Poincaré Embeddings for Learning Hierarchical Representations

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.