9 citations · 9 across the 2 of their papers we have counts for
5 papers
PXtal: Learning to Align Powder X-Ray Diffraction and Crystal Structures under Information Asymmetry across Modalities
Zhuoran Yang, Christopher M. Collins, Bei Peng +3
Scientific multimodal learning commonly assumes that paired views are comparably informative. Powder X-ray diffraction (PXRD) makes this mismatch explicit: compressing a three-dime…
How Does the Lagrangian Guide Safe Reinforcement Learning through Diffusion Models?
Xiaoyuan Cheng, Wenxuan Yuan, Boyang Li +7
Diffusion policy sampling enables reinforcement learning (RL) to represent multimodal action distributions beyond suboptimal unimodal Gaussian policies. However, existing diffusion…
Heuristic Transformer: Belief Augmented In-Context Reinforcement Learning
Oliver Dippel, Alexei Lisitsa, Bei Peng
Transformers have demonstrated exceptional in-context learning (ICL) capabilities, enabling applications across natural language processing, computer vision, and sequential decisio…
MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures
Elena Zamaraeva, Christopher M. Collins, George R. Darling +8
Geometry optimization of atomic structures is a common and crucial task in computational chemistry and materials design. Following the learning to optimize paradigm, we propose a n…
A Knowledge-Informed Deep Learning Paradigm for Generalizable and Stability-Optimized Car-Following Models
Chengming Wang, Dongyao Jia, Wei Wang +3
Car-following models (CFMs) are fundamental to traffic flow analysis and autonomous driving. Although calibrated physics-based and trained data-driven CFMs can replicate human driv…