Deep Generative Models with Stick Breaking Priors
Bayesian nonparametric models are attractive for their data dependent capacity, but their implementation can be problematic due to computational or analytical obstacles. We make progress on this problem by extending Stochastic Gradient Variational Bayes (Kingma Welling, 2013) a 'black box' method for approximate posterior inference, to stick breaking priors (Ishwaran James, 2001) This innovation allows us to define deep generative models (DGMs) with infinite dimensional latent variables. We experimentally demonstrate that DGMs with Dirichlet process priors learn highly discriminative latent representations that are well suited for semi supervised settings and often outperform the popular Gaussian alternative.