May 14, 2024, 4:43 a.m. | Owen Davis, Gianluca Geraci, Mohammad Motamed

cs.LG updates on arXiv.org arxiv.org

arXiv:2405.06727v1 Announce Type: cross
Abstract: In this work, we consider the approximation of a large class of bounded functions, with minimal regularity assumptions, by ReLU neural networks. We show that the approximation error can be bounded from above by a quantity proportional to the uniform norm of the target function and inversely proportional to the product of network width and depth. We inherit this approximation error bound from Fourier features residual networks, a type of neural network that uses complex …

abstract approximation arxiv assumptions class complexity cs.lg error function functions low networks neural networks norm relu show spaces stat.ml type uniform work

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