An Introduction to Variational Autoencoders

FnTML 2019 pp. 307-392

doi:10.1561/2200000056 /ftml/2019/kingma2019ftml-introduction/

Abstract

Variational autoencoders provide a principled framework for learning deep latent-variable models and corresponding inference models. In this work, we provide an introduction to variational autoencoders and some important extensions.

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Cite

Text

Kingma and Welling. "An Introduction to Variational Autoencoders." Foundations and Trends in Machine Learning, 2019. doi:10.1561/2200000056

Markdown

[Kingma and Welling. "An Introduction to Variational Autoencoders." Foundations and Trends in Machine Learning, 2019.](https://mlanthology.org/ftml/2019/kingma2019ftml-introduction/) doi:10.1561/2200000056

BibTeX

@article{kingma2019ftml-introduction,
  title     = {{An Introduction to Variational Autoencoders}},
  author    = {Kingma, Diederik P. and Welling, Max},
  journal   = {Foundations and Trends in Machine Learning},
  year      = {2019},
  pages     = {307-392},
  doi       = {10.1561/2200000056},
  volume    = {12},
  url       = {https://mlanthology.org/ftml/2019/kingma2019ftml-introduction/}
}