【AI最新動向 2026年1月9日】論文5件・GitHub5件

📝 この記事のポイント

• 🚀 AI技術の最新動向 – 2026年1月9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 bassrehab/mhc-visualizer leockl/sklearn-diagnose rasidi3112/IMAGINE-CUP-MICROSOFT-2026 Rufina46/time-series-synthetic yogender-ai/knn-cat-dog-demo 📌 関連記事もチェック 📚 最新研究論文 1. Optimal Lower Bounds for Online Multicalibration 著者: Natalie Collina, Jiuyao Lu, Georgy Noarov We prove tight lower bounds for online multicalibration, establishing an information-theoretic separation from marginal calibration. In the general setting where group functions can depend on both context and the learner's predictions, we prove an $Ω(T^{2/3})$ lower bound on expected multicalibrat… 論文を読む → 2. GDPO: Group reward-Decoupled Normalization Policy Optimization for Multi-reward RL Optimization 著者: Shih-Yang Liu, Xin Dong, Ximing Lu As language models become increasingly capable, users expect them to provide not only accurate responses but also behaviors aligned with diverse human preferences across a variety of scenarios. To achieve this, Reinforcement learning (RL) pipelines have begun incorporating multiple rewards, each cap… 論文を読む → 3. RoboVIP: Multi-View Video Generation with Visual Identity Prompting Augments Robot Manipulation 著者: Boyang Wang, Haoran Zhang, Shujie Zhang The diversity, quantity, and quality of manipulation data are critical for training effective robot policies. However, due to hardware and physical setup constraints, collecting large-scale real-world manipulation data remains difficult to scale across diverse environments. Recent work uses text-pro… 論文を読む → 💻 注目のGitHubプロジェクト 1. bassrehab/mhc-visualizer Interactive visualization of Manifold-Constrained Hyper-Connections (mHC) for stable deep network training ⭐ 15 stars | 🔀 1 forks リポジトリを見る → 2. leockl/sklearn-diagnose 🔍 AI-powered diagnosis for Scikit-learn models: Detect overfitting, data leakage, class imbalance & more with LLM-generated insights ⭐ 7 stars | 🔀 0 forks リポジトリを見る → 3. rasidi3112/IMAGINE-CUP-MICROSOFT-2026 🌾 AI-powered plant disease detection app for farmers in West Nusa Tenggara, Indonesia. Built with Flutter, Azure Custom Vision & Azure OpenAI. ⭐ 5 stars | 🔀 0 forks リポジトリを見る → 4. Rufina46/time-series-synthetic Open-source synthetic time-series generator for ML testing ⭐ 5 stars | 🔀 0 forks リポジトリを見る → 5. yogender-ai/knn-cat-dog-demo KNN algorithm from scratch for cat vs dog image classification using Python. Machine learning, distance-based classification, and computer vision experiment. ⭐ 3 stars | 🔀 0 forks リポジトリを見る → 📚 あわせて読みたい コールセンター応答率2倍!? 音声認識AI導入でオペレーター3割減の衝撃！ 【衝撃】画像認識AI頂上決戦！Gemini圧勝の理由 製造業の品質検査、AIでコスト激減！？【完全自動化ガイド】。

📑 目次

1. 💻 注目のGitHubプロジェクト
   1. bassrehab/mhc-visualizer
   2. leockl/sklearn-diagnose
   3. rasidi3112/IMAGINE-CUP-MICROSOFT-2026
   4. Rufina46/time-series-synthetic
   5. yogender-ai/knn-cat-dog-demo
2. 📌 関連記事もチェック

1. Optimal Lower Bounds for Online Multicalibration

著者: Natalie Collina, Jiuyao Lu, Georgy Noarov

We prove tight lower bounds for online multicalibration, establishing an information-theoretic separation from marginal calibration.
In the general setting where group functions can depend on both context and the learner's predictions, we prove an $Ω(T^{2/3})$ lower bound on expected multicalibrat…

論文を読む →

2. GDPO: Group reward-Decoupled Normalization Policy Optimization for Multi-reward RL Optimization

著者: Shih-Yang Liu, Xin Dong, Ximing Lu

As language models become increasingly capable, users expect them to provide not only accurate responses but also behaviors aligned with diverse human preferences across a variety of scenarios. To achieve this, Reinforcement learning (RL) pipelines have begun incorporating multiple rewards, each cap…

論文を読む →

3. RoboVIP: Multi-View Video Generation with Visual Identity Prompting Augments Robot Manipulation

著者: Boyang Wang, Haoran Zhang, Shujie Zhang

The diversity, quantity, and quality of manipulation data are critical for training effective robot policies. However, due to hardware and physical setup constraints, collecting large-scale real-world manipulation data remains difficult to scale across diverse environments. Recent work uses text-pro…

論文を読む →

1. bassrehab/mhc-visualizer

Interactive visualization of Manifold-Constrained Hyper-Connections (mHC) for stable deep network training

⭐ 15 stars | 🔀 1 forks

リポジトリを見る →

2. leockl/sklearn-diagnose

🔍 AI-powered diagnosis for Scikit-learn models: Detect overfitting, data leakage, class imbalance & more with LLM-generated insights

⭐ 7 stars | 🔀 0 forks

リポジトリを見る →

3. rasidi3112/IMAGINE-CUP-MICROSOFT-2026

🌾 AI-powered plant disease detection app for farmers in West Nusa Tenggara, Indonesia. Built with Flutter, Azure Custom Vision & Azure OpenAI.

⭐ 5 stars | 🔀 0 forks

リポジトリを見る →

4. Rufina46/time-series-synthetic

Open-source synthetic time-series generator for ML testing

⭐ 5 stars | 🔀 0 forks

リポジトリを見る →

5. yogender-ai/knn-cat-dog-demo

KNN algorithm from scratch for cat vs dog image classification using Python. Machine learning, distance-based classification, and computer vision experiment.

⭐ 3 stars | 🔀 0 forks

リポジトリを見る →

📚 関連記事もチェック