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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.

He initialization Lipschitz constants ReLU neural networks deep-learning-theory network sensitivity random initialization

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arXiv cs.LGNear-optimal estimates for the $\ell^p$-Lipschitz constants of deep random ReLU neural networks ↗SEP 11 04:00 UTC