Ordering Dimensions with Nested Dropout Normalizing Flows
Machine Learning
2020-06-17 v1 Machine Learning
Abstract
The latent space of normalizing flows must be of the same dimensionality as their output space. This constraint presents a problem if we want to learn low-dimensional, semantically meaningful representations. Recent work has provided compact representations by fitting flows constrained to manifolds, but hasn't defined a density off that manifold. In this work we consider flows with full support in data space, but with ordered latent variables. Like in PCA, the leading latent dimensions define a sequence of manifolds that lie close to the data. We note a trade-off between the flow likelihood and the quality of the ordering, depending on the parameterization of the flow.
Cite
@article{arxiv.2006.08777,
title = {Ordering Dimensions with Nested Dropout Normalizing Flows},
author = {Artur Bekasov and Iain Murray},
journal= {arXiv preprint arXiv:2006.08777},
year = {2020}
}