11 citations · 18 across the 6 of their papers we have counts for
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cs.LG2020★ 11 cited
Universal Approximation in Dropout Neural Networks
Oxana A. Manita, Mark A. Peletier, Jacobus W. Portegies +2
We prove two universal approximation theorems for a range of dropout neural networks. These are feed-forward neural networks in which each edge is given a random -valued f…
cs.LG2020
Asymptotic convergence rate of Dropout on shallow linear neural networks
Albert Senen-Cerda, Jaron Sanders
We analyze the convergence rate of gradient flows on objective functions induced by Dropout and Dropconnect, when applying them to shallow linear Neural Networks (NNs) - which can…
math.OC2020★ 6 cited
Almost Sure Convergence of Dropout Algorithms for Neural Networks
Albert Senen-Cerda, Jaron Sanders
We investigate the convergence and convergence rate of stochastic training algorithms for Neural Networks (NNs) that have been inspired by Dropout (Hinton et al., 2012). With the g…