Concentration inequalities and optimal number of layers for stochastic deep neural networks
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Abstract (arXiv)
We state concentration inequalities for the output of the hidden layers of a stochastic deep neural network (SDNN), as well as for the output of the whole SDNN. These results allow us to introduce an expected classifier (EC), and to give probabilistic upper bound for the classification error of the EC. We also state the optimal number of layers for the SDNN via an optimal stopping procedure. We apply our analysis to a stochastic version of a feedforward neural network with ReLU activation function.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 21 of its 53 references that are in the map. First posted on arXiv 22 Jun 2022.
Builds on
21 of 53 references.
References · 21
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9 more
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