The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
An Efficient Learning Procedure for Deep Boltzmann Machines
Open accessAbstract
We present a new learning algorithm for Boltzmann machines that contain many layers of hidden variables. Data-dependent statistics are estimated using a variational approximation that tends to focus on a single mode, and data-independent statistics are estimated using persistent Markov chains. The use of two quite different techniques for estimating the two types of statistic that enter into the gradient of the log likelihood makes it practical to learn Boltzmann machines with multiple hidden layers and millions of parameters. The learning can be made more efficient by using a layer-by-layer pretraining phase that initializes the weights sensibly. The pretraining also allows the variational inference to be initialized sensibly with a single bottom-up pass. We present results on the MNIST and NORB data sets showing that deep Boltzmann machines learn very good generative models of handwritten digits and 3D objects. We also show that the features discovered by deep Boltzmann machines are a very effective way to initialize the hidden layers of feedforward neural nets, which are then discriminatively fine-tuned.
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Cites
A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336
Self-citationA Stochastic Approximation Method
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“Instead of viewing this learning procedure as a gross approximation to maximum likelihood learning, it can be viewed as a much better approximation to minimizing the difference of two divergences (Hinton, 2002), and so it is called contrastive divergence (CD) learning.”
BackgroundMethodInfluentialSelf-citationGreedy layer-wise training of deep networks
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A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants
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Self-citationRestricted Boltzmann machines for collaborative filtering
Salakhutdinov, R., Mnih, A., & Hinton, G. (2007) · Proceedings of the 24th international conference on Machine learning · doi:10.1145/1273496.1273596 · Cited by 1,885
Self-citationLearning methods for generic object recognition with invariance to pose and lighting
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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
Deep learning for computer vision: A brief review
Voulodimos, A., Doulamis, N., Doulamis, A., & Protopapadakis, E. (2018) · Computational Intelligence and Neuroscience · doi:10.1155/2018/7068349 · Cited by 3,403
Remote Sensing Image Scene Classification: Benchmark and State of the Art
Cheng, G., Han, J., & Lu, X. (2017) · Proceedings of the IEEE · doi:10.1109/jproc.2017.2675998 · Cited by 2,926
Deep learning for visual understanding: A review
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Deep learning for computational biology
Angermueller, C., Pärnamaa, T., Parts, L., & Stegle, O. (2016) · Molecular Systems Biology · doi:10.15252/msb.20156651 · Cited by 1,473
Metadata
- Authors
- Salakhutdinov, R., & Hinton, G.
- Venue
- Neural Computation
- Publisher
- MIT Press - Journals
- Published
- 2012-08
- Volume
- 24
- Issue
- 8
- Pages
- 1967-2006
- ISSN
- 0899-7667 (ISSN-L)
- 1530-888X
- OpenAlex
- W2071709160
- Cited by
- 484
- References
- 8
- FWCI
- 27.86
- Type
- journal article
- Language
- en
- Open access
- green
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