare）</news:title>
   <news:publication_date>2026-04-16T23:04:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680153</loc>
  <lastmod>2026-04-16T23:03:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>木構造受容野を用いたスパースな深層フィードフォワードネットワークの構築（Building Sparse Deep Feedforward Networks using Tree Receptive Fields）</news:title>
   <news:publication_date>2026-04-16T23:03:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680151</loc>
  <lastmod>2026-04-16T23:03:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層畳み込み残差ノイズ除去ネットワークによる画像デモザイキングとノイズ除去の統合（Deep Image Demosaicking using a Cascade of Convolutional Residual Denoising Networks）</news:title>
   <news:publication_date>2026-04-16T23:03:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680149</loc>
  <lastmod>2026-04-16T23:02:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>潜在ツリー型変分オートエンコーダによる多面クラスタリングの提案（LEARNING LATENT SUPERSTRUCTURES IN VARIATIONAL AUTOENCODERS FOR DEEP MULTIDIMENSIONAL CLUSTERING）</news:title>
   <news:publication_date>2026-04-16T23:02:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680147</loc>
  <lastmod>2026-04-16T23:02:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>リアルタイム心血管MRIにおける時空間アーティファクト抑制の深層学習（Real-time Cardiovascular MR with Spatio-temporal Artifact Suppression using Deep Learning）</news:title>
   <news:publication_date>2026-04-16T23:02:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680145</loc>
  <lastmod>2026-04-16T23:01:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層ニューラルネットワークによる量子制御補正の近似（Approximation of quantum control correction scheme using deep neural networks）</news:title>
   <news:publication_date>2026-04-16T23:01:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680143</loc>
  <lastmod>2026-04-16T23:01:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチレベルの文脈を組み合わせたスーパーピクセルによる自然画像ラベリング（Combining Multi-level Contexts of Superpixel using Convolutional Neural Networks to perform Natural Scene Labeling）</news:title>
   <news:publication_date>2026-04-16T23:01:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680141</loc>
  <lastmod>2026-04-16T22:10:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>xDeepFM：明示的・暗黙的な特徴量相互作用を統合する推薦モデル（xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems）</news:title>
   <news:publication_date>2026-04-16T22:10:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680139</loc>
  <lastmod>2026-04-16T22:10:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>広帯域二層反射防止構造によるシリコン真空窓の実現（A 1.6:1 Bandwidth Two-Layer Antireflection Structure for Silicon Matched to the 190–310 GHz Atmospheric Window）</news:title>
   <news:publication_date>2026-04-16T22:10:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680137</loc>
  <lastmod>2026-04-16T22:09:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>量子制御学習における幾何学的表現と時系列表現の比較（Geometrical versus time-series representation of data in quantum control learning）</news:title>
   <news:publication_date>2026-04-16T22:09:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680135</loc>
  <lastmod>2026-04-16T22:08:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ISISの最盛期におけるアラビア語Twitter議論と示唆（ISIS at its apogee: the Arabic discourse on Twitter and what we can learn from that about ISIS support and Foreign Fighters）</news:title>
   <news:publication_date>2026-04-16T22:08:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680133</loc>
  <lastmod>2026-04-16T22:08:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>アドバーサリアル・データ・プログラミング（Adversarial Data Programming: Using GANs to Relax the Bottleneck of Curated Labeled Data）</news:title>
   <news:publication_date>2026-04-16T22:08:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680131</loc>
  <lastmod>2026-04-16T22:08:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率母関数を用いた感染症モデル入門（A primer on the use of probability generating functions in infectious disease modeling）</news:title>
   <news:publication_date>2026-04-16T22:08:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680129</loc>
  <lastmod>2026-04-16T22:08:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>畳み込み非負値行列因子分解の乗法更新則とβダイバージェンス（Multiplicative Updates for Convolutional NMF Under β-Divergence）</news:title>
   <news:publication_date>2026-04-16T22:08:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680127</loc>
  <lastmod>2026-04-16T21:16:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>C-LSTMによるFPGA上の効率的なLSTM実装（C-LSTM: Enabling Efficient LSTM using Structured Compression Techniques on FPGAs）</news:title>
   <news:publication_date>2026-04-16T21:16:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680125</loc>
