Tractable Probabilistic Graph Representation Learning with Graph-Induced Sum-Product Networks

International Conference on Learning Representations

We propose a probabilistic model that bridges graph machine learning and sum-product networks to tractably answer probabilistic queries. Our Graph-induced Sum-Product Network can solve unsupervised and supervised graph tasks and it is especially effective in modeling the missing data distribution as well as exploiting unlabeled data in a scarce supervision scenario.

Federico Errica
Federico Errica
Research Scientist

My research interests include distributed robotics, mobile computing and programmable matter.