It is shown that over-parameterized neural networks can achieve minimax
...
We study the approximation capacity of some variation spaces correspondi...
This paper analyzes the convergence rate of a deep Galerkin method for t...
We study the uniform approximation of echo state networks with randomly
...
We study how well generative adversarial networks (GAN) learn probabilit...
This paper studies the approximation capacity of ReLU neural networks wi...
Recently, transformer architecture has demonstrated its significance in ...
We derive nearly sharp bounds for the bidirectional GAN (BiGAN) estimati...
Using deep neural networks to solve PDEs has attracted a lot of attentio...
Reconstructing a band-limited function from its finite sample data is a
...
This paper studies how well generative adversarial networks (GANs) learn...
We study the efficacy and efficiency of deep generative networks for
app...
We construct deep ReLU neural networks to approximate functions in dilat...
Existing domain adaptation methods aim at learning features that can be
...
Deep neural networks (DNNs) although achieving human-level performance i...