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:
- Moebius transformations (described in arXiv:2002.02428) ("m")
- Circular rational-quadratic splines (described in arXiv:2002.02428) ("o")
S2:
- Autorregressive flow for N-Spheres (arXiv:2002.02428) ("n")
- Exponential map flow (arXiv:0906.0874/arXiv:2002.02428) ("v")
- Neural Manifold Ordinary Differential Equations arXiv:2006.10254 ("c")
Interval Flows:
- "Neural Spline Flows" (Rational-quadratic splines) arXiv:1906.04032 ("r")
Simplex Flows:
- Autoregressive simplex flow arXiv:2008.05456 ("w")