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 language | English (US) |
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Title of host publication | Journal of Machine Learning Research |
Publisher | Microtome [email protected] |
Pages | 268-276 |
Number of pages | 9 |
State | Published - Jan 1 2015 |
Externally published | Yes |