Preprint 23/2015

Universal Approximation of Markov Kernels by Shallow Stochastic Feedforward Networks

Guido Montúfar

Contact the author: Please use for correspondence this email.
Submission date: 24. Mar. 2015
Pages: 15
Keywords and phrases: stochastic feedforward network, universal approximation, sufficiency bounds
Download full preprint: PDF (318 kB)

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 is a universal approximator of Markov kernels. We show that each possible probabilistic assignment of the states of n output units, given the states of k 1 input units, can be approximated arbitrarily well by a network with 2k1(2n1 1) hidden units.

24.11.2021, 02:18