Error bounds for ReLU networks with depth and width parameters

DOI10.1007/s13160-022-00515-0OpenAlexW4281936743MaRDI QIDQ2111556

Sunghwan Moon, Jae-Mo Kang

Publication date: 17 January 2023

Published in: Japan Journal of Industrial and Applied Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s13160-022-00515-0

zbMATH Keywords

approximation theoryCybenkodeep neural netsReLU network

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Rate of convergence, degree of approximation (41A25)

Cites Work

• Functions, spaces, and expansions. Mathematical tools in physics and engineering
• Approximation and estimation bounds for artificial neural networks
• Multilayer feedforward networks are universal approximators
• Theory of deep convolutional neural networks: downsampling
• Nonparametric regression using deep neural networks with ReLU activation function
• Error bounds for approximations with deep ReLU networks
• Universality of deep convolutional neural networks
• On deep learning as a remedy for the curse of dimensionality in nonparametric regression
• Deep vs. shallow networks: An approximation theory perspective
• New Error Bounds for Deep ReLU Networks Using Sparse Grids
• Approximation by superpositions of a sigmoidal function
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