University of Oulu

W. Peng, T. Varanka, A. Mostafa, H. Shi and G. Zhao, "Hyperbolic Deep Neural Networks: A Survey," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 12, pp. 10023-10044, 1 Dec. 2022, doi: 10.1109/TPAMI.2021.3136921.

Hyperbolic deep neural networks : a survey

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Author: Peng, Wei1; Varanka, Tuomas1; Mostafa, Abdelrahman1;
Organizations: 1Center for Machine Vision and Signal Analysis, University of Oulu, Oulu, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 1.1 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2022
Publish Date: 2022-10-05


Recently, hyperbolic deep neural networks (HDNNs) have been gaining momentum as the deep representations in the hyperbolic space provide high fidelity embeddings with few dimensions, especially for data possessing hierarchical structure. Such a hyperbolic neural architecture is quickly extended to many different scientific fields, including natural language processing, single-cell RNA-sequence analysis, graph embedding, financial analysis, and computer vision. The promising results demonstrate its superior capability, significant compactness of the model, and a substantially better physical interpretability than its counterpart in the Euclidean space. To stimulate future research, this paper presents a coherent and a comprehensive review of the literature around the neural components in the construction of HDNN, as well as the generalization of the leading deep approaches to the hyperbolic space. It also presents current applications of various tasks, together with insightful observations and identifying open questions and promising future directions.

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Series: IEEE transactions on pattern analysis and machine intelligence
ISSN: 0162-8828
ISSN-E: 2160-9292
ISSN-L: 0162-8828
Volume: 44
Issue: 12
Pages: 10023 - 10044
DOI: 10.1109/tpami.2021.3136921
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
Funding: This work is supported by the Academy of Finland for ICT 2023 project (grant 328115), Academy Professor project EmotionAI (grants 336116, 345122), project MiGA (grant 316765) and Infotech Oulu.
Academy of Finland Grant Number: 328115
Detailed Information: 328115 (Academy of Finland Funding decision)
336116 (Academy of Finland Funding decision)
345122 (Academy of Finland Funding decision)
316765 (Academy of Finland Funding decision)
Copyright information: © The Author(s) 2021. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see