Representing Hyperbolic Space Accurately using Multi-Component FloatsDownload PDF

21 May 2021, 20:47 (modified: 22 Jan 2022, 18:32)NeurIPS 2021 PosterReaders: Everyone
Keywords: Hyperbolic Embedding, Multi-component floating-point, Representation learning
Abstract: Hyperbolic space is particularly useful for embedding data with hierarchical structure; however, representing hyperbolic space with ordinary floating-point numbers greatly affects the performance due to its \emph{ineluctable} numerical errors. Simply increasing the precision of floats fails to solve the problem and incurs a high computation cost for simulating greater-than-double-precision floats on hardware such as GPUs, which does not support them. In this paper, we propose a simple, feasible-on-GPUs, and easy-to-understand solution for numerically accurate learning on hyperbolic space. We do this with a new approach to represent hyperbolic space using multi-component floating-point (MCF) in the Poincar{\'e} upper-half space model. Theoretically and experimentally we show our model has small numerical error, and on embedding tasks across various datasets, models represented by multi-component floating-points gain more capacity and run significantly faster on GPUs than prior work.
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Code: https://github.com/ydtydr/HyperbolicTiling_Learning
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