Sylvester Normalizing Flows for Variational Inference

Rianne van den Berg, Leonard Hasenclever, Jakub Tomczak, Max Welling
Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:392-401, 2018.

Abstract

Variational inference relies on flexible ap- proximate posterior distributions. Normaliz- ing flows provide a general recipe to con- struct flexible variational posteriors. We in- troduce 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 pla- nar flows and inverse autoregressive flows and demonstrate that they compare favorably on several datasets.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR16-berg18a, title = {Sylvester Normalizing Flows for Variational Inference}, author = {Berg, Rianne van den and Hasenclever, Leonard and Tomczak, Jakub and Welling, Max}, booktitle = {Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence}, pages = {392--401}, year = {2018}, editor = {Globerson, Amir and Silva, Ricardo}, volume = {R16}, series = {Proceedings of Machine Learning Research}, month = {06--10 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r16/main/assets/berg18a/berg18a.pdf}, url = {https://proceedings.mlr.press/r16/berg18a.html}, abstract = {Variational inference relies on flexible ap- proximate posterior distributions. Normaliz- ing flows provide a general recipe to con- struct flexible variational posteriors. We in- troduce 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 pla- nar flows and inverse autoregressive flows and demonstrate that they compare favorably on several datasets.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Sylvester Normalizing Flows for Variational Inference %A Rianne van den Berg %A Leonard Hasenclever %A Jakub Tomczak %A Max Welling %B Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2018 %E Amir Globerson %E Ricardo Silva %F pmlr-vR16-berg18a %I PMLR %P 392--401 %U https://proceedings.mlr.press/r16/berg18a.html %V R16 %X Variational inference relies on flexible ap- proximate posterior distributions. Normaliz- ing flows provide a general recipe to con- struct flexible variational posteriors. We in- troduce 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 pla- nar flows and inverse autoregressive flows and demonstrate that they compare favorably on several datasets. %Z Reissued by PMLR on 04 October 2026.
APA
Berg, R.v.d., Hasenclever, L., Tomczak, J. & Welling, M.. (2018). Sylvester Normalizing Flows for Variational Inference. Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R16:392-401 Available from https://proceedings.mlr.press/r16/berg18a.html. Reissued by PMLR on 04 October 2026.

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