title: Poincaré Wasserstein Autoencoder
abstract: This work presents the Poincaré Wasserstein Autoencoder, a reformulation of
  the recently proposed Wasserstein autoencoder framework on a non-Euclidean
  manifold, the Poincaré ball model of the hyperbolic space H n . By assuming the
  latent space to be hyperbolic, we can use its intrinsic hierarchy to impose structure
  on the learned latent space representations. We show that for datasets with latent
  hierarchies, we can recover the structure in a low-dimensional latent space. We
  also demonstrate the model in the visual domain to analyze some of its properties
  and show competitive results on a graph link prediction task.
authors: Ivan Ovinnikov
hIndex: Ivan Ovinnikov 2