[1705.08039] Poincaré Embeddings for Learning Hierarchical Representations
▻https://arxiv.org/abs/1705.08039
Representation learning has become an invaluable approach for learning from symbolic data such as text and graphs. However, while complex symbolic datasets often exhibit a latent hierarchical structure, state-of-the-art methods typically learn embeddings in Euclidean vector spaces, which do not account for this property. For this purpose, 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 Poincar\’e ball.
