Neural Spline Flows [article]

Conor Durkan, Artur Bekasov, Iain Murray, George Papamakarios
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
more » ... flexibility of both coupling and autoregressive transforms while retaining analytic invertibility. We demonstrate that neural spline flows improve density estimation, variational inference, and generative modeling of images.
arXiv:1906.04032v2 fatcat:dw56ywfo2nao7frnmlfgo366yq