cs.LG(2024-03-15)
📊 共 11 篇论文 | 🔗 2 篇有代码
🎯 兴趣领域导航
支柱二:RL算法与架构 (RL & Architecture) (5 🔗1)
支柱九:具身大模型 (Embodied Foundation Models) (4 🔗1)
支柱一:机器人控制 (Robot Control) (1)
支柱八:物理动画 (Physics-based Animation) (1)
🔬 支柱二:RL算法与架构 (RL & Architecture) (5 篇)
| # | 题目 | 一句话要点 | 标签 | 🔗 | ⭐ |
|---|---|---|---|---|---|
| 1 | AD3: Implicit Action is the Key for World Models to Distinguish the Diverse Visual Distractors | 提出AD3以解决视觉控制中的同质干扰物识别问题 | world model world models | ||
| 2 | Parameter Efficient Reinforcement Learning from Human Feedback | 提出参数高效的强化学习方法以降低人类反馈的计算成本 | reinforcement learning RLHF | ||
| 3 | Online Policy Learning from Offline Preferences | 提出虚拟偏好框架以解决离线偏好学习的泛化问题 | reinforcement learning policy learning | ||
| 4 | Towards Adversarially Robust Dataset Distillation by Curvature Regularization | 提出通过曲率正则化实现对抗鲁棒的数据集蒸馏 | distillation | ✅ | |
| 5 | Graph Enhanced Reinforcement Learning for Effective Group Formation in Collaborative Problem Solving | 提出图增强强化学习以解决协作问题中的有效组队问题 | reinforcement learning |
🔬 支柱九:具身大模型 (Embodied Foundation Models) (4 篇)
| # | 题目 | 一句话要点 | 标签 | 🔗 | ⭐ |
|---|---|---|---|---|---|
| 6 | Benchmarking Zero-Shot Robustness of Multimodal Foundation Models: A Pilot Study | 评估多模态基础模型的零-shot鲁棒性以应对现实挑战 | foundation model multimodal | ||
| 7 | Block Verification Accelerates Speculative Decoding | 提出区块验证以加速推测解码过程 | large language model | ||
| 8 | SocialGenPod: Privacy-Friendly Generative AI Social Web Applications with Decentralised Personal Data Stores | 提出SocialGenPod以解决用户数据隐私与可移植性问题 | large language model | ✅ | |
| 9 | pTNAS: Progressive Neural Architecture Search for Tabular Data | 提出pTNAS以解决表格数据神经架构搜索效率问题 | foundation model |
🔬 支柱一:机器人控制 (Robot Control) (1 篇)
| # | 题目 | 一句话要点 | 标签 | 🔗 | ⭐ |
|---|---|---|---|---|---|
| 10 | Quality-Diversity Actor-Critic: Learning High-Performing and Diverse Behaviors via Value and Successor Features Critics | 提出QDAC以解决深度强化学习中的多样性与性能问题 | locomotion reinforcement learning deep reinforcement learning |
🔬 支柱八:物理动画 (Physics-based Animation) (1 篇)
| # | 题目 | 一句话要点 | 标签 | 🔗 | ⭐ |
|---|---|---|---|---|---|
| 11 | How Suboptimal is Training rPPG Models with Videos and Targets from Different Body Sites? | 提出使用不同身体部位PPG信号训练rPPG模型以提高准确性 | PULSE |