arXiv Machine Learning July 14, 2026 By Yanming Lai, Defeng Sun, Yang Wang

Approximation of Analytic Functions by ReLU Neural Networks with Adjustable Depth and Width

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arXiv:2607. 10589v1 Announce Type: cross Abstract: In contrast to most studies on neural network approximation theory that characterize results through a single parameter, such as the total number of network parameters, \cite{shen2020deep} pioneered the characterization of approximation rates as a joint function of the width parameter $N$ and the depth parameter $L$, thereby granting greater architectural flexibility.

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