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- W2948659923 abstract "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." @default.
- W2948659923 created "2019-06-14" @default.
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- W2948659923 date "2019-06-10" @default.
- W2948659923 modified "2023-09-23" @default.
- W2948659923 title "Neural Spline Flows" @default.
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