Publications [#338702] of Lawrence Carin

Papers Published

1. Gan, Z; Henao, R; Carlson, D; Carin, L, Learning deep sigmoid belief networks with data augmentation, Artificial Intelligence and Statistics, vol. 38 (2015), pp. 268-276
   (last updated on 2024/12/31)

   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.