MCMC for Hierarchical Semi-Markov Conditional Random fields

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MCMC for Hierarchical Semi-Markov Conditional Random fields

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 len...

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Bibliographic Details
Main Authors:	Truyen, Tran, Phung, Dinh, Venkatesh, Svetha, Bui, Hung H.
Other Authors:	Li Deng
Format:	Conference Paper
Published:	unknown 2009
Online Access:	http://hdl.handle.net/20.500.11937/42222

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