MiS Preprint Repository
We have decided to discontinue the publication of preprints on our preprint server end of 2024. The publication culture within mathematics has changed so much due to the rise of repositories such as ArXiV (www.arxiv.org) that we are encouraging all institute members to make their preprints available there. An institute's repository in its previous form is, therefore, unnecessary. The preprints published to date will remain available here, but we will not add any new preprints here.
Universal Approximation of Markov Kernels by Shallow Stochastic Feedforward Networks
Guido MontúfarAbstract
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
- Received:
- 24.03.15
- Published:
- 26.03.15
- Keywords:
- stochastic feedforward network, universal approximation, sufficiency bounds