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: | , , , |
|---|---|
| Other Authors: | |
| Format: | Conference Paper |
| Published: |
unknown
2009
|
| Online Access: | http://hdl.handle.net/20.500.11937/42222 |
| _version_ | 1848756360584364032 |
|---|---|
| author | Truyen, Tran Phung, Dinh Venkatesh, Svetha Bui, Hung H. |
| author2 | Li Deng |
| author_facet | Li Deng Truyen, Tran Phung, Dinh Venkatesh, Svetha Bui, Hung H. |
| author_sort | Truyen, Tran |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T09:10:58Z |
| format | Conference Paper |
| id | curtin-20.500.11937-42222 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:10:58Z |
| publishDate | 2009 |
| publisher | unknown |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-422222022-12-09T07:12:34Z MCMC for Hierarchical Semi-Markov Conditional Random fields Truyen, Tran Phung, Dinh Venkatesh, Svetha Bui, Hung H. Li Deng Dong Yu Geoff Hinton 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. 2009 Conference Paper http://hdl.handle.net/20.500.11937/42222 unknown fulltext |
| spellingShingle | Truyen, Tran Phung, Dinh Venkatesh, Svetha Bui, Hung H. MCMC for Hierarchical Semi-Markov Conditional Random fields |
| title | MCMC for Hierarchical Semi-Markov Conditional Random fields |
| title_full | MCMC for Hierarchical Semi-Markov Conditional Random fields |
| title_fullStr | MCMC for Hierarchical Semi-Markov Conditional Random fields |
| title_full_unstemmed | MCMC for Hierarchical Semi-Markov Conditional Random fields |
| title_short | MCMC for Hierarchical Semi-Markov Conditional Random fields |
| title_sort | mcmc for hierarchical semi-markov conditional random fields |
| url | http://hdl.handle.net/20.500.11937/42222 |