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Lipschitz constants

topic1 events
papersSEP 11 04:00 UTC

Near-optimal bounds on Lipschitz constants of deep random ReLU networks

This preprint analyzes the ℓ^p-Lipschitz constants of ReLU neural networks mapping from R^d to R when the weights are randomly initialized using a variant of the He scheme, covering p from 1 to infinity. The author derives estimates that are near-optimal, meaning the upper and lower bounds match up to constant factors. The work targets theoretical understanding of how depth and width affect the sensitivity of randomly initialized networks.

research/neural-networks/theory/lipschitz