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Neural Spline Flows
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 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.Comment: Published at the 33rd Conference on Neural Information Processing
Systems (NeurIPS 2019), Vancouver, Canad
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