| 1 |
Explainable Reinforcement Learning via Physics-Aware Policy Distillation |
通过物理感知的策略蒸馏提升深度强化学习可解释性 |
reinforcement learning deep reinforcement learning DRL |
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| 2 |
MEGA-CL: A Molecular Foundation Model for Generalizable ADMET Prediction through Graph External Attention and Contrastive Learning |
提出MEGA-CL以解决小分子ADMET预测的挑战 |
contrastive learning foundation model |
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| 3 |
Stress-Testing EEG Foundation Models for Clinical Decoding: Dataset Identity and Targeted Negative Controls |
评估EEG基础模型在临床解码中的有效性与鲁棒性 |
Mamba foundation model |
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| 4 |
ACRL: Adaptive Control of Training-Inference Discrepancy for Stable Reinforcement Learning |
提出自适应控制方法以解决强化学习训练不稳定问题 |
reinforcement learning large language model |
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| 5 |
Context Is King: How In-Context Specification Shapes the Geometry of Concepts |
提出上下文规范以重塑概念几何结构 |
world model world models large language model |
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| 6 |
FlowCTS: On-policy Continuous Trajectory Supervision of Flow Models |
提出FlowCTS以解决流模型的稀疏奖励和偏见问题 |
distillation large language model |
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| 7 |
Unsupervised Graph Representation Learning with Complementary View Alignment |
提出AlignGAE以解决异质图表示学习问题 |
representation learning |
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| 8 |
ML-based Predictive Models for Power Consumption in Virtualised O-RANs |
提出基于机器学习的模型以预测虚拟化O-RAN中的功耗 |
predictive model |
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| 9 |
Constrained Reinforcement Learning Using Successor Representations |
提出SafeDSR以解决强化学习中的安全约束问题 |
reinforcement learning |
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| 10 |
WorldDiT: A Unified Diffusion Architecture for World and Action Modeling |
提出WorldDiT以解决机器人控制中的视觉与动作建模问题 |
world model world models |
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