Near-optimal estimates for the $\ell^p$-Lipschitz constants of deep random ReLU neural networks
The analysis is extended to networks with possibly non-zero biases drawn from arbitrary symmetric distributions and derives high probability upper and lower bounds for wide networks that differ at most by a factor that is logarithmic in the network's width and linear in its depth.
Sjoerd Dirksen, Patrick Finke, Paul Geuchen et al.
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