<?xml version="1.0" encoding="UTF-8"?>
<!--generator='jetpack-15.7-beta.2'-->
<!--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/676945</loc>
  <lastmod>2026-04-08T03:12:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>品質重視の機械学習ジョブスケジューリング（SLAQ: Quality-Driven Scheduling for Distributed Machine Learning）</news:title>
   <news:publication_date>2026-04-08T03:12:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676943</loc>
  <lastmod>2026-04-08T03:12:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>近傍のトリプル系 Wolf 1130 における超大質量で冷たい白色矮星の発見（Wolf 1130: A Nearby Triple System Containing a Cool, Ultramassive White Dwarf）</news:title>
   <news:publication_date>2026-04-08T03:12:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676941</loc>
  <lastmod>2026-04-08T03:12:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>進化させる方策勾配：Evolved Policy Gradients（Evolved Policy Gradients）</news:title>
   <news:publication_date>2026-04-08T03:12:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676939</loc>
  <lastmod>2026-04-08T03:11:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>MONKによる外れ値耐性カーネル平均埋め込み推定（MONK – Outlier-Robust Mean Embedding Estimation by Median-of-Means）</news:title>
   <news:publication_date>2026-04-08T03:11:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676937</loc>
  <lastmod>2026-04-08T03:11:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>星の嵐のあと：E+Aポストスター バースト銀河に残る塵とガス（After the Fall: The Dust and Gas in E+A Post-Starburst Galaxies）</news:title>
   <news:publication_date>2026-04-08T03:11:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676935</loc>
  <lastmod>2026-04-08T03:10:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的分散削減ハミルトニアンモンテカルロ法（Stochastic Variance-Reduced Hamilton Monte Carlo Methods）</news:title>
   <news:publication_date>2026-04-08T03:10:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676933</loc>
  <lastmod>2026-04-08T03:10:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的分散削減三次正則化ニュートン法（Stochastic Variance-Reduced Cubic Regularized Newton Method）</news:title>
   <news:publication_date>2026-04-08T03:10:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676931</loc>
  <lastmod>2026-04-08T02:18:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>制約付き被積分関数のための四重積分改善（Improving Quadrature for Constrained Integrands）</news:title>
   <news:publication_date>2026-04-08T02:18:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676929</loc>
  <lastmod>2026-04-08T02:18:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>漸進的強化学習と蒸留による多技能モーション制御（PROGRESSIVE REINFORCEMENT LEARNING WITH DISTILLATION FOR MULTI-SKILLED MOTION CONTROL）</news:title>
   <news:publication_date>2026-04-08T02:18:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676927</loc>
  <lastmod>2026-04-08T02:17:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>物体認識のための深層予測符号化ネットワーク（Deep Predictive Coding Network for Object Recognition）</news:title>
   <news:publication_date>2026-04-08T02:17:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676925</loc>
  <lastmod>2026-04-08T02:17:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多次元時空分数拡散波方程式の従属化原理（SUBORDINATION PRINCIPLES FOR THE MULTI-DIMENSIONAL SPACE-TIME-FRACTIONAL DIFFUSION-WAVE EQUATION）</news:title>
   <news:publication_date>2026-04-08T02:17:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676923</loc>
  <lastmod>2026-04-08T02:17:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>線形符号の復号における深層学習—シンドロームベースのアプローチ（Deep Learning for Decoding of Linear Codes - A Syndrome-Based Approach）</news:title>
   <news:publication_date>2026-04-08T02:17:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676921</loc>
  <lastmod>2026-04-08T02:16:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>意味注釈コーパスの簡潔な概観 (A Short Survey on Sense-Annotated Corpora)</news:title>
   <news:publication_date>2026-04-08T02:16:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676919</loc>
  <lastmod>2026-04-08T02:16:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>離散連続かつ歪んだデータにおける不確実性の定量化（Quantifying Uncertainty in Discrete-Continuous and Skewed Data with Bayesian Deep Learning）</news:title>
   <news:publication_date>2026-04-08T02:16:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676917</loc>
  <lastmod>2026-04-08T01:25:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>サブステーション信号名の照合とBagged Token Classifier（Substation Signal Matching with a Bagged Token Classifier）</news:title>
   <news:publication_date>2026-04-08T01:25:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676915</loc>
  <lastmod>2026-04-08T01:25:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Tensor Comprehensionsの実務的意味合い（Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions）</news:title>
   <news:publication_date>2026-04-08T01:25:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676913</loc>
  <lastmod>2026-04-08T01:25:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多項式時間で学習する導出ω木言語の照会学習（Query Learning of Derived ω-Tree Languages in Polynomial Time）</news:title>
   <news:publication_date>2026-04-08T01:25:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676911</loc>
  <lastmod>2026-04-08T01:24:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オンライン分散削減による確率的最適化（Online Variance Reduction for Stochastic Optimization）</news:title>
   <news:publication_date>2026-04-08T01:24:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676909</loc>
  <lastmod>2026-04-08T01:23:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>知覚最適化を組み込んだGANによる同時デモザイク＆ノイズ除去（Joint Demosaicing and Denoising with Perceptual Optimization on a Generative Adversarial Network）</news:title>
   <news:publication_date>2026-04-08T01:23:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676907</loc>
  <lastmod>2026-04-08T01:23:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>重ね合わせで学ぶホークス過程の確率的最適化（Superposition-Assisted Stochastic Optimization for Hawkes Processes）</news:title>
   <news:publication_date>2026-04-08T01:23:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676905</loc>
  <lastmod>2026-04-08T01:22:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>カードリティ（個数）制約を深層構造化予測へ組み込む手法の要点（Predict and Constrain: Modeling Cardinality in Deep Structured Prediction）</news:title>
   <news:publication_date>2026-04-08T01:22:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676903</loc>
  <lastmod>2026-04-08T00:31:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>注意力に基づく深層多数インスタンス学習（Attention-based Deep Multiple Instance Learning）</news:title>
   <news:publication_date>2026-04-08T00:31:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676901</loc>
  <lastmod>2026-04-08T00:30:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Hadamard Responseによるローカル差分プライバシー下での分布推定（Hadamard Response: Estimating Distributions Privately, Efficiently, and with Little Communication）</news:title>
   <news:publication_date>2026-04-08T00:30:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676899</loc>
  <lastmod>2026-04-08T00:30:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>周期軌道付近における量子–古典対応の定量化（Quantum-classical correspondence in the vicinity of periodic orbits）</news:title>
   <news:publication_date>2026-04-08T00:30:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676897</loc>
  <lastmod>2026-04-08T00:29:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習型探索アルゴリズムの新展開：MCTSnetによる検索の“学習化”（Learning to Search with MCTSnets）</news:title>
   <news:publication_date>2026-04-08T00:29:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676895</loc>
  <lastmod>2026-04-08T00:29:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>脳画像レジストレーションの学習的解法（BIRNet: Brain Image Registration Using Dual-Supervised Fully Convolutional Networks）</news:title>
   <news:publication_date>2026-04-08T00:29:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676893</loc>
  <lastmod>2026-04-08T00:28:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>相互作用系のためのニューラル関係推論（Neural Relational Inference for Interacting Systems）</news:title>
   <news:publication_date>2026-04-08T00:28:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676891</loc>
  <lastmod>2026-04-08T00:28:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ラベル無しデータでの予測ランク集約と評価（Unsupervised Evaluation and Weighted Aggregation of Ranked Predictions）</news:title>
   <news:publication_date>2026-04-08T00:28:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676889</loc>
  <lastmod>2026-04-07T23:37:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>変数選択とタスクグルーピングによるマルチタスク学習（Variable Selection and Task Grouping for Multi-Task Learning）</news:title>
   <news:publication_date>2026-04-07T23:37:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676887</loc>
  <lastmod>2026-04-07T23:36:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>イディオム翻訳のための並列コーパス構築（Examining the Tip of the Iceberg: A Data Set for Idiom Translation）</news:title>
   <news:publication_date>2026-04-07T23:36:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676885</loc>
  <lastmod>2026-04-07T23:36:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>科学論文から要旨に値する文を自動抽出する注意機構ベースの手法（Attention based Sentence Extraction from Scientific Articles using Pseudo-Labeled data）</news:title>
   <news:publication_date>2026-04-07T23:36:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676883</loc>
  <lastmod>2026-04-07T23:34:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>弱点を逆手に取るニューラルネットワークの権利主張技術（Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooring）</news:title>
   <news:publication_date>2026-04-07T23:34:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676881</loc>
  <lastmod>2026-04-07T23:34:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多対多の関連を扱う多視点特徴学習の確率的枠組み（A probabilistic framework for multi-view feature learning with many-to-many associations via neural networks）</news:title>
   <news:publication_date>2026-04-07T23:34:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676879</loc>
  <lastmod>2026-04-07T23:34:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>顔の加齢と血縁関係のモデリングに関するサーベイ（Modeling of Facial Aging and Kinship: A Survey）</news:title>
   <news:publication_date>2026-04-07T23:34:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676877</loc>
  <lastmod>2026-04-07T23:33:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>キュレーテッドメディアからの弱教師あり集団特徴学習（Weakly Supervised Collective Feature Learning from Curated Media）</news:title>
   <news:publication_date>2026-04-07T23:33:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676875</loc>
  <lastmod>2026-04-07T22:42:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多様な凸関数下でのオンライン勾配法の対数後悔（Logarithmic Regret for Online Gradient Descent Beyond Strong Convexity）</news:title>
   <news:publication_date>2026-04-07T22:42:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676873</loc>
  <lastmod>2026-04-07T22:42:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Barista：深層学習を“視覚的に設計・訓練”するツール（Barista – a Graphical Tool for Designing and Training Deep Neural Networks）</news:title>
   <news:publication_date>2026-04-07T22:42:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676871</loc>
  <lastmod>2026-04-07T22:41:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>経験的損失のランドスケープによる高速な大域的収束（Fast Global Convergence via Landscape of Empirical Loss）</news:title>
   <news:publication_date>2026-04-07T22:41:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676869</loc>
  <lastmod>2026-04-07T22:40:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>QUIJOTE実験によるCMB Bモード偏光検出と前景特性の解明（THE QUIJOTE EXPERIMENT: PROSPECTS FOR CMB B-MODE POLARIZATION DETECTION AND FOREGROUNDS CHARACTERIZATION）</news:title>
   <news:publication_date>2026-04-07T22:40:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676867</loc>
  <lastmod>2026-04-07T22:40:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>最適モデル平均化の使いどころと限界（When and when not to use optimal model averaging）</news:title>
   <news:publication_date>2026-04-07T22:40:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676865</loc>
  <lastmod>2026-04-07T22:40:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ドックレス自転車シェアの深層強化学習による再配置枠組み（A Deep Reinforcement Learning Framework for Rebalancing Dockless Bike Sharing Systems）</news:title>
   <news:publication_date>2026-04-07T22:40:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676863</loc>
  <lastmod>2026-04-07T22:39:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>第一次情報に基づく生成対抗ネットワークの最適化（First Order Generative Adversarial Networks）</news:title>
   <news:publication_date>2026-04-07T22:39:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676861</loc>
  <lastmod>2026-04-07T21:48:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>フランス語の文境界検出におけるサブワード情報と畳み込みニューラルネットワーク（Sentence Boundary Detection for French with Subword-Level Information）</news:title>
   <news:publication_date>2026-04-07T21:48:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676859</loc>
  <lastmod>2026-04-07T21:48:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>構造化カリキュラム学習による英日音声翻訳の実装（Structured-based Curriculum Learning for End-to-end English-Japanese Speech Translation）</news:title>
   <news:publication_date>2026-04-07T21:48:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676857</loc>
  <lastmod>2026-04-07T21:48:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多様性駆動型探索戦略（Diversity-Driven Exploration Strategy for Deep Reinforcement Learning）</news:title>
   <news:publication_date>2026-04-07T21:48:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676855</loc>
  <lastmod>2026-04-07T21:45:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>より厳密な変分下界は必ずしも良くない（Tighter Variational Bounds are Not Necessarily Better）</news:title>
   <news:publication_date>2026-04-07T21:45:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676853</loc>
  <lastmod>2026-04-07T21:45:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ミツバチ標識の自動位置検出とデコード（Automatic localization and decoding of honeybee markers using deep convolutional neural networks）</news:title>
   <news:publication_date>2026-04-07T21:45:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676851</loc>
  <lastmod>2026-04-07T21:45:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>バイナリ向け敵対的事例によるエンドツーエンド深層学習マルウェア検知器の欺瞞（Deceiving End-to-End Deep Learning Malware Detectors using Adversarial Examples）</news:title>
   <news:publication_date>2026-04-07T21:45:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676849</loc>
  <lastmod>2026-04-07T21:44:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>クラウドソーシングにおける最小化最大誤差率の解析と作業者クラスタリングモデルの応用（Analysis of Minimax Error Rate for Crowdsourcing and Its Application to Worker Clustering Model）</news:title>
   <news:publication_date>2026-04-07T21:44:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676839</loc>
  <lastmod>2026-04-07T20:52:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>業界級顔認証システムへのクエリ不要攻撃（Query-Free Attacks on Industry-Grade Face Recognition Systems under Resource Constraints）</news:title>
   <news:publication_date>2026-04-07T20:52:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676837</loc>
  <lastmod>2026-04-07T20:52:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Flipped-Adversarial AutoEncodersの解説（Flipped-Adversarial AutoEncoders）</news:title>
   <news:publication_date>2026-04-07T20:52:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676835</loc>
  <lastmod>2026-04-07T20:52:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>テンソル分解の新手法：Legendre分解が切り開く非負値データ解析（Legendre Decomposition for Tensors）</news:title>
   <news:publication_date>2026-04-07T20:52:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676833</loc>
  <lastmod>2026-04-07T20:51:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>RoboChain: 人とロボットの協働で安全にデータ共有する枠組み（RoboChain: A Secure Data-Sharing Framework for Human-Robot Interaction）</news:title>
   <news:publication_date>2026-04-07T20:51:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676831</loc>
  <lastmod>2026-04-07T20:51:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層学習を用いた位相マイクロフォンアレイによる音源定位（Phased Microphone Array for Sound Source Localization with Deep Learning）</news:title>
   <news:publication_date>2026-04-07T20:51:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676829</loc>
  <lastmod>2026-04-07T20:50:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非凸・非滑らか最適化のための単純な近接確率的勾配法（A Simple Proximal Stochastic Gradient Method for Nonsmooth Nonconvex Optimization）</news:title>
   <news:publication_date>2026-04-07T20:50:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676827</loc>
  <lastmod>2026-04-07T20:50:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフ上関数最大化のための上昇アルゴリズム（Graph-Based Ascent Algorithms for Function Maximization）</news:title>
   <news:publication_date>2026-04-07T20:50:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676825</loc>
  <lastmod>2026-04-07T19:59:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>情報スケーリング則による深層ニューラルネットワークの情報論的理解（Information Scaling Law of Deep Neural Networks）</news:title>
   <news:publication_date>2026-04-07T19:59:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676823</loc>
  <lastmod>2026-04-07T19:58:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非滑らかな関数を深層学習が効率的に学ぶ理由（DEEP NEURAL NETWORKS LEARN NON-SMOOTH FUNCTIONS EFFECTIVELY）</news:title>
   <news:publication_date>2026-04-07T19:58:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676821</loc>
