Random Fully Connected Neural Networks as Perturbatively Solvable Hierarchies

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. [abs] [ pdf ][ bib ] © JMLR 2024. (edit, beta)