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== History ==
The autoencoder has also been called the autoassociator,<ref>{{Cite journal |last1=Japkowicz |first1=Nathalie |last2=Hanson |first2=Stephen José |last3=Gluck |first3=Mark A. |date=2000-03-01 |title=Nonlinear Autoassociation Is Not Equivalent to PCA |url=https://doi.org/10.1162/089976600300015691 |journal=Neural Computation |volume=12 |issue=3 |pages=531–545 |doi=10.1162/089976600300015691 |pmid=10769321 |s2cid=18490972 |issn=0899-7667}}</ref> or Diabolo network.<ref>{{Cite journal |last1=Schwenk |first1=Holger |last2=Bengio |first2=Yoshua |date=1997 |title=Training Methods for Adaptive Boosting of Neural Networks |url=https://proceedings.neurips.cc/paper/1997/hash/9cb67ffb59554ab1dabb65bcb370ddd9-Abstract.html |journal=Advances in Neural Information Processing Systems |publisher=MIT Press |volume=10}}</ref><ref name="bengio" /> Its first applications date to the 1980s.<ref name=":0" /><ref>[https://www.cs.toronto.edu/~fritz/absps/cogscibm.pdf Ackley, D. H., Hinton, G. E., & Sejnowski, T. J. (1985). A learning algorithm for Boltzmann machines. Cognitive Science, 9, 147-169.]</ref><ref>{{Cite journal |last=Schmidhuber |first=Jürgen |date=January 2015 |title=Deep learning in neural networks: An overview |journal=Neural Networks |volume=61 |pages=85–117 |arxiv=1404.7828 |doi=10.1016/j.neunet.2014.09.003 |pmid=25462637 |s2cid=11715509}}</ref><ref>Hinton, G. E., & Zemel, R. S. (1994). Autoencoders, minimum description length and Helmholtz free energy. In ''Advances in neural information processing systems 6'' (pp. 3-10).</ref> Their most traditional application was [[dimensionality reduction]] or [[feature learning]], but the concept became widely used for learning [[generative model]]s of data.<ref name="VAE">{{cite arXiv |eprint=1312.6114 |class=stat.ML |author1=Diederik P Kingma |first2=Max |last2=Welling |title=Auto-Encoding Variational Bayes |date=2013}}</ref><ref name="gan_faces">Generating Faces with Torch, Boesen A., Larsen L. and Sonderby S.K., 2015 {{url|http://torch.ch/blog/2015/11/13/gan.html}}</ref> Some of the most powerful [[Artificial intelligence|AIs]] in the 2010s involved autoencoders stacked inside [[Deep learning|deep]] neural networks.<ref name="domingos">{{cite book |last1=Domingos |first1=Pedro |title=The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World |title-link=The Master Algorithm |date=2015 |publisher=Basic Books |isbn=978-046506192-1 |at="Deeper into the Brain" subsection |chapter=4 |author-link=Pedro Domingos}}</ref>
==Variations==
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