@inproceedings{c8b67f6ab5974f83b5af2b5035be801d,
title = "Monte Carlo Variational Auto-Encoders",
abstract = "Variational auto-encoders (VAE) are popular deep latent variable models which are trained by maximizing an Evidence Lower Bound (ELBO). To obtain tighter ELBO and hence better variational approximations, it has been proposed to use importance sampling to get a lower variance estimate of the evidence. However, importance sampling is known to perform poorly in high dimensions. While it has been suggested many times in the literature to use more sophisticated algorithms such as Annealed Importance Sampling (AIS) and its Sequential Importance Sampling (SIS) extensions, the potential benefits brought by these advanced techniques have never been realized for VAE: the AIS estimate cannot be easily differentiated, while SIS requires the specification of carefully chosen backward Markov kernels. In this paper, we address both issues and demonstrate the performance of the resulting Monte Carlo VAEs on a variety of applications.",
author = "Achille Thin and Nikita Kotelevskii and Alain Durmus and Maxim Panov and Eric Moulines and Arnaud Doucet",
note = "Publisher Copyright: Copyright {\textcopyright} 2021 by the author(s); 38th International Conference on Machine Learning, ICML 2021 ; Conference date: 18-07-2021 Through 24-07-2021",
year = "2021",
month = jan,
day = "1",
language = "English",
series = "Proceedings of Machine Learning Research",
publisher = "ML Research Press",
pages = "10247--10257",
booktitle = "Proceedings of the 38th International Conference on Machine Learning, ICML 2021",
}