The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Stacked Convolutional Auto-Encoders for Hierarchical Feature Extraction
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Citations
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
Object recognition from local scale-invariant features
Lowe, D. G. (1999) · Proceedings of the Seventh IEEE International Conference on Computer Vision · doi:10.1109/iccv.1999.790410 · Cited by 16,472
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
Fukushima, K. (1980) · Biological Cybernetics · doi:10.1007/bf00344251 · Cited by 5,759
Training Products of Experts by Minimizing Contrastive Divergence
Hinton, G. E. (2002) · Neural Computation · doi:10.1162/089976602760128018 · Cited by 4,879 · cited 1×
“Methods using this paradigm include stacks of: Low-Complexity Coding and Decoding machines (LOCOCODE) [10], Predictability Minimization layers [23,24], Restricted Boltzmann Machines (RBMs) [8], auto-encoders [20] and energy based models [15].”
MethodGreedy 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
(2011) · doi:10.5591/978-1-57735-516-8/ijcai11-210 · Cited by 1,229
Self-citation
Cited by
A survey of deep neural network architectures and their applications
Liu, W., Wang, Z., Liu, X., Zeng, N., Liu, Y., & Alsaadi, F. E. (2017) · Neurocomputing · doi:10.1016/j.neucom.2016.12.038 · Cited by 3,239
Deep Clustering for Unsupervised Learning of Visual Features
Caron, M., Bojanowski, P., Joulin, A., & Douze, M. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01264-9_9 · Cited by 2,674
Deep learning for visual understanding: A review
Guo, Y., Liu, Y., Oerlemans, A., Lao, S., Wu, S., & Lew, M. S. (2016) · Neurocomputing · doi:10.1016/j.neucom.2015.09.116 · Cited by 2,504
Unsupervised Representation Learning by Predicting Image Rotations
Gidaris, S., Singh, P., & Komodakis, N. (2018) · arXiv · doi:10.48550/arxiv.1803.07728 · Cited by 2,477
“…methods (Dosovitskiy et al., 2014; Liao et al., 2016; Yang et al., 2016), reconstruction based methods (Bengio et al., 2007; Huang et al., 2007; Masci et al., 2011), and methods that involve learning generative probabilistic models Goodfellow et al. (2014); Donahue et al. (2016); Radford et…”
MethodDeep Learning for Health Informatics
Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., & Yang, G.-Z. (2017) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2016.2636665 · Cited by 2,000
Metadata
- Authors
- Masci, J., Meier, U., Cireşan, D., & Schmidhuber, J.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2011
- Pages
- 52-59
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2136655611
- Cited by
- 1,955
- References
- 7
- FWCI
- 4.54
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Masci, J., Meier, U., Cireşan, D., & Schmidhuber, J. (2011). Stacked Convolutional Auto-Encoders for Hierarchical Feature Extraction. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-642-21735-7_7
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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.
