【AI最新動向 2026年1月12日】論文5件・GitHub5件

2026年1月12日2026年2月11日

📝 この記事のポイント

🚀 AI技術の最新動向 – 2026年1月12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 panyisheng095-ux/VisionQuant-Pro superdoc-dev/docx-corpus samouraiworld/awesome-mistral leockl/sklearn-diagnose rasidi3112/IMAGINE-CUP-MICROSOFT-2026 📌 関連記事もチェック 📚 最新研究論文 1. Manifold limit for the training of shallow graph convolutional neural networks 著者: Johanna Tengler, Christoph Brune, José A. Iglesias We study the discrete-to-continuum consistency of the training of shallow graph convolutional neural networks (GCNNs) on proximity graphs of sampled point clouds under a manifold assumption. Graph convolution is defined spectrally via the graph Laplacian, whose low-frequency spectrum approximates th… 論文を読む → 2. AdaFuse: Adaptive Ensemble Decoding with Test-Time Scaling for LLMs 著者: Chengming Cui, Tianxin Wei, Ziyi Chen Large language models (LLMs) exhibit complementary strengths arising from differences in pretraining data, model architectures, and decoding behaviors. Inference-time ensembling provides a practical way to combine these capabilities without retraining. However, existing ensemble approaches suffer fr… 論文を読む → 3. LookAroundNet: Extending Temporal Context with Transformers for Clinically Viable EEG Seizure Detection 著者: Þór Sverrisson, Steinn Guðmundsson Automated seizure detection from electroencephalography (EEG) remains difficult due to the large variability of seizure dynamics across patients, recording conditions, and clinical settings. We introduce LookAroundNet, a transformer-based seizure detector that uses a wider temporal window of EEG dat… 論文を読む → 💻 注目のGitHubプロジェクト 1. panyisheng095-ux/VisionQuant-Pro 🤖 基于深度学习的AI量化投资系统 | Vision-Based Quantitative Trading System with Deep Learning ⭐ 39 stars | 🔀 3 forks リポジトリを見る → 2. superdoc-dev/docx-corpus The largest open corpus of .docx files for document processing research ⭐ 26 stars | 🔀 1 forks リポジトリを見る → 3. samouraiworld/awesome-mistral A curated list of awesome resources, tools, libraries, and projects for the Mistral AI ecosystem. ⭐ 23 stars | 🔀 0 forks リポジトリを見る → 4. leockl/sklearn-diagnose 🔍 AI-powered diagnosis for Scikit-learn models: Detect overfitting, data leakage, class imbalance & more with LLM-generated insights ⭐ 10 stars | 🔀 0 forks リポジトリを見る → 5. rasidi3112/IMAGINE-CUP-MICROSOFT-2026 🌾 AI-powered plant disease detection app for farmers in West Nusa Tenggara, Indonesia. Built with Flutter, Azure Custom Vision & Azure OpenAI. ⭐ 7 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技術の最新動向 – 2026年1月12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. panyisheng095-ux/VisionQuant-Pro
2. superdoc-dev/docx-corpus
3. samouraiworld/awesome-mistral
4. leockl/sklearn-diagnose
5. rasidi3112/IMAGINE-CUP-MICROSOFT-2026
📌 関連記事もチェック

📚 最新研究論文

1. Manifold limit for the training of shallow graph convolutional neural networks

著者: Johanna Tengler, Christoph Brune, José A. Iglesias

We study the discrete-to-continuum consistency of the training of shallow graph convolutional neural networks (GCNNs) on proximity graphs of sampled point clouds under a manifold assumption. Graph convolution is defined spectrally via the graph Laplacian, whose low-frequency spectrum approximates th…

論文を読む →

2. AdaFuse: Adaptive Ensemble Decoding with Test-Time Scaling for LLMs

著者: Chengming Cui, Tianxin Wei, Ziyi Chen

Large language models (LLMs) exhibit complementary strengths arising from differences in pretraining data, model architectures, and decoding behaviors. Inference-time ensembling provides a practical way to combine these capabilities without retraining. However, existing ensemble approaches suffer fr…

論文を読む →

3. LookAroundNet: Extending Temporal Context with Transformers for Clinically Viable EEG Seizure Detection

著者: Þór Sverrisson, Steinn Guðmundsson

Automated seizure detection from electroencephalography (EEG) remains difficult due to the large variability of seizure dynamics across patients, recording conditions, and clinical settings. We introduce LookAroundNet, a transformer-based seizure detector that uses a wider temporal window of EEG dat…

論文を読む →