Conference item
Online variational filtering and parameter learning
- Abstract:
-
We present a variational method for online state estimation and parameter learning in state-space models (SSMs), a ubiquitous class of latent variable models for sequential data. As per standard batch variational techniques, we use stochastic gradients to simultaneously optimize a lower bound on the log evidence with respect to both model parameters and a variational approximation of the states' posterior distribution. However, unlike existing approaches, our method is able to operate in an e...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
-
-
(Accepted manuscript, pdf, 4.1MB)
-
Authors
Funding
+ Engineering and Physical Sciences Research Council
More from this funder
Grant:
56726
EP/R013616/1
Bibliographic Details
- Publisher:
- Curran Associates Publisher's website
- Host title:
- Advances in Neural Information Processing Systems 34 (NeurIPS 2021)
- Volume:
- 34
- Pages:
- 18633-18645
- Publication date:
- 2022-05-31
- Acceptance date:
- 2021-10-04
- Event title:
- 35th Conference on Neural Information Processing Systems (NeurIPS 2021)
- Event location:
- Virtual event
- Event website:
- https://nips.cc/Conferences/2021/
- Event start date:
- 2021-12-06
- Event end date:
- 2021-12-14
- ISSN:
-
1049-5258
- ISBN:
- 9781713845393
Item Description
- Language:
- English
- Keywords:
- Pubs id:
-
1266431
- Local pid:
- pubs:1266431
- Deposit date:
- 2022-12-08
Terms of use
- Copyright holder:
- Campbell et al. and NIPS
- Copyright date:
- 2021
- Rights statement:
- Copyright © (2021) by individual authors and Neural Information Processing Systems Foundation Inc. All rights reserved.
- Notes:
- This is the accepted manuscript version of the paper. The final version is available from the Neural Information Processing Systems Foundation at: https://proceedings.neurips.cc/paper/2021/hash/9a6a1aaafe73c572b7374828b03a1881-Abstract.html
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