cs.LG(2025-09-21)

📊 共 12 篇论文 | 🔗 2 篇有代码

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支柱二:RL算法与架构 (RL & Architecture) (6) 支柱九:具身大模型 (Embodied Foundation Models) (4 🔗2) 支柱四:生成式动作 (Generative Motion) (1) 支柱八:物理动画 (Physics-based Animation) (1)

🔬 支柱二:RL算法与架构 (RL & Architecture) (6 篇)

#题目一句话要点标签🔗
1 Causal Representation Learning from Multimodal Clinical Records under Non-Random Modality Missingness 针对临床记录缺失场景,提出因果表征学习框架,提升多模态融合效果。 representation learning contrastive learning large language model
2 GRPOformer: Advancing Hyperparameter Optimization via Group Relative Policy Optimization GRPOformer:通过群组相对策略优化提升超参数优化性能 reinforcement learning large language model
3 The Complexity of Finding Local Optima in Contrastive Learning 证明对比学习中局部最优解的复杂性 contrastive learning
4 TraceHiding: Scalable Machine Unlearning for Mobility Data 提出TraceHiding框架以解决移动数据的机器遗忘问题 teacher-student distillation
5 On the Limits of Tabular Hardness Metrics for Deep RL: A Study with the Pharos Benchmark 研究表明表格型硬度指标难以有效评估深度强化学习环境难度,并提出Pharos基准。 reinforcement learning deep reinforcement learning
6 NeuFACO: Neural Focused Ant Colony Optimization for Traveling Salesman Problem 提出NeuFACO,结合神经启发式与蚁群优化求解旅行商问题 reinforcement learning PPO

🔬 支柱九:具身大模型 (Embodied Foundation Models) (4 篇)

#题目一句话要点标签🔗
7 PTQTP: Post-Training Quantization to Trit-Planes for Large Language Models 提出PTQTP:一种面向大语言模型的后训练三元平面量化方法,实现高效推理。 large language model
8 Adaptive Graph Convolution and Semantic-Guided Attention for Multimodal Risk Detection in Social Networks 提出自适应图卷积和语义引导注意力机制,用于社交网络中的多模态风险检测 multimodal
9 TSGym: Design Choices for Deep Multivariate Time-Series Forecasting TSGym:通过细粒度组件选择与自动模型构建,提升多元时间序列预测性能。 large language model foundation model
10 SignalLLM: A General-Purpose LLM Agent Framework for Automated Signal Processing 提出SignalLLM:一个面向自动化信号处理的通用LLM Agent框架 large language model

🔬 支柱四:生成式动作 (Generative Motion) (1 篇)

#题目一句话要点标签🔗
11 Ultra-short-term solar power forecasting by deep learning and data reconstruction 提出基于深度学习和数据重构的超短期太阳能发电功率预测方法 penetration

🔬 支柱八:物理动画 (Physics-based Animation) (1 篇)

#题目一句话要点标签🔗
12 Dendritic Resonate-and-Fire Neuron for Effective and Efficient Long Sequence Modeling 提出D-RF神经元模型,有效且高效地处理长序列建模任务。 spatiotemporal

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