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A General Constructive Upper Bound on Shallow Neural Nets Complexity
Frantisek Hakl, Vit Fojtik
https://arxiv.org/abs/2510.06372 https://arxiv.org/pdf/2510…

A General Constructive Upper Bound on Shallow Neural Nets Complexity
We provide an upper bound on the number of neurons required in a shallow neural network to approximate a continuous function on a compact set with a given accuracy. This method, inspired by a specific proof of the Stone-Weierstrass theorem, is constructive and more general than previous bounds of this character, as it applies to any continuous function on any compact set.