Sylvester Normalizing Flows for Variational Inference - van den Berg, Rianne; Hasenclever, Leonard; M. Tomczak, Jakub; Welling, Max (2018 Uncertainty in Artificial Intelligence, 2018)
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.