
Improving Variational Inference with Inverse Autoregressive Flow
The framework of normalizing flows provides a general strategy for flexi...
06/15/2016 ∙ by Diederik P. Kingma, et al. ∙ 0 ∙ shareread it

Inferring the quantum density matrix with machine learning
We introduce two methods for estimating the density matrix for a quantum...
04/11/2019 ∙ by Kyle Cranmer, et al. ∙ 0 ∙ shareread it

Variational Inference with Normalizing Flows
The choice of approximate posterior distribution is one of the core prob...
05/21/2015 ∙ by Danilo Jimenez Rezende, et al. ∙ 0 ∙ shareread it

MultiSource MultiSink Nash Flows Over Time
Nash flows over time describe the behavior of selfish users eager to rea...
07/03/2018 ∙ by Leon Sering, et al. ∙ 0 ∙ shareread it

Developing Synthesis Flows Without Human Knowledge
Design flows are the explicit combinations of design transformations, pr...
04/16/2018 ∙ by Cunxi Yu, et al. ∙ 0 ∙ shareread it

Neural Spline Flows
A normalizing flow models a complex probability density as an invertible...
06/10/2019 ∙ by Conor Durkan, et al. ∙ 5 ∙ shareread it

Discrete Flows: Invertible Generative Models of Discrete Data
While normalizing flows have led to significant advances in modeling hig...
05/24/2019 ∙ by Dustin Tran, et al. ∙ 6 ∙ shareread it
Sylvester Normalizing Flows for Variational Inference
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 wellknown singleunit 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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