# Nickel 2017: Poincaré Embeddings for Learning Hierarchical Representations

> Maximilian Nickel, Douwe Kiela, 2017. 1 records on Mysteries Department cite it. Listed in the bibliography of the methods survey on emerging methods.

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# Poincaré Embeddings for Learning Hierarchical Representations

Maximilian Nickel, Douwe Kiela, 2017. _NeurIPS 2017_.

A machine-learning paper that embeds hierarchical data, such as taxonomies, in hyperbolic space rather than ordinary Euclidean space.

**Voynich Manuscript** ArticleChecked against the original0

## What it's used for here

Listed in the bibliography of the methods survey on emerging methods.

## Cited by on this site 1 record

[1 methods surveys]

Mathematics

SRC

## Cite it

```
Nickel, M., & Kiela, D. (2017). Poincaré Embeddings for Learning Hierarchical Representations. NeurIPS 2017. https://arxiv.org/abs/1705.08039
```

[Read it free at arxiv.org](https://arxiv.org/abs/1705.08039)

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**SRC** 0D8MS9M60TT1

Kind

Journal article

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arXiv-nonexclusive

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Against the published article

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The record

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