  Normalizing flows are a powerful tool for building expressive distributions
in high dimensions. So far, most of the literature has concentrated on learning
flows on Euclidean spaces. Some problems however, such as those involving
angles, are defined on spaces with more complex geometries, such as tori or
spheres. In this paper, we propose and compare expressive and numerically
stable flows on such spaces. Our flows are built recursively on the dimension
of the space, starting from flows on circles, closed intervals or spheres.
