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Variational EM Learning of DSBNs with Conditional Deep Boltzmann Machines

  • Xing Zhang
  • Siwei Lyu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8681)

Abstract

Variational EM (VEM) is an efficient parameter learning scheme for sigmoid belief networks with many layers of latent variables. The choice of the inference model that forms the variational lower bound of the log likelihood is critical in VEM learning. The mean field approximations and wake-sleep algorithm use simple models that are computationally efficient, but may be poor approximations to the true posterior densities when the latent variables have strong mutual dependencies. In this paper, we describe a variational EM learning method of DSBNs with a new inference model known as the conditional deep Boltzmann machine (cDBM), which is an undirected graphical model capable of representing complex dependencies among latent variables. We show that this algorithm does not require the computation of the intractable partition function in the undirected cDBM model, and can be accelerated with contrastive learning. Performances of the proposed method are evaluated and compared on handwritten digit data.

Keywords

Latent Variable Inference Model Latent Variable Model Complex Dependency Visible Variable 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Xing Zhang
    • 1
  • Siwei Lyu
    • 1
  1. 1.Computer Science DepartmentState University of New YorkUSA

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