Learning deep sigmoid belief networks with data augmentation

Zhe Gan, Ricardo Henao, David Carlson, Lawrence Carin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

34 Scopus citations

Abstract

Deep directed generative models are developed. The multi-layered model is designed by stacking sigmoid belief networks, with sparsity-encouraging priors placed on the model parameters. Learning and inference of layer-wise model parameters are implemented in a Bayesian setting. By exploring the idea of data augmentation and introducing auxiliary Polya-Gamma variables, simple and efficient Gibbs sampling and mean-field variational Bayes (VB) inference are implemented. To address large-scale datasets, an online version of VB is also developed. Experimental results are presented for three publicly available datasets: MNIST, Caltech 101 Silhouettes and OCR letters.
Original languageEnglish (US)
Title of host publicationJournal of Machine Learning Research
PublisherMicrotome Publishinginfo@mtome.com
Pages268-276
Number of pages9
StatePublished - Jan 1 2015
Externally publishedYes

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