【AI最新動向 2026年3月5日】論文5件・GitHub5件

2026年3月5日2026年3月6日

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🚀 AI技術の最新動向 – 2026年3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 kossisoroyce/timber Scottcjn/legend-of-elya-n64 hanxiao/pacmap-mlx ErdemYavuz55/drug-review-sentiment-analysis georgeguimaraes/hallmark 📌 関連記事もチェック 📚 最新研究論文 1. SimpliHuMoN: Simplifying Human Motion Prediction 著者: Aadya Agrawal, Alexander Schwing Human motion prediction combines the tasks of trajectory forecasting and human pose prediction. For each of the two tasks, specialized models have been developed. Combining these models for holistic human motion prediction is non-trivial, and recent methods have struggled to compete on established b… 論文を読む → 2. Accurate and Efficient Hybrid-Ensemble Atmospheric Data Assimilation in Latent Space with Uncertainty Quantification 著者: Hang Fan, Juan Nathaniel, Yi Xiao Data assimilation (DA) combines model forecasts and observations to estimate the optimal state of the atmosphere with its uncertainty, providing initial conditions for weather prediction and reanalyses for climate research. Yet, existing traditional and machine-learning DA methods struggle to achiev… 論文を読む → 3. SELDON: Supernova Explosions Learned by Deep ODE Networks 著者: Jiezhong Wu, Jack O'Brien, Jennifer Li The discovery rate of optical transients will explode to 10 million public alerts per night once the Vera C. Rubin Observatory's Legacy Survey of Space and Time comes online, overwhelming the traditional physics-based inference pipelines. A continuous-time forecasting AI model is of interest because… 論文を読む → 💻 注目のGitHubプロジェクト 1. kossisoroyce/timber Ollama for classical ML models. AOT compiler that turns XGBoost, LightGBM, scikit-learn, CatBoost & ONNX models into native C99 inference code. One command to load, one command to serve. 336x faster than Python inference. ⭐ 549 stars | 🔀 14 forks リポジトリを見る → 2. Scottcjn/legend-of-elya-n64 Legend of Elya — N64 game with a real 819K-parameter transformer running on the VR4300 MIPS III CPU. Zelda-style dungeon, AI NPCs, byte-level inference at 60 tok/s. Built with libdragon SDK. ⭐ 26 stars | 🔀 2 forks リポジトリを見る → 3. hanxiao/pacmap-mlx PaCMAP in pure MLX for Apple Silicon. Pure GPU, no scipy/numba. ⭐ 17 stars | 🔀 0 forks リポジトリを見る → 4. ErdemYavuz55/drug-review-sentiment-analysis End-to-end sentiment analysis pipeline on 160K+ drug reviews using TF-IDF, Word2Vec, and fine-tuned BERT for binary and multi-class classification. ⭐ 9 stars | 🔀 0 forks リポジトリを見る → 5. georgeguimaraes/hallmark Hallucination detection for Elixir, powered by Vectara's HHEM model ⭐ 8 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最新動向】一歩先の未来へ！私が触れた最先端AIの世界。

🚀 AI技術の最新動向 – 2026年3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. kossisoroyce/timber
2. Scottcjn/legend-of-elya-n64
3. hanxiao/pacmap-mlx
4. ErdemYavuz55/drug-review-sentiment-analysis
5. georgeguimaraes/hallmark
📌 関連記事もチェック

📚 最新研究論文

1. SimpliHuMoN: Simplifying Human Motion Prediction

著者: Aadya Agrawal, Alexander Schwing

Human motion prediction combines the tasks of trajectory forecasting and human pose prediction. For each of the two tasks, specialized models have been developed. Combining these models for holistic human motion prediction is non-trivial, and recent methods have struggled to compete on established b…

論文を読む →

2. Accurate and Efficient Hybrid-Ensemble Atmospheric Data Assimilation in Latent Space with Uncertainty Quantification

著者: Hang Fan, Juan Nathaniel, Yi Xiao

Data assimilation (DA) combines model forecasts and observations to estimate the optimal state of the atmosphere with its uncertainty, providing initial conditions for weather prediction and reanalyses for climate research. Yet, existing traditional and machine-learning DA methods struggle to achiev…

論文を読む →

3. SELDON: Supernova Explosions Learned by Deep ODE Networks

著者: Jiezhong Wu, Jack O'Brien, Jennifer Li

The discovery rate of optical transients will explode to 10 million public alerts per night once the Vera C. Rubin Observatory's Legacy Survey of Space and Time comes online, overwhelming the traditional physics-based inference pipelines. A continuous-time forecasting AI model is of interest because…

論文を読む →

💻 注目のGitHubプロジェクト

1. kossisoroyce/timber

Ollama for classical ML models. AOT compiler that turns XGBoost, LightGBM, scikit-learn, CatBoost & ONNX models into native C99 inference code. One command to load, one command to serve. 336x faster than Python inference.

⭐ 549 stars | 🔀 14 forks

リポジトリを見る →

2. Scottcjn/legend-of-elya-n64

Legend of Elya — N64 game with a real 819K-parameter transformer running on the VR4300 MIPS III CPU. Zelda-style dungeon, AI NPCs, byte-level inference at 60 tok/s. Built with libdragon SDK.

⭐ 26 stars | 🔀 2 forks

リポジトリを見る →