Ng, YC;
Chilinski, P;
Silva, R;
(2016)
Scaling Factorial Hidden Markov Models: Stochastic Variational Inference without Messages.
In:
Proceedings of the 29th Conference on Neural Information Processing Systems (NIPS 2016).
Neural Information Processing Systems Foundation: Barcelona, Spain.
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Abstract
Factorial Hidden Markov Models (FHMMs) are powerful models for sequential data but they do not scale well with long sequences. We propose a scalable inference and learning algorithm for FHMMs that draws on ideas from the stochastic variational inference, neural network and copula literatures. Unlike existing approaches, the proposed algorithm requires no message passing procedure among latent variables and can be distributed to a network of computers to speed up learning. Our experiments corroborate that the proposed algorithm does not introduce further approximation bias compared to the proven structured mean-field algorithm, and achieves better performance with long sequences and large FHMMs.
Type: | Proceedings paper |
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Title: | Scaling Factorial Hidden Markov Models: Stochastic Variational Inference without Messages |
Event: | 29th Conference on Neural Information Processing Systems (NIPS 2016) |
Open access status: | An open access version is available from UCL Discovery |
Publisher version: | http://papers.nips.cc/book/advances-in-neural-info... |
Language: | English |
UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
URI: | https://discovery.ucl.ac.uk/id/eprint/1529378 |
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