cs.LG(2025-04-09)
📊 共 7 篇论文 | 🔗 1 篇有代码
🎯 兴趣领域导航
支柱九:具身大模型 (Embodied Foundation Models) (4 🔗1)
支柱二:RL算法与架构 (RL & Architecture) (2)
支柱六:视频提取与匹配 (Video Extraction) (1)
🔬 支柱九:具身大模型 (Embodied Foundation Models) (4 篇)
| # | 题目 | 一句话要点 | 标签 | 🔗 | ⭐ |
|---|---|---|---|---|---|
| 1 | ColonScopeX: Leveraging Explainable Expert Systems with Multimodal Data for Improved Early Diagnosis of Colorectal Cancer | ColonScopeX:利用可解释专家系统与多模态数据改进结直肠癌早期诊断 | multimodal | ||
| 2 | A Neuro-inspired Interpretation of Unlearning in Large Language Models through Sample-level Unlearning Difficulty | 提出基于神经科学启发的MRD指标,提升大语言模型中样本级别Unlearning的效率与效果。 | large language model | ||
| 3 | Sculpting Subspaces: Constrained Full Fine-Tuning in LLMs for Continual Learning | 提出基于自适应SVD的约束全参数微调方法,解决LLM持续学习中的灾难性遗忘问题。 | large language model instruction following | ||
| 4 | Holistic Capability Preservation: Towards Compact Yet Comprehensive Reasoning Models | 提出Ring-Lite-Distill:一种紧凑且全面的轻量级推理模型 | large language model instruction following | ✅ |
🔬 支柱二:RL算法与架构 (RL & Architecture) (2 篇)
| # | 题目 | 一句话要点 | 标签 | 🔗 | ⭐ |
|---|---|---|---|---|---|
| 5 | Neural Motion Simulator: Pushing the Limit of World Models in Reinforcement Learning | 提出神经运动模拟器MoSim,提升具身智能体在强化学习中的世界模型性能 | reinforcement learning world model | ||
| 6 | Bridging the Gap Between Preference Alignment and Machine Unlearning | 提出U2A框架,通过选择性遗忘负样本提升大语言模型偏好对齐性能 | reinforcement learning RLHF large language model |
🔬 支柱六:视频提取与匹配 (Video Extraction) (1 篇)
| # | 题目 | 一句话要点 | 标签 | 🔗 | ⭐ |
|---|---|---|---|---|---|
| 7 | A Graph-Enhanced DeepONet Approach for Real-Time Estimating Hydrogen-Enriched Natural Gas Flow under Variable Operations | 提出图增强DeepONet框架,用于实时估计氢气掺混天然气管道中的氢气浓度。 | sparse sensors |