  <lastmod>2026-04-16T21:13:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>アナログHTMの学習を伴わない特徴抽出とメムリスタ・CMOS回路設計（Feature extraction without learning in an analog Spatial Pooler）</news:title>
   <news:publication_date>2026-04-16T21:13:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680123</loc>
  <lastmod>2026-04-16T21:13:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>信号処理における分割凸フィッティング（Signal Processing and Piecewise Convex Estimation）</news:title>
   <news:publication_date>2026-04-16T21:13:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680121</loc>
  <lastmod>2026-04-16T21:12:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>検索順位学習データセットにおける特徴選択とモデル比較（Feature Selection and Model Comparison on Microsoft Learning-to-Rank Data Sets）</news:title>
   <news:publication_date>2026-04-16T21:12:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680119</loc>
  <lastmod>2026-04-16T21:12:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>網膜OCTのトポロジー保証付きセグメンテーション（Topology guaranteed segmentation of the human retina from OCT using convolutional neural networks）</news:title>
   <news:publication_date>2026-04-16T21:12:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680117</loc>
  <lastmod>2026-04-16T21:12:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>MT-Spike：時間を使うスパイキングニューラルネットで多層学習を実現する（MT-Spike: A Multilayer Time-based Spiking Neuromorphic Architecture with Temporal Error Backpropagation）</news:title>
   <news:publication_date>2026-04-16T21:12:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680115</loc>
  <lastmod>2026-04-16T21:11:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>協働的マルチタスク訓練による敵対的攻撃への防御（Defending against Adversarial Attack towards Deep Neural Networks via Collaborative Multi-Task Training）</news:title>
   <news:publication_date>2026-04-16T21:11:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680113</loc>
  <lastmod>2026-04-16T20:20:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>特徴蒸留：敵対的例に対するDNN志向JPEG圧縮（Feature Distillation: DNN-Oriented JPEG Compression Against Adversarial Examples）</news:title>
   <news:publication_date>2026-04-16T20:20:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680111</loc>
  <lastmod>2026-04-16T20:20:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>別ラベルからのアップリフトモデリング（Uplift Modeling from Separate Labels）</news:title>
   <news:publication_date>2026-04-16T20:20:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680109</loc>
  <lastmod>2026-04-16T20:19:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>近傍を学習するランキング手法の要点（Ranking with Adaptive Neighbors）</news:title>
   <news:publication_date>2026-04-16T20:19:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680107</loc>
  <lastmod>2026-04-16T20:18:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>安定性に基づくモデル非依存プライベート学習（Model-Agnostic Private Learning via Stability）</news:title>
   <news:publication_date>2026-04-16T20:18:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680105</loc>
  <lastmod>2026-04-16T20:18:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>不正確な事前分布に対するロバスト性（Robustness to Incorrect Priors in Partially Observed Stochastic Control）</news:title>
   <news:publication_date>2026-04-16T20:18:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680103</loc>
  <lastmod>2026-04-16T20:17:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DNNに適したJPEG圧縮の再設計—DeepN-JPEGの要点と実務インパクト（DeepN-JPEG: A Deep Neural Network Favorable JPEG-based Image Compression Framework）</news:title>
   <news:publication_date>2026-04-16T20:17:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680101</loc>
  <lastmod>2026-04-16T20:17:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>バケット縮約による近似推論（Bucket Renormalization for Approximate Inference）</news:title>
   <news:publication_date>2026-04-16T20:17:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680099</loc>
  <lastmod>2026-04-16T19:25:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>顔画像における注目領域を用いた軟生体情報分類（SAF-BAGE: Salient Approach for Facial Soft-Biometric Classification - Age, Gender, and Facial Expression）</news:title>
   <news:publication_date>2026-04-16T19:25:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680097</loc>
  <lastmod>2026-04-16T19:18:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>アルゴリズムによる社会介入の提案（Thesis Proposal: Algorithmic Social Intervention）</news:title>
   <news:publication_date>2026-04-16T19:18:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680095</loc>
  <lastmod>2026-04-16T19:17:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数顕著物体の検出・ランキング・即時数の再考（Revisiting Salient Object Detection: Simultaneous Detection, Ranking, and Subitizing of Multiple Salient Objects）</news:title>
   <news:publication_date>2026-04-16T19:17:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680093</loc>
