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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Cites
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“An RBM is an energy-based model for unsupervised learning (Hinton, 2002; Smolensky, 1986).”
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Cited by
Representation Learning: A Review and New Perspectives
Bengio, Y., Courville, A., & Vincent, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.50 · Cited by 13,001
Learning Deep Architectures for AI
Bengio, Y. (2009) · now publishers, Inc. eBooks · doi:10.1561/9781601982957 · Cited by 4,941
A Practical Guide to Training Restricted Boltzmann Machines
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A review of classification algorithms for EEG-based brain–computer interfaces: A 10 year update
Lotte, F., Bougrain, L., Cichocki, A., Clerc, M., Congedo, M., Rakotomamonjy, A., & Yger, F. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aab2f2 · Cited by 2,239
(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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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
