19 citations · 53 across the 21 of their papers we have counts for
4 papers · 1 filter
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…
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…
Modeling Rydberg Gases using Random Sequential Adsorption on Random Graphs
Daan Rutten, Jaron Sanders
The statistics of strongly interacting, ultracold Rydberg gases are governed by the interplay of two factors: geometrical restrictions induced by blockade effects, and quantum mech…
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…