@inproceedings{vandenberg2018sylvester,
author = {van den Berg, Rianne and Hasenclever, Leonard and M. Tomczak, Jakub and Welling, Max},
title = {Sylvester Normalizing Flows for Variational Inference},
booktitle = {2018 Uncertainty in Artificial Intelligence},
year = {2018},
month = {January},
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.},
publisher = {Association For Uncertainty in Artificial Intelligence (AUAI)},
url = {http://approjects.co.za/?big=en-us/research/publication/sylvester-normalizing-flows-for-variational-inference/},
pages = {393-402},
}