【AI最新動向 2025年12月29日】論文5件・GitHub5件

📝 この記事のポイント

• 🚀 AI技術の最新動向 – 2025年12月29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 Trhova/Multi-omics Ramakm/AI-ML-Book-References reward-scope-ai/reward-scope HadiHz88/LU-M1S1 GustyCube/ERR-EVAL 📌 関連記事もチェック 📚 最新研究論文 1. Agentic Structured Graph Traversal for Root Cause Analysis of Code-related Incidents in Cloud Applications 著者: Shengkun Cui, Rahul Krishna, Saurabh Jha Cloud incidents pose major operational challenges in production, with unresolved production cloud incidents cost on average over $2M per hour. Prior research identifies code- and configuration-related issues as the predominant category of root causes in cloud incidents. This paper introduces PRAXIS,… 論文を読む → 2. Pruning as a Game: Equilibrium-Driven Sparsification of Neural Networks 著者: Zubair Shah, Noaman Khan Neural network pruning is widely used to reduce model size and computational cost. Yet, most existing methods treat sparsity as an externally imposed constraint, enforced through heuristic importance scores or training-time regularization. In this work, we propose a fundamentally different perspecti… 論文を読む → 3. Explainable Multimodal Regression via Information Decomposition 著者: Zhaozhao Ma, Shujian Yu Multimodal regression aims to predict a continuous target from heterogeneous input sources and typically relies on fusion strategies such as early or late fusion. However, existing methods lack principled tools to disentangle and quantify the individual contributions of each modality and their inter… 論文を読む → 💻 注目のGitHubプロジェクト 1. Trhova/Multi-omics Intuitive guide to multi-omics integration with toy examples: supervised latent components (DIABLO), unsupervised shared/partial/unique structure (DIVAS), VAEs/conditional VAEs, and key baselines (MOFA, JIVE, SNF) with practical tips + code. ⭐ 27 stars | 🔀 4 forks リポジトリを見る → 2. Ramakm/AI-ML-Book-References This repository is for all those AI enthusiastics who actually loves to read books and learn. ⭐ 16 stars | 🔀 4 forks リポジトリを見る → 3. reward-scope-ai/reward-scope Real-time reward debugging and hacking detection for reinforcement learning ⭐ 15 stars | 🔀 2 forks リポジトリを見る → 4. HadiHz88/LU-M1S1 Organized course materials, notes, and summaries for Lebanese University (LU) Computer Science Master M1S1 (2024–2025), structured for easy Git pulls and updates. ⭐ 6 stars | 🔀 0 forks リポジトリを見る → 5. GustyCube/ERR-EVAL Benchmark for evaluating AI epistemic reliability – testing how well LLMs handle uncertainty, avoid hallucinations, and acknowledge what they don't know. ⭐ 5 stars | 🔀 1 forks リポジトリを見る → 📚 あわせて読みたい コールセンター応答率2倍!? 音声認識AI導入でオペレーター3割減の衝撃！ 【衝撃】画像認識AI頂上決戦！Gemini圧勝の理由 製造業の品質検査、AIでコスト激減！？【完全自動化ガイド】。

📑 目次

1. 💻 注目のGitHubプロジェクト
   1. Trhova/Multi-omics
   2. Ramakm/AI-ML-Book-References
   3. reward-scope-ai/reward-scope
   4. HadiHz88/LU-M1S1
   5. GustyCube/ERR-EVAL
2. 📌 関連記事もチェック

1. Agentic Structured Graph Traversal for Root Cause Analysis of Code-related Incidents in Cloud Applications

著者: Shengkun Cui, Rahul Krishna, Saurabh Jha

Cloud incidents pose major operational challenges in production, with unresolved production cloud incidents cost on average over $2M per hour. Prior research identifies code- and configuration-related issues as the predominant category of root causes in cloud incidents. This paper introduces PRAXIS,…

論文を読む →

2. Pruning as a Game: Equilibrium-Driven Sparsification of Neural Networks

著者: Zubair Shah, Noaman Khan

Neural network pruning is widely used to reduce model size and computational cost. Yet, most existing methods treat sparsity as an externally imposed constraint, enforced through heuristic importance scores or training-time regularization. In this work, we propose a fundamentally different perspecti…

論文を読む →

3. Explainable Multimodal Regression via Information Decomposition

著者: Zhaozhao Ma, Shujian Yu

Multimodal regression aims to predict a continuous target from heterogeneous input sources and typically relies on fusion strategies such as early or late fusion. However, existing methods lack principled tools to disentangle and quantify the individual contributions of each modality and their inter…

論文を読む →

1. Trhova/Multi-omics

Intuitive guide to multi-omics integration with toy examples: supervised latent components (DIABLO), unsupervised shared/partial/unique structure (DIVAS), VAEs/conditional VAEs, and key baselines (MOFA, JIVE, SNF) with practical tips + code.

⭐ 27 stars | 🔀 4 forks

リポジトリを見る →

2. Ramakm/AI-ML-Book-References

This repository is for all those AI enthusiastics who actually loves to read books and learn.

⭐ 16 stars | 🔀 4 forks

リポジトリを見る →

3. reward-scope-ai/reward-scope

Real-time reward debugging and hacking detection for reinforcement learning

⭐ 15 stars | 🔀 2 forks

リポジトリを見る →

4. HadiHz88/LU-M1S1

Organized course materials, notes, and summaries for Lebanese University (LU) Computer Science Master M1S1 (2024–2025), structured for easy Git pulls and updates.

⭐ 6 stars | 🔀 0 forks

リポジトリを見る →

5. GustyCube/ERR-EVAL

Benchmark for evaluating AI epistemic reliability – testing how well LLMs handle uncertainty, avoid hallucinations, and acknowledge what they don't know.

⭐ 5 stars | 🔀 1 forks

リポジトリを見る →

📚 関連記事もチェック