cs.LG(2025-12-27)
📊 共 11 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)
支柱一:机器人控制 (Robot Control) (3)
支柱二:RL算法与架构 (RL & Architecture) (2)
支柱七:动作重定向 (Motion Retargeting) (1)
支柱四:生成式动作 (Generative Motion) (1)
🔬 支柱九:具身大模型 (Embodied Foundation Models) (4 篇)
| # | 题目 | 一句话要点 | 标签 | 🔗 | ⭐ |
|---|---|---|---|---|---|
| 1 | Scaling Unverifiable Rewards: A Case Study on Visual Insights | 提出Selective TTS,通过阶段性推理优化解决不可验证奖励下的多阶段任务。 | large language model | ||
| 2 | AdaFRUGAL: Adaptive Memory-Efficient Training with Dynamic Control | AdaFRUGAL:通过动态控制实现自适应、内存高效的大模型训练 | large language model | ||
| 3 | TimePerceiver: An Encoder-Decoder Framework for Generalized Time-Series Forecasting | TimePerceiver:一种用于广义时间序列预测的Encoder-Decoder框架 | TAMP | ✅ | |
| 4 | The Bayesian Geometry of Transformer Attention | Transformer通过几何机制实现贝叶斯推理,显著优于MLP | large language model |
🔬 支柱一:机器人控制 (Robot Control) (3 篇)
| # | 题目 | 一句话要点 | 标签 | 🔗 | ⭐ |
|---|---|---|---|---|---|
| 5 | Communication Compression for Distributed Learning with Aggregate and Server-Guided Feedback | 提出CAFe和CAFe-S框架,解决联邦学习中通信压缩和隐私保护的难题。 | shared control | ||
| 6 | Decomposing Task Vectors for Refined Model Editing | 提出任务向量分解方法,实现对预训练模型行为的精确控制与编辑 | manipulation | ||
| 7 | Geometric Scaling of Bayesian Inference in LLMs | 探讨大语言模型中贝叶斯推断的几何特征 | manipulation |
🔬 支柱二:RL算法与架构 (RL & Architecture) (2 篇)
| # | 题目 | 一句话要点 | 标签 | 🔗 | ⭐ |
|---|---|---|---|---|---|
| 8 | Toward Real-World IoT Security: Concept Drift-Resilient IoT Botnet Detection via Latent Space Representation Learning and Alignment | 提出基于潜在空间对齐的IoT僵尸网络检测框架,解决概念漂移问题 | representation learning | ||
| 9 | Predictive Modeling of Power Outages during Extreme Events: Integrating Weather and Socio-Economic Factors | 提出一种融合天气与社会经济因素的预测模型,用于极端事件下的电力中断预测。 | predictive model |
🔬 支柱七:动作重定向 (Motion Retargeting) (1 篇)
| # | 题目 | 一句话要点 | 标签 | 🔗 | ⭐ |
|---|---|---|---|---|---|
| 10 | Uncertainty-Aware Flow Field Reconstruction Using SVGP Kolmogorov-Arnold Networks | 提出基于SVGP-KAN的不确定性感知流场重建方法,解决时序稀疏数据下的流场重建问题。 | structure preservation PULSE |
🔬 支柱四:生成式动作 (Generative Motion) (1 篇)
| # | 题目 | 一句话要点 | 标签 | 🔗 | ⭐ |
|---|---|---|---|---|---|
| 11 | Quantum Generative Models for Computational Fluid Dynamics: A First Exploration of Latent Space Learning in Lattice Boltzmann Simulations | 首次探索量子生成模型在CFD中的应用,用于学习LBM模拟的潜在空间表示 | VQ-VAE |