1 citations · 1 across the 4 of their papers we have counts for
4 papers
Mixup-Augmented Meta-Learning for Sample-Efficient Fine-Tuning of Protein Simulators
Jingbang Chen, Yian Wang, Xingwei Qu +4
Molecular dynamics simulations have emerged as a fundamental instrument for studying biomolecules. At the same time, it is desirable to perform simulations of a collection of parti…
SafeDreamer: Safe Reinforcement Learning with World Models
Weidong Huang, Jiaming Ji, Chunhe Xia +2
The deployment of Reinforcement Learning (RL) in real-world applications is constrained by its failure to satisfy safety criteria. Existing Safe Reinforcement Learning (SafeRL) met…
Deep Reinforcement Learning with Task-Adaptive Retrieval via Hypernetwork
Yonggang Jin, Chenxu Wang, Tianyu Zheng +5
Deep reinforcement learning algorithms are usually impeded by sampling inefficiency, heavily depending on multiple interactions with the environment to acquire accurate decision-ma…
ASP: Learn a Universal Neural Solver!
Chenguang Wang, Zhouliang Yu, Stephen McAleer +2
Applying machine learning to combinatorial optimization problems has the potential to improve both efficiency and accuracy. However, existing learning-based solvers often struggle…