Random Fully Connected Neural Networks as Perturbatively Solvable Hierarchies
Boris Hanin.
Year: 2024, Volume: 25, Issue: 267, Pages: 1−58
Abstract
We study the distribution of fully connected neural networks with Gaussian random weights/biases and L hidden layers, each of width proportional to a large parameter n. For polynomially bounded non-linearities we give sharp estimates in powers of 1/n for the joint cumulants of the network output and its derivatives. We further show that network cumulants form a perturbatively solvable hierarchy in powers of 1/n. That is, the k-th order cumulants in each layer are determined to leading order in 1/n by cumulants of order at most k computed at the previous layer. By explicitly deriving and then solving several such recursions, we find that the depth-to-width ratio L/n plays the role of an effective network depth, controlling both the distance to Gaussianity and the size of inter-neuron correlations.