  <lastmod>2026-04-07T19:57:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非対応（Unpaired）画像間変換の最適化アーキテクチャ（An Optimized Architecture for Unpaired Image-to-Image Translation）</news:title>
   <news:publication_date>2026-04-07T19:57:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676819</loc>
  <lastmod>2026-04-07T19:56:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューラルネットワークの表現力を位相幾何で測る（On Characterizing the Capacity of Neural Networks using Algebraic Topology）</news:title>
   <news:publication_date>2026-04-07T19:56:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676817</loc>
  <lastmod>2026-04-07T19:56:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高信頼度で敵対的事例を予測する手法（Predicting Adversarial Examples with High Confidence）</news:title>
   <news:publication_date>2026-04-07T19:56:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676815</loc>
  <lastmod>2026-04-07T19:56:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>注意に基づく誘導された構造的スパース化（ATTENTION-BASED GUIDED STRUCTURED SPARSITY OF DEEP NEURAL NETWORKS）</news:title>
   <news:publication_date>2026-04-07T19:56:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676813</loc>
  <lastmod>2026-04-07T19:56:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>極端なスケール変化下におけるテクスチャ分類とGANet（Texture Classification in Extreme Scale Variations using GANet）</news:title>
   <news:publication_date>2026-04-07T19:56:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676811</loc>
  <lastmod>2026-04-07T19:04:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>H&amp;amp;E染色組織切片における複数核表現型の深層学習による分離（Deep Learning Models Delineates Multiple Nuclear Phenotypes in H&amp;amp;E Stained Histology Sections）</news:title>
   <news:publication_date>2026-04-07T19:04:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676809</loc>
  <lastmod>2026-04-07T19:04:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>宇宙機テレメトリ異常検知におけるLSTMと非パラメトリック動的閾値（Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding）</news:title>
   <news:publication_date>2026-04-07T19:04:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676807</loc>
  <lastmod>2026-04-07T19:04:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SIGNSGDによる勾配圧縮と分散学習の実務的利点（SIGNSGD: Compressed Optimisation for Non-Convex Problems）</news:title>
   <news:publication_date>2026-04-07T19:04:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676805</loc>
  <lastmod>2026-04-07T19:03:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>皮質の安静時ネットワークの成熟軌跡は媒介する周波数帯に依存する（Maturation Trajectories of Cortical Resting-State Networks Depend on the Mediating Frequency Band）</news:title>
   <news:publication_date>2026-04-07T19:03:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676803</loc>
  <lastmod>2026-04-07T19:03:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>二層畳み込み線形分類器の一般化バイアスの理解に向けて (Towards Understanding the Generalization Bias of Two Layer Convolutional Linear Classifiers with Gradient Descent)</news:title>
   <news:publication_date>2026-04-07T19:03:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676801</loc>
  <lastmod>2026-04-07T19:02:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習における公平性強化介入の比較研究 (A comparative study of fairness-enhancing interventions in machine learning)</news:title>
   <news:publication_date>2026-04-07T19:02:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676799</loc>
  <lastmod>2026-04-07T19:02:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Neural Tensor Factorization（Neural Tensor Factorization）</news:title>
   <news:publication_date>2026-04-07T19:02:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676797</loc>
  <lastmod>2026-04-07T18:10:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>敵対的正則化グラフオートエンコーダによるグラフ埋め込み（Adversarially Regularized Graph Autoencoder for Graph Embedding）</news:title>
   <news:publication_date>2026-04-07T18:10:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676795</loc>
  <lastmod>2026-04-07T18:10:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>LSALSAによる高速な信号分離と疎表現推論（LSALSA: Accelerated Source Separation via Learned Sparse Coding）</news:title>
   <news:publication_date>2026-04-07T18:10:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676793</loc>
  <lastmod>2026-04-07T18:09:26Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ベイズ的探索で学習を効率化するBDQN（Efficient Exploration through Bayesian Deep Q-Networks）</news:title>
   <news:publication_date>2026-04-07T18:09:26Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676791</loc>
  <lastmod>2026-04-07T18:08:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>3D点群の再帰スライスネットワーク（Recurrent Slice Networks for 3D Segmentation of Point Clouds）</news:title>
   <news:publication_date>2026-04-07T18:08:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676789</loc>
  <lastmod>2026-04-07T18:08:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非パラメトリック混合モデルの識別性とベイズ最適クラスタリング（Identifiability of Nonparametric Mixture Models and Bayes Optimal Clustering）</news:title>
   <news:publication_date>2026-04-07T18:08:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676787</loc>
  <lastmod>2026-04-07T18:08:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフ上の歩行を学ぶM-WalkとMCTSの統合（M-Walk: Learning to Walk over Graphs using Monte Carlo Tree Search）</news:title>
   <news:publication_date>2026-04-07T18:08:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676785</loc>
  <lastmod>2026-04-07T18:07:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>トリプレットに基づく変分オートエンコーダ（TVAE: Triplet-Based Variational Autoencoder Using Metric Learning）</news:title>
   <news:publication_date>2026-04-07T18:07:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676783</loc>
  <lastmod>2026-04-07T17:15:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>画像の再ターゲティング可能性を定量化する（Image Retargetability: Predicting and Leveraging Image Retargetability）</news:title>
   <news:publication_date>2026-04-07T17:15:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676781</loc>
  <lastmod>2026-04-07T17:15:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グラフ全変動を用いた非滑らか汎関数の正則化のためのCut‑Pursuitアルゴリズム（Cut‑Pursuit Algorithm for Regularizing Nonsmooth Functionals with Graph Total Variation）</news:title>
   <news:publication_date>2026-04-07T17:15:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676779</loc>
  <lastmod>2026-04-07T17:15:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>類似ペアと未ラベルデータから学ぶ分類（Classification from Pairwise Similarity and Unlabeled Data）</news:title>
   <news:publication_date>2026-04-07T17:15:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676777</loc>
  <lastmod>2026-04-07T17:13:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Tempered Adversarial Networksの要点と実務的示唆（Tempered Adversarial Networks）</news:title>
   <news:publication_date>2026-04-07T17:13:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676775</loc>
  <lastmod>2026-04-07T17:13:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>条件付けされたメトリック非依存埋め込みによる少数ショット学習の実務的意義（Few-Shot Learning with Metric-Agnostic Conditional Embeddings）</news:title>
   <news:publication_date>2026-04-07T17:13:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676773</loc>
  <lastmod>2026-04-07T17:13:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューラルネットワークに基づく表現学習によるオープンセット認識（Learning a Neural-network-based Representation for Open Set Recognition）</news:title>
   <news:publication_date>2026-04-07T17:13:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676771</loc>
  <lastmod>2026-04-07T17:13:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GW170817連星中性子星合体に伴うスーパーカミオカンデのニュートリノ探索（SEARCH FOR NEUTRINOS IN SUPER-KAMIOKANDE ASSOCIATED WITH THE GW170817 NEUTRON-STAR MERGER）</news:title>
   <news:publication_date>2026-04-07T17:13:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676769</loc>
  <lastmod>2026-04-07T16:20:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>接合木変分オートエンコーダによる分子グラフ生成（Junction Tree Variational Autoencoder for Molecular Graph Generation）</news:title>
   <news:publication_date>2026-04-07T16:20:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676767</loc>
  <lastmod>2026-04-07T16:20:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>TVMが開く深層学習の最適化自動化（TVM: An Automated End-to-End Optimizing Compiler for Deep Learning）</news:title>
   <news:publication_date>2026-04-07T16:20:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676765</loc>
  <lastmod>2026-04-07T16:20:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>コスト意識型学習による複数実験での識別性向上（Cost-Aware Learning for Improved Identifiability with Multiple Experiments）</news:title>
   <news:publication_date>2026-04-07T16:20:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676763</loc>
  <lastmod>2026-04-07T16:19:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>エントロピーペナルティ付き半正定値計画（Entropy-Penalized Semidefinite Programming）</news:title>
   <news:publication_date>2026-04-07T16:19:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676761</loc>
  <lastmod>2026-04-07T16:19:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>部分的に明らかにされた単位区間グラフ上のマルチアームバンディット（Multi-Armed Bandits on Partially Revealed Unit Interval Graphs）</news:title>
   <news:publication_date>2026-04-07T16:19:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676759</loc>
  <lastmod>2026-04-07T16:19:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自然言語と入出力例からプログラムを合成する手法（Neural Program Search: Solving Programming Tasks from Description and Examples）</news:title>
   <news:publication_date>2026-04-07T16:19:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676757</loc>
  <lastmod>2026-04-07T16:18:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>解釈可能な部分代替による低コストな透明性獲得 (Gaining Free or Low-Cost Transparency with Interpretable Partial Substitute)</news:title>
   <news:publication_date>2026-04-07T16:18:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676755</loc>
  <lastmod>2026-04-07T15:27:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模確率的準ニュートン法と適応ステップ長（Stochastic quasi-Newton with adaptive step lengths for large-scale problems）</news:title>
   <news:publication_date>2026-04-07T15:27:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676753</loc>
  <lastmod>2026-04-07T15:27:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>効率的なモデルベース深層強化学習と変分状態タビュレーション（Efficient Model–Based Deep Reinforcement Learning with Variational State Tabulation）</news:title>
   <news:publication_date>2026-04-07T15:27:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676751</loc>
  <lastmod>2026-04-07T15:26:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>LHeCが拓く高エネルギーDISの未来（Future Deep Inelastic Scattering with the LHeC）</news:title>
   <news:publication_date>2026-04-07T15:26:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676749</loc>
  <lastmod>2026-04-07T15:26:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高速近接点法による正確なワッサースタイン距離の計算（A Fast Proximal Point Method for Computing Exact Wasserstein Distance）</news:title>
   <news:publication_date>2026-04-07T15:26:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676747</loc>
  <lastmod>2026-04-07T15:25:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>次元に依存しないPAC-Bayesianによる平均推定（Dimension-free PAC-Bayesian bounds for the estimation of the mean of a random vector）</news:title>
   <news:publication_date>2026-04-07T15:25:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676745</loc>
  <lastmod>2026-04-07T15:25:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高緯度銀河面における平均星間偏光の局所測定 (Local measurements of the mean interstellar polarization at high Galactic latitudes)</news:title>
   <news:publication_date>2026-04-07T15:25:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676743</loc>
  <lastmod>2026-04-07T15:25:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ツイート単位でボットを見抜く文脈型LSTM（Deep Neural Networks for Bot Detection）</news:title>
   <news:publication_date>2026-04-07T15:25:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676741</loc>
  <lastmod>2026-04-07T14:33:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>強化学習による車両巡回配送問題の解法（Reinforcement Learning for Solving the Vehicle Routing Problem）</news:title>
   <news:publication_date>2026-04-07T14:33:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676739</loc>
  <lastmod>2026-04-07T14:33:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>セッション単位で見るボットと人間の行動動態（Measuring bot and human behavioral dynamics）</news:title>
   <news:publication_date>2026-04-07T14:33:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676737</loc>
  <lastmod>2026-04-07T14:32:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習による宇宙構造形成の学習（Machine learning cosmological structure formation）</news:title>
   <news:publication_date>2026-04-07T14:32:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676735</loc>
  <lastmod>2026-04-07T14:31:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SparseMAPによる微分可能なスパース構造推論 (SparseMAP: Differentiable Sparse Structured Inference)</news:title>
   <news:publication_date>2026-04-07T14:31:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676733</loc>
  <lastmod>2026-04-07T14:31:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>正直で最適な無後悔フレームワーク（Let’s be Honest: An Optimal No-Regret Framework for Zero-Sum Games）</news:title>
   <news:publication_date>2026-04-07T14:31:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676731</loc>
  <lastmod>2026-04-07T14:31:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>拒否オプションをスパースに学ぶ線形計画アプローチ（Sparse Reject Option Classifier Using Successive Linear Programming）</news:title>
   <news:publication_date>2026-04-07T14:31:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676729</loc>
  <lastmod>2026-04-07T14:30:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>データ表現に複雑性を埋め込む：モデルではなくデータで扱う（Embedding Complexity In the Data Representation Instead of In the Model: A Case Study Using Heterogeneous Medical Data）</news:title>
   <news:publication_date>2026-04-07T14:30:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676727</loc>
  <lastmod>2026-04-07T13:39:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模カテゴリ分布の確率的推論（Augment and Reduce: Stochastic Inference for Large Categorical Distributions）</news:title>
   <news:publication_date>2026-04-07T13:39:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676725</loc>
  <lastmod>2026-04-07T13:38:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>敵対的音声合成（Adversarial Audio Synthesis）</news:title>
   <news:publication_date>2026-04-07T13:38:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676723</loc>
  <lastmod>2026-04-07T13:38:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オーディオブックのためのエンドツーエンド音声翻訳技術（END-TO-END AUTOMATIC SPEECH TRANSLATION OF AUDIOBOOKS）</news:title>
   <news:publication_date>2026-04-07T13:38:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676721</loc>
  <lastmod>2026-04-07T13:37:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>画像ベース合成による深層3D人体姿勢推定（Image-based Synthesis for Deep 3D Human Pose Estimation）</news:title>
   <news:publication_date>2026-04-07T13:37:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676719</loc>
  <lastmod>2026-04-07T13:36:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>銀行業務向けの行動ベース基盤を速めるclient2vec (client2vec: Towards Systematic Baselines for Banking Applications)</news:title>
   <news:publication_date>2026-04-07T13:36:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676717</loc>
  <lastmod>2026-04-07T13:36:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模な未ラベルデータを活かすインタラクティブ画像検索の高速化（Fast Interactive Image Retrieval using large-scale unlabeled data）</news:title>
   <news:publication_date>2026-04-07T13:36:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676715</loc>
  <lastmod>2026-04-07T13:35:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>不確実性とハイブリッド力学下における効率的な階層的ロボット運動計画（Efficient Hierarchical Robot Motion Planning Under Uncertainty and Hybrid Dynamics）</news:title>
   <news:publication_date>2026-04-07T13:35:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676713</loc>
  <lastmod>2026-04-07T12:43:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>制御のための状態表現学習の概観 (State Representation Learning for Control: An Overview)</news:title>
   <news:publication_date>2026-04-07T12:43:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676711</loc>
  <lastmod>2026-04-07T12:43:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>電気自動車運転者のクラスタリングによる負荷予測と制御応用（Electric Vehicle Driver Clustering using Statistical Model and Machine Learning）</news:title>
   <news:publication_date>2026-04-07T12:43:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676709</loc>
  <lastmod>2026-04-07T12:42:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>モデル識別のための実験設計手法（Design of Experiments for Model Discrimination）</news:title>
   <news:publication_date>2026-04-07T12:42:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676707</loc>
  <lastmod>2026-04-07T12:42:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>地下実験向け高効率ガスターゲット装置（A high-efficiency gas target setup for underground experiments）</news:title>
   <news:publication_date>2026-04-07T12:42:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676705</loc>
  <lastmod>2026-04-07T12:41:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>エージェントベースモデルで電流を理解する（Understanding Electric Current Using Agent-Based Models: Connecting the Micro-level with Flow Rate）</news:title>
