Online Structure Learning for Sum-Product Networks with Gaussian Leaves

Wilson Hsu, Agastya Kalra, Pascal Poupart

Nov 04, 2016 (modified: Jan 18, 2017) ICLR 2017 conference submission readers: everyone
  • Abstract: Sum-product networks have recently emerged as an attractive representation due to their dual view as a special type of deep neural network with clear semantics and a special type of probabilistic graphical model for which inference is always tractable. Those properties follow from some conditions (i.e., completeness and decomposability) that must be respected by the structure of the network. As a result, it is not easy to specify a valid sum-product network by hand and therefore structure learning techniques are typically used in practice. This paper describes the first {\em online} structure learning technique for continuous SPNs with Gaussian leaves. We also introduce an accompanying new parameter learning technique.
  • TL;DR: This paper describes the first online structure learning technique for continuous SPNs with Gaussian leaves.
  • Conflicts: uwaterloo.ca
  • Keywords: Unsupervised Learning, Deep learning

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