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.

翻译：双曲空间中的深度学习在机器学习、多媒体及计算机视觉领域正迅速获得关注。深度网络通常在欧氏空间中运行，隐含假设数据位于规则网格上。最新研究表明，双曲几何为深度学习提供了可行的替代基础，尤其适用于数据具有层次结构且嵌入维度较低的场景。然而，目前尚无类似知名深度学习库那样可直接构建双曲网络模块的开源库。我们提出双曲学习库HypLL，汇聚双曲深度学习的研究进展。HypLL基于PyTorch构建，设计上注重易用性，旨在吸引广泛受众关注这一开放性的前沿研究方向。代码已开源：https://github.com/maxvanspengler/hyperbolic_learning_library。