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Neural Spline Flows
[article]
2019
arXiv
pre-print
A normalizing flow models a complex probability density as an invertible transformation of a simple base density. Flows based on either coupling or autoregressive transforms both offer exact density evaluation and sampling, but rely on the parameterization of an easily invertible elementwise transformation, whose choice determines the flexibility of these models. Building upon recent work, we propose a fully-differentiable module based on monotonic rational-quadratic splines, which enhances the
arXiv:1906.04032v2
fatcat:dw56ywfo2nao7frnmlfgo366yq