We propose a general approach to evaluating the performance of robust
es...
In this paper, we introduce CDII-PINNs, a computationally efficient meth...
We study the properties of differentiable neural networks activated by
r...
In this paper, we focus on approximating a natural class of functions th...
With recent study of the deep learning in scientific computation, the PI...
This paper analyzes the convergence rate of a deep Galerkin method for t...
We propose a penalized nonparametric approach to estimating the quantile...
We propose a deep generative approach to nonparametric estimation of
con...
Efficient quantum compiling tactics greatly enhance the capability of qu...
This paper studies the approximation capacity of ReLU neural networks wi...
Conditional distribution is a fundamental quantity for describing the
re...
In this work we propose a nonconvex two-stage stochastic
alternating min...
In this paper, we consider recovering n dimensional signals from m binar...
In this work, we consider the algorithm to the (nonlinear) regression
pr...
Deep Ritz methods (DRM) have been proven numerically to be efficient in
...
We derive nearly sharp bounds for the bidirectional GAN (BiGAN) estimati...
We propose a deep generative approach to sampling from a conditional
dis...
We propose a relative entropy gradient sampler (REGS) for sampling from
...
Recovering sparse signals from observed data is an important topic in
si...
In recent years, physical informed neural networks (PINNs) have been sho...
Using deep neural networks to solve PDEs has attracted a lot of attentio...
In this paper, we study the properties of robust nonparametric estimatio...
This paper considers the problem of nonparametric quantile regression un...
Schrödinger-Föllmer sampler (SFS) is a novel and efficient approach
for ...
Sampling from probability distributions is an important problem in stati...
We propose to learn a generative model via entropy interpolation with a
...
This paper studies how well generative adversarial networks (GANs) learn...
In this paper, we consider the problem of binary classification with a c...
In this paper, we study the properties of nonparametric least squares
re...
Using deep neural networks to solve PDEs has attracted a lot of attentio...
In this paper, we construct neural networks with ReLU, sine and 2^x as
a...
We propose an Euler particle transport (EPT) approach for generative
lea...
The success of deep supervised learning depends on its automatic data
re...
The main goal of 1-bit compressive sampling is to decode n dimensional
s...
We develop a constructive approach for ℓ_0-penalized estimation in the
s...
We propose a unified framework for implicit
generative modeling (UnifiGe...
Screening and working set techniques are important approaches to reducin...
Feature selection is important for modeling high-dimensional data, where...
Sparse phase retrieval plays an important role in many fields of applied...
To address the challenges in learning deep generative models (e.g.,the
b...
We propose a semismooth Newton algorithm for pathwise optimization (SNAP...
In Genome-Wide Association Studies (GWAS) where multiple correlated trai...
In this study, we consider the problem of variable selection and estimat...
Both the smoothly clipped absolute deviation (SCAD) and the minimax conc...
In 1-bit compressive sensing (1-bit CS) where target signal is coded int...
We develop a primal dual active set with continuation algorithm for solv...
In this paper, we consider the problem of recovering a sparse vector fro...