The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
To recognize shapes, first learn to generate images
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Cites
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892
Self-citationA 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-citationTraining Products of Experts by Minimizing Contrastive Divergence
Hinton, G. E. (2002) · Neural Computation · doi:10.1162/089976602760128018 · Cited by 4,879 · cited 1×
“The true classes are arranged in standard scan order. detectors and no direct interactions between the pixels, there is a simple and efficient way to learn a good set of feature detectors from a set of training images (Hinton, 2002).”
BackgroundSelf-citationGreedy layer-wise training of deep networks
Bengio, Y., Lamblin, P., Popovici, D., & Larochelle, H. (2007) · Advances in Neural Information Processing Systems 19 · doi:10.7551/mitpress/7503.003.0024 · Cited by 4,841
Learning methods for generic object recognition with invariance to pose and lighting
LeCun, Y., Huang, F. J., & Bottou, L. (2004) · Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004. · doi:10.1109/cvpr.2004.1315150 · Cited by 1,324
Dependence on REM Sleep of Overnight Improvement of a Perceptual Skill
Karni, A., Tanne, D., Rubenstein, B. S., Askenasy, J. J. M., & Sagi, D. (1994) · Science · doi:10.1126/science.8036518 · Cited by 1,002
Learning in spiking neural networks by reinforcement of stochastic synaptic transmission
Seung, H. S. (2003) · Neuron · doi:10.1016/s0896-6273(03)00761-x · Cited by 340
Cited by
Whatever next? Predictive brains, situated agents, and the future of cognitive science
Clark, A. (2013) · Behavioral and Brain Sciences · doi:10.1017/s0140525x12000477 · Cited by 5,924
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
Hinton, G. E. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-35289-8_32 · Cited by 2,852
Self-citationAn empirical evaluation of deep architectures on problems with many factors of variation
Larochelle, H., Erhan, D., Courville, A., Bergstra, J., & Bengio, Y. (2007) · Proceedings of the 24th international conference on Machine learning · doi:10.1145/1273496.1273556 · Cited by 992
(2007) · Cited by 937
Metadata
- Authors
- Hinton, G. E.
- Venue
- Progress in Brain Research
- Publisher
- Elsevier
- Published
- 2007
- Volume
- 165
- Pages
- 535-547
- ISSN
- 0079-6123 (ISSN-L)
- OpenAlex
- W1543614656
- Cited by
- 309
- References
- 9
- FWCI
- 15.76
- Type
- book chapter
- Language
- en
- Open access
- closed
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- Hinton, G. E. (2007). To recognize shapes, first learn to generate images. Progress in Brain Research. https://doi.org/10.1016/s0079-6123(06)65034-6
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
