## Abstract

Sum-Product Networks (SPNs) are a highly efficient type of a deep probabilistic

model that allows exact inference in time linear in the size of the network. In

previous work, several heuristic structure learning approaches for SPNs have been

developed, which are prone to overfitting compared to a purely Bayesian model.

In this work, we propose a principled approach to structure learning in SPNs by

introducing infinite Sum-Product Trees (SPTs). Our approach is the first correct and

successful extension of SPNs to a Bayesian nonparametric model. We show that

infinite SPTs can be used successfully to discover SPN structures and outperform

infinite Gaussian mixture models in the task of density estimation.

model that allows exact inference in time linear in the size of the network. In

previous work, several heuristic structure learning approaches for SPNs have been

developed, which are prone to overfitting compared to a purely Bayesian model.

In this work, we propose a principled approach to structure learning in SPNs by

introducing infinite Sum-Product Trees (SPTs). Our approach is the first correct and

successful extension of SPNs to a Bayesian nonparametric model. We show that

infinite SPTs can be used successfully to discover SPN structures and outperform

infinite Gaussian mixture models in the task of density estimation.

Original language | English |
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Title of host publication | Neural Information Processing Systems (NIPS) workshop |

Publication status | Published - 2016 |