A General Constructive Upper Bound on Shallow Neural Nets Complexity
arXiv:2510.06372v1 Announce Type: new
Abstract: 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.
Abstract: 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.
Frantisek Hakl, Vit Fojtik
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