The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
Autoencoders
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Citations
Cites
Reducing the dimensionality of data with neural networks
Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928
Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Foundations
Rumelhart, D. E., McClelland, J. L., & AU (1986) · doi:10.7551/mitpress/5236.001.0001 · Cited by 15,686
A Global Geometric Framework for Nonlinear Dimensionality Reduction
Tenenbaum, J. B., Silva, V. D., & Langford, J. C. (2000) · Science · doi:10.1126/science.290.5500.2319 · Cited by 13,585
Wold, S., Esbensen, K., & Geladi, P. (1987) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/0169-7439(87)80084-9 · Cited by 11,606
(2006) · Springer eBooks · Cited by 8,123
Deep Learning Face Attributes in the Wild
Liu, Z., Luo, P., Wang, X., & Tang, X. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.425 · Cited by 7,747
Extracting and composing robust features with denoising autoencoders
Vincent, P., Larochelle, H., Bengio, Y., & Manzagol, P.-A. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390294 · Cited by 7,436
Proceedings of the 25th international conference on Machine learning - ICML '08
(2008) · doi:10.1145/1390156 · Cited by 6,680
Image Style Transfer Using Convolutional Neural Networks
Gatys, L. A., Ecker, A. S., & Bethge, M. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.265 · Cited by 6,025
Divergence measures based on the Shannon entropy
Lin, J. (1991) · IEEE Transactions on Information Theory · doi:10.1109/18.61115 · Cited by 5,140
Cited by
Autoencoders and their applications in machine learning: a survey
Berahmand, K., Daneshfar, F., Salehi, E. S., Li, Y., & Xu, Y. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-023-10662-6 · Cited by 593
Data-centric Artificial Intelligence: A Survey
Zha, D., Bhat, Z. P., Lai, K.-H., Yang, F., Jiang, Z., Zhong, S., & Hu, X. (2025) · ACM Computing Surveys · doi:10.1145/3711118 · Cited by 334
Wang, F., Jiang, Y., Zhang, R., Wei, A., Xie, J., & Pang, X. (2025) · Sensors · doi:10.3390/s25010190 · Cited by 119
Speech based detection of Alzheimer's disease: a survey of AI techniques, datasets and challenges
Ding, K., Chetty, M., Noori Hoshyar, A., Bhattacharya, T., & Klein, B. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10961-6 · Cited by 82
GenConViT: Deepfake Video Detection Using Generative Convolutional Vision Transformer
Deressa, D. W., Mareen, H., Lambert, P., Atnafu, S., Akhtar, Z., & Van Wallendael, G. (2025) · Applied Sciences · doi:10.3390/app15126622 · Cited by 46
Metadata
- Authors
- Bank, D., Koenigstein, N., & Giryes, R.
- Venue
- Machine Learning for Data Science Handbook
- Publisher
- Springer International Publishing
- Published
- 2023
- Pages
- 353-374
- OpenAlex
- W4385958045
- Cited by
- 406
- References
- 30
- FWCI
- 234.17
- Type
- book chapter
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Autoencoders. https://app.cochat.ai/papers/10.1007/978-3-031-24628-9_16
- The paper
- Bank, D., Koenigstein, N., & Giryes, R. (2023). Autoencoders. Machine Learning for Data Science Handbook. https://doi.org/10.1007/978-3-031-24628-9_16
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.
