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What regularized auto-encoders learn from the data-generating distribution
Abstract
What do auto-encoders learn about the underlying data-generating distribution? Recent work suggests that some auto-encoder variants do a good job of capturing the local manifold structure of data. ...
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Cited by
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Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space
Nguyen, A., Clune, J., Bengio, Y., Dosovitskiy, A., & Yosinski, J. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.374 · Cited by 578
Deep learning in the construction industry: A review of present status and future innovations
Akinosho, T. D., Oyedele, L. O., Bilal, M., Ajayi, A. O., Delgado, M. D., Akinade, O. O., & Ahmed, A. A. (2020) · Journal of Building Engineering · doi:10.1016/j.jobe.2020.101827 · Cited by 537
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Metadata
- Authors
- AlainGuillaume, & BengioYoshua
- Venue
- Journal of Machine Learning Research
- Publisher
- The MIT Press
- Published
- 2014-01-01
- ISSN
- 1532-4435 (ISSN-L)
- DOI
- 10.5555/2627435.2750359
- OpenAlex
- W3007103623
- Cited by
- 162
- References
- 0
- FWCI
- 7.59
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: What regularized auto-encoders learn from the data-generating distribution. https://app.cochat.ai/papers/10.5555/2627435.2750359
- The paper
- AlainGuillaume, & BengioYoshua. (2014). What regularized auto-encoders learn from the data-generating distribution. Journal of Machine Learning Research. https://doi.org/10.5555/2627435.2750359
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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.
