4 papers
Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation
Yi Zhang, Peng Wang, Difan Zou
Modeling physical systems in a generative manner offers several advantages, including the ability to handle partial observations, generate diverse solutions, and address both forwa…
Kernel Regression in Structured Non-IID Settings: Theory and Implications for Denoising Score Learning
Dechen Zhang, Zhenmei Shi, Yi Zhang +2
Kernel ridge regression (KRR) is a foundational tool in machine learning, with recent work emphasizing its connections to neural networks. However, existing theory primarily addres…
On the Collapse Errors Induced by the Deterministic Sampler for Diffusion Models
Yi Zhang, Zhenyu Liao, Jingfeng Wu +1
Despite the widespread adoption of deterministic samplers in diffusion models (DMs), their potential limitations remain largely unexplored. In this paper, we identify collapse erro…
Reverse Transition Kernel: A Flexible Framework to Accelerate Diffusion Inference
Xunpeng Huang, Difan Zou, Hanze Dong +3
To generate data from trained diffusion models, most inference algorithms, such as DDPM, DDIM, and other variants, rely on discretizing the reverse SDEs or their equivalent ODEs. I…