# A General Constructive Upper Bound on Shallow Neural Nets Complexity

arXiv: https://arxiv.org/abs/2510.06372 · HTML: https://mlsanity.com/paper/2510.06372/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 0 of its 1 references that are in the map.
- First posted on arXiv 7 Oct 2025.

## Abstract (arXiv)

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.