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MCMC for Hierarchical Semi-Markov Conditional Random Fields

Tran, Truyen, Phung, Quoc-Dinh, Venkatesh, Svetha and Bui, Hung H. 2009, MCMC for Hierarchical Semi-Markov Conditional Random Fields, in NIPS'09 : Proceedings of the 2009 Deep Learning for Speech Recognition and Related Applications, [The Workshop], [Whistler, British Colombia], pp. 1-8.

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Title MCMC for Hierarchical Semi-Markov Conditional Random Fields
Author(s) Tran, TruyenORCID iD for Tran, Truyen orcid.org/0000-0001-6531-8907
Phung, Quoc-DinhORCID iD for Phung, Quoc-Dinh orcid.org/0000-0002-9977-8247
Venkatesh, Svetha
Bui, Hung H.
Conference name Deep Learning for Speech Recognition and Related Applications. Workshop (2009 : Whistler, British Colombia)
Conference location Whistler, BC, Canada
Conference dates 2009/12/12 - 2009/12/12
Title of proceedings NIPS'09 : Proceedings of the 2009 Deep Learning for Speech Recognition and Related Applications
Publication date 2009
Start page 1
End page 8
Total pages 8
Publisher [The Workshop]
Place of publication [Whistler, British Colombia]
Summary Deep architecture such as hierarchical semi-Markov models is an important class of models for nested sequential data. Current exact inference schemes either cost cubic time in sequence length, or exponential time in model depth. These costs are prohibitive for large-scale problems with arbitrary length and depth. In this contribution, we propose a new approximation technique that may have the potential to achieve sub-cubic time complexity in length and linear time depth, at the cost of some loss of quality. The idea is based on two well-known methods: Gibbs sampling and Rao-Blackwellisation. We provide some simulation-based evaluation of the quality of the RGBS with respect to run time and sequence length.
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 0 Not Applicable
HERDC Research category E2.1 Full written paper - non-refereed / Abstract reviewed
Persistent URL http://hdl.handle.net/10536/DRO/DU:30074712

Document type: Conference Paper
Collection: School of Information Technology
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