Learning the Structure of Sum-Product Networks via an SVD-based Algorithm

Adel, T. , Balduzzi, D. and Ghodsi, A. (2015) Learning the Structure of Sum-Product Networks via an SVD-based Algorithm. In: 31st Conference on Uncertainty in Artificial Intelligence (UAI 2015), Amsterdam, The Netherlands, 12-16 Jul 2015, pp. 32-41. ISBN 9780996643108

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Publisher's URL: https://auai.org/~w-auai/uai2015/proceedings.shtml

Abstract

Sum-product networks (SPNs) are a recently developed class of deep probabilistic models where inference is tractable. We present two new structure learning algorithms for sum-product networks, in the generative and discriminative settings, that are based on recursively extracting rank-one submatrices from data. The proposed algorithms find the subSPNs that are the most coherent jointly in the instances and variables - that is, whose instances are most strongly correlated over the given variables. Experimental results show that SPNs learned using the proposed generative algorithm have better likelihood and inference results - and also much faster - than previous approaches. Finally, we apply the discriminative SPN structure learning algorithm to handwritten digit recognition tasks, where it achieves state-of-the-art performance for an SPN.

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Hesham, Dr Tameem Adel
Authors: Adel, T., Balduzzi, D., and Ghodsi, A.
College/School:College of Science and Engineering > School of Computing Science
ISBN:9780996643108

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