   <news:publication_date>2026-04-07T12:41:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676703</loc>
  <lastmod>2026-04-07T12:41:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Twitterにおける電話番号スパム発信者の集合分類（Collective Classification of Spam Campaigners on Twitter: A Hierarchical Meta-Path Based Approach）</news:title>
   <news:publication_date>2026-04-07T12:41:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676701</loc>
  <lastmod>2026-04-07T12:41:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>橋梁の設計フェーズを変える機械学習による構造形式分類（Bridge type classification: Supervised learning on a modified NBI dataset）</news:title>
   <news:publication_date>2026-04-07T12:41:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676699</loc>
  <lastmod>2026-04-07T11:49:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文脈付き推薦のためのポリシー勾配法（Policy Gradients for Contextual Recommendations）</news:title>
   <news:publication_date>2026-04-07T11:49:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676697</loc>
  <lastmod>2026-04-07T11:49:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>畳み込みフィルタの分解による軽量化と安定化（DCFNet: Deep Neural Network with Decomposed Convolutional Filters）</news:title>
   <news:publication_date>2026-04-07T11:49:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676695</loc>
  <lastmod>2026-04-07T11:48:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>モジュラー型ロボットの階層学習（HIERARCHICAL LEARNING FOR MODULAR ROBOTS）</news:title>
   <news:publication_date>2026-04-07T11:48:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676693</loc>
  <lastmod>2026-04-07T11:48:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Electron Cryo-Subtomogramsのための教師あり深層学習によるセマンティックセグメンテーション（Deep learning based supervised semantic segmentation of Electron Cryo-Subtomograms）</news:title>
   <news:publication_date>2026-04-07T11:48:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676691</loc>
  <lastmod>2026-04-07T11:47:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スピーカー認証における線形回帰バックエンド（Linear Regression for Speaker Verification）</news:title>
   <news:publication_date>2026-04-07T11:47:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676689</loc>
  <lastmod>2026-04-07T11:47:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複雑事象認識の実践と進化（The Complex Event Recognition Group）</news:title>
   <news:publication_date>2026-04-07T11:47:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676687</loc>
  <lastmod>2026-04-07T11:46:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>車両の3次元検出のための一般的なパイプライン（A General Pipeline for 3D Detection of Vehicles）</news:title>
   <news:publication_date>2026-04-07T11:46:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676685</loc>
  <lastmod>2026-04-07T10:55:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ビットコインの短期ボラティリティ予測と板情報の活用（Bitcoin Volatility Forecasting with a Glimpse into Buy and Sell Orders）</news:title>
   <news:publication_date>2026-04-07T10:55:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676683</loc>
  <lastmod>2026-04-07T10:47:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非対話型ローカル差分プライバシー下の経験的リスク最小化（Empirical Risk Minimization in Non-interactive Local Differential Privacy: Efficiency and High Dimensional Case）</news:title>
   <news:publication_date>2026-04-07T10:47:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676681</loc>
  <lastmod>2026-04-07T10:46:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ランダム化ブロック立方ニュートン法（Randomized Block Cubic Newton Method）</news:title>
   <news:publication_date>2026-04-07T10:46:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676679</loc>
  <lastmod>2026-04-07T10:46:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高速確率的行列反転と加速BFGSの理論と実践 (Accelerated Stochastic Matrix Inversion: General Theory and Speeding up BFGS Rules for Faster Second-Order Optimization)</news:title>
   <news:publication_date>2026-04-07T10:46:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676677</loc>
  <lastmod>2026-04-07T10:45:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層生成モデルを用いたブラインド画像復元（Blind Image Deconvolution using Deep Generative Priors）</news:title>
   <news:publication_date>2026-04-07T10:45:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676675</loc>
  <lastmod>2026-04-07T10:44:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>実験を通じた勾配取得：LSTMとメモリ型PPOによるブラックボックス量子制御（Taking gradients through experiments: LSTMs and memory proximal policy optimization for black-box quantum control）</news:title>
   <news:publication_date>2026-04-07T10:44:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676673</loc>
  <lastmod>2026-04-07T10:44:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文脈付きバンディットの実務比較（A Contextual Bandit Bake-off）</news:title>
   <news:publication_date>2026-04-07T10:44:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676671</loc>
  <lastmod>2026-04-07T09:53:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模コンピュータネットワークの時変機能的結合推定（Inferring the time-varying functional connectivity of large-scale computer networks from emitted events）</news:title>
   <news:publication_date>2026-04-07T09:53:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676669</loc>
  <lastmod>2026-04-07T09:52:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Lipschitz-マージントレーニング（Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks）</news:title>
   <news:publication_date>2026-04-07T09:52:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676667</loc>
  <lastmod>2026-04-07T09:52:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>音楽領域における一つの深層表現で全てをまかなえるか（One Deep Music Representation to Rule Them All?）</news:title>
   <news:publication_date>2026-04-07T09:52:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676665</loc>
  <lastmod>2026-04-07T09:51:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>言語に依存しない特徴の自動生成によるクロスリンガル分類の実用化（Automatic Generation of Language-Independent Features for Cross-Lingual Classification）</news:title>
   <news:publication_date>2026-04-07T09:51:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676663</loc>
  <lastmod>2026-04-07T09:50:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文脈認識学習による転移可能特徴の乳がん組織画像分類（Context-Aware Learning using Transferable Features for Classification of Breast Cancer Histology Images）</news:title>
   <news:publication_date>2026-04-07T09:50:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676661</loc>
  <lastmod>2026-04-07T09:50:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>公平かつ多様なDPPに基づくデータ要約（Fair and Diverse DPP-based Data Summarization）</news:title>
   <news:publication_date>2026-04-07T09:50:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676659</loc>
  <lastmod>2026-04-07T09:50:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>バイアススパン制約下での効率的な探索と活用（Efficient Bias-Span-Constrained Exploration-Exploitation）</news:title>
   <news:publication_date>2026-04-07T09:50:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676657</loc>
  <lastmod>2026-04-07T08:58:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Wasserstein距離による強化学習の正則化とマルチポリシー学習への応用（Reinforcement Learning with Wasserstein Distance Regularisation）</news:title>
   <news:publication_date>2026-04-07T08:58:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676655</loc>
  <lastmod>2026-04-07T08:49:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模理論のためのウォッチリスト指導（ProofWatch: Watchlist Guidance for Large Theories in E）</news:title>
   <news:publication_date>2026-04-07T08:49:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676653</loc>
  <lastmod>2026-04-07T08:49:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>潜在変数を考慮した時変ネットワーク推定（Latent Variable Time-varying Network Inference）</news:title>
   <news:publication_date>2026-04-07T08:49:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676651</loc>
  <lastmod>2026-04-07T08:49:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ノード埋め込みと同時クラスタ学習を行うGEMSEC（GEMSEC: Graph Embedding with Self Clustering）</news:title>
   <news:publication_date>2026-04-07T08:49:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676649</loc>
  <lastmod>2026-04-07T08:48:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>一般線形動的システムに対するスペクトルフィルタリング（Spectral Filtering for General Linear Dynamical Systems）</news:title>
   <news:publication_date>2026-04-07T08:48:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676647</loc>
  <lastmod>2026-04-07T08:48:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>メールを部署別に仕分けるニューラルネットワーク（Email Classification into Relevant Category Using Neural Networks）</news:title>
   <news:publication_date>2026-04-07T08:48:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676645</loc>
  <lastmod>2026-04-07T08:48:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SPINEによる帰納的ネットワーク埋め込みの実務的意義（SPINE: Structural Identity Preserved Inductive Network Embedding）</news:title>
   <news:publication_date>2026-04-07T08:48:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676643</loc>
  <lastmod>2026-04-07T07:56:16Z</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が摂動論を学ぶ様子の可視化（Visualizing Neural Network Developing Perturbation Theory）</news:title>
   <news:publication_date>2026-04-07T07:56:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676641</loc>
  <lastmod>2026-04-07T07:49:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>極端な多ラベル分類に対する疎な重み付き近傍法の再考（Revisiting the Vector Space Model: Sparse Weighted Nearest-Neighbor Method for Extreme Multi-Label Classification）</news:title>
   <news:publication_date>2026-04-07T07:49:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676639</loc>
  <lastmod>2026-04-07T07:48:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ハイパーキュービック量子化ハッシングにおける回転の必要性（On the Needs for Rotations in Hypercubic Quantization Hashing）</news:title>
   <news:publication_date>2026-04-07T07:48:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676637</loc>
  <lastmod>2026-04-07T07:48:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マスクベース特徴符号化による物体検出 (Object Detection with Mask-based Feature Encoding)</news:title>
   <news:publication_date>2026-04-07T07:48:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676635</loc>
  <lastmod>2026-04-07T07:47:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>テストエージェント：適応的・自律的・知的なテストケース（Test Agents: Adaptive, Autonomous and Intelligent Test Cases）</news:title>
   <news:publication_date>2026-04-07T07:47:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676633</loc>
  <lastmod>2026-04-07T07:47:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>トリプレット安全スクリーニングによる距離計量学習の高速化（Safe Triplet Screening for Distance Metric Learning）</news:title>
   <news:publication_date>2026-04-07T07:47:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676631</loc>
  <lastmod>2026-04-07T07:46:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層特徴圧縮による協調型物体検出の効率化（DEEP FEATURE COMPRESSION FOR COLLABORATIVE OBJECT DETECTION）</news:title>
   <news:publication_date>2026-04-07T07:46:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676629</loc>
  <lastmod>2026-04-07T06:54:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ラベルシフトの検出と補正—ブラックボックス予測器を用いた実務的手法（Detecting and Correcting for Label Shift with Black Box Predictors）</news:title>
   <news:publication_date>2026-04-07T06:54:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676627</loc>
  <lastmod>2026-04-07T06:53:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>季節性KPIのラベルなし異常検知に対する変分オートエンコーダの応用（Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications）</news:title>
   <news:publication_date>2026-04-07T06:53:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676625</loc>
  <lastmod>2026-04-07T06:53:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>天候データが太陽光発電予測に与える価値の評価（Assessing the Utility of Weather Data for Photovoltaic Power Prediction）</news:title>
   <news:publication_date>2026-04-07T06:53:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676623</loc>
  <lastmod>2026-04-07T06:52:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>身体化エージェントにおける多機能性：三段階のニューラルリユース（Multifunctionality in embodied agents: Three levels of neural reuse）</news:title>
   <news:publication_date>2026-04-07T06:52:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676621</loc>
  <lastmod>2026-04-07T06:52:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自己適応型計算基盤が変える高性能コンピューティングの実務応用（SAPA: Self-Aware Polymorphic Architecture）</news:title>
   <news:publication_date>2026-04-07T06:52:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676619</loc>
  <lastmod>2026-04-07T06:52:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Nearest-Neighbor Q-Learning（Q-learning with Nearest Neighbors）</news:title>
   <news:publication_date>2026-04-07T06:52:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676617</loc>
  <lastmod>2026-04-07T06:51:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ツリーモデルの説明を一貫して個別化する方法（Consistent Individualized Feature Attribution for Tree Ensembles）</news:title>
   <news:publication_date>2026-04-07T06:51:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676615</loc>
  <lastmod>2026-04-07T06:00:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Random Hinge Forestの解説（Random Hinge Forest for Differentiable Learning）</news:title>
   <news:publication_date>2026-04-07T06:00:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676613</loc>
  <lastmod>2026-04-07T05:59:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ClosNetsによる全結合層の事前疎化による高速学習（ClosNets: a Priori Sparse Topologies for Faster DNN Training）</news:title>
   <news:publication_date>2026-04-07T05:59:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676611</loc>
  <lastmod>2026-04-07T05:59:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Answerer in Questioner’s Mind（Answerer in Questioner’s Mind: Information Theoretic Approach to Goal-Oriented Visual Dialog）</news:title>
   <news:publication_date>2026-04-07T05:59:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676609</loc>
  <lastmod>2026-04-07T05:59:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Pseudo-Recursalによる破滅的忘却問題の解決（Pseudo-Recursal: Solving the Catastrophic Forgetting Problem in Deep Neural Networks）</news:title>
   <news:publication_date>2026-04-07T05:59:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676607</loc>
  <lastmod>2026-04-07T05:58:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>5Gミリ波ネットワークにおける超高信頼通信：リスク感度学習のアプローチ（Ultra-reliable communication in 5G mmWave networks: A risk-sensitive approach）</news:title>
   <news:publication_date>2026-04-07T05:58:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676605</loc>
  <lastmod>2026-04-07T05:58:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>特権情報を活かすガウス過程分類（Gaussian Process Classification with Privileged Information by Soft-to-Hard Labeling Transfer）</news:title>
   <news:publication_date>2026-04-07T05:58:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676603</loc>
  <lastmod>2026-04-07T05:58:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>区間ラベルを扱うオンラインランキング学習法の本質（PRIL: Perceptron Ranking Using Interval Labeled Data）</news:title>
   <news:publication_date>2026-04-07T05:58:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676601</loc>
  <lastmod>2026-04-07T05:07:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>フォローアップ喪失情報の回復（Recovering Loss to Followup Information Using Denoising Autoencoders）</news:title>
   <news:publication_date>2026-04-07T05:07:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676599</loc>
  <lastmod>2026-04-07T05:06:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>IoTエージェントの変動性を機械学習で扱う（Machine Learning-based Variability Handling in IoT Agents）</news:title>
   <news:publication_date>2026-04-07T05:06:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676597</loc>
  <lastmod>2026-04-07T05:06:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Katyusha X: 実践的なモメンタム手法による確率的非凸和最適化（Katyusha X: Practical Momentum Method for Stochastic Sum-of-Nonconvex Optimization）</news:title>
   <news:publication_date>2026-04-07T05:06:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676595</loc>
  <lastmod>2026-04-07T05:05:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>医療オントロジーの動的トピック発見とクエリ生成（MedTQ: Dynamic Topic Discovery and Query Generation for Medical Ontologies）</news:title>
   <news:publication_date>2026-04-07T05:05:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676593</loc>
  <lastmod>2026-04-07T05:05:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Sasaki多様体におけるスカラー曲率と適正性（Scalar Curvature and Properness on Sasaki Manifolds）</news:title>
   <news:publication_date>2026-04-07T05:05:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676591</loc>
  <lastmod>2026-04-07T05:05:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>領域検出によるガウス型マルコフ確率場のモデル選択（Region Detection in Markov Random Fields: Gaussian Case）</news:title>
   <news:publication_date>2026-04-07T05:05:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676589</loc>
  <lastmod>2026-04-07T05:04:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>凸ポリオミノの大偏差原理が示すもの（Large Deviations of Convex Polyominoes）</news:title>
   <news:publication_date>2026-04-07T05:04:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676587</loc>
  <lastmod>2026-04-07T04:12:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>希少成分のスペクトル回復と較正を可能にするBTEM＋T-PLSの実践的意義（Band Target Entropy Minimization and Target Partial Least Squares for Spectral Recovery and Calibration）</news:title>
