  This article gives a new proof that fully connected neural networks with
random weights and biases converge to Gaussian processes in the regime where
the input dimension, output dimension, and depth are kept fixed, while the
hidden layer widths tend to infinity. Unlike prior work, convergence is shown
assuming only moment conditions for the distribution of weights and for quite
general non-linearities.
