5 citations · 9 across the 5 of their papers we have counts for
5 papers
Fast Convergence for High-Order ODE Solvers in Diffusion Probabilistic Models
Daniel Zhengyu Huang, Jiaoyang Huang, Zhengjiang Lin
Diffusion probabilistic models generate samples by learning to reverse a noise-injection process that transforms data into noise. A key development is the reformulation of the reve…
Fisher-Rao Gradient Flow: Geodesic Convexity and Functional Inequalities
José A. Carrillo, Yifan Chen, Daniel Zhengyu Huang +2
The dynamics of probability density functions have been extensively studied in computational science and engineering to understand physical phenomena and facilitate algorithmic des…
Efficient, Multimodal, and Derivative-Free Bayesian Inference With Fisher-Rao Gradient Flows
Yifan Chen, Daniel Zhengyu Huang, Jiaoyang Huang +2
In this paper, we study efficient approximate sampling for probability distributions known up to normalization constants. We specifically focus on a problem class arising in Bayesi…
Convergence Analysis of Probability Flow ODE for Score-based Generative Models
Daniel Zhengyu Huang, Jiaoyang Huang, Zhengjiang Lin
Score-based generative models have emerged as a powerful approach for sampling high-dimensional probability distributions. Despite their effectiveness, their theoretical underpinni…
Sampling via Gradient Flows in the Space of Probability Measures
Yifan Chen, Daniel Zhengyu Huang, Jiaoyang Huang +2
Sampling a target probability distribution with an unknown normalization constant is a fundamental challenge in computational science and engineering. Recent work shows that algori…