   <news:publication_date>2026-04-07T04:12:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676585</loc>
  <lastmod>2026-04-07T04:12:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>探索的データ解析のための『Uncharted Forest』手法（Uncharted Forest: a Technique for Exploratory Data Analysis）</news:title>
   <news:publication_date>2026-04-07T04:12:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676583</loc>
  <lastmod>2026-04-07T04:12:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>リソース制約IoTプラットフォームにおけるエッジ・ホスト分割と特徴空間符号化（Edge-Host Partitioning of Deep Neural Networks with Feature Space Encoding for Resource-Constrained Internet-of-Things Platforms）</news:title>
   <news:publication_date>2026-04-07T04:12:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676581</loc>
  <lastmod>2026-04-07T04:11:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散確率的マルチタスク学習とグラフ正則化（Distributed Stochastic Multi-Task Learning with Graph Regularization）</news:title>
   <news:publication_date>2026-04-07T04:11:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676579</loc>
  <lastmod>2026-04-07T04:11:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>数値積分で高精度化するカーネル近似（Quadrature-based features for kernel approximation）</news:title>
   <news:publication_date>2026-04-07T04:11:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676577</loc>
  <lastmod>2026-04-07T04:11:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ThUndervoltによるDNNアクセラレータの低電圧運用（ThUndervolt: Enabling Aggressive Voltage Underscaling and Timing Error Resilience for Energy Efficient Deep Learning Accelerators）</news:title>
   <news:publication_date>2026-04-07T04:11:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676575</loc>
  <lastmod>2026-04-07T04:10:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>物理制約を組み込んだデータ駆動型粗視化モデルの発見（Physics-constrained, data-driven discovery of coarse-grained dynamics）</news:title>
   <news:publication_date>2026-04-07T04:10:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676573</loc>
  <lastmod>2026-04-07T03:19:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>視覚対話の生成モデルFLIPDIAL（FLIPDIAL: A Generative Model for Visual Dialogue）</news:title>
   <news:publication_date>2026-04-07T03:19:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676571</loc>
  <lastmod>2026-04-07T03:19:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SGDとHogwild!：有界勾配の仮定を外しても収束する（SGD and Hogwild! Convergence Without the Bounded Gradients Assumption）</news:title>
   <news:publication_date>2026-04-07T03:19:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676569</loc>
  <lastmod>2026-04-07T03:18:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>永久故障がシストリックアレイ型ニューラルネットワークアクセラレータに与える影響の解析と緩和（Analyzing and Mitigating the Impact of Permanent Faults on a Systolic Array Based Neural Network Accelerator）</news:title>
   <news:publication_date>2026-04-07T03:18:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676567</loc>
  <lastmod>2026-04-07T03:18:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>影響指向の説明手法が示す、深層畳み込みネットワークの「中身」の読み解き方（Influence-Directed Explanations for Deep Convolutional Networks）</news:title>
   <news:publication_date>2026-04-07T03:18:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676565</loc>
  <lastmod>2026-04-07T03:18:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>カリキュラム学習を転移学習で実現する意義（Curriculum Learning by Transfer Learning）</news:title>
   <news:publication_date>2026-04-07T03:18:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676563</loc>
  <lastmod>2026-04-07T03:17:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>薬剤応答予測におけるアンサンブル学習と薬剤誘導遺伝子発現シグネチャ（Drug response prediction by ensemble learning and drug-induced gene expression signatures）</news:title>
   <news:publication_date>2026-04-07T03:17:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676561</loc>
  <lastmod>2026-04-07T03:17:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多核子移動反応による中性子豊富希少同位体生成（Neutron-rich rare isotope production with stable and radioactive beams in the mass range A∼40–60 at beam energy around 15 MeV/nucleon）</news:title>
   <news:publication_date>2026-04-07T03:17:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676559</loc>
  <lastmod>2026-04-07T02:26:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>カーネル化された結合主義モデルと層別学習による深層学習（On Kernel Method-Based Connectionist Models and Supervised Deep Learning Without Backpropagation）</news:title>
   <news:publication_date>2026-04-07T02:26:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676557</loc>
  <lastmod>2026-04-07T02:25:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューラルアーキテクチャ探索を効率化するBOと最適輸送（Neural Architecture Search with Bayesian Optimisation and Optimal Transport）</news:title>
   <news:publication_date>2026-04-07T02:25:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676555</loc>
  <lastmod>2026-04-07T02:25:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>学習型畳み込みニューラルネットワークによるデモザイシングの革新（Learning Deep Convolutional Networks for Demosaicing）</news:title>
   <news:publication_date>2026-04-07T02:25:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676553</loc>
  <lastmod>2026-04-07T02:24:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GUIベースの回帰テストに機械学習を組み合わせる意義（Machine Learning and Evolutionary Computing for GUI-based Regression Testing）</news:title>
   <news:publication_date>2026-04-07T02:24:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676551</loc>
  <lastmod>2026-04-07T02:24:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Wassersteinオートエンコーダの潜在空間について (On the Latent Space of Wasserstein Auto-Encoders)</news:title>
   <news:publication_date>2026-04-07T02:24:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676549</loc>
  <lastmod>2026-04-07T02:23:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>P2P貸付における私的情報、信用リスク、ネットワーク構造（Private Information, Credit Risk and Graph Structure in P2P Lending Networks）</news:title>
   <news:publication_date>2026-04-07T02:23:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676547</loc>
  <lastmod>2026-04-07T02:23:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>次元削減における公平な主成分分析の凸定式化（Convex Formulations for Fair Principal Component Analysis）</news:title>
   <news:publication_date>2026-04-07T02:23:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676545</loc>
  <lastmod>2026-04-07T01:32:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチセット正準相関解析の簡潔な解説（Multiset Canonical Correlation Analysis simply explained）</news:title>
   <news:publication_date>2026-04-07T01:32:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676543</loc>
  <lastmod>2026-04-07T01:32:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模行動空間を扱う対話システム向けサンプル効率の良い深層強化学習（Sample efficient deep reinforcement learning for dialogue systems with large action spaces）</news:title>
   <news:publication_date>2026-04-07T01:32:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676541</loc>
  <lastmod>2026-04-07T01:30:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層学習による皮膚疾患の教師あり分類（Supervised classification of dermatological diseases via Deep learning）</news:title>
   <news:publication_date>2026-04-07T01:30:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676539</loc>
  <lastmod>2026-04-07T01:30:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>境界や欠陥を持つサーフェスコードの効率的機械学習表現（Efficient Machine Learning Representations of Surface Code with Boundaries, Defects, Domain Walls and Twists）</news:title>
   <news:publication_date>2026-04-07T01:30:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676537</loc>
  <lastmod>2026-04-07T01:30:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時間発展ネットワークの生成モデルと応用（A Generative Model for Dynamic Networks with Applications）</news:title>
   <news:publication_date>2026-04-07T01:30:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676535</loc>
  <lastmod>2026-04-07T01:29:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>人と機械の収斂時代におけるグローバルガバナンスのシステム（Systems of Global Governance in the Era of Human-Machine Convergence）</news:title>
   <news:publication_date>2026-04-07T01:29:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676533</loc>
  <lastmod>2026-04-07T01:29:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>教育建築と新興技術：革新的教室モデル（Educational Architecture and Emerging Technologies: Innovative Classroom-Models）</news:title>
   <news:publication_date>2026-04-07T01:29:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676531</loc>
  <lastmod>2026-04-07T00:38:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>英語のIS-A文から形式的オントロジーを学ぶ手法（Formal Ontology Learning from English IS-A Sentences）</news:title>
   <news:publication_date>2026-04-07T00:38:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676529</loc>
  <lastmod>2026-04-07T00:38:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ReLUネットワークを「尺度不変空間」で最適化する考え方（G-SGD: OPTIMIZING RELU NEURAL NETWORKS IN ITS POSITIVELY SCALE-INVARIANT SPACE）</news:title>
   <news:publication_date>2026-04-07T00:38:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676527</loc>
  <lastmod>2026-04-07T00:37:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>不均衡データ下におけるリアルタイム事故発生確率予測のためのPCAに基づく欠損値補完（PCA-Based Missing Information Imputation for Real-Time Crash Likelihood Prediction Under Imbalanced Data）</news:title>
   <news:publication_date>2026-04-07T00:37:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676525</loc>
  <lastmod>2026-04-07T00:37:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>群作用に対する等変性と畳み込みの一般化（On the Generalization of Equivariance and Convolution in Neural Networks to the Action of Compact Groups）</news:title>
   <news:publication_date>2026-04-07T00:37:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676523</loc>
  <lastmod>2026-04-07T00:36:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>区間定常性を想定したバンディット問題における変化検出付き適応手法（Nearly Optimal Adaptive Procedure with Change Detection for Piecewise-Stationary Bandit）</news:title>
   <news:publication_date>2026-04-07T00:36:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676521</loc>
  <lastmod>2026-04-07T00:36:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>プログラム翻訳のための木構造ニューラルネットワーク（Tree-to-tree Neural Networks for Program Translation）</news:title>
   <news:publication_date>2026-04-07T00:36:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676519</loc>
  <lastmod>2026-04-07T00:36:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>治療推奨における二重制御メモリ拡張ニューラルネットワーク（Dual Control Memory Augmented Neural Networks for Treatment Recommendations）</news:title>
   <news:publication_date>2026-04-07T00:36:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676517</loc>
  <lastmod>2026-04-06T23:44:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>1ステップ貪欲を超えて：強化学習におけるマルチステップ方策改善（Beyond the One-Step Greedy Approach in Reinforcement Learning）</news:title>
   <news:publication_date>2026-04-06T23:44:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676515</loc>
  <lastmod>2026-04-06T23:36:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>構造化予測と注意における微分可能な動的計画法（Differentiable Dynamic Programming for Structured Prediction and Attention）</news:title>
   <news:publication_date>2026-04-06T23:36:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676513</loc>
  <lastmod>2026-04-06T23:36:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SAT問題を「学習」して解く神経モデルの示唆（LEARNING A SAT SOLVER FROM SINGLE-BIT SUPERVISION）</news:title>
   <news:publication_date>2026-04-06T23:36:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676511</loc>
  <lastmod>2026-04-06T23:35:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>代理損失最小化からベイズ最適分類器への収束速度について（On the Rates of Convergence from Surrogate Risk Minimizers to the Bayes Optimal Classifier）</news:title>
   <news:publication_date>2026-04-06T23:35:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676509</loc>
  <lastmod>2026-04-06T23:35:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>圧縮センシングを用いたスペクトラムセンシングの実用性と課題（Adaptive Compressive Spectrum Sensing for Wideband Cognitive Radios）</news:title>
   <news:publication_date>2026-04-06T23:35:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676507</loc>
  <lastmod>2026-04-06T23:34:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>グローバルモデル解釈のための再帰的分割（Global Model Interpretation via Recursive Partitioning）</news:title>
   <news:publication_date>2026-04-06T23:34:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676505</loc>
  <lastmod>2026-04-06T23:34:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>シンプレクティックによる最適化の再構築（On Symplectic Optimization）</news:title>
   <news:publication_date>2026-04-06T23:34:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676503</loc>
  <lastmod>2026-04-06T22:42:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>t-指数ベイズ的ランダムキッチンシンクスによる深層学習の頑健化（Deep Learning with t-Exponential Bayesian Kitchen Sinks）</news:title>
   <news:publication_date>2026-04-06T22:42:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676501</loc>
  <lastmod>2026-04-06T22:42:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>低ランク手法によるイベント検出と部分空間近接テスト（Low-Rank Methods in Event Detection and Subsampled Point-to-Subspace Proximity Tests）</news:title>
   <news:publication_date>2026-04-06T22:42:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676499</loc>
  <lastmod>2026-04-06T22:41:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>量子相関を用いたカオスとエルゴディシティの探査（Quantum correlations as probes of chaos and ergodicity）</news:title>
   <news:publication_date>2026-04-06T22:41:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676497</loc>
  <lastmod>2026-04-06T22:41:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>複数の変化点を検出する手法と神経記録への応用（Detecting Multiple Change Points Using Adaptive Regression Splines with Application to Neural Recordings）</news:title>
   <news:publication_date>2026-04-06T22:41:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676495</loc>
  <lastmod>2026-04-06T22:41:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>逆最適輸送によるマッチング学習（Learning to Match via Inverse Optimal Transport）</news:title>
   <news:publication_date>2026-04-06T22:41:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676493</loc>
  <lastmod>2026-04-06T22:41:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>時系列相関空間の学習（Learning Correlation Space for Time Series）</news:title>
   <news:publication_date>2026-04-06T22:41:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676491</loc>
  <lastmod>2026-04-06T22:40:35Z</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ReLUニューラルネットワークの表現力と複雑度の限界（On the Universal Approximability and Complexity Bounds of Quantized ReLU Neural Networks）</news:title>
   <news:publication_date>2026-04-06T22:40:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676480</loc>
  <lastmod>2026-04-06T21:49:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>極めて深いReLUネットワークによる連続関数の最適近似（Optimal approximation of continuous functions by very deep ReLU networks）</news:title>
   <news:publication_date>2026-04-06T21:49:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676478</loc>
  <lastmod>2026-04-06T21:49:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>気管支鏡における逐次ファインチューニングで転移学習を最適化する手法（Optimize transfer learning for lung diseases in bronchoscopy using a new concept: sequential fine-tuning）</news:title>
   <news:publication_date>2026-04-06T21:49:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676476</loc>
  <lastmod>2026-04-06T21:48:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Combinets: 訓練済みニューラルネットワークの再結合による創造性（Combinets: Creativity via Recombination of Neural Networks）</news:title>
   <news:publication_date>2026-04-06T21:48:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676474</loc>
  <lastmod>2026-04-06T21:47:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層可視ドメイン適応の総説（Deep Visual Domain Adaptation: A Survey）</news:title>
   <news:publication_date>2026-04-06T21:47:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676472</loc>
  <lastmod>2026-04-06T21:47:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高次元線形分類における特徴分散型SVRGの提案（Feature-Distributed SVRG for High-Dimensional Linear Classification）</news:title>
   <news:publication_date>2026-04-06T21:47:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676470</loc>
  <lastmod>2026-04-06T21:47:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>文書分類における分散機械学習の適用（Document Classification Using Distributed Machine Learning）</news:title>
   <news:publication_date>2026-04-06T21:47:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676468</loc>
  <lastmod>2026-04-06T21:47:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>概念空間で学ぶ深層メタラーニング（Deep Meta-Learning: Learning to Learn in the Concept Space）</news:title>
   <news:publication_date>2026-04-06T21:47:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676466</loc>
  <lastmod>2026-04-06T20:55:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>対話的ニューラル機械翻訳における作業量削減のためのオンライン学習 (Online Learning for Effort Reduction in Interactive Neural Machine Translation)</news:title>
   <news:publication_date>2026-04-06T20:55:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676464</loc>
  <lastmod>2026-04-06T20:55:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>肺結節のセグメンテーション・属性推定・悪性度予測の統合学習（JOINT LEARNING FOR PULMONARY NODULE SEGMENTATION, ATTRIBUTES AND MALIGNANCY PREDICTION）</news:title>
   <news:publication_date>2026-04-06T20:55:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676462</loc>
