Detailed Instructions: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Problem:This work presents the Poincare Wasserstein Autoencoder, a reformulation of
the recently proposed Wasserstein autoencoder framework on a non-Euclidean
manifold, the Poincare 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.
Solution:
Wasserstein Autoencoder with hyperbolic latent space