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    MCMC for Hierarchical Semi-Markov Conditional Random fields

    133775_15619_truyen_phung_venkatesh_bui...pdf (187.6Kb)
    Access Status
    Open access
    Authors
    Truyen, Tran
    Phung, Dinh
    Venkatesh, Svetha
    Bui, Hung H.
    Date
    2009
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    Truyen, Tran The and Phung, Dinh Q. and Venkatesh, Svetha and Bui, Hung H. 2009. MCMC for Hierarchical Semi-Markov Conditional Random fields, in Li Deng, Dong Yu and Geoff Hinton (ed), NIPS 2009, Dec 12 2009, pp. 1-8. Whistler, BC, Canada.
    Source Title
    Deep learning for speech recognition and related applications
    Source Conference
    NIPS 2009
    Faculty
    School of Science and Computing
    Department of Computing
    Faculty of Science and Engineering
    URI
    http://hdl.handle.net/20.500.11937/42222
    Collection
    • Curtin Research Publications
    Abstract

    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.

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