Methods › Natural Language Processing › Static Word Embeddings › Poincaré Embeddings
Poincaré Embeddings
Introduced by Maximilian Nickel et al. in Poincaré Embeddings for Learning Hierarchical Representations
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Poincaré Embeddings learn hierarchical representations of symbolic data by embedding them into hyperbolic space -- or more precisely into an n-dimensional Poincaré ball. Due to the underlying hyperbolic geometry, this allows for learning of parsimonious representations of symbolic data by simultaneously capturing hierarchy and similarity. Embeddings are learnt based on Riemannian optimization.
Papers archive 2025-07-28
1 shown of 1, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Poincaré Embeddings for Learning Hierarchical Representations 22 May 2017 · 9 repositories · arXiv:1705.08039Syntology ran 6 of 14 samples · 8 unverified · 6 pointer-only (licence)
Tasks archive 2025-07-28
3 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Graph Embedding | 1 |
| Representation Learning | 1 |
| Riemannian optimization | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections