6 papers · 1 filter
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…
F-Adapter: Frequency-Adaptive Parameter-Efficient Fine-Tuning in Scientific Machine Learning
Hangwei Zhang, Chun Kang, Yan Wang +1
Parameter-efficient fine-tuning (PEFT) of powerful pre-trained models for complex downstream tasks has proven effective in vision and language processing, yet this paradigm remains…
Hierarchical Koopman Diffusion: Fast Generation with Interpretable Diffusion Trajectory
Hanru Bai, Weiyang Ding, Difan Zou
Diffusion models have achieved impressive success in high-fidelity image generation but suffer from slow sampling due to their inherently iterative denoising process. While recent…
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…
On the Interpolation Error of Nonlinear Attention versus Linear Regression
Zhenyu Liao, Jiaqing Liu, TianQi Hou +2
Attention has become the core building block of modern machine learning (ML) by efficiently capturing the long-range dependencies among input tokens. Its inherently parallelizable…
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…