【AI最新動向 2026年2月16日】論文5件・GitHub5件

2026年2月16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-Enginevixhal-baraiya/microgpt-cKuberwastaken/picogptScottT2-spec/malaria-cell-detectionmirzayasirabdullahbaig07/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 LuThe 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 CanThe 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 MiksikVideo 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-EngineA 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-cThe 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/picogptGPT 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-detectionCNN-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-NLPThis 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 →。

🚀 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 →