mlsanityData to 7 Oct 2026

HypLL: The Hyperbolic Learning Library

arXiv 2306.06154 ↗First posted 9 Jun 2023cs.LG cs.AIcited by 6

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 22 of its 27 references that are in the map. First posted on arXiv 9 Jun 2023.

Builds on

22 of 27 references.

References · 22

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,414
  3. 2016Geometric deep learning: going beyond Euclidean datacited by 1,042
  4. 2017Poincar'e Embeddings for Learning Hierarchical Representationscited by 674
  5. 2018Hyperbolic Neural Networkscited by 393
  6. 2019Hyperbolic Graph Neural Networkscited by 244
  7. 2019Hyperbolic Image Embeddingscited by 219
  8. 2015MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systemscited by 195
  9. 2018Riemannian Adaptive Optimization Methodscited by 187
  10. 2018Hyperbolic Entailment Cones for Learning Hierarchical Embeddingscited by 160
  11. 2018Poincar'e GloVe: Hyperbolic Word Embeddingscited by 155
  12. 2021Hyperbolic Deep Neural Networks: A Surveycited by 138
10 more
  1. 2020Geoopt: Riemannian Optimization in PyTorchcited by 109
  2. 2022Hyperbolic Vision Transformers: Combining Improvements in Metric Learningcited by 109
  3. 2019Constant Curvature Graph Convolutional Networkscited by 106
  4. 2022Hyperbolic Image Segmentationcited by 101
  5. 2023Hyperbolic Deep Learning in Computer Vision: A Surveycited by 72
  6. 2018geomstats: a Python Package for Riemannian Geometry in Machine Learningcited by 71
  7. 2020Differentiating through the Fr'echet Meancited by 52
  8. 2022Hyperbolic Graph Neural Networks: A Review of Methods and Applicationscited by 40
  9. 2023Poincar'e ResNetcited by 24
  10. 2021Fully Hyperbolic Graph Convolution Network for Recommendationcited by 13

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 2; 2026: 2.

6 papers.

  1. 2024When Cars meet Drones: Hyperbolic Federated Learning for Source-Free Domain Adaptation in Adverse Weathercited by 4
  2. 2024Klein Model for Hyperbolic Neural Networkscited by 3
  3. 2025Manify: A Python Library for Learning Non-Euclidean Representationscited by 2
  4. 2025HyperHELM: Hyperbolic Hierarchy Encoding for mRNA Language Modelingcited by 0
  5. 2026Group-Equivariant Poincar'e Convolutional Networkscited by 0
  6. 2026hyperbolix: Hyperbolic Deep Learning in JAXcited by 0