The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Training restricted Boltzmann machines using approximations to the likelihood gradient
Abstract
A new algorithm for training Restricted Boltzmann Machines is introduced. The algorithm, named Persistent Contrastive Divergence, is different from the standard Contrastive Divergence algorithms in that it aims to draw samples from almost exactly the model distribution. It is compared to some standard Contrastive Divergence and Pseudo-Likelihood algorithms on the tasks of modeling and classifying various types of data. The Persistent Contrastive Divergence algorithm outperforms the other algorithms, and is equally fast and simple.
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A review of classification algorithms for EEG-based brain–computer interfaces: A 10 year update
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(2015) · Cited by 989
Metadata
- Authors
- Tieleman, T.
- Venue
- Proceedings of the 25th international conference on Machine learning - ICML '08
- Publisher
- ACM Press
- Published
- 2008
- Pages
- 1064-1071
- OpenAlex
- W2116825644
- Cited by
- 837
- References
- 18
- FWCI
- 16.62
- Type
- conference paper
- Language
- en
- Open access
- closed
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