  <lastmod>2026-04-06T20:55:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散型ワン・クラス学習（DISTRIBUTED ONE-CLASS LEARNING）</news:title>
   <news:publication_date>2026-04-06T20:55:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676460</loc>
  <lastmod>2026-04-06T20:54:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>線形グラフ埋め込みにおけるノルム正則化の重要性（THE IMPORTANCE OF NORM REGULARIZATION IN LINEAR GRAPH EMBEDDING）</news:title>
   <news:publication_date>2026-04-06T20:54:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676458</loc>
  <lastmod>2026-04-06T20:54:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>汎用分類器の品質基準（CRITÈRES DE QUALITÉ D’UN CLASSIFIEUR GÉNÉRALISTE）</news:title>
   <news:publication_date>2026-04-06T20:54:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676456</loc>
  <lastmod>2026-04-06T20:53:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Persistence Fisher Kernelによる持続的ホモロジーの新たな距離設計（Persistence Fisher Kernel: A Riemannian Manifold Kernel for Persistence Diagrams）</news:title>
   <news:publication_date>2026-04-06T20:53:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676454</loc>
  <lastmod>2026-04-06T20:53:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチラベルデータ管理のためのツール群と実務的ガイド（Tips, guidelines and tools for managing multi-label datasets）</news:title>
   <news:publication_date>2026-04-06T20:53:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676452</loc>
  <lastmod>2026-04-06T20:02:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>蛍光顕微鏡画像における尿細管セグメンテーション（Tubule Segmentation of Fluorescence Microscopy Images Based on Convolutional Neural Networks With Inhomogeneity Correction）</news:title>
   <news:publication_date>2026-04-06T20:02:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676450</loc>
  <lastmod>2026-04-06T20:02:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習支援進化アルゴリズムで描くアモルファスLixSiの第一原理相図（Constructing first-principles phase diagrams of amorphous LixSi using machine-learning-assisted sampling with an evolutionary algorithm）</news:title>
   <news:publication_date>2026-04-06T20:02:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676448</loc>
  <lastmod>2026-04-06T20:02:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>情報最大化ニューラルネットワークによる自動物理推論 (Automatic physical inference with information maximising neural networks)</news:title>
   <news:publication_date>2026-04-06T20:02:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676446</loc>
  <lastmod>2026-04-06T20:01:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>AdaBoostM1とJ48による頑健化手法（Enhanced version of AdaBoostM1 with J48 Tree learning method）</news:title>
   <news:publication_date>2026-04-06T20:01:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676444</loc>
  <lastmod>2026-04-06T20:00:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>サブスペース学習における揺らぎを扱う新しいカーネル（Disturbance Grassmann Kernels for Subspace-Based Learning）</news:title>
   <news:publication_date>2026-04-06T20:00:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676442</loc>
  <lastmod>2026-04-06T20:00:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>単調性情報を活用したベイズ最適化と機械学習ハイパーパラメータ調整への応用 (Bayesian Optimization Using Monotonicity Information and Its Application in Machine Learning Hyperparameter Tuning)</news:title>
   <news:publication_date>2026-04-06T20:00:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676440</loc>
  <lastmod>2026-04-06T20:00:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>弱いラベルで強い検出器を育てる協調学習（Collaborative Learning for Weakly Supervised Object Detection）</news:title>
   <news:publication_date>2026-04-06T20:00:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676438</loc>
  <lastmod>2026-04-06T19:09:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>放射線学におけるディープラーニングの概観（Deep learning in radiology: an overview of the concepts and a survey of the state of the art）</news:title>
   <news:publication_date>2026-04-06T19:09:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676436</loc>
  <lastmod>2026-04-06T19:08:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多変量MRIと深層学習で切り分ける乳房組織の特徴（Multiparametric Deep Learning Tissue Signatures for a Radiological Biomarker of Breast Cancer: Preliminary Results）</news:title>
   <news:publication_date>2026-04-06T19:08:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676434</loc>
  <lastmod>2026-04-06T19:07:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所対比学習が変える少数ショット認識の考え方（Local Contrast Learning）</news:title>
   <news:publication_date>2026-04-06T19:07:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676432</loc>
  <lastmod>2026-04-06T19:07:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ツァリスエントロピー正則化MDPにおける経路一貫性学習（Path Consistency Learning in Tsallis Entropy Regularized MDPs）</news:title>
   <news:publication_date>2026-04-06T19:07:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676430</loc>
  <lastmod>2026-04-06T19:07:10Z</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・オートエンコーダの理論と実践（Coulomb Autoencoders）</news:title>
   <news:publication_date>2026-04-06T19:07:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676428</loc>
  <lastmod>2026-04-06T19:06:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>二足歩行の「ストレンジアトラクター」モデルが示す運動制御の本質（The Strange Attractor Model of Bipedal Locomotion and its Consequences on Motor Control）</news:title>
   <news:publication_date>2026-04-06T19:06:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676426</loc>
  <lastmod>2026-04-06T18:15:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>より頑健な二重ロバスト型オフポリシー評価（More Robust Doubly Robust Off-policy Evaluation）</news:title>
   <news:publication_date>2026-04-06T18:15:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676424</loc>
  <lastmod>2026-04-06T18:15:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>AMC: モバイル機器向けモデル圧縮の自動化（AMC: AutoML for Model Compression and Acceleration on Mobile Devices）</news:title>
   <news:publication_date>2026-04-06T18:15:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676422</loc>
  <lastmod>2026-04-06T18:14:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高分解能ハイパースペクトル画像の分類におけるGANと確率的グラフモデルの統合（Generative Adversarial Networks and Probabilistic Graph Models for Hyperspectral Image Classification）</news:title>
   <news:publication_date>2026-04-06T18:14:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676420</loc>
  <lastmod>2026-04-06T18:14:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>二層隠れ層ネットワークにおける線形分離を達成するためのノード上限の一般化（Generalization of an Upper Bound on the Number of Nodes Needed to Achieve Linear Separability）</news:title>
   <news:publication_date>2026-04-06T18:14:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676418</loc>
  <lastmod>2026-04-06T18:13:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>わずかな非線形性が生む悪い局所最適解（SMALL NONLINEARITIES IN ACTIVATION FUNCTIONS CREATE BAD LOCAL MINIMA IN NEURAL NETWORKS）</news:title>
   <news:publication_date>2026-04-06T18:13:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676416</loc>
  <lastmod>2026-04-06T18:13:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>生物学的マニフォールド整列を目指すGAN（MAGAN: Aligning Biological Manifolds）</news:title>
   <news:publication_date>2026-04-06T18:13:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676414</loc>
  <lastmod>2026-04-06T18:13:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>可逆オートエンコーダを用いたドメイン適応（Invertible Autoencoder for domain adaptation）</news:title>
   <news:publication_date>2026-04-06T18:13:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676412</loc>
  <lastmod>2026-04-06T17:21:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スマートフォンセンサで歩数を高精度に数えるためのLSTMモデル（An LSTM Recurrent Network for Step Counting）</news:title>
   <news:publication_date>2026-04-06T17:21:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676410</loc>
  <lastmod>2026-04-06T17:21:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>離散最適化の継続法と近傍分類への応用（A Continuation Method for Discrete Optimization and its Application to Nearest Neighbor Classification）</news:title>
   <news:publication_date>2026-04-06T17:21:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676408</loc>
  <lastmod>2026-04-06T17:21:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GraphVAEによる小規模グラフ生成の試み (GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders)</news:title>
   <news:publication_date>2026-04-06T17:21:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676406</loc>
  <lastmod>2026-04-06T17:20:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>差分プライバシーで示す敵対的事例への認証付き堅牢性（Certified Robustness to Adversarial Examples with Differential Privacy）</news:title>
   <news:publication_date>2026-04-06T17:20:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676404</loc>
  <lastmod>2026-04-06T17:19:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>通信・計算効率の良い勾配符号化（Communication-Computation Efficient Gradient Coding）</news:title>
   <news:publication_date>2026-04-06T17:19:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676402</loc>
  <lastmod>2026-04-06T17:19:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>局所計量と影響領域による距離学習（Learning Local Metrics and Influential Regions for Classification）</news:title>
   <news:publication_date>2026-04-06T17:19:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676400</loc>
  <lastmod>2026-04-06T17:19:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Lipschitzマージン比を最大化することで伸びる分類器の頑健性（Metric Learning via Maximizing the Lipschitz Margin Ratio）</news:title>
   <news:publication_date>2026-04-06T17:19:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676398</loc>
  <lastmod>2026-04-06T16:27:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Reweighted Autoencoded Variational Bayesによる分子シミュレーションの高速化（Reweighted Autoencoded Variational Bayes for Enhanced Sampling）</news:title>
   <news:publication_date>2026-04-06T16:27:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676396</loc>
  <lastmod>2026-04-06T16:19:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模暗黙行列のスペクトル密度推定（Estimating the Spectral Density of Large Implicit Matrices）</news:title>
   <news:publication_date>2026-04-06T16:19:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676394</loc>
  <lastmod>2026-04-06T16:19:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GAN評価指標の長所と短所（Pros and Cons of GAN Evaluation Measures）</news:title>
   <news:publication_date>2026-04-06T16:19:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676392</loc>
  <lastmod>2026-04-06T16:18:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スパースな行列乗算のコーディング（Coded Sparse Matrix Multiplication）</news:title>
   <news:publication_date>2026-04-06T16:18:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676390</loc>
  <lastmod>2026-04-06T16:17:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>移動エージェントの実時間位置推定における狭義機械学習（Narrow artificial intelligence with machine learning for real time estimation of a mobile agent’s location using Hidden Markov Models）</news:title>
   <news:publication_date>2026-04-06T16:17:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676388</loc>
  <lastmod>2026-04-06T16:17:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>UMAPによる次元削減の実践（UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction）</news:title>
   <news:publication_date>2026-04-06T16:17:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676386</loc>
  <lastmod>2026-04-06T16:16:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大学生の学業成功と専攻を予測する方法（Predicting University Students’ Academic Success and Major using Random Forests）</news:title>
   <news:publication_date>2026-04-06T16:16:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676384</loc>
  <lastmod>2026-04-06T15:25:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>LOFAR低周波観測が示したSS 433と超新星残骸W50の新像（LOFAR 150-MHz observations of SS 433 and W 50）</news:title>
   <news:publication_date>2026-04-06T15:25:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676382</loc>
  <lastmod>2026-04-06T15:25:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>遠方クエーサーを取り巻く広がるLyα放射の発見（Extended and broad Lyα emission around a BAL quasar at z ∼5）</news:title>
   <news:publication_date>2026-04-06T15:25:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676380</loc>
  <lastmod>2026-04-06T15:25:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>衝撃波が示す宇宙プラズマの平衡化と電波ハローの境界（BOW SHOCK IN MERGING CLUSTER A520: THE EDGE OF THE RADIO HALO AND THE ELECTRON–ION EQUILIBRATION TIMESCALE）</news:title>
   <news:publication_date>2026-04-06T15:25:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676378</loc>
  <lastmod>2026-04-06T15:24:13Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>同一・異種判定問題が畳み込みニューラルネットワークに与える負荷（Same-different problems strain convolutional neural networks）</news:title>
   <news:publication_date>2026-04-06T15:24:13Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676376</loc>
  <lastmod>2026-04-06T15:23:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>少数派を勝たせる文脈探索の新境地（Make the Minority Great Again: First-Order Regret Bound for Contextual Bandits）</news:title>
   <news:publication_date>2026-04-06T15:23:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676374</loc>
  <lastmod>2026-04-06T15:23:52Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>少ないラベルで学べるハイブリッド生成モデルの提案（GENERATIVE SCATTERNET HYBRID DEEP LEARNING (G-SHDL) NETWORK WITH STRUCTURAL PRIORS FOR SEMANTIC IMAGE SEGMENTATION）</news:title>
   <news:publication_date>2026-04-06T15:23:52Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676372</loc>
  <lastmod>2026-04-06T15:23:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ATPboostによる前提選択の二値学習とATPフィードバック (ATPboost: Learning Premise Selection in Binary Setting with ATP Feedback)</news:title>
   <news:publication_date>2026-04-06T15:23:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676370</loc>
  <lastmod>2026-04-06T14:32:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>テキストデータに対する情報プランニング（Information Planning for Text Data）</news:title>
   <news:publication_date>2026-04-06T14:32:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676368</loc>
  <lastmod>2026-04-06T14:31:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルウェアフロー検出のための深層学習（Deep Learning for Malicious Flow Detection）</news:title>
   <news:publication_date>2026-04-06T14:31:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676366</loc>
  <lastmod>2026-04-06T14:31:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>顧客離脱予測における時間依存データとXGBoost（Predicting Customer Churn: Extreme Gradient Boosting with Temporal Data）</news:title>
   <news:publication_date>2026-04-06T14:31:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676364</loc>
  <lastmod>2026-04-06T14:30:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率微分方程式のブラックボックス変分推論（Black-box Variational Inference for Stochastic Differential Equations）</news:title>
   <news:publication_date>2026-04-06T14:30:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676362</loc>
  <lastmod>2026-04-06T14:30:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大規模制約付き線形回帰の再検討（Large Scale Constrained Linear Regression Revisited: Faster Algorithms via Preconditioning）</news:title>
   <news:publication_date>2026-04-06T14:30:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676360</loc>
  <lastmod>2026-04-06T14:30:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>歴史的文書におけるテキスト行検出の二段階手法 (A Two-Stage Method for Text Line Detection in Historical Documents)</news:title>
   <news:publication_date>2026-04-06T14:30:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676358</loc>
  <lastmod>2026-04-06T13:38:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>音声広告の品質を予測する試み（Predicting Audio Advertisement Quality）</news:title>
   <news:publication_date>2026-04-06T13:38:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676356</loc>
  <lastmod>2026-04-06T13:38:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>粒子検出器シミュレーションの生成と精緻化（Generating and refining particle detector simulations using the Wasserstein distance in adversarial networks）</news:title>
   <news:publication_date>2026-04-06T13:38:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676354</loc>
  <lastmod>2026-04-06T13:37:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>環境資源が進化的深層知能に与える影響（Nature vs. Nurture: The Role of Environmental Resources in Evolutionary Deep Intelligence）</news:title>
   <news:publication_date>2026-04-06T13:37:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676352</loc>
  <lastmod>2026-04-06T13:36:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>線形再帰ニューラルネットワークの可能性（The Power of Linear Recurrent Neural Networks）</news:title>
   <news:publication_date>2026-04-06T13:36:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676350</loc>
  <lastmod>2026-04-06T13:36:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>順位に基づくベイズ推論（BAYESIAN INFERENCE FOR BIVARIATE RANKS）</news:title>
   <news:publication_date>2026-04-06T13:36:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676348</loc>
  <lastmod>2026-04-06T13:36:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>深層学習の数の認知に関する認知的欠陥（Cognitive Deficit of Deep Learning in Numerosity）</news:title>
   <news:publication_date>2026-04-06T13:36:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676346</loc>
  <lastmod>2026-04-06T13:36:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>無監督深層ドメイン適応による歩行者検出の実務的意義（Unsupervised Deep Domain Adaptation for Pedestrian Detection）</news:title>
