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Releases: thoglu/jammy_flows

v.1.0.0

11 Nov 13:41
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First release with basic funtionality.

General:

  • Autoregressive conditional structure is taken care of behind the scenes and connects manifolds
  • Coverage is straightforward. Everything (including spherical, interval and simplex flows) is based on a Gaussian base distribution (arXiv:2008.0582).
  • Bisection & Newton iterations for differentiable inverse (used for certain non-analytic inverse flow functions)
  • amortizable MLPs that can use low-rank approximations
  • amortizable PDFs - the total PDF can be the output of another neural network
  • unit tests that make sure backwards / and forward flow passes of all implemented flow-layers agree
  • include log-lambda as an additional flow parameter to define parametrized Poisson-Processes
  • easily extendible: define new Euclidean / spherical flow layers by subclassing Euclidean or spherical base classes

Euclidean flows:

  • Generic affine flow (Multivariate normal distribution) ("t")
  • Gaussianization flow arXiv:2003.01941 ("g")
  • Hybrid of nonlinear scalings and rotations ("Polynomial Stretch flow") ("p")

Spherical flows:

S1:

S2:

Interval Flows:

Simplex Flows: