4 citations · 6 across the 4 of their papers we have counts for
8 papers · 1 filter
Analysis of Deep Neural Networks with Quasi-optimal polynomial approximation rates
Joseph Daws, Clayton Webster
We show the existence of a deep neural network capable of approximating a wide class of high-dimensional approximations. The construction of the proposed neural network is based on…
Closure Learning for Nonlinear Model Reduction Using Deep Residual Neural Network
Xuping Xie, Clayton G. Webster, Traian Iliescu
Developing accurate, efficient, and robust closure models is essential in the construction of reduced order models (ROMs) for realistic nonlinear systems, which generally require d…
Neural network integral representations with the ReLU activation function
Armenak Petrosyan, Anton Dereventsov, Clayton Webster
In this effort, we derive a formula for the integral representation of a shallow neural network with the ReLU activation function. We assume that the outer weighs admit a finite $L…
A Weighted -Minimization Approach For Wavelet Reconstruction of Signals and Images
Joseph Daws, Armenak Petrosyan, Hoang Tran +1
In this effort, we propose a convex optimization approach based on weighted -regularization for reconstructing objects of interest, such as signals or images, that are spar…
A nonlocal feature-driven exemplar-based approach for image inpainting
Viktor Reshniak, Jeremy Trageser, Clayton G. Webster
We present a nonlocal variational image completion technique which admits simultaneous inpainting of multiple structures and textures in a unified framework. The recovery of geomet…
Analytic Continuation of Noisy Data Using Adams Bashforth ResNet
Xuping Xie, Feng Bao, Thomas Maier +1
We propose a data-driven learning framework for the analytic continuation problem in numerical quantum many-body physics. Designing an accurate and efficient framework for the anal…