Sylvester Normalizing Flows for Variational Inference
2018-03-15Code Available1· sign in to hype
Rianne van den Berg, Leonard Hasenclever, Jakub M. Tomczak, Max Welling
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- github.com/riannevdberg/sylvester-flowsOfficialIn paperpytorch★ 0
- github.com/CW-Huang/CP-Flowpytorch★ 85
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.