   <news:publication_date>2026-04-06T13:36:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676344</loc>
  <lastmod>2026-04-06T12:44:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ビデオイベント認識と異常検出の統合モデル（Video Event Recognition and Anomaly Detection by Combining Gaussian Process and Hierarchical Dirichlet Process Models）</news:title>
   <news:publication_date>2026-04-06T12:44:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676342</loc>
  <lastmod>2026-04-06T12:44:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>トリプレットに基づく深層類似度学習と人物再識別（Triplet-based Deep Similarity Learning for Person Re-Identification）</news:title>
   <news:publication_date>2026-04-06T12:44:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676340</loc>
  <lastmod>2026-04-06T12:43:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>効率的ニューラルアーキテクチャ探索（Efficient Neural Architecture Search via Parameter Sharing）</news:title>
   <news:publication_date>2026-04-06T12:43:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676338</loc>
  <lastmod>2026-04-06T12:43:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>特徴表現と距離尺度を同時学習する複数対象追跡（Multiple Target Tracking by Learning Feature Representation and Distance Metric Jointly）</news:title>
   <news:publication_date>2026-04-06T12:43:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676336</loc>
  <lastmod>2026-04-06T12:43:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Piecewise Flat Embeddingによる画像分割の革新（Piecewise Flat Embedding for Image Segmentation）</news:title>
   <news:publication_date>2026-04-06T12:43:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676334</loc>
  <lastmod>2026-04-06T12:42:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>手術残時間を動画だけで予測するRSDNet（RSDNet: Learning to Predict Remaining Surgery Duration from Laparoscopic Videos Without Manual Annotations）</news:title>
   <news:publication_date>2026-04-06T12:42:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676332</loc>
  <lastmod>2026-04-06T12:42:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>光子の横運動量を含めたW+W−生成の再評価（Production of W+W−pairs via γ*γ*→W+W−subprocess with photon transverse momenta）</news:title>
   <news:publication_date>2026-04-06T12:42:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676330</loc>
  <lastmod>2026-04-06T11:51:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>離散化を「置換」から「拡張」へ：D-MIATが示した特徴拡張の新パラダイム（Using Discretization for Extending the Set of Predictive Features）</news:title>
   <news:publication_date>2026-04-06T11:51:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676328</loc>
  <lastmod>2026-04-06T11:50:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>長文の意味を捉えるRNNベースのセマンティック変分オートエンコーダ（Recurrent Neural Network-Based Semantic Variational Autoencoder for Sequence-to-Sequence Learning）</news:title>
   <news:publication_date>2026-04-06T11:50:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676326</loc>
  <lastmod>2026-04-06T11:50:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>フルフレームによるシーン座標回帰で画像ベース位置推定が変わる（Full-Frame Scene Coordinate Regression for Image-Based Localization）</news:title>
   <news:publication_date>2026-04-06T11:50:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676324</loc>
  <lastmod>2026-04-06T11:49:41Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>縦断データの深層クラスタリング（Deep clustering of longitudinal data）</news:title>
   <news:publication_date>2026-04-06T11:49:41Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676322</loc>
  <lastmod>2026-04-06T11:49:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>曲線登録を組み込んだ結合低ランク因子分解（Curve Registered Coupled Low Rank Factorization）</news:title>
   <news:publication_date>2026-04-06T11:49:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676320</loc>
  <lastmod>2026-04-06T11:49:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>二者確率的ゲームを調整するソフトQ学習（Balancing Two-Player Stochastic Games with Soft Q-Learning）</news:title>
   <news:publication_date>2026-04-06T11:49:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676318</loc>
  <lastmod>2026-04-06T11:48:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ロバストな「オプション」を学習する方法（Learning Robust Options）</news:title>
   <news:publication_date>2026-04-06T11:48:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676316</loc>
  <lastmod>2026-04-06T10:57:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ハチの全個体を生涯追跡する技術とその意義（Tracking all members of a honey bee colony over their lifetime using learned models of correspondence）</news:title>
   <news:publication_date>2026-04-06T10:57:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676314</loc>
  <lastmod>2026-04-06T10:57:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自己束縛型近似接尾辞木による系列予測（Self-Bounded Prediction Suffix Tree via Approximate String Matching）</news:title>
   <news:publication_date>2026-04-06T10:57:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676312</loc>
  <lastmod>2026-04-06T10:56:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Q(σ, λ)による強化学習の多段階TD学習とエリジビリティトレースの統合（A Unified Approach for Multi-step Temporal-Difference Learning with Eligibility Traces in Reinforcement Learning）</news:title>
   <news:publication_date>2026-04-06T10:56:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676310</loc>
  <lastmod>2026-04-06T10:55:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層的クロス（布）シミュレーションと深層ニューラルネットワークの融合（Hierarchical Cloth Simulation using Deep Neural Networks）</news:title>
   <news:publication_date>2026-04-06T10:55:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676308</loc>
  <lastmod>2026-04-06T10:55:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スマートメーター・データ解析の俯瞰と実務的示唆（Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges）</news:title>
   <news:publication_date>2026-04-06T10:55:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676306</loc>
  <lastmod>2026-04-06T10:55:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>敵対的メトリック学習（Adversarial Metric Learning）</news:title>
   <news:publication_date>2026-04-06T10:55:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676304</loc>
  <lastmod>2026-04-06T10:54:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>URLの文字列だけで悪意あるURLを見抜く仕組み（URLNet: Learning a URL Representation with Deep Learning for Malicious URL Detection）</news:title>
   <news:publication_date>2026-04-06T10:54:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676302</loc>
  <lastmod>2026-04-06T10:03:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>再サンプリング特徴とコピー・ムーブ解析による画像改ざん検出の強化 (Boosting Image Forgery Detection using Resampling Features and Copy-move Analysis)</news:title>
   <news:publication_date>2026-04-06T10:03:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676300</loc>
  <lastmod>2026-04-06T10:03:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ピオンのパートン分布の統計的アプローチ（Statistical approach of pion parton distributions from Drell–Yan process）</news:title>
   <news:publication_date>2026-04-06T10:03:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676298</loc>
  <lastmod>2026-04-06T10:03:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>中間表現から敏感情報を取り除く深層特徴抽出（Deep Private-Feature Extractor (DPFE)）</news:title>
   <news:publication_date>2026-04-06T10:03:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676296</loc>
  <lastmod>2026-04-06T10:02:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>バッチカルマン正規化（Batch Kalman Normalization: Towards Training Deep Neural Networks with Micro-Batches）</news:title>
   <news:publication_date>2026-04-06T10:02:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676294</loc>
  <lastmod>2026-04-06T10:02:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>楽曲に潜む反復を機械が読む仕組み（Neural Dynamic Programming for Musical Self Similarity）</news:title>
   <news:publication_date>2026-04-06T10:02:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676292</loc>
  <lastmod>2026-04-06T10:02:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Relational Autoencoderによる特徴抽出の現実的意義（Relational Autoencoder for Feature Extraction）</news:title>
   <news:publication_date>2026-04-06T10:02:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676290</loc>
  <lastmod>2026-04-06T10:01:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>LibriSpeechにフランス語翻訳を付与した多言語コーパス（Augmenting Librispeech with French Translations: A Multimodal Corpus for Direct Speech Translation Evaluation）</news:title>
   <news:publication_date>2026-04-06T10:01:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676288</loc>
  <lastmod>2026-04-06T09:10:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>大振幅集団運動による核反応経路の導出と再量子化（Nuclear reaction path and requantization of TDDFT）</news:title>
   <news:publication_date>2026-04-06T09:10:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676286</loc>
  <lastmod>2026-04-06T09:09:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチエージェントに基づくゼロリソースニューラル機械翻訳（Zero-Resource Neural Machine Translation with Multi-Agent Communication Game）</news:title>
   <news:publication_date>2026-04-06T09:09:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676284</loc>
  <lastmod>2026-04-06T09:09:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ロバストかつスパースなGLM推定を大規模データで実現する手法（Robust and Sparse Regression in GLM by Stochastic Optimization）</news:title>
   <news:publication_date>2026-04-06T09:09:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676282</loc>
  <lastmod>2026-04-06T09:08:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>プラットフォームにおける「マッチング学習」の実務的示唆（Learning to Match）</news:title>
   <news:publication_date>2026-04-06T09:08:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676280</loc>
  <lastmod>2026-04-06T09:08:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>CALYPSO構造予測の高速化：ポテンシャルエネルギー面のデータ駆動学習による加速 (Accelerating CALYPSO Structure Prediction by Data-driven Learning of Potential Energy Surface)</news:title>
   <news:publication_date>2026-04-06T09:08:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676278</loc>
  <lastmod>2026-04-06T09:08:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>畳み込みハッシュによる自動シーン照合（Convolutional Hashing for Automated Scene Matching）</news:title>
   <news:publication_date>2026-04-06T09:08:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676276</loc>
  <lastmod>2026-04-06T09:07:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>歯科X線画像の自動歯分割が拓く診断支援の地平（Automatic segmenting teeth in X-ray images: Trends, a novel data set, benchmarking and future perspectives）</news:title>
   <news:publication_date>2026-04-06T09:07:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676274</loc>
  <lastmod>2026-04-06T08:16:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>擬似教師付き学習とグラフベース正則化による潜在表現学習とクラスタリング（Learning Latent Representations in Neural Networks for Clustering Through Pseudo Supervision and Graph-Based Activity Regularization）</news:title>
   <news:publication_date>2026-04-06T08:16:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676272</loc>
  <lastmod>2026-04-06T08:16:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Apache SystemMLによる大規模深層学習の実運用アプローチ（Deep Learning with Apache SystemML）</news:title>
   <news:publication_date>2026-04-06T08:16:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676270</loc>
  <lastmod>2026-04-06T08:16:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>物理測定で制約された現実的な地質生成（Generating Realistic Geology Conditioned on Physical Measurements with GANs）</news:title>
   <news:publication_date>2026-04-06T08:16:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676268</loc>
  <lastmod>2026-04-06T08:15:47Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ポイズニング攻撃に対する異常検知による学習用敵対的例の検出（Detection of Adversarial Training Examples in Poisoning Attacks through Anomaly Detection）</news:title>
   <news:publication_date>2026-04-06T08:15:47Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676266</loc>
  <lastmod>2026-04-06T08:15:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>動的価格設定におけるトンプソン・サンプリングの実装と効果（Thompson Sampling for Dynamic Pricing）</news:title>
   <news:publication_date>2026-04-06T08:15:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676264</loc>
  <lastmod>2026-04-06T08:15:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>コーディング手法を用いた分散処理の高速化（Leveraging Coding Techniques for Speeding up Distributed Computing）</news:title>
   <news:publication_date>2026-04-06T08:15:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676262</loc>
  <lastmod>2026-04-06T08:15:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ディープヘッジによる実務的なヘッジ戦略の再定義（Deep Hedging）</news:title>
   <news:publication_date>2026-04-06T08:15:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676260</loc>
  <lastmod>2026-04-06T07:23:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>少数ショットでニューラルネットワークをスクラッチ学習する擬似例最適化（Few-shot learning of neural networks from scratch by pseudo example optimization）</news:title>
   <news:publication_date>2026-04-06T07:23:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676258</loc>
  <lastmod>2026-04-06T07:23:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>電力市場における仮想取引のアルゴリズム入札（Algorithmic Bidding for Virtual Trading in Electricity Markets）</news:title>
   <news:publication_date>2026-04-06T07:23:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676256</loc>
  <lastmod>2026-04-06T07:22:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>個別化医療を制御問題として考える（Precision medicine as a control problem: Using simulation and deep reinforcement learning to discover adaptive, personalized multi-cytokine therapy for sepsis）</news:title>
   <news:publication_date>2026-04-06T07:22:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676254</loc>
  <lastmod>2026-04-06T07:21:58Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>弱凸関数上での確率的部分勾配法の収束率 O(k−1/4)（Stochastic subgradient method converges at the rate O(k−1/4) on weakly convex functions）</news:title>
   <news:publication_date>2026-04-06T07:21:58Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676252</loc>
  <lastmod>2026-04-06T07:21:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>原子分解能TEM画像で局所構造を見抜く深層学習（A deep learning approach to identify local structures in atomic-resolution transmission electron microscopy images）</news:title>
   <news:publication_date>2026-04-06T07:21:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676250</loc>
  <lastmod>2026-04-06T07:21:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>全変動正則化に基づく一般化加法モデルの統計的学習可能性（Statistical Learnability of Generalized Additive Models based on Total Variation Regularization）</news:title>
   <news:publication_date>2026-04-06T07:21:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676248</loc>
  <lastmod>2026-04-06T07:21:04Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>強化学習のための高速生成モデルの学習と照会（Learning and Querying Fast Generative Models for Reinforcement Learning）</news:title>
   <news:publication_date>2026-04-06T07:21:04Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676246</loc>
  <lastmod>2026-04-06T06:30:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オープンデータとグレイデータの管理責任（Open Data, Grey Data, and Stewardship）</news:title>
   <news:publication_date>2026-04-06T06:30:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676244</loc>
  <lastmod>2026-04-06T06:29:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>データから学ぶスパースなウェーブレット表現（Learning Sparse Wavelet Representations）</news:title>
   <news:publication_date>2026-04-06T06:29:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676242</loc>
  <lastmod>2026-04-06T06:29:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>TSViz：時系列解析の深層学習の可視化手法（TSViz: Demystification of Deep Learning Models for Time-Series Analysis）</news:title>
   <news:publication_date>2026-04-06T06:29:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676240</loc>
  <lastmod>2026-04-06T06:28:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューラルネットワークによるKohn–Sham交換相関ポテンシャルと訓練外転移性 (Neural-network Kohn-Sham exchange-correlation potential and its out-of-training transferability)</news:title>
   <news:publication_date>2026-04-06T06:28:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676238</loc>
  <lastmod>2026-04-06T06:28:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>拡散MRIを用いた無監督学習による軽度外傷性脳損傷（mTBI）同定の新アプローチ（A Deep Unsupervised Learning Approach Toward MTBI Identification Using Diffusion MRI）</news:title>
   <news:publication_date>2026-04-06T06:28:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676236</loc>
  <lastmod>2026-04-06T06:28:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ネットワークを回転させる：重みの統合改善と忘却の軽減（Rotate your Networks: Better Weight Consolidation and Less Catastrophic Forgetting）</news:title>
   <news:publication_date>2026-04-06T06:28:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676234</loc>
  <lastmod>2026-04-06T06:27:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルコフ過程の状態圧縮を実現するスペクトル法（Spectral State Compression of Markov Processes）</news:title>
   <news:publication_date>2026-04-06T06:27:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676232</loc>
  <lastmod>2026-04-06T05:35:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>圧縮ストリーミングデータのオンライン分解（Online Decomposition of Compressive Streaming Data Using n-ℓ1 Cluster-Weighted Minimization）</news:title>
   <news:publication_date>2026-04-06T05:35:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676230</loc>
