In this episode, we dive into Variational Autoencoders, a class of neural networks that can learn to compress data completely unsupervised! VAE's are a very ...
J. Huggins, M. Kasprzak, T. Campbell, and T. Broderick. (2019)cite arxiv:1910.04102Comment: A python package for carrying out our validated variational inference workflow -- including doing black-box variational inference and computing the bounds we develop in this paper -- is available at https://github.com/jhuggins/viabel. The same repository also contains code for reproducing all of our experiments.
D. Burt, C. Rasmussen, and M. Van Der Wilk. Proceedings of the 36th International Conference on Machine Learning, volume 97 of Proceedings of Machine Learning Research, page 862--871. Long Beach, California, USA, PMLR, (09--15 Jun 2019)
J. Hron, A. Matthews, and Z. Ghahramani. Proceedings of the 35th International Conference on Machine Learning, volume 80 of Proceedings of Machine Learning Research, page 2019--2028. Stockholmsmässan, Stockholm Sweden, PMLR, (10--15 Jul 2018)
Y. Yang, R. Bamler, and S. Mandt. (2020)cite arxiv:2006.04240Comment: 8 pages + detailed supplement with additional qualitative and quantitative results.