  <lastmod>2026-04-16T19:16:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>垂直メニュー選択における人間のパフォーマンス予測（Predicting Human Performance in Vertical Menu Selection Using Deep Learning）</news:title>
   <news:publication_date>2026-04-16T19:16:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680091</loc>
  <lastmod>2026-04-16T19:15:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>画像から集団感情を推定するマルチモーダル手法（A Multi-Modal Approach to Infer Image Affect）</news:title>
   <news:publication_date>2026-04-16T19:15:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680089</loc>
  <lastmod>2026-04-16T19:15:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>音楽の長期構造を学習する階層潜在ベクトルモデル（A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music）</news:title>
   <news:publication_date>2026-04-16T19:15:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680087</loc>
  <lastmod>2026-04-16T19:14:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>心理学的知見を応用した実行可能な分析（Applications of Psychological Science for Actionable Analytics）</news:title>
   <news:publication_date>2026-04-16T19:14:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680085</loc>
  <lastmod>2026-04-16T18:23:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模ソーシャルデータの落とし穴：公開Redditコーパスの欠損と研究への影響（Caveat Emptor, Computational Social Science: Large-Scale Missing Data in a Widely-Published Reddit Corpus）</news:title>
   <news:publication_date>2026-04-16T18:23:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680083</loc>
  <lastmod>2026-04-16T18:23:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>探索方策を学習するメタポリシー勾配（Learning to Explore with Meta-Policy Gradient）</news:title>
   <news:publication_date>2026-04-16T18:23:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680081</loc>
  <lastmod>2026-04-16T18:22:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非自律的敵対システムの解析（Analysis of Nonautonomous Adversarial Systems）</news:title>
   <news:publication_date>2026-04-16T18:22:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680079</loc>
  <lastmod>2026-04-16T18:21:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自動化されたデータ駆動型無機材料設計とAFLOW（Autonomous data-driven design of inorganic materials with AFLOW）</news:title>
   <news:publication_date>2026-04-16T18:21:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680077</loc>
  <lastmod>2026-04-16T18:21:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>コロイド・顆粒性ガラスにおけるスピンガラス様エージング（Spin-glass–like aging in colloidal and granular glasses）</news:title>
   <news:publication_date>2026-04-16T18:21:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680075</loc>
  <lastmod>2026-04-16T18:21:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多数の戦略的エージェントを持つシステムにおける分散学習（Decentralised Learning in Systems with Many, Many Strategic Agents）</news:title>
   <news:publication_date>2026-04-16T18:21:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680073</loc>
  <lastmod>2026-04-16T18:21:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ゼロ過剰データに効く疎カーネル付き変分ガウス過程（Variational zero-inflated Gaussian processes with sparse kernels）</news:title>
   <news:publication_date>2026-04-16T18:21:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680071</loc>
  <lastmod>2026-04-16T17:29:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習とソフトウェア定義ネットワークで守るIoTの未来（Securing the Internet of Things in the Age of Machine Learning and Software-defined Networking）</news:title>
   <news:publication_date>2026-04-16T17:29:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680069</loc>
  <lastmod>2026-04-16T17:29:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的疾患進行モデルによる臨床予測の実用化（A Probabilistic Disease Progression Model for Predicting Future Clinical Outcome）</news:title>
   <news:publication_date>2026-04-16T17:29:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680067</loc>
  <lastmod>2026-04-16T17:29:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>テンソル列（Tensor Train）を使った主成分分析による次元削減の再定義（Principal Component Analysis with Tensor Train Subspace）</news:title>
   <news:publication_date>2026-04-16T17:29:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680065</loc>
  <lastmod>2026-04-16T17:28:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>タスク簡易化によるクローズドループ物体把持学習（Comparing Task Simplifications to Learn Closed-Loop Object Picking Using Deep Reinforcement Learning）</news:title>
   <news:publication_date>2026-04-16T17:28:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680063</loc>
  <lastmod>2026-04-16T17:27:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>トランジット系惑星のJWST早期公開科学計画（Transiting Exoplanet Community Early Release Science Program for JWST）</news:title>
