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- 🚀 AI技術の最新動向 – 2025年12月23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 JarvisPei/SCOPE ahmetkumass/yolo-gen Shaivpidadi/refrag AmirhosseinHonardoust/Machine-Learning-Warning-Systems AmirhosseinHonardoust/Tumor-Doppelganger-Studio 📌 関連記事もチェック 📚 最新研究論文 1. Scalably Enhancing the Clinical Validity of a Task Benchmark with Physician Oversight 著者: Junze Ye, Daniel Tawfik, Alex J. Goodell Automating the calculation of clinical risk scores offers a significant opportunity to reduce physician administrative burden and enhance patient care. The current standard for evaluating this capability is MedCalc-Bench, a large-scale dataset constructed using LLM-based feature extraction and rule-… 論文を読む → 2. Pushing the Frontier of Audiovisual Perception with Large-Scale Multimodal Correspondence Learning 著者: Apoorv Vyas, Heng-Jui Chang, Cheng-Fu Yang We introduce Perception Encoder Audiovisual, PE-AV, a new family of encoders for audio and video understanding trained with scaled contrastive learning. Built on PE, PE-AV makes several key contributions to extend representations to audio, and natively support joint embeddings across audio-video, au… 論文を読む → 3. WorldWarp: Propagating 3D Geometry with Asynchronous Video Diffusion 著者: Hanyang Kong, Xingyi Yang, Xiaoxu Zheng Generating long-range, geometrically consistent video presents a fundamental dilemma: while consistency demands strict adherence to 3D geometry in pixel space, state-of-the-art generative models operate most effectively in a camera-conditioned latent space. This disconnect causes current methods to … 論文を読む → 💻 注目のGitHubプロジェクト 1. JarvisPei/SCOPE SCOPE: Self-evolving Context Optimization via Prompt Evolution – A framework for automatic prompt optimization ⭐ 11 stars | 🔀 2 forks リポジトリを見る → 2. ahmetkumass/yolo-gen Train YOLO + VLM with one command. Auto-generate vision-language training data from YOLO labels – no extra labeling needed. ⭐ 9 stars | 🔀 1 forks リポジトリを見る → 3. Shaivpidadi/refrag REFRAG: LLM-powered representations for better RAG retrieval. Improve precision, reduce context size, same speed. ⭐ 9 stars | 🔀 4 forks リポジトリを見る → 4. AmirhosseinHonardoust/Machine-Learning-Warning-Systems A long-form article and practical framework for designing machine learning systems that warn instead of decide. Covers regimes vs decimals, levers over labels, reversible alerts, anti-coercion UI patterns, auditability, and the “Warning Card” template, so ML preserves human agency while staying useful under uncertainty. ⭐ 8 stars | 🔀 0 forks リポジトリを見る → 5. AmirhosseinHonardoust/Tumor-Doppelganger-Studio Similarity-first interpretability studio for breast tumor samples: pick a case, find its closest “twins” (benign/malignant look-alikes), visualize neighborhood structure, compare feature fingerprints, and run minimal-change counterfactual edits toward a target class. Educational demo only, not for diagnosis. ⭐ 7 stars | 🔀 0 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】。
🚀 AI技術の最新動向 – 2025年12月23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
- JarvisPei/SCOPE
- ahmetkumass/yolo-gen
- Shaivpidadi/refrag
- AmirhosseinHonardoust/Machine-Learning-Warning-Systems
- AmirhosseinHonardoust/Tumor-Doppelganger-Studio
- 📌 関連記事もチェック
📚 最新研究論文
1. Scalably Enhancing the Clinical Validity of a Task Benchmark with Physician Oversight
著者: Junze Ye, Daniel Tawfik, Alex J. Goodell
Automating the calculation of clinical risk scores offers a significant opportunity to reduce physician administrative burden and enhance patient care. The current standard for evaluating this capability is MedCalc-Bench, a large-scale dataset constructed using LLM-based feature extraction and rule-…
2. Pushing the Frontier of Audiovisual Perception with Large-Scale Multimodal Correspondence Learning
著者: Apoorv Vyas, Heng-Jui Chang, Cheng-Fu Yang
We introduce Perception Encoder Audiovisual, PE-AV, a new family of encoders for audio and video understanding trained with scaled contrastive learning. Built on PE, PE-AV makes several key contributions to extend representations to audio, and natively support joint embeddings across audio-video, au…
3. WorldWarp: Propagating 3D Geometry with Asynchronous Video Diffusion
著者: Hanyang Kong, Xingyi Yang, Xiaoxu Zheng
Generating long-range, geometrically consistent video presents a fundamental dilemma: while consistency demands strict adherence to 3D geometry in pixel space, state-of-the-art generative models operate most effectively in a camera-conditioned latent space. This disconnect causes current methods to …
💻 注目のGitHubプロジェクト
1. JarvisPei/SCOPE
SCOPE: Self-evolving Context Optimization via Prompt Evolution – A framework for automatic prompt optimization
⭐ 11 stars | 🔀 2 forks
2. ahmetkumass/yolo-gen
Train YOLO + VLM with one command. Auto-generate vision-language training data from YOLO labels – no extra labeling needed.
⭐ 9 stars | 🔀 1 forks
3. Shaivpidadi/refrag
REFRAG: LLM-powered representations for better RAG retrieval. Improve precision, reduce context size, same speed.
⭐ 9 stars | 🔀 4 forks
4. AmirhosseinHonardoust/Machine-Learning-Warning-Systems
A long-form article and practical framework for designing machine learning systems that warn instead of decide. Covers regimes vs decimals, levers over labels, reversible alerts, anti-coercion UI patterns, auditability, and the “Warning Card” template, so ML preserves human agency while staying useful under uncertainty.
⭐ 8 stars | 🔀 0 forks
5. AmirhosseinHonardoust/Tumor-Doppelganger-Studio
Similarity-first interpretability studio for breast tumor samples: pick a case, find its closest “twins” (benign/malignant look-alikes), visualize neighborhood structure, compare feature fingerprints, and run minimal-change counterfactual edits toward a target class. Educational demo only, not for diagnosis.
⭐ 7 stars | 🔀 0 forks
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