Sylvester Normalizing Flows for Variational Inference

Rianne van den Berg; Leonard Hasenclever; Jakub M. Tomczak; Max Welling

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arXiv · 1803.05649

Sylvester Normalizing Flows for Variational Inference

Rianne van den Berg·Leonard Hasenclever·Jakub M. Tomczak·Max Welling

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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.

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BibTeX RIS

Rianne van den Berg, Leonard Hasenclever, Jakub M. Tomczak, Max Welling. 2018-03-15. Sylvester Normalizing Flows for Variational Inference. https://arxiv.org/abs/1803.05649

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