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06/14/2023
Nonparametric regression using over-parameterized shallow ReLU neural networks
It is shown that over-parameterized neural networks can achieve minimax ...
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04/04/2023
Optimal rates of approximation by shallow ReLU^k neural networks and applications to nonparametric regression
We study the approximation capacity of some variation spaces correspondi...
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02/05/2023
Convergence Analysis of the Deep Galerkin Method for Weak Solutions
This paper analyzes the convergence rate of a deep Galerkin method for t...
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06/12/2022
Universality and approximation bounds for echo state networks with random weights
We study the uniform approximation of echo state networks with randomly ...
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05/25/2022
Learning Distributions by Generative Adversarial Networks: Approximation and Generalization
We study how well generative adversarial networks (GAN) learn probabilit...
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01/24/2022
Approximation bounds for norm constrained neural networks with applications to regression and GANs
This paper studies the approximation capacity of ReLU neural networks wi...
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11/22/2021
DBIA: Data-free Backdoor Injection Attack against Transformer Networks
Recently, transformer architecture has demonstrated its significance in ...
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10/24/2021
Non-Asymptotic Error Bounds for Bidirectional GANs
We derive nearly sharp bounds for the bidirectional GAN (BiGAN) estimati...
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07/30/2021
Error Analysis of Deep Ritz Methods for Elliptic Equations
Using deep neural networks to solve PDEs has attracted a lot of attentio...
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06/16/2021
Exponential Approximation of Band-limited Signals from Nonuniform Sampling
Reconstructing a band-limited function from its finite sample data is a ...
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05/27/2021
An error analysis of generative adversarial networks for learning distributions
This paper studies how well generative adversarial networks (GANs) learn...
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01/29/2021
On the capacity of deep generative networks for approximating distributions
We study the efficacy and efficiency of deep generative networks for app...
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05/25/2020
Approximation in shift-invariant spaces with deep ReLU neural networks
We construct deep ReLU neural networks to approximate functions in dilat...
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11/28/2019
Light-weight Calibrator: a Separable Component for Unsupervised Domain Adaptation
Existing domain adaptation methods aim at learning features that can be ...
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10/17/2018
