Geometric separation and constructive universal approximation with two hidden layers

Abstract

We give a geometric construction of neural networks that separate disjoint compact subsets of ${\mathbb{R}}^n$, and use it to obtain a constructive universal approximation theorem. Specifically, we show that networks with two hidden layers and either a sigmoidal activation (i.e., strictly monotone bounded continuous) or the ReLU activation can approximate any real-valued continuous function on an arbitrary compact set $K\subset {\mathbb{R}}^n$ to any prescribed accuracy in the uniform norm. For finite $K$, the construction simplifies and yields a sharp depth-2 (single hidden layer) approximation result.