2 citations · 3 across the 2 of their papers we have counts for
2 papers
cs.LG2015★ 1 cited
Universal Approximation of Markov Kernels by Shallow Stochastic Feedforward Networks
Guido Montufar
We establish upper bounds for the minimal number of hidden units for which a binary stochastic feedforward network with sigmoid activation probabilities and a single hidden layer i…
stat.ML2012★ 2 cited
When Does a Mixture of Products Contain a Product of Mixtures?
Guido F. Montufar, Jason Morton
We derive relations between theoretical properties of restricted Boltzmann machines (RBMs), popular machine learning models which form the building blocks of deep learning models,…