A Universal Approximation Theorem of Deep Neural Networks for Expressing Probability Distributions

This paper studies the universal approximation property of deep neural networks for representing probability distributions. Given a target distribution π and a source distribution pz both defined on ℝd, we prove under some assumptions that there exists a deep neural network g:ℝdrightarrow ℝ with ReLU activation such that the push-forward measure (nabla g)_# pz of pz under the map nabla g is arbitrarily close to the target measure π. The closeness are measured by three classes of integral probability metrics between probability distributions: 1-Wasserstein distance, maximum mean distance (MMD) and kernelized Stein discrepancy (KSD). We prove upper bounds for the size (width and depth) of the deep neural network in terms of the dimension d and the approximation error varepsilon with respect to the three discrepancies. In particular, the size of neural network can grow exponentially in d when 1-Wasserstein distance is used as the discrepancy, whereas for both MMD and KSD the size of neural network only depends on d at most polynomially. Our proof relies on convergence estimates of empirical measures under aforementioned discrepancies and semi-discrete optimal transport.