4 papers
Generalization of Diffusion Models Arises with a Balanced Representation Space
Zekai Zhang, Xiao Li, Xiang Li +4
Diffusion models excel at generating high-quality, diverse samples, yet they risk memorizing training data when overfit to the training objective. We analyze the distinctions betwe…
Learning Structure, Energy, and Dynamics: A Survey of Artificial Intelligence for Protein Dynamics
Haocheng Tang, Liang Shi, Ya-Shi Zhang +3
Protein dynamics underlie many biological functions, yet remain difficult to characterize due to the high computational cost of molecular dynamics simulations and the scarcity of d…
Atomic Trajectory Modeling with State Space Models for Biomolecular Dynamics
Liang Shi, Jiarui Lu, Junqi Liu +3
Understanding the dynamic behavior of biomolecules is fundamental to elucidating biological function and facilitating drug discovery. While Molecular Dynamics (MD) simulations prov…
A Closer Look at Model Collapse: From a Generalization-to-Memorization Perspective
Lianghe Shi, Meng Wu, Huijie Zhang +3
The widespread use of diffusion models has led to an abundance of AI-generated data, raising concerns about model collapse -- a phenomenon in which recursive iterations of training…