   <news:publication_date>2026-04-16T17:27:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680061</loc>
  <lastmod>2026-04-16T17:27:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>条件付き活性化による多様なニューロン表現（Conditional Activation for Diverse Neurons in Heterogeneous Networks）</news:title>
   <news:publication_date>2026-04-16T17:27:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680059</loc>
  <lastmod>2026-04-16T17:27:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>LCANetによるエンドツーエンドのリップリーディング（LCANet: End-to-End Lipreading with Cascaded Attention-CTC）</news:title>
   <news:publication_date>2026-04-16T17:27:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680057</loc>
  <lastmod>2026-04-16T16:27:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>麻酔薬分子は液体無秩序相に入りやすい（Common anesthetic molecules prefer to partition in liquid disorder phase in a composite multicomponent membrane）</news:title>
   <news:publication_date>2026-04-16T16:27:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680055</loc>
  <lastmod>2026-04-16T16:27:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Information-Corrected Estimation（Information-Corrected Estimation: A Generalization Error Reducing Parameter Estimation Method）</news:title>
   <news:publication_date>2026-04-16T16:27:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680053</loc>
  <lastmod>2026-04-16T16:27:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>頭部CTにおける重大所見検出の深層学習アルゴリズムの開発と検証（Development and Validation of Deep Learning Algorithms for Detection of Critical Findings in Head CT Scans）</news:title>
   <news:publication_date>2026-04-16T16:27:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680051</loc>
  <lastmod>2026-04-16T16:25:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>構造非拘束モデリング：因果グラフの敵対的学習（Structural Agnostic Modeling: Adversarial Learning of Causal Graphs）</news:title>
   <news:publication_date>2026-04-16T16:25:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680049</loc>
  <lastmod>2026-04-16T16:25:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>能動的報酬評価を考慮した強化学習とMCTSの接合（Active Reinforcement Learning with Monte-Carlo Tree Search）</news:title>
   <news:publication_date>2026-04-16T16:25:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680047</loc>
  <lastmod>2026-04-16T16:24:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>FCNの量子化が生む過学習抑制と高精度セグメンテーション（Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation）</news:title>
   <news:publication_date>2026-04-16T16:24:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680045</loc>
  <lastmod>2026-04-16T15:33:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>診断駆動型カーネル混合による非定常エミュレータ（DIAGNOSTICS-DRIVEN NONSTATIONARY EMULATORS USING KERNEL MIXTURES）</news:title>
   <news:publication_date>2026-04-16T15:33:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680043</loc>
  <lastmod>2026-04-16T15:31:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CNNがマンモグラム分類で利用する視覚プリミティブを専門家が同定する研究（Expert identification of visual primitives used by CNNs during mammogram classification）</news:title>
   <news:publication_date>2026-04-16T15:31:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680041</loc>
  <lastmod>2026-04-16T15:23:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>音声から音楽ジャンルを認識する学習（Learning to Recognize Musical Genre from Audio）</news:title>
   <news:publication_date>2026-04-16T15:23:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680039</loc>
  <lastmod>2026-04-16T15:23:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数ソースからのドメイン適応におけるターゲットシフトへの最適輸送の適用（Optimal Transport for Multi-source Domain Adaptation under Target Shift）</news:title>
   <news:publication_date>2026-04-16T15:23:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680037</loc>
  <lastmod>2026-04-16T15:21:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習に基づく多変量アウトカムの関連性測定法（A machine learning-based approach for estimating and testing associations with multivariate outcomes）</news:title>
   <news:publication_date>2026-04-16T15:21:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680035</loc>
  <lastmod>2026-04-16T15:21:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>立体3D動画の視線予測を学習で実現するモデル（A Learning-Based Visual Saliency Prediction Model for Stereoscopic 3D Video）</news:title>
   <news:publication_date>2026-04-16T15:21:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/680033</loc>
  <lastmod>2026-04-16T15:21:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>データベース内でのエンティティ連携を実現するIDEL（IDEL: In-Database Entity Linking with Neural Embeddings）</news:title>
   <news:publication_date>2026-04-16T15:21:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
</urlset>
