37 citations · 135 across the 10 of their papers we have counts for
10 papers
Optimal rates of approximation by shallow ReLU neural networks and applications to nonparametric regression
Yunfei Yang, Ding-Xuan Zhou
We study the approximation capacity of some variation spaces corresponding to shallow ReLU neural networks. It is shown that sufficiently smooth functions are contained in thes…
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…
Approximation bounds for norm constrained neural networks with applications to regression and GANs
Yuling Jiao, Yang Wang, Yunfei Yang
This paper studies the approximation capacity of ReLU neural networks with norm constraint on the weights. We prove upper and lower bounds on the approximation error of these netwo…
Non-Asymptotic Error Bounds for Bidirectional GANs
Shiao Liu, Yunfei Yang, Jian Huang +2
We derive nearly sharp bounds for the bidirectional GAN (BiGAN) estimation error under the Dudley distance between the latent joint distribution and the data joint distribution wit…
Exponential Approximation of Band-limited Functions from Nonuniform Sampling by Regularization Methods
Yunfei Yang, Haizhang Zhang
Reconstructing a band-limited function from its finite sample data is a fundamental task in signal analysis. A Gaussian regularized Shannon sampling series has been proved to be ab…
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…
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…
Approximation in shift-invariant spaces with deep ReLU neural networks
Yunfei Yang, Zhen Li, Yang Wang
We study the expressive power of deep ReLU neural networks for approximating functions in dilated shift-invariant spaces, which are widely used in signal processing, image processi…
Light-weight Calibrator: a Separable Component for Unsupervised Domain Adaptation
Shaokai Ye, Kailu Wu, Mu Zhou +6
Existing domain adaptation methods aim at learning features that can be generalized among domains. These methods commonly require to update source classifier to adapt to the target…
Progressive Weight Pruning of Deep Neural Networks using ADMM
Shaokai Ye, Tianyun Zhang, Kaiqi Zhang +10
Deep neural networks (DNNs) although achieving human-level performance in many domains, have very large model size that hinders their broader applications on edge computing devices…