160 citations · 204 across the 5 of their papers we have counts for
5 papers
Fast Training of Diffusion Models with Masked Transformers
Hongkai Zheng, Weili Nie, Arash Vahdat +1
We propose an efficient approach to train large diffusion models with masked transformers. While masked transformers have been extensively explored for representation learning, the…
Fast Sampling of Diffusion Models via Operator Learning
Hongkai Zheng, Weili Nie, Arash Vahdat +2
Diffusion models have found widespread adoption in various areas. However, their sampling process is slow because it requires hundreds to thousands of network evaluations to emulat…
Langevin Monte Carlo for Contextual Bandits
Pan Xu, Hongkai Zheng, Eric Mazumdar +2
We study the efficiency of Thompson sampling for contextual bandits. Existing Thompson sampling-based algorithms need to construct a Laplace approximation (i.e., a Gaussian distrib…
Physics-Informed Neural Operator for Learning Partial Differential Equations
Zongyi Li, Hongkai Zheng, Nikola Kovachki +5
In this paper, we propose physics-informed neural operators (PINO) that combine training data and physics constraints to learn the solution operator of a given family of parametric…
Implicit competitive regularization in GANs
Florian Schäfer, Hongkai Zheng, Anima Anandkumar
To improve the stability of GAN training we need to understand why they can produce realistic samples. Presently, this is attributed to properties of the divergence obtained under…