  <lastmod>2026-04-06T05:35:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>オンライン学習の全体像と実務への示唆（Online Learning: A Comprehensive Survey）</news:title>
   <news:publication_date>2026-04-06T05:35:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676228</loc>
  <lastmod>2026-04-06T05:33:56Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>無監督で銀河を分類する新しい視点（Unsupervised Classification of Galaxies. I. ICA feature selection）</news:title>
   <news:publication_date>2026-04-06T05:33:56Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676226</loc>
  <lastmod>2026-04-06T05:33:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>リザバーコンピュータで学ぶカオスアトラクタの再現と応用（Using a reservoir computer to learn chaotic attractors, with applications to chaos synchronisation and cryptography）</news:title>
   <news:publication_date>2026-04-06T05:33:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676224</loc>
  <lastmod>2026-04-06T05:33:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>mGPfusionによるタンパク質安定性予測（mGPfusion: Predicting protein stability changes with Gaussian process kernel learning and data fusion）</news:title>
   <news:publication_date>2026-04-06T05:33:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676222</loc>
  <lastmod>2026-04-06T05:33:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューラルネットワークによるリノーマライゼーション群（Neural Network Renormalization Group）</news:title>
   <news:publication_date>2026-04-06T05:33:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676220</loc>
  <lastmod>2026-04-06T05:32:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スマートなエネルギー管理による効率化の実務応用（Smart energy management as a means towards improved energy efficiency）</news:title>
   <news:publication_date>2026-04-06T05:32:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676218</loc>
  <lastmod>2026-04-06T04:41:33Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>治療効果の異質性に対するデータ適応型二重ロバスト計算法（Data-adaptive doubly robust instrumental variable methods for treatment effect heterogeneity）</news:title>
   <news:publication_date>2026-04-06T04:41:33Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676216</loc>
  <lastmod>2026-04-06T04:41:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>同期トロン優勢超新星残骸の深部X線観測（A DEEP X-RAY VIEW OF THE SYNCHROTRON-DOMINATED SUPERNOVA REMNANT G330.2+1.0）</news:title>
   <news:publication_date>2026-04-06T04:41:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676214</loc>
  <lastmod>2026-04-06T04:41:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>機械学習によるCME到着予測の新手法（A NEW TOOL FOR CME ARRIVAL TIME PREDICTION USING MACHINE LEARNING ALGORITHMS: CAT-PUMA）</news:title>
   <news:publication_date>2026-04-06T04:41:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676212</loc>
  <lastmod>2026-04-06T04:39:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>注目領域を活用した頑健な視覚追跡の強化（SALIENCY-ENHANCED ROBUST VISUAL TRACKING）</news:title>
   <news:publication_date>2026-04-06T04:39:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676210</loc>
  <lastmod>2026-04-06T04:39:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>フィギュアスケート映像の自動採点学習（Learning to Score Figure Skating Sport Videos）</news:title>
   <news:publication_date>2026-04-06T04:39:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676208</loc>
  <lastmod>2026-04-06T04:39:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>トランスダクティブ敵対ネットワーク（Transductive Adversarial Networks）</news:title>
   <news:publication_date>2026-04-06T04:39:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676206</loc>
  <lastmod>2026-04-06T04:39:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>初期宇宙における低エディントン比クエーサーの発見（Low Eddington Ratio Quasar at z ∼6）</news:title>
   <news:publication_date>2026-04-06T04:39:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676204</loc>
  <lastmod>2026-04-06T03:47:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>シンプルニューラルネットワークによる帰納的バイアスの学習（Learning Inductive Biases with Simple Neural Networks）</news:title>
   <news:publication_date>2026-04-06T03:47:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676202</loc>
  <lastmod>2026-04-06T03:47:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Intel Xeon Phiでのストリーム化アプリケーションのチューニング（Tuning Streamed Applications on Intel Xeon Phi: A Machine Learning Based Approach）</news:title>
   <news:publication_date>2026-04-06T03:47:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676200</loc>
  <lastmod>2026-04-06T03:47:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>IoT向けD2D通信の自律送信電力割当（Autonomous Power Allocation based on Distributed Deep Learning for Device-to-Device Communication Underlaying Cellular Network）</news:title>
   <news:publication_date>2026-04-06T03:47:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676198</loc>
  <lastmod>2026-04-06T03:45:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多くの機能的制約を持つ凸問題へのプリマル・デュアル確率的勾配法（PRIMAL-DUAL STOCHASTIC GRADIENT METHOD FOR CONVEX PROGRAMS WITH MANY FUNCTIONAL CONSTRAINTS）</news:title>
   <news:publication_date>2026-04-06T03:45:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676196</loc>
  <lastmod>2026-04-06T03:45:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ハッシュ法からCNNへ：ハッシュを介した2値重みネットワークの学習（From Hashing to CNNs: Training Binary Weight Networks via Hashing）</news:title>
   <news:publication_date>2026-04-06T03:45:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676194</loc>
  <lastmod>2026-04-06T03:45:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高等教育における複雑系教育の先駆的経験に関するインタビュー研究（An interview based study of pioneering experiences in teaching and learning Complex Systems in Higher Education）</news:title>
   <news:publication_date>2026-04-06T03:45:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676192</loc>
  <lastmod>2026-04-06T03:45:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自然画像事前分布を用いた深層画像超解像（Deep Image Super Resolution via Natural Image Priors）</news:title>
   <news:publication_date>2026-04-06T03:45:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676190</loc>
  <lastmod>2026-04-06T02:53:12Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>組合せ子抽象を取り入れたニューラル実行器の普遍性向上（IMPROVING THE UNIVERSALITY AND LEARNABILITY OF NEURAL PROGRAMMER-INTERPRETERS WITH COMBINATOR ABSTRACTION）</news:title>
   <news:publication_date>2026-04-06T02:53:12Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676188</loc>
  <lastmod>2026-04-06T02:52:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>最適部分抽出によるロジスティック回帰の効率的推定（More Efficient Estimation for Logistic Regression with Optimal Subsamples）</news:title>
   <news:publication_date>2026-04-06T02:52:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676186</loc>
  <lastmod>2026-04-06T02:51:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ミニバッチ確率的ADMMによる非凸・非滑らか最適化のスケール解法（Mini-batch Stochastic ADMMs for Nonconvex Nonsmooth Optimization）</news:title>
   <news:publication_date>2026-04-06T02:51:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676184</loc>
  <lastmod>2026-04-06T02:51:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>蛾の嗅覚学習に学ぶ生物学的学習メカニズム（Biological Mechanisms for Learning: A Computational Model of Olfactory Learning in the Manduca sexta Moth）</news:title>
   <news:publication_date>2026-04-06T02:51:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676182</loc>
  <lastmod>2026-04-06T02:50:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ブラックホールの影は事象の地平線の幾何を探るか（Does the black hole shadow probe the event horizon geometry?）</news:title>
   <news:publication_date>2026-04-06T02:50:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676180</loc>
  <lastmod>2026-04-06T02:50:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ノイズ付きラベルから学ぶ半教師あり二段階アプローチ（A Semi-Supervised Two-Stage Approach to Learning from Noisy Labels）</news:title>
   <news:publication_date>2026-04-06T02:50:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676178</loc>
  <lastmod>2026-04-06T02:50:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>渦を味方にする集団泳法と深層強化学習（Efficient collective swimming by harnessing vortices through deep reinforcement learning）</news:title>
   <news:publication_date>2026-04-06T02:50:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676176</loc>
  <lastmod>2026-04-06T01:58:28Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>PPFNetによる3D点群対応の高精度化（PPFNet: Global Context Aware Local Features for Robust 3D Point Matching）</news:title>
   <news:publication_date>2026-04-06T01:58:28Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676174</loc>
  <lastmod>2026-04-06T01:58:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>非コヒーレント・ダイヤモンドネットワークの一般化自由度（Generalized Degrees of Freedom of Noncoherent Diamond Networks）</news:title>
   <news:publication_date>2026-04-06T01:58:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676172</loc>
  <lastmod>2026-04-06T01:57:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ジオメトリスコア：GAN比較の新手法（Geometry Score: A Method For Comparing Generative Adversarial Networks）</news:title>
   <news:publication_date>2026-04-06T01:57:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676170</loc>
  <lastmod>2026-04-06T01:56:57Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>アクセントと音響の同時モデル化による多アクセント音声認識（JOINT MODELING OF ACCENTS AND ACOUSTICS FOR MULTI-ACCENT SPEECH RECOGNITION）</news:title>
   <news:publication_date>2026-04-06T01:56:57Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676168</loc>
  <lastmod>2026-04-06T01:56:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スパース符号化に基づく特徴点検出器（SCK: A Sparse Coding Based Key-Point Detector）</news:title>
   <news:publication_date>2026-04-06T01:56:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676166</loc>
  <lastmod>2026-04-06T01:56:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>教育技術設計におけるパターン志向アプローチ（A Patterns Based Approach for Design of Educational Technologies）</news:title>
   <news:publication_date>2026-04-06T01:56:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676164</loc>
  <lastmod>2026-04-06T01:56:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>勾配共役事前分布と多層ニューラルネットワーク（Gradient Conjugate Priors and Multi-layer Neural Networks）</news:title>
   <news:publication_date>2026-04-06T01:56:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676162</loc>
  <lastmod>2026-04-06T01:04:31Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>同次リーマン多様体における非自明な零空間の構造解析（HOMOGENEOUS RIEMANNIAN MANIFOLDS WITH NON-TRIVIAL NULLITY）</news:title>
   <news:publication_date>2026-04-06T01:04:31Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676160</loc>
  <lastmod>2026-04-06T01:04:01Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>遅延を最小化する安全な符号化分散計算（Minimizing Latency for Secure Coded Computing Using Secret Sharing via Staircase Codes）</news:title>
   <news:publication_date>2026-04-06T01:04:01Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676158</loc>
  <lastmod>2026-04-06T01:03:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>日本式掛け算の教育的価値（On the Japanese Multiplication Method）</news:title>
   <news:publication_date>2026-04-06T01:03:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676156</loc>
  <lastmod>2026-04-06T01:03:11Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ウェハスケール静電四極子アレイによる多重イオンビーム操作（WAFERSCALE ELECTROSTATIC QUADRUPOLE ARRAY FOR MULTIPLE ION BEAM MANIPULATION）</news:title>
   <news:publication_date>2026-04-06T01:03:11Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676154</loc>
  <lastmod>2026-04-06T01:03:00Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スパイキングニューラルネットワークを深くする技術（Going Deeper in Spiking Neural Networks: VGG and Residual Architectures）</news:title>
   <news:publication_date>2026-04-06T01:03:00Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676152</loc>
  <lastmod>2026-04-06T01:02:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>タイル化深層ネットワークによる空間適応型画像圧縮（SPATIALLY ADAPTIVE IMAGE COMPRESSION USING A TILED DEEP NETWORK）</news:title>
   <news:publication_date>2026-04-06T01:02:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676150</loc>
  <lastmod>2026-04-06T01:02:03Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マスク付き条件付きニューラルネットワークによる音響事象認識（Recognition of Acoustic Events Using Masked Conditional Neural Networks）</news:title>
   <news:publication_date>2026-04-06T01:02:03Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676148</loc>
  <lastmod>2026-04-06T00:11:09Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Encoder-Decoder と Atrous Separable Convolution による意味画像セグメンテーションの改良（Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation）</news:title>
   <news:publication_date>2026-04-06T00:11:09Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676146</loc>
  <lastmod>2026-04-06T00:10:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>OOV対策としての単語表現強化（ENHANCE WORD REPRESENTATION FOR OUT-OF-VOCABULARY ON UBUNTU DIALOGUE CORPUS）</news:title>
   <news:publication_date>2026-04-06T00:10:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676144</loc>
  <lastmod>2026-04-06T00:09:43Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Deep Versus Wide Convolutional Neural Networks for Object Recognition on Neuromorphic System（Deep Versus Wide Convolutional Neural Networks for Object Recognition on Neuromorphic System）</news:title>
   <news:publication_date>2026-04-06T00:09:43Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676142</loc>
  <lastmod>2026-04-06T00:09:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>教師なし学習で高速化する変形医用画像レジストレーション（An Unsupervised Learning Model for Deformable Medical Image Registration）</news:title>
   <news:publication_date>2026-04-06T00:09:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676140</loc>
  <lastmod>2026-04-06T00:09:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>過去の誤りから学ぶ：ASR出力改善のためのノイズ・クリーン句コンテキストモデル（Learning from Past Mistakes: Improving Automatic Speech Recognition Output via Noisy-Clean Phrase Context Modeling）</news:title>
   <news:publication_date>2026-04-06T00:09:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676138</loc>
  <lastmod>2026-04-06T00:09:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>フレーズ化するか否か――用語依存性が検索精度に与える影響（To Phrase or Not to Phrase – Impact of User versus System Term Dependence Upon Retrieval）</news:title>
   <news:publication_date>2026-04-06T00:09:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676136</loc>
  <lastmod>2026-04-05T23:17:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>NGC 2841における巨大な恒星ディスクの発見（THE DRAGONFLY NEARBY GALAXIES SURVEY. IV. A GIANT STELLAR DISK IN NGC 2841）</news:title>
   <news:publication_date>2026-04-05T23:17:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676134</loc>
  <lastmod>2026-04-05T23:17:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>画像から場面グラフを生成するための敵対的三要素生成（Generating Triples with Adversarial Networks for Scene Graph Construction）</news:title>
   <news:publication_date>2026-04-05T23:17:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676132</loc>
  <lastmod>2026-04-05T23:17:06Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>参照スペクトルサンプルを用いない相関関数による自己一貫的な赤方偏移推定（Self-consistent redshift estimation using correlation functions without a spectroscopic reference sample）</news:title>
   <news:publication_date>2026-04-05T23:17:06Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676130</loc>
  <lastmod>2026-04-05T23:16:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>プライバシーポリシーの自動分析と提示（Polisis: Automated Analysis and Presentation of Privacy Policies Using Deep Learning）</news:title>
   <news:publication_date>2026-04-05T23:16:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676128</loc>
  <lastmod>2026-04-05T23:16:29Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>無意識的データ歪みに対する第I種過誤の意図的制御（Intentional Control of Type I Error over Unconscious Data Distortion: a Neyman-Pearson Approach to Text Classification）</news:title>
   <news:publication_date>2026-04-05T23:16:29Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676126</loc>
  <lastmod>2026-04-05T23:16:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>視覚的自己正則化（VISER: Visual Self-Regularization）</news:title>
   <news:publication_date>2026-04-05T23:16:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676124</loc>
  <lastmod>2026-04-05T23:16:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>協調型機械学習で大規模マルチモーダルコーパスの社会的信号アノテーションを高速化する（Applying Cooperative Machine Learning to Speed Up the Annotation of Social Signals in Large Multi-modal Corpora）</news:title>
   <news:publication_date>2026-04-05T23:16:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676122</loc>
  <lastmod>2026-04-05T22:24:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Neyman–Pearson基準に基づく分類と標本サイズ要件（Neyman-Pearson classification: parametrics and sample size requirement）</news:title>
   <news:publication_date>2026-04-05T22:24:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676120</loc>
  <lastmod>2026-04-05T22:24:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SAGA/Prox-SVRGの局所収束性と加速（Local Convergence Properties of SAGA/Prox-SVRG and Acceleration）</news:title>
   <news:publication_date>2026-04-05T22:24:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676118</loc>
  <lastmod>2026-04-05T22:24:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>分散SVMの安全設計をゲーム理論で考える（A Game-Theoretic Approach to Design Secure and Resilient Distributed Support Vector Machines）</news:title>
   <news:publication_date>2026-04-05T22:24:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676116</loc>
