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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.
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
SRCMathematics
Cite it
Nickel, M., & Kiela, D. (2017). Poincaré Embeddings for Learning Hierarchical Representations. NeurIPS 2017. https://arxiv.org/abs/1705.08039
@article{nickel2017poincare,
author = {Nickel, Maximilian and Kiela, Douwe},
title = {Poincaré Embeddings for Learning Hierarchical Representations},
journal = {NeurIPS 2017},
year = {2017},
url = {https://arxiv.org/abs/1705.08039}
}TY - JOUR TI - Poincaré Embeddings for Learning Hierarchical Representations AU - Nickel, Maximilian AU - Kiela, Douwe PY - 2017 JO - NeurIPS 2017 UR - https://arxiv.org/abs/1705.08039 ER -
Nickel, Maximilian and Douwe Kiela. “Poincaré Embeddings for Learning Hierarchical Representations.” NeurIPS 2017 (2017).