【AI最新動向 2026年2月16日】論文5件・GitHub5件

2026年2月16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16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 milanm/AutoGrad-Engine vixhal-baraiya/microgpt-c Kuberwastaken/picogpt ScottT2-spec/malaria-cell-detection mirzayasirabdullahbaig07/GenAI-NLP 📌 関連記事もチェック 📚 最新研究論文 1. Imitating What Works: Simulation-Filtered Modular Policy Learning from Human Videos 著者: Albert J. Zhai, Kuo-Hao Zeng, Jiasen Lu The ability to learn manipulation skills by watching videos of humans has the potential to unlock a new source of highly scalable data for robot learning. Here, we tackle prehensile manipulation, in which tasks involve grasping an object before performing various post-grasp motions. Human videos off… 論文を読む → 2. Semantic Chunking and the Entropy of Natural Language 著者: Weishun Zhong, Doron Sivan, Tankut Can The entropy rate of printed English is famously estimated to be about one bit per character, a benchmark that modern large language models (LLMs) have only recently approached. This entropy rate implies that English contains nearly 80 percent redundancy relative to the five bits per character expect… 論文を読む → 3. CoPE-VideoLM: Codec Primitives For Efficient Video Language Models 著者: Sayan Deb Sarkar, Rémi Pautrat, Ondrej Miksik Video Language Models (VideoLMs) empower AI systems to understand temporal dynamics in videos. To fit to the maximum context window constraint, current methods use keyframe sampling which can miss both macro-level events and micro-level details due to the sparse temporal coverage. Furthermore, proce… 論文を読む → 💻 注目のGitHubプロジェクト 1. milanm/AutoGrad-Engine A complete GPT language model (training and inference) in ~600 lines of pure C#, zero dependencies ⭐ 279 stars | 🔀 28 forks リポジトリを見る → 2. vixhal-baraiya/microgpt-c The most atomic way to train and inference a GPT in pure, dependency-free C ⭐ 118 stars | 🔀 22 forks リポジトリを見る → 3. Kuberwastaken/picogpt GPT in a QR Code ; The actual most atomic way to train and inference a GPT in pure, dependency-free JS/Python. ⭐ 80 stars | 🔀 9 forks リポジトリを見る → 4. ScottT2-spec/malaria-cell-detection CNN-based malaria detection from blood cell microscope images — 95.43% test accuracy on NIH dataset (27,558 images) ⭐ 11 stars | 🔀 0 forks リポジトリを見る → 5. mirzayasirabdullahbaig07/GenAI-NLP This repository contains structured notes, implementations, and projects based on a complete Natural Language Processing (NLP) course. ⭐ 9 stars | 🔀 0 forks リポジトリを見る → 📚 あわせて読みたい「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三つ巴！Geminiと私が出会って、ストーリー作りは変わった？。

🚀 AI技術の最新動向 – 2026年2月16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. milanm/AutoGrad-Engine
2. vixhal-baraiya/microgpt-c
3. Kuberwastaken/picogpt
4. ScottT2-spec/malaria-cell-detection
5. mirzayasirabdullahbaig07/GenAI-NLP
📌 関連記事もチェック

📚 最新研究論文

1. Imitating What Works: Simulation-Filtered Modular Policy Learning from Human Videos

著者: Albert J. Zhai, Kuo-Hao Zeng, Jiasen Lu

The ability to learn manipulation skills by watching videos of humans has the potential to unlock a new source of highly scalable data for robot learning. Here, we tackle prehensile manipulation, in which tasks involve grasping an object before performing various post-grasp motions. Human videos off…

論文を読む →

2. Semantic Chunking and the Entropy of Natural Language

著者: Weishun Zhong, Doron Sivan, Tankut Can

The entropy rate of printed English is famously estimated to be about one bit per character, a benchmark that modern large language models (LLMs) have only recently approached. This entropy rate implies that English contains nearly 80 percent redundancy relative to the five bits per character expect…

論文を読む →

3. CoPE-VideoLM: Codec Primitives For Efficient Video Language Models

著者: Sayan Deb Sarkar, Rémi Pautrat, Ondrej Miksik

Video Language Models (VideoLMs) empower AI systems to understand temporal dynamics in videos. To fit to the maximum context window constraint, current methods use keyframe sampling which can miss both macro-level events and micro-level details due to the sparse temporal coverage. Furthermore, proce…

論文を読む →