Finite Neural Networks as Mixtures of Gaussian Processes: From Provable Error Bounds to Prior Selection
arXiv:2407.18707v3 Announce Type: replace-cross Abstract: Infinitely wide or deep neural networks (NNs) with independent and identically distributed (i.i.d.) parameters have been shown to be equivalent to Gaussian processes. Because of the favorable properties of Gaussian processes, this equivalence is commonly employed to analyze neural networks and has led to various breakthroughs over the years. However, neural networks and Gaussian processes are equivalent only in the limit; in the finite case there are currently no methods available to approximate […]