Related papers: HypLL: The Hyperbolic Learning Library

HypLL: The Hyperbolic Learning Library

URL: http://arxiv.org/abs/2306.06154v3
Date: Tue, 19 Dec 2023 15:37:22 GMT
Title: HypLL: The Hyperbolic Learning Library
Authors: Max van Spengler, Philipp Wirth, Pascal Mettes
Abstract summary: We present HypLL, the Hyperbolic Learning Library to bring the progress on hyperbolic deep learning together. HypLL is built on top of PyTorch, with an emphasis on ease-of-use to attract a broad audience towards this new and open-ended research direction.
Score: 14.760891078342166
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Deep learning in hyperbolic space is quickly gaining traction in the fields of machine learning, multimedia, and computer vision. Deep networks commonly operate in Euclidean space, implicitly assuming that data lies on regular grids. Recent advances have shown that hyperbolic geometry provides a viable alternative foundation for deep learning, especially when data is hierarchical in nature and when working with few embedding dimensions. Currently however, no accessible open-source library exists to build hyperbolic network modules akin to well-known deep learning libraries. We present HypLL, the Hyperbolic Learning Library to bring the progress on hyperbolic deep learning together. HypLL is built on top of PyTorch, with an emphasis in its design for ease-of-use, in order to attract a broad audience towards this new and open-ended research direction. The code is available at: https://github.com/maxvanspengler/hyperbolic_learning_library.

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