The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
An empirical evaluation of deep architectures on problems with many factors of variation
Abstract
Recently, several learning algorithms relying on models with deep architectures have been proposed. Though they have demonstrated impressive performance, to date, they have only been evaluated on relatively simple problems such as digit recognition in a controlled environment, for which many machine learning algorithms already report reasonable results. Here, we present a series of experiments which indicate that these models show promise in solving harder learning problems that exhibit many factors of variation. These models are compared with well-established algorithms such as Support Vector Machines and single hidden-layer feed-forward neural networks.
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Citations
Cites
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“Although P (x) is not tractable in an RBM, the Contrastive Divergence gradient (Hinton, 2002) is a good (a) RBM for x (b) RBM for h(1) (c) RBM for h(2) and y”
BackgroundGreedy layer-wise training of deep networks
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Self-citationLearning methods for generic object recognition with invariance to pose and lighting
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Training Invariant Support Vector Machines
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Learning a Nonlinear Embedding by Preserving Class Neighbourhood Structure
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Exponential Family Harmoniums with an Application to Information Retrieval
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To recognize shapes, first learn to generate images
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Cited by
(2010) · Cited by 14,134
(2010) · International Conference on Machine Learning · Cited by 13,995
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(2012) · Cited by 8,009
Extracting and composing robust features with denoising autoencoders
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Metadata
- Authors
- Larochelle, H., Erhan, D., Courville, A., Bergstra, J., & Bengio, Y.
- Venue
- Proceedings of the 24th international conference on Machine learning
- Publisher
- ACM
- Published
- 2007-06-20
- Pages
- 473-480
- OpenAlex
- W1994197834
- Cited by
- 992
- References
- 14
- FWCI
- 11.71
- Type
- conference paper
- Language
- en
- Open access
- closed
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