PhD forum: Mean Field Variational inference using Bregman ADMM for distributed camera network

Behnam Babagholami-Mohamadabadi, Sejong Yoon, Vladimir Pavlovic

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Bayesian models provide a framework for probabilistic modelling of complex datasets. However, many of such models are computationally demanding especially in the presence of large datasets. On the other hand, in sensor network applications, statistical (Bayesian) parameter estimation usually needs distributed algorithms, in which both data and computation are distributed across the nodes of the net- work. In this paper we propose a general framework for distributed Bayesian learning using Bregman Alternating Direction Method of Multipliers (B-ADMM). We demonstrate the utility of our framework, with Mean Field Variational Bayes (MFVB) as the primitive for distributed a fine structure from motion (SfM).

Original languageEnglish (US)
Title of host publication9th International Conference on Distributed Smart Cameras, ICDSC 2015
PublisherAssociation for Computing Machinery
Pages209-210
Number of pages2
ISBN (Electronic)9781450336819
DOIs
StatePublished - Sep 8 2015
Event9th International Conference on Distributed Smart Cameras, ICDSC 2015 - Seville, Spain
Duration: Sep 8 2015Sep 11 2015

Publication series

NameACM International Conference Proceeding Series
Volume08-11-Sep-2015

Other

Other9th International Conference on Distributed Smart Cameras, ICDSC 2015
CountrySpain
CitySeville
Period9/8/159/11/15

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

Keywords

  • Distributed learning
  • Probabilistic principal component analysis
  • Variational method

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