@inproceedings{chen2023the,
author = {Chen, Sitan and Chewi, Sinho and Lee, Holden and Li, Yuanzhi and Lu, Jianfeng and Salim, Adil},
title = {The probability flow ODE is provably fast},
booktitle = {NeurIPS 2023},
year = {2023},
month = {May},
abstract = {We provide the first polynomial-time convergence guarantees for the probability flow ODE implementation (together with a corrector step) of score-based generative modeling. Our analysis is carried out in the wake of recent results obtaining such guarantees for the SDE-based implementation (i.e., denoising diffusion probabilistic modeling or DDPM), but requires the development of novel techniques for studying deterministic dynamics without contractivity. Through the use of a specially chosen corrector step based on the underdamped Langevin diffusion, we obtain better dimension dependence than prior works on DDPM (O(d−−√) vs. O(d), assuming smoothness of the data distribution), highlighting potential advantages of the ODE framework.},
url = {http://approjects.co.za/?big=en-us/research/publication/the-probability-flow-ode-is-provably-fast/},
}