2025-draft version
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.
2510.06372
Country:
- Europe > Czechia > Prague (0.08)
- Europe > Germany > North Rhine-Westphalia > Upper Bavaria > Munich (0.04)
Technology: Information Technology > Artificial Intelligence > Machine Learning > Neural Networks (1.00)