Learning Relational Sum-Product Networks

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Learning Relational Sum-Product Networks
Sum-product networks (SPNs) are a recently-proposed deep architecture that guarantees tractable inference, even on certain high-treewidth models. SPNs are a propositional architecture, treating the instances as independent and identically distributed. In this paper, we introduce Relational SumProduct Networks (RSPNs), a new tractable first-order probabilistic architecture. RSPNs generalize SPNs by modeling a set of instances jointly, allowing them to influence each other’s probability distributions, as well as modeling probabilities of relations between objects. We also present LearnRSPN, the first algorithm for learning high-treewidth tractable statistical relational models. LearnRSPN is a recursive topdown structure learning algorithm for RSPNs, based on Gens and Domingos’ LearnSPN algorithm for propositional SPN learning. We evaluate the algorithm on three datasets; the RSPN learning algorithm outperforms Markov Logic Networks in both running time and predictive accuracy.
Aniruddh Nath, Pedro M. Domingos
Added 27 Mar 2016
Updated 27 Mar 2016
Type Journal
Year 2015
Where AAAI
Authors Aniruddh Nath, Pedro M. Domingos
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