The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Self-organizing neural network that discovers surfaces in random-dot stereograms
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Citations
Cites
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-citation
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
A simple framework for contrastive learning of visual representations
Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020) · arXiv · doi:10.48550/arxiv.2002.05709 · Cited by 10,857
Self-citationAn Information-Maximization Approach to Blind Separation and Blind Deconvolution
Bell, A. J., & Sejnowski, T. J. (1995) · Neural Computation · doi:10.1162/neco.1995.7.6.1129 · Cited by 8,986
Masked Autoencoders Are Scalable Vision Learners
He, K., Chen, X., Xie, S., Li, Y., Dollar, P., & Girshick, R. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01553 · Cited by 8,489
“Recently, contrastive learning [3, 22] has been popular, e.g ., [62, 43, 23, 7], which models image similarity and dissimilarity (or only similarity [21, 8]) between two or more views.”
BackgroundBootstrap Your Own Latent: A New Approach to Self-Supervised Learning
Grill, J.-B., Strub, F., Altché, F., Tallec, C., Richemond, P. H., Buchatskaya, E., Doersch, C., Pires, B. A., Guo, Z. D., Azar, M. G., et al. (2020) · arXiv · doi:10.48550/arxiv.2006.07733 · Cited by 3,846
Metadata
- Authors
- Becker, S., & Hinton, G. E.
- Venue
- Nature
- Published
- 1992-01
- Volume
- 355
- Issue
- 6356
- Pages
- 161-163
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W2063971957
- Cited by
- 495
- References
- 1
- FWCI
- 16.54
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Self-organizing neural network that discovers surfaces in random-dot stereograms. https://app.cochat.ai/papers/10.1038/355161a0
- The paper
- Becker, S., & Hinton, G. E. (1992). Self-organizing neural network that discovers surfaces in random-dot stereograms. Nature. https://doi.org/10.1038/355161a0
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.
