5 citations · 6 across the 13 of their papers we have counts for
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Designing Reinforcement Learning for Diffusion Models: A Unified Path-Space View
Yixian Xu, Yuanrui Zhang, Shengjie Luo +2
Reinforcement learning (RL) post-training provides a direct way to align diffusion models with human preferences and task-specific rewards. However, current RL algorithms for diffu…
Quotient-Space Diffusion Models
Yixian Xu, Yusong Wang, Shengjie Luo +4
Diffusion-based generative models have reformed generative AI, and also enabled new capabilities in the science domain, e.g., fast generation of 3D structures of molecules. In such…
Diagnosing and Improving Diffusion Models by Estimating the Optimal Loss Value
Yixian Xu, Shengjie Luo, Liwei Wang +2
Diffusion models have achieved remarkable success in generative modeling. Despite more stable training, the loss of diffusion models is not indicative of absolute data-fitting qual…
Bridging Geometric States via Geometric Diffusion Bridge
Shengjie Luo, Yixian Xu, Di He +3
The accurate prediction of geometric state evolution in complex systems is critical for advancing scientific domains such as quantum chemistry and material modeling. Traditional ex…
One Transformer Can Understand Both 2D & 3D Molecular Data
Shengjie Luo, Tianlang Chen, Yixian Xu +4
Unlike vision and language data which usually has a unique format, molecules can naturally be characterized using different chemical formulations. One can view a molecule as a 2D g…