  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.
