HypLL: The Hyperbolic Learning Library

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.

arXiv commentACM Multimedia Open-Source Software Competition 2023

References in corpus (6)

• Geometric deep learning: going beyond Euclidean data
• MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems
• Riemannian Adaptive Optimization Methods
• Geomstats: A Python Package for Riemannian Geometry in Machine Learning
• Geoopt: Riemannian Optimization in PyTorch
• Constant Curvature Graph Convolutional Networks