# Doubly infinite residual neural networks: a diffusion process approach

@article{Peluchetti2021DoublyIR, title={Doubly infinite residual neural networks: a diffusion process approach}, author={Stefano Peluchetti and Stefano Favaro and Philipp Hennig}, journal={J. Mach. Learn. Res.}, year={2021}, volume={22}, pages={175:1-175:48} }

Modern neural networks featuring a large number of layers (depth) and units per layer (width) have achieved a remarkable performance across many domains. While there exists a vast literature on the interplay between infinitely wide neural networks and Gaussian processes, a little is known about analogous interplays with respect to infinitely deep neural networks. Neural networks with independent and identically distributed (i.i.d.) initializations exhibit undesirable forward and backward…

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