16 citations · 21 across the 2 of their papers we have counts for
3 papers
cs.LG2022★ 5 cited
Universality and approximation bounds for echo state networks with random weights
Zhen Li, Yunfei Yang
We study the uniform approximation of echo state networks with randomly generated internal weights. These models, in which only the readout weights are optimized during training, h…
cs.LG2021★ 16 cited
An error analysis of generative adversarial networks for learning distributions
Jian Huang, Yuling Jiao, Zhen Li +3
This paper studies how well generative adversarial networks (GANs) learn probability distributions from finite samples. Our main results establish the convergence rates of GANs und…
cs.LG2021
On the capacity of deep generative networks for approximating distributions
Yunfei Yang, Zhen Li, Yang Wang
We study the efficacy and efficiency of deep generative networks for approximating probability distributions. We prove that neural networks can transform a low-dimensional source d…