Dropout neural networks approximate the unit ball of W^{n,∞}([0,1]^d) by ReLU networks with random edge retention; Sobolev rates and confidence bounds
Read the original at arxiv.org→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,...
Original headline: "Approximation Property of Dropout Neural Networks: Sobolev Rates and Confidence Bounds"
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- Oct 5, 04:00 UTC arXiv cs.LG lead source Approximation Property of Dropout Neural Networks: Sobolev Rates and Confidence Bounds