1 code implementation • 15 Dec 2023 • Peter Sorrenson, Felix Draxler, Armand Rousselot, Sander Hummerich, Ullrich Köthe
Many real world data, particularly in the natural sciences and computer vision, lie on known Riemannian manifolds such as spheres, tori or the group of rotation matrices.
1 code implementation • 25 Oct 2023 • Felix Draxler, Peter Sorrenson, Lea Zimmermann, Armand Rousselot, Ullrich Köthe
Normalizing Flows are generative models that directly maximize the likelihood.
2 code implementations • 2 Jun 2023 • Peter Sorrenson, Felix Draxler, Armand Rousselot, Sander Hummerich, Lea Zimmermann, Ullrich Köthe
Normalizing Flows explicitly maximize a full-dimensional likelihood on the training data.
1 code implementation • 16 Apr 2021 • Barry M. Dillon, Tilman Plehn, Christof Sauer, Peter Sorrenson
In particular, the Dirichlet setup solves the problem and improves both the performance and the interpretability of the networks.
1 code implementation • ICLR 2020 • Peter Sorrenson, Carsten Rother, Ullrich Köthe
Furthermore, the recovered informative latent variables will be in one-to-one correspondence with the true latent variables of the generating process, up to a trivial component-wise transformation.