【AI最新動向 2026年1月13日】論文5件・GitHub5件

2026年1月13日2026年2月10日

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🚀 AI技術の最新動向 – 2026年1月13日世界中から収集したAI・機械学習の最新情報をお届けします 📑 目次 💻 注目のGitHubプロジェクト panyisheng095-ux/VisionQuant-Pro superdoc-dev/docx-corpus samouraiworld/awesome-mistral leockl/sklearn-diagnose w0rng/gofeat 📌 関連記事もチェック 📚 最新研究論文 1. A Complete Decomposition of Stochastic Differential Equations 著者: Samuel Duffield We show that any stochastic differential equation with prescribed time-dependent marginal distributions admits a decomposition into three components: a unique scalar field governing marginal evolution, a symmetric positive-semidefinite diffusion matrix field and a skew-symmetric matrix field…. 論文を読む → 2. MHLA: Restoring Expressivity of Linear Attention via Token-Level Multi-Head 著者: Kewei Zhang, Ye Huang, Yufan Deng While the Transformer architecture dominates many fields, its quadratic self-attention complexity hinders its use in large-scale applications. Linear attention offers an efficient alternative, but its direct application often degrades performance, with existing fixes typically re-introducing computa… 論文を読む → 3. Optimal Learning Rate Schedule for Balancing Effort and Performance 著者: Valentina Njaradi, Rodrigo Carrasco-Davis, Peter E. Latham Learning how to learn efficiently is a fundamental challenge for biological agents and a growing concern for artificial ones. To learn effectively, an agent must regulate its learning speed, balancing the benefits of rapid improvement against the costs of effort, instability, or resource use. We int… 論文を読む → 💻 注目のGitHubプロジェクト 1. panyisheng095-ux/VisionQuant-Pro 🤖 基于深度学习的AI量化投资系统 | Vision-Based Quantitative Trading System with Deep Learning ⭐ 50 stars | 🔀 3 forks リポジトリを見る → 2. superdoc-dev/docx-corpus The largest open corpus of .docx files for document processing research ⭐ 32 stars | 🔀 1 forks リポジトリを見る → 3. samouraiworld/awesome-mistral A curated list of awesome resources, tools, libraries, and projects for the Mistral AI ecosystem. ⭐ 29 stars | 🔀 0 forks リポジトリを見る → 4. leockl/sklearn-diagnose 🔍 AI-powered diagnosis for Scikit-learn models: Detect overfitting, data leakage, class imbalance & more with LLM-generated insights ⭐ 10 stars | 🔀 0 forks リポジトリを見る → 5. w0rng/gofeat Embedded feature store for Go ⭐ 8 stars | 🔀 0 forks リポジトリを見る → 📚 あわせて読みたい【AI最新動向】一歩先の未来へ！私が触れた最先端AIの世界「プロンプトは戦略だ！」と学んで劇的効率UP！私のAI活用術私が実感！AIで物流の悩み解消、時間もコストも浮いた話。

🚀 AI技術の最新動向 – 2026年1月13日

世界中から収集したAI・機械学習の最新情報をお届けします

📑 目次

💻 注目のGitHubプロジェクト
1. panyisheng095-ux/VisionQuant-Pro
2. superdoc-dev/docx-corpus
3. samouraiworld/awesome-mistral
4. leockl/sklearn-diagnose
5. w0rng/gofeat
📌 関連記事もチェック

📚 最新研究論文

1. A Complete Decomposition of Stochastic Differential Equations

著者: Samuel Duffield

We show that any stochastic differential equation with prescribed time-dependent marginal distributions admits a decomposition into three components: a unique scalar field governing marginal evolution, a symmetric positive-semidefinite diffusion matrix field and a skew-symmetric matrix field….

論文を読む →

2. MHLA: Restoring Expressivity of Linear Attention via Token-Level Multi-Head

著者: Kewei Zhang, Ye Huang, Yufan Deng

While the Transformer architecture dominates many fields, its quadratic self-attention complexity hinders its use in large-scale applications. Linear attention offers an efficient alternative, but its direct application often degrades performance, with existing fixes typically re-introducing computa…

論文を読む →

3. Optimal Learning Rate Schedule for Balancing Effort and Performance

著者: Valentina Njaradi, Rodrigo Carrasco-Davis, Peter E. Latham

Learning how to learn efficiently is a fundamental challenge for biological agents and a growing concern for artificial ones. To learn effectively, an agent must regulate its learning speed, balancing the benefits of rapid improvement against the costs of effort, instability, or resource use. We int…

論文を読む →