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...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Published: |
unknown
2009
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| Online Access: | http://hdl.handle.net/20.500.11937/42222 |