【AI最新動向 2026年2月4日】論文5件・GitHub5件

2026年2月4日2026年3月27日

📝 この記事のポイント

🚀 AI技術の最新動向 – 2026年2月4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 justalittle180-max/Album jmbarrancoml/awesome-european-ai berrzebb/zeroquant phydra-labs/phydrax 📌 関連記事もチェック 📚 最新研究論文 1. PLATE: Plasticity-Tunable Efficient Adapters for Geometry-Aware Continual Learning 著者: Romain Cosentino We develop a continual learning method for pretrained models that emph{requires no access to old-task data}, addressing a practical barrier in foundation model adaptation where pretraining distributions are often unavailable. Our key observation is that pretrained networks exhibit substantial emph… 論文を読む → 2. Investigating Quantum Circuit Designs Using Neuro-Evolution 著者: Devroop Kar, Daniel Krutz, Travis Desell Designing effective quantum circuits remains a central challenge in quantum computing, as circuit structure strongly influences expressivity, trainability, and hardware feasibility. Current approaches, whether using manually designed circuit templates, fixed heuristics, or automated rules, face limi… 論文を読む → 3. Understanding and Exploiting Weight Update Sparsity for Communication-Efficient Distributed RL 著者: Erfan Miahi, Eugene Belilovsky Reinforcement learning (RL) is a critical component for post-training large language models (LLMs). However, in bandwidth-constrained distributed RL, scalability is often bottlenecked by the synchronization of policy weights from trainers to inference workers, particularly over commodity networks or… 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. ⭐ 25 stars | 🔀 0 forks リポジトリを見る → 2. justalittle180-max/Album Album | Formula | Ai | Photo | Artivicial Intelligence | Creative | Tools | Generation | ⭐ 19 stars | 🔀 0 forks リポジトリを見る → 3. 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 → 4. berrzebb/zeroquant Rust 기반 고성능 자동화 트레이딩 시스템 ⭐ 7 stars | 🔀 5 forks リポジトリを見る → 5. phydra-labs/phydrax Modular Physics-ML Components in JAX ⭐ 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4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. justalittle180-max/Album
3. jmbarrancoml/awesome-european-ai
4. berrzebb/zeroquant
5. phydra-labs/phydrax
📌 関連記事もチェック

📚 最新研究論文

1. PLATE: Plasticity-Tunable Efficient Adapters for Geometry-Aware Continual Learning

著者: Romain Cosentino

We develop a continual learning method for pretrained models that emph{requires no access to old-task data}, addressing a practical barrier in foundation model adaptation where pretraining distributions are often unavailable. Our key observation is that pretrained networks exhibit substantial emph…

論文を読む →

2. Investigating Quantum Circuit Designs Using Neuro-Evolution

著者: Devroop Kar, Daniel Krutz, Travis Desell

Designing effective quantum circuits remains a central challenge in quantum computing, as circuit structure strongly influences expressivity, trainability, and hardware feasibility. Current approaches, whether using manually designed circuit templates, fixed heuristics, or automated rules, face limi…

論文を読む →

3. Understanding and Exploiting Weight Update Sparsity for Communication-Efficient Distributed RL

著者: Erfan Miahi, Eugene Belilovsky

Reinforcement learning (RL) is a critical component for post-training large language models (LLMs). However, in bandwidth-constrained distributed RL, scalability is often bottlenecked by the synchronization of policy weights from trainers to inference workers, particularly over commodity networks or…

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.

⭐ 25 stars | 🔀 0 forks

リポジトリを見る →