Sylvester Normalizing Flows for Variational Inference
Rianne van den Berg, Leonard Hasenclever, Jakub M. Tomczak, Max Welling, Globerson, A., Silva, R. · UvA-DARE (University of Amsterdam) · 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.