• Corpus ID: 235658959

Hyperbolic Busemann Learning with Ideal Prototypes

@inproceedings{Atigh2021HyperbolicBL,
  title={Hyperbolic Busemann Learning with Ideal Prototypes},
  author={Mina Ghadimi Atigh and Martin Keller-Ressel and Pascal Mettes},
  booktitle={Neural Information Processing Systems},
  year={2021}
}
Hyperbolic space has become a popular choice of manifold for representation learning of arbitrary data, from tree-like structures and text to graphs. Building on the success of deep learning with prototypes in Euclidean and hyperspherical spaces, a few recent works have proposed hyperbolic prototypes for classification. Such approaches enable effective learning in low-dimensional output spaces and can exploit hierarchical relations amongst classes, but require privileged information about class… 

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