Sylvester Normalizing Flows for Variational Inference
Abstract
Variational inference relies on flexible approximate posterior distributions. Normalizing flows provide a general recipe to construct flexible variational posteriors. We introduce Sylvester normalizing flows, which can be seen as a generalization of planar flows. Sylvester normalizing flows remove the well-known single-unit bottleneck from planar flows, making a single transformation much more flexible. We compare the performance of Sylvester normalizing flows against planar flows and inverse autoregressive flows and demonstrate that they compare favorably on several datasets.
Cite
Text
van den Berg et al. "Sylvester Normalizing Flows for Variational Inference." Conference on Uncertainty in Artificial Intelligence, 2018.Markdown
[van den Berg et al. "Sylvester Normalizing Flows for Variational Inference." Conference on Uncertainty in Artificial Intelligence, 2018.](https://mlanthology.org/uai/2018/vandenberg2018uai-sylvester/)BibTeX
@inproceedings{vandenberg2018uai-sylvester,
title = {{Sylvester Normalizing Flows for Variational Inference}},
author = {van den Berg, Rianne and Hasenclever, Leonard and Tomczak, Jakub M. and Welling, Max},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2018},
pages = {393-402},
url = {https://mlanthology.org/uai/2018/vandenberg2018uai-sylvester/}
}