  We study the extent to which wide neural networks may be approximated by
Gaussian processes when initialized with random weights. It is a
well-established fact that as the width of a network goes to infinity, its law
converges to that of a Gaussian process. We make this quantitative by
establishing explicit convergence rates for the central limit theorem in an
infinite-dimensional functional space, metrized with a natural transportation
distance. We identify two regimes of interest; when the activation function is
polynomial, its degree determines the rate of convergence, while for
non-polynomial activations, the rate is governed by the smoothness of the
function.

Probability;Machine Learning;Machine Learning