  <lastmod>2026-04-05T22:23:24Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>線形分類器の予測誤差とAUCを直接効率的に最適化する方法（Directly and Efficiently Optimizing Prediction Error and AUC of Linear Classifiers）</news:title>
   <news:publication_date>2026-04-05T22:23:24Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676114</loc>
  <lastmod>2026-04-05T22:23:10Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>重なりパッチを持つ単層畳み込みの学習（Learning One Convolutional Layer with Overlapping Patches）</news:title>
   <news:publication_date>2026-04-05T22:23:10Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676112</loc>
  <lastmod>2026-04-05T22:22:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>変分推論の評価手法の実用化 — PSISとVSBCで「働くか」を確かめる（Yes, but Did It Work?: Evaluating Variational Inference）</news:title>
   <news:publication_date>2026-04-05T22:22:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676110</loc>
  <lastmod>2026-04-05T22:22:38Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>半償却（Semi‑Amortized）変分オートエンコーダの実務的要点（Semi‑Amortized Variational Autoencoders）</news:title>
   <news:publication_date>2026-04-05T22:22:38Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676108</loc>
  <lastmod>2026-04-05T21:31:48Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>FixaTonsによる注視データとスキャンパス類似度の標準化（FixaTons: A collection of Human Fixations Datasets and Metrics for Scanpath Similarity）</news:title>
   <news:publication_date>2026-04-05T21:31:48Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676106</loc>
  <lastmod>2026-04-05T21:31:39Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>空間マッピングによる3D→2D変換と深層学習応用（A Spatial Mapping Algorithm with Applications in Deep Learning-Based Structure Classification）</news:title>
   <news:publication_date>2026-04-05T21:31:39Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676104</loc>
  <lastmod>2026-04-05T21:31:15Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DBpediaの事物分類を深層ニューラルネットワークで行う手法（Classification of Things in DBpedia Using Deep Neural Networks）</news:title>
   <news:publication_date>2026-04-05T21:31:15Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676102</loc>
  <lastmod>2026-04-05T21:30:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>防御的プログラミングによるマルウェア緩和の再考（A Praise for Defensive Programming: Leveraging Uncertainty for Effective Malware Mitigation）</news:title>
   <news:publication_date>2026-04-05T21:30:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676100</loc>
  <lastmod>2026-04-05T21:30:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ルーディン–シャピロ多項式の単位円上での振る舞いに関する改良結果（IMPROVED RESULTS ON THE OSCILLATION OF THE MODULUS OF THE RUDIN-SHAPIRO POLYNOMIALS ON THE UNIT CIRCLE）</news:title>
   <news:publication_date>2026-04-05T21:30:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676098</loc>
  <lastmod>2026-04-05T21:30:14Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DeepHeartによる心血管リスク予測の半教師ありシーケンス学習（DeepHeart: Semi-Supervised Sequence Learning for Cardiovascular Risk Prediction）</news:title>
   <news:publication_date>2026-04-05T21:30:14Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676096</loc>
  <lastmod>2026-04-05T21:29:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>物理学の「革命」を社会と共に読み直す（The “revolution” in physics of the early Nineteenth century revisited in the context of science-and-society interaction）</news:title>
   <news:publication_date>2026-04-05T21:29:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676094</loc>
  <lastmod>2026-04-05T20:38:44Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>有界ツリーワイズのベイズネットワーク効率的学習（Efficient Learning of Bounded-Treewidth Bayesian Networks from Complete and Incomplete Data Sets）</news:title>
   <news:publication_date>2026-04-05T20:38:44Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676092</loc>
  <lastmod>2026-04-05T20:30:05Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>アクティブマニフォールドによる次元削減（Dimension Reduction Using Active Manifolds）</news:title>
   <news:publication_date>2026-04-05T20:30:05Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676090</loc>
  <lastmod>2026-04-05T20:29:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>カドリーモデリング：サブポピュレーションと予測モデルを同時に発見する手法（Cadre Modeling: Simultaneously Discovering Subpopulations and Predictive Models）</news:title>
   <news:publication_date>2026-04-05T20:29:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676088</loc>
  <lastmod>2026-04-05T20:29:16Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DNAメチル化データのための二項HMMのスペクトル学習（Spectral Learning of Binomial HMMs for DNA Methylation Data）</news:title>
   <news:publication_date>2026-04-05T20:29:16Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676086</loc>
  <lastmod>2026-04-05T20:28:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>画像ハッシュ化のための深層強化学習（Deep Reinforcement Learning for Image Hashing）</news:title>
   <news:publication_date>2026-04-05T20:28:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676084</loc>
  <lastmod>2026-04-05T20:28:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>二つのコンパクト星の合体が示す高密度核物理学への手がかり（The merger of two compact stars: a tool for dense matter nuclear physics）</news:title>
   <news:publication_date>2026-04-05T20:28:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676082</loc>
  <lastmod>2026-04-05T20:27:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>SCH-GANによる半教師ありクロスモーダルハッシングの提案（SCH-GAN: Semi-supervised Cross-modal Hashing by Generative Adversarial Network）</news:title>
   <news:publication_date>2026-04-05T20:27:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676080</loc>
  <lastmod>2026-04-05T19:36:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>プライバシーを守る推薦サービスの実装技術（CryptoRec: Privacy-preserving Recommendation as a Service）</news:title>
   <news:publication_date>2026-04-05T19:36:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676078</loc>
  <lastmod>2026-04-05T19:36:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>確率的逆畳み込みニューラルネットワークのアンサンブル学習によるモード崩壊抑制（Stochastic Deconvolutional Neural Network Ensemble Training on Generative Pseudo-Adversarial Networks）</news:title>
   <news:publication_date>2026-04-05T19:36:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676076</loc>
  <lastmod>2026-04-05T19:36:18Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>10億規模近似最近傍探索における倒立インデックスの再検討（Revisiting the Inverted Indices for Billion-Scale Approximate Nearest Neighbors）</news:title>
   <news:publication_date>2026-04-05T19:36:18Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676074</loc>
  <lastmod>2026-04-05T19:35:49Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ShakeDrop正則化の実務的意義（ShakeDrop Regularization）</news:title>
   <news:publication_date>2026-04-05T19:35:49Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676072</loc>
  <lastmod>2026-04-05T19:35:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>選択的深層畳み込み特徴からコンパクトな二値表現へ（From Selective Deep Convolutional Features to Compact Binary Representations for Image Retrieval）</news:title>
   <news:publication_date>2026-04-05T19:35:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676070</loc>
  <lastmod>2026-04-05T19:35:22Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>異常な光子・クォーク結合が生むcos 2φh非対称性（Parton distributions and cos 2φh asymmetry induced by anomalous photon-quark coupling）</news:title>
   <news:publication_date>2026-04-05T19:35:22Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676068</loc>
  <lastmod>2026-04-05T19:34:36Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>モジュール型ロボットのための深層強化学習評価フレームワーク（EVALUATION OF DEEP REINFORCEMENT LEARNING METHODS FOR MODULAR ROBOTS）</news:title>
   <news:publication_date>2026-04-05T19:34:36Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676066</loc>
  <lastmod>2026-04-05T18:43:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>多視点ベイズ相関成分解析の要点（MULTI-VIEW BAYESIAN CORRELATED COMPONENT ANALYSIS）</news:title>
   <news:publication_date>2026-04-05T18:43:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676064</loc>
  <lastmod>2026-04-05T18:43:40Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ニューラルによるプログラム合成の最近の進展（Recent Advances in Neural Program Synthesis）</news:title>
   <news:publication_date>2026-04-05T18:43:40Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676062</loc>
  <lastmod>2026-04-05T18:43:27Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>同符号Wボソン散乱で見るEFTによるBSM探索の現実性（Same-sign WW scattering at the LHC: can we discover BSM effects before discovering new states?）</news:title>
   <news:publication_date>2026-04-05T18:43:27Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676060</loc>
  <lastmod>2026-04-05T18:42:30Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>Manoa：ペタスケール異種クラスターの協調設計（A Novel Co-design Peta-scale Heterogeneous Cluster for Deep Learning Training）</news:title>
   <news:publication_date>2026-04-05T18:42:30Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676058</loc>
  <lastmod>2026-04-05T18:42:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>咬翼片X線画像のセマンティックセグメンテーション（Bitewing Radiography Semantic Segmentation Base on Conditional Generative Adversarial Nets）</news:title>
   <news:publication_date>2026-04-05T18:42:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676056</loc>
  <lastmod>2026-04-05T18:42:08Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>エネルギー効率に優れたCMOSメムリスタ型シナプスによる混合信号ニューロモルフィックSoC（Energy-Efficient CMOS Memristive Synapses for Mixed-Signal Neuromorphic System-on-a-Chip）</news:title>
   <news:publication_date>2026-04-05T18:42:08Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676054</loc>
  <lastmod>2026-04-05T18:41:55Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>モックアップからの自動GUIプロトタイピング（Machine Learning-Based Prototyping of Graphical User Interfaces for Mobile Apps）</news:title>
   <news:publication_date>2026-04-05T18:41:55Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676052</loc>
  <lastmod>2026-04-05T17:50:17Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>階層的二値オートエンコーダによる自己教師ありビデオハッシュ（Self-Supervised Video Hashing with Hierarchical Binary Auto-encoder）</news:title>
   <news:publication_date>2026-04-05T17:50:17Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676050</loc>
  <lastmod>2026-04-05T17:40:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>臨床ノートからICD-9コードを自動付与する深層学習の実証評価（An Empirical Evaluation of Deep Learning for ICD-9 Code Assignment using MIMIC-III Clinical Notes）</news:title>
   <news:publication_date>2026-04-05T17:40:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676048</loc>
  <lastmod>2026-04-05T17:40:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>商用ゲームログを公開して競争で切り拓くゲームデータマイニング（Game Data Mining Competition on Churn Prediction and Survival Analysis using Commercial Game Log Data）</news:title>
   <news:publication_date>2026-04-05T17:40:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676046</loc>
  <lastmod>2026-04-05T17:39:02Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>マルチエージェントのゲーム理論的ログ線形学習から強化学習へ（From Game-theoretic Multi-agent Log-Linear Learning to Reinforcement Learning）</news:title>
   <news:publication_date>2026-04-05T17:39:02Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676044</loc>
  <lastmod>2026-04-05T17:38:54Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>3D点群の特徴記述子の現在地（3D Point Cloud Descriptors in Hand-crafted and Deep Learning Age: State-of-the-Art）</news:title>
   <news:publication_date>2026-04-05T17:38:54Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676042</loc>
  <lastmod>2026-04-05T17:38:46Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DeepRoad：GANを用いた自動運転システムのメタモルフィックテスト（DeepRoad: GAN-based Metamorphic Autonomous Driving System Testing）</news:title>
   <news:publication_date>2026-04-05T17:38:46Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676040</loc>
  <lastmod>2026-04-05T17:38:32Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>スペクトル画像の自然色可視化を可能にするGAN（Spectral Image Visualization Using Generative Adversarial Networks）</news:title>
   <news:publication_date>2026-04-05T17:38:32Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676038</loc>
  <lastmod>2026-04-05T16:46:59Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>DRLによるナビゲーションの批判的検証（A Critical Investigation of Deep Reinforcement Learning for Navigation）</news:title>
   <news:publication_date>2026-04-05T16:46:59Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676036</loc>
  <lastmod>2026-04-05T16:46:51Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>普遍的な深層ニューラルネットワーク圧縮（Universal Deep Neural Network Compression）</news:title>
   <news:publication_date>2026-04-05T16:46:51Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676034</loc>
  <lastmod>2026-04-05T16:46:37Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>役割ベースのグラフ埋め込みの学習（Learning Role-based Graph Embeddings）</news:title>
   <news:publication_date>2026-04-05T16:46:37Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676032</loc>
  <lastmod>2026-04-05T16:45:50Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>相互作用粒子系の拡散係数推定（Learning interacting particle systems: diffusion parameter estimation for aggregation equations）</news:title>
   <news:publication_date>2026-04-05T16:45:50Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676030</loc>
  <lastmod>2026-04-05T16:45:42Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>宇宙からの複雑なメッセージは無害化できない（INTERSTELLAR COMMUNICATION. IX. MESSAGE DECONTAMINATION IS IMPOSSIBLE）</news:title>
   <news:publication_date>2026-04-05T16:45:42Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676028</loc>
  <lastmod>2026-04-05T16:45:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>実用的な転移学習を用いたベイズ最適化（Practical Transfer Learning for Bayesian Optimization）</news:title>
   <news:publication_date>2026-04-05T16:45:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676026</loc>
  <lastmod>2026-04-05T16:45:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>重要特徴を学習する注意付き専門家混合モデル（Granger-causal Attentive Mixtures of Experts）</news:title>
   <news:publication_date>2026-04-05T16:45:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676024</loc>
  <lastmod>2026-04-05T15:53:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>テキストを使った因果推論の方法（How to Make Causal Inferences Using Texts）</news:title>
   <news:publication_date>2026-04-05T15:53:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676022</loc>
  <lastmod>2026-04-05T15:53:20Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>GW170817キロノバの最初の数時間が示すもの（Early Emission From the GW170817 Kilonova）</news:title>
   <news:publication_date>2026-04-05T15:53:20Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676020</loc>
  <lastmod>2026-04-05T15:52:34Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>小天体におけるマグマ上昇を支配する因子：平均粒径の役割（Magma ascent in planetesimals: control by grain size）</news:title>
   <news:publication_date>2026-04-05T15:52:34Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676018</loc>
  <lastmod>2026-04-05T15:52:21Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高赤shift塵埃星形成銀河の平均形態は指数ディスクである（ALMA 26 Arcmin2 Survey of GOODS-S at One-millimeter (ASAGAO): Average Morphology of High-z Dusty Star-Forming Galaxies Is an Exponential-Disk (n ≃1)）</news:title>
   <news:publication_date>2026-04-05T15:52:21Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676016</loc>
  <lastmod>2026-04-05T15:52:07Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>高赤方偏移星形成銀河におけるイオン化ガスと分子ガスの運動学（Ionized and Molecular Gas Kinematics in a z = 1.4 Star-Forming Galaxy）</news:title>
   <news:publication_date>2026-04-05T15:52:07Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676014</loc>
  <lastmod>2026-04-05T15:51:53Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>ビレフリンジェント電子からマージナルあるいは非フェルミ液への遷移（From Birefringent Electrons to a Marginal or Non-Fermi Liquid of Relativistic Spin-1/2 Fermions: An Emergent Superuniversality）</news:title>
   <news:publication_date>2026-04-05T15:51:53Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676012</loc>
  <lastmod>2026-04-05T15:51:23Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>臨界パーコレーションを用いた深層ネットワーク学習の解析（CRITICAL PERCOLATION AS A FRAMEWORK TO ANALYZE THE TRAINING OF DEEP NETWORKS）</news:title>
   <news:publication_date>2026-04-05T15:51:23Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676010</loc>
  <lastmod>2026-04-05T15:00:45Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>潜在ヘッド表現による非平面依存構文解析（Non-Projective Dependency Parsing via Latent Heads Representation）</news:title>
   <news:publication_date>2026-04-05T15:00:45Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676008</loc>
  <lastmod>2026-04-05T15:00:35Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>自己バックホール型ミリ波ネットワークにおける経路選択とレート配分（Path Selection and Rate Allocation in Self-Backhauled mmWave Networks）</news:title>
   <news:publication_date>2026-04-05T15:00:35Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
 <url>
  <loc>https://aibr.jp/archives/676006</loc>
  <lastmod>2026-04-05T15:00:19Z</lastmod>
  <news:news>
   <news:publication>
    <news:name>AI Benchmark Research</news:name>
    <news:language>ja</news:language>
   </news:publication>
   <news:title>無秩序な原子ワイヤで観測されたトポロジカル・アンダーソン絶縁体（Observation of the topological Anderson insulator in disordered atomic wires）</news:title>
   <news:publication_date>2026-04-05T15:00:19Z</news:publication_date>
   <news:genres>Blog</news:genres>
  </news:news>
 </url>
</urlset>
