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Found 15 papers, 2 papers with code
On the rates of convergence for learning with convolutional neural networks
Our second result gives new analysis on the covering number of feed-forward neural networks with CNNs as special cases.
Nonparametric regression using over-parameterized shallow ReLU neural networks
It is shown that over-parameterized neural networks can achieve minimax optimal rates of convergence (up to logarithmic factors) for learning functions from certain smooth function classes, if the weights are suitably constrained or regularized.
Optimal rates of approximation by shallow ReLU$^k$ neural networks and applications to nonparametric regression
It is also proven that over-parameterized (deep or shallow) neural networks can achieve nearly optimal rates for nonparametric regression.
Convergence Analysis of the Deep Galerkin Method for Weak Solutions
This paper analyzes the convergence rate of a deep Galerkin method for the weak solution (DGMW) of second-order elliptic partial differential equations on $\mathbb{R}^d$ with Dirichlet, Neumann, and Robin boundary conditions, respectively.
Universality and approximation bounds for echo state networks with random weights
We study the uniform approximation of echo state networks with randomly generated internal weights.
Learning Distributions by Generative Adversarial Networks: Approximation and Generalization
We study how well generative adversarial networks (GAN) learn probability distributions from finite samples by analyzing the convergence rates of these models.
Approximation bounds for norm constrained neural networks with applications to regression and GANs
This paper studies the approximation capacity of ReLU neural networks with norm constraint on the weights.
DBIA: Data-free Backdoor Injection Attack against Transformer Networks
In this paper, we propose DBIA, a novel data-free backdoor attack against the CV-oriented transformer networks, leveraging the inherent attention mechanism of transformers to generate triggers and injecting the backdoor using the poisoned surrogate dataset.
Non-Asymptotic Error Bounds for Bidirectional GANs
Our results are also applicable to the Wasserstein bidirectional GAN if the target distribution is assumed to have a bounded support.
Exponential Approximation of Band-limited Functions from Nonuniform Sampling by Regularization Methods
Specifically, we show that one can recover a band-limited function by Gaussian or hyper-Gaussian regularized nonuniform sampling series with an exponential convergence rate.
An error analysis of generative adversarial networks for learning distributions
This paper studies how well generative adversarial networks (GANs) learn probability distributions from finite samples.
On the capacity of deep generative networks for approximating distributions
Furthermore, it is shown that the approximation error in Wasserstein distance grows at most linearly on the ambient dimension and that the approximation order only depends on the intrinsic dimension of the target distribution.
Approximation in shift-invariant spaces with deep ReLU neural networks
We also give lower bounds of the $L^p (1\le p \le \infty)$ approximation error for Sobolev spaces, which show that our construction of neural network is asymptotically optimal up to a logarithmic factor.
Light-weight Calibrator: a Separable Component for Unsupervised Domain Adaptation
Existing domain adaptation methods aim at learning features that can be generalized among domains.
Ranked #3 on
Domain Adaptation
on USPS-to-MNIST
Progressive Weight Pruning of Deep Neural Networks using ADMM
Motivated by dynamic programming, the proposed method reaches extremely high pruning rate by using partial prunings with moderate pruning rates.
