PyTorch implementation of Poincaré embeddings for learning hierarchical representations.

Representation learning has become an invaluable approach for learning from symbolic data such as text and graphs. However, state-of-the-art embedding methods typically do not account for latent hierarchical structures which are characteristic for many complex symbolic datasets. In this work, we introduce a new approach for learning hierarchical representations of symbolic data by embedding them into hyperbolic space — or more precisely into an n-dimensional Poincaré ball.