Autoencoder-Enhanced Sum-Product Networks

Abstract

Sum-product networks (SPNs) are probabilistic models that guarantee exact inference in time linear in the size of the network. We use autoencoders in concert with SPNs to model high-dimensional, high-arity random vectors (e.g., image data). Experiments show that our proposed model, the autoencoder-SPN (AESPN), which combines two SPNs and an autoencoder, produces better samples than an SPN alone. This is true whether we sample all variables, or whether a set of unknown query variables is sampled, given a set of known evidence variables.