Reinterpreting Importance-Weighted Autoencoders
Machine Learning
2017-08-16 v2
Abstract
The standard interpretation of importance-weighted autoencoders is that they maximize a tighter lower bound on the marginal likelihood than the standard evidence lower bound. We give an alternate interpretation of this procedure: that it optimizes the standard variational lower bound, but using a more complex distribution. We formally derive this result, present a tighter lower bound, and visualize the implicit importance-weighted distribution.
Cite
@article{arxiv.1704.02916,
title = {Reinterpreting Importance-Weighted Autoencoders},
author = {Chris Cremer and Quaid Morris and David Duvenaud},
journal= {arXiv preprint arXiv:1704.02916},
year = {2017}
}
Comments
ICLR 2017 Workshop