Name
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. |
Year | Venue | DocType |
|---|---|---|
2019 | ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 32 (NIPS 2019) | Conference |
Volume | ISSN | Citations |
32 | 1049-5258 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Conor Durkan | 1 | 0 | 1.69 |
| Bekasov, Artur | 2 | 0 | 0.34 |
| Iain Murray | 3 | 673 | 53.11 |
| George Papamakarios | 4 | 25 | 5.25 |