📝 この記事のポイント
- 🚀 AI技術の最新動向 – 2026年1月26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 stong/gradscii-art fangchenantek/Psychosis-of-AI sidmohan0/tesserack Colev2/Neural-Networks yaelelmatad/RunTime-Public 📌 関連記事もチェック 📚 最新研究論文 1. AnyView: Synthesizing Any Novel View in Dynamic Scenes 著者: Basile Van Hoorick, Dian Chen, Shun Iwase Modern generative video models excel at producing convincing, high-quality outputs, but struggle to maintain multi-view and spatiotemporal consistency in highly dynamic real-world environments. In this work, we introduce textbf{AnyView}, a diffusion-based video generation framework for emph{dynami… 論文を読む → 2. A Scalable Measure of Loss Landscape Curvature for Analyzing the Training Dynamics of LLMs 著者: Dayal Singh Kalra, Jean-Christophe Gagnon-Audet, Andrey Gromov Understanding the curvature evolution of the loss landscape is fundamental to analyzing the training dynamics of neural networks. The most commonly studied measure, Hessian sharpness ($λ_{max}^H$) — the largest eigenvalue of the loss Hessian — determines local training stability and interacts wit… 論文を読む → 3. Latent Diffusion for Internet of Things Attack Data Generation in Intrusion Detection 著者: Estela Sánchez-Carballo, Francisco M. Melgarejo-Meseguer, José Luis Rojo-Álvarez Intrusion Detection Systems (IDSs) are a key component for protecting Internet of Things (IoT) environments. However, in Machine Learning-based (ML-based) IDSs, performance is often degraded by the strong class imbalance between benign and attack traffic. Although data augmentation has been widely e… 論文を読む → 💻 注目のGitHubプロジェクト 1. stong/gradscii-art An extremely good ASCII art generator, based on machine learning ⭐ 157 stars | 🔀 3 forks リポジトリを見る → 2. fangchenantek/Psychosis-of-AI 一份来自2026年的AI精神病理学诊断报告 / A Pathological Diagnosis of AI Civilization ⭐ 35 stars | 🔀 0 forks リポジトリを見る → 3. sidmohan0/tesserack AI learns to play Pokemon Red entirely in your browser. WebGPU LLM + TensorFlow.js neural network + WASM emulator. No servers required. ⭐ 24 stars | 🔀 3 forks リポジトリを見る → 4. Colev2/Neural-Networks Assignments on Neural Networks course at CSD AUTH ⭐ 7 stars | 🔀 0 forks リポジトリを見る → 5. yaelelmatad/RunTime-Public RunTime: intensity-free, TPP-aligned Transformer for probabilistic forecasting on irregular event streams, regular event streams, or general regression tasks. ⭐ 5 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 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1月26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
- stong/gradscii-art
- fangchenantek/Psychosis-of-AI
- sidmohan0/tesserack
- Colev2/Neural-Networks
- yaelelmatad/RunTime-Public
- 📌 関連記事もチェック
📚 最新研究論文
1. AnyView: Synthesizing Any Novel View in Dynamic Scenes
著者: Basile Van Hoorick, Dian Chen, Shun Iwase
Modern generative video models excel at producing convincing, high-quality outputs, but struggle to maintain multi-view and spatiotemporal consistency in highly dynamic real-world environments. In this work, we introduce textbf{AnyView}, a diffusion-based video generation framework for emph{dynami…
2. A Scalable Measure of Loss Landscape Curvature for Analyzing the Training Dynamics of LLMs
著者: Dayal Singh Kalra, Jean-Christophe Gagnon-Audet, Andrey Gromov
Understanding the curvature evolution of the loss landscape is fundamental to analyzing the training dynamics of neural networks. The most commonly studied measure, Hessian sharpness ($λ_{max}^H$) — the largest eigenvalue of the loss Hessian — determines local training stability and interacts wit…
3. Latent Diffusion for Internet of Things Attack Data Generation in Intrusion Detection
著者: Estela Sánchez-Carballo, Francisco M. Melgarejo-Meseguer, José Luis Rojo-Álvarez
Intrusion Detection Systems (IDSs) are a key component for protecting Internet of Things (IoT) environments. However, in Machine Learning-based (ML-based) IDSs, performance is often degraded by the strong class imbalance between benign and attack traffic. Although data augmentation has been widely e…
💻 注目のGitHubプロジェクト
1. stong/gradscii-art
An extremely good ASCII art generator, based on machine learning
⭐ 157 stars | 🔀 3 forks
2. fangchenantek/Psychosis-of-AI
一份来自2026年的AI精神病理学诊断报告 / A Pathological Diagnosis of AI Civilization
⭐ 35 stars | 🔀 0 forks
3. sidmohan0/tesserack
AI learns to play Pokemon Red entirely in your browser. WebGPU LLM + TensorFlow.js neural network + WASM emulator. No servers required.
⭐ 24 stars | 🔀 3 forks
4. Colev2/Neural-Networks
Assignments on Neural Networks course at CSD AUTH
⭐ 7 stars | 🔀 0 forks
5. yaelelmatad/RunTime-Public
RunTime: intensity-free, TPP-aligned Transformer for probabilistic forecasting on irregular event streams, regular event streams, or general regression tasks.
⭐ 5 stars | 🔀 0 forks
📚 あわせて読みたい
コメント