【AI最新動向 2026年2月5日】論文5件・GitHub5件

2026年2月5日2026年2月9日

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🚀 AI技術の最新動向 – 2026年2月5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 PACKIARAJ-R/ML-Image-Classification-using-CNN jmbarrancoml/awesome-european-ai phydra-labs/phydrax marimo-team/modernaicourse Dipta04/Project_Machine_Learning 📌 関連記事もチェック 📚 最新研究論文 1. Reinforced Attention Learning 著者: Bangzheng Li, Jianmo Ni, Chen Qu Post-training with Reinforcement Learning (RL) has substantially improved reasoning in Large Language Models (LLMs) via test-time scaling. However, extending this paradigm to Multimodal LLMs (MLLMs) through verbose rationales yields limited gains for perception and can even degrade performance. We… 論文を読む → 2. Protein Autoregressive Modeling via Multiscale Structure Generation 著者: Yanru Qu, Cheng-Yen Hsieh, Zaixiang Zheng We present protein autoregressive modeling (PAR), the first multi-scale autoregressive framework for protein backbone generation via coarse-to-fine next-scale prediction. Using the hierarchical nature of proteins, PAR generates structures that mimic sculpting a statue, forming a coarse topology and … 論文を読む → 3. Contrastive Continual Learning for Model Adaptability in Internet of Things 著者: Ajesh Koyatan Chathoth Internet of Things (IoT) deployments operate in nonstationary, dynamic environments where factors such as sensor drift, evolving user behavior, and heterogeneous user privacy requirements can affect application utility. Continual learning (CL) addresses this by adapting models over time without cata… 論文を読む → 💻 注目のGitHubプロジェクト 1. PACKIARAJ-R/ML-Image-Classification-using-CNN Built a CNN-based image classification system with data preprocessing, model training, evaluation, and result visualization using accuracy curves and confusion matrix for reliable image recognition. ⭐ 27 stars | 🔀 0 forks リポジトリを見る → 2. jmbarrancoml/awesome-european-ai A curated list of European AI companies, research labs, open source projects, and resources ⭐ 10 stars | 🔀 0 forks リポジトリを見る → 3. phydra-labs/phydrax Modular Physics-ML Components in JAX ⭐ 8 stars | 🔀 0 forks リポジトリを見る → 4. marimo-team/modernaicourse A companion to CMU professor Zico Kolter's Intro to Modern AI. Learn the basics of machine learning, then train your own LLM from scratch. ⭐ 7 stars | 🔀 0 forks リポジトリを見る → 5. Dipta04/Project_Machine_Learning HSC Result Predictor using Random Forest Regression to forecast student exam scores based on historical data and performance indicators. ⭐ 7 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2月5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. PACKIARAJ-R/ML-Image-Classification-using-CNN
2. jmbarrancoml/awesome-european-ai
3. phydra-labs/phydrax
4. marimo-team/modernaicourse
5. Dipta04/Project_Machine_Learning
📌 関連記事もチェック

📚 最新研究論文

1. Reinforced Attention Learning

著者: Bangzheng Li, Jianmo Ni, Chen Qu

Post-training with Reinforcement Learning (RL) has substantially improved reasoning in Large Language Models (LLMs) via test-time scaling. However, extending this paradigm to Multimodal LLMs (MLLMs) through verbose rationales yields limited gains for perception and can even degrade performance.
We…

論文を読む →

2. Protein Autoregressive Modeling via Multiscale Structure Generation

著者: Yanru Qu, Cheng-Yen Hsieh, Zaixiang Zheng

We present protein autoregressive modeling (PAR), the first multi-scale autoregressive framework for protein backbone generation via coarse-to-fine next-scale prediction. Using the hierarchical nature of proteins, PAR generates structures that mimic sculpting a statue, forming a coarse topology and …

論文を読む →

3. Contrastive Continual Learning for Model Adaptability in Internet of Things

著者: Ajesh Koyatan Chathoth

Internet of Things (IoT) deployments operate in nonstationary, dynamic environments where factors such as sensor drift, evolving user behavior, and heterogeneous user privacy requirements can affect application utility. Continual learning (CL) addresses this by adapting models over time without cata…

論文を読む →

💻 注目のGitHubプロジェクト

1. PACKIARAJ-R/ML-Image-Classification-using-CNN

Built a CNN-based image classification system with data preprocessing, model training, evaluation, and result visualization using accuracy curves and confusion matrix for reliable image recognition.

⭐ 27 stars | 🔀 0 forks

リポジトリを見る →