6 papers
Tucker Diffusion Model for High-dimensional Tensor Generation
Jianhua Guo, Xinbing Kong, Zeyu Li +1
Statistical inference on large-dimensional tensor data has been extensively studied in the literature and widely used in economics, biology, machine learning, and other fields, but…
Constrained Particle Seeking: Solving Diffusion Inverse Problems with Just Forward Passes
Hongkun Dou, Zike Chen, Zeyu Li +3
Diffusion models have gained prominence as powerful generative tools for solving inverse problems due to their ability to model complex data distributions. However, existing method…
Variational Trajectory Optimization of Anisotropic Diffusion Schedules
Pengxi Liu, Zeyu Michael Li, Xiang Cheng
We introduce a variational framework for diffusion models with anisotropic noise schedules parameterized by a matrix-valued path that allocates noise across subspaces. Ce…
You Only Look One Step: Accelerating Backpropagation in Diffusion Sampling with Gradient Shortcuts
Hongkun Dou, Zeyu Li, Xingyu Jiang +4
Diffusion models (DMs) have recently demonstrated remarkable success in modeling large-scale data distributions. However, many downstream tasks require guiding the generated conten…
A data-driven sparse learning approach to reduce chemical reaction mechanisms
Shen Fang, Siyi Zhang, Zeyu Li +4
Reduction of detailed chemical reaction mechanisms is one of the key methods for mitigating the computational cost of reactive flow simulations. Exploitation of species and element…
Physics-aligned Schrödinger bridge
Zeyu Li, Hongkun Dou, Shen Fang +3
The reconstruction of physical fields from sparse measurements is pivotal in both scientific research and engineering applications. Traditional methods are increasingly supplemente…