Error bounds for ReLU networks with depth and width parameters
type: Item
instance of: scholarly article
title: Error bounds for ReLU networks with depth and width parameters
author: Jae-Mo Kang
author: Sunghwan Moon
published in: Japan Journal of Industrial and Applied Mathematics
publication date: 2023-01-17T00:00:00Z
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 41A25
zbMATH DE Number: 7642723
zbMATH Keywords: deep neural nets
zbMATH Keywords: ReLU network
zbMATH Keywords: approximation theory
zbMATH Keywords: Cybenko
MaRDI profile type: Publication
full work available at URL: s13160-022-00515-0
OpenAlex ID: W4281936743
cites work: Approximation and estimation bounds for artificial neural networks
cites work: On deep learning as a remedy for the curse of dimensionality in nonparametric regression
cites work: Q3998932
cites work: Functions, spaces, and expansions. Mathematical tools in physics and engineering
cites work: Approximation by superpositions of a sigmoidal function
cites work: Q5270493
cites work: Multilayer feedforward networks are universal approximators
cites work: Deep vs. shallow networks: an approximation theory perspective
cites work: New Error Bounds for Deep ReLU Networks Using Sparse Grids
cites work: Nonparametric regression using deep neural networks with ReLU activation function
cites work: Error bounds for approximations with deep ReLU networks
cites work: Universality of deep convolutional neural networks
cites work: Theory of deep convolutional neural networks: downsampling
DOI: 10.1007/S13160-022-00515-0
description: scientific article; zbMATH DE number 7642723