Approximation Property of Dropout Neural Networks: Sobolev Rates and Confidence Bounds
arXiv:2610.02253v1 Announce Type: new Abstract: The universal approximation property of dropout neural networks does not by itself describe the network size required for an accurate random realization. In this work, we study approximation of the unit ball of $W^{n,\infty}([0,1]^d)$ by ReLU networks…
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- 2026-10-05 04:00 · arXiv cs.LG
Approximation Property of Dropout Neural Networks: Sobolev Rates and Confidence Bounds