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Hyperbolic Representation Learning Papers

This document contains detailed paper listings organized by subcategories. For the overview, table of contents, and latest venue updates, see README.md.

Table of Contents

1. Surveys, Books, Tools, Tutorials
 1.1 Surveys  1.2 Books
 1.3 Tools  1.4 Tutorials
2. Core Methods and Geometry
 2.1 Hyperbolic Shallow Models and Embeddings  2.2 Hyperbolic Neural Networks
 2.3 Hyperbolic Graph Neural Networks  2.4 Attention, Transformers, and Sequence Models
 2.5 Theory, Analysis, and Numerical Stability  2.6 Hierarchy and Tree Modeling
 2.7 Mixed-Curvature and Alternative Geometries  2.8 Hyperbolic Generative Models
 2.9 Hyperbolic Classifiers and Decision Models  2.10 Hyperbolic Operations and Representation Utilities
 2.11 LLMs and Foundation Models
3. Domain Applications
 3.1 Language and Text Applications  3.2 Computer Vision, Graphics, and Robotics
 3.3 Graphs and Networked Data  3.4 Recommender Systems
 3.5 Knowledge Graphs  3.6 Biology and Molecular Learning
 3.7 Code Representation
4. Task-Oriented Settings
 4.1 Multi-label and Hierarchical Classification  4.2 Metric Learning, Retrieval, and Clustering
 4.3 Data-Driven Geometry Learning and Adaptation  4.4 Few-Shot and Low-Data Learning
 4.5 Open-Vocabulary, Zero-Shot, and Category Discovery  4.6 Safety, Robustness, and Privacy
 4.7 Environmental Monitoring  4.8 Multi-Criteria Learning

1. Surveys, Books, Tools, Tutorials

1.1 Surveys

  1. Hyperbolic Deep Learning for Foundation Models: A Survey, KDD 2025
    Neil He, Hiren Madhu, Ngoc Bui, Menglin Yang, Rex Ying

  2. Hyperbolic Deep Learning in Computer Vision: A Survey, arxiv 2023
    Pascal Mettes, Mina Ghadimi Atigh, Martin Keller-Ressel, Jeffrey Gu, Serena Yeung

  3. Hyperbolic Graph Neural Networks: A Review of Methods and Application, arxiv 2022. GitHub
    Menglin Yang, Min Zhou, Zhihao Li, Jiahong Liu, Lujia Pan, Hui Xiong, Irwin King

  4. Hyperbolic Deep Neural Networks: A Survey, TPAMI 2022. GitHub
    Wei Peng, Tuomas Varanka, Abdelrahman Mostafa, Henglin Shi, Guoying Zhao

  5. Hyperbolic Geometry in Computer Vision: A Survey, arxiv 2023.
    Pengfei Fang, Mehrtash Harandi, Trung Le, Dinh Phung

1.2 Books

  1. An Introduction to Geometric Topology, 2022
    Bruno Martelli

  2. Hyperbolic Geometry, 2020.
    Brice Loustau

  3. Manifolds and Differential Geometry, 2009.
    Jeffrey M. Lee

  4. Introduction to Hyperbolic Geometry, 1995.
    A Ramsay, RD Richtmyer

1.3 Tools

  1. Geoopt: Riemannian Adaptive Optimization Methods ICLR 2019
    Max Kochurov and Rasul Karimov and Serge Kozlukov

  2. Curvature Learning Framework
    Alibaba

  3. GraphZoo: A Development Toolkit for Graph Neural Networks with Hyperbolic Geometries WWW 2022
    Anoushka Vyas, Nurendra Choudhary, Mehrdad Khatir, Chandan K. Reddy

  4. HypLL: The Hyperbolic Learning Library, GitHub
    Max van Spengler, Philipp Wirth, Pascal Mettes

  5. Geomstats, Pymanopt

  6. Manify: A Python Library for Learning Non-Euclidean Representations
    Philippe Chlenski, Kaizhu Du, Dylan Satow, Itsik Pe'er

1.4 Tutorials

  1. Hyperbolic Deep Learning for Foundation Models
    Neil He, Menglin Yang, Rex Ying, Hiren Madhu, Ngoc Bui

  2. Hyperbolic Deep Learning for Computer Vision
    Pascal Mettes, Max van Spengler, Yunhui Guo, Stella Yu

  3. Hyperbolic networks: Theory, Architecture and Applications
    Nurendra Choudhary, Nikhil Rao, Karthik Subbian, Srinivasan H. Sengamedu, Chandan Reddy

  4. Hyperbolic Graph Neural Networks: A Tutorial on Methods and Applications, KDD 2023
    Min Zhou, Menglin Yang, Bo Xiong, Hui Xiong, Irwin King

  5. Hyperbolic Representation Learning for Computer Vision. Tutorial 2022
    Pascal Mettes, Mina Ghadimi Atigh, Martin Keller-Ressel, Jeffrey Gu, Serena Yeung@ECCV2022
    https://hyperbolic-representation-learning.readthedocs.io/en/latest/

  6. Hyperbolic Graph Representation Learning. Tutorial 2022
    Min Zhou, Menglin Yang, Lujia Pan, Irwin King @ ECML-PKDD 2022

  7. Hyperbolic Neural Network. Tutorial 2022
    Nurendra Choudhary, Nikhil Rao, Karthik Subbian, Srinivasan Sengamedu, Chandan Reddy @ KDD 2022

  8. Hyperbolic Hyperbolic embeddings in machine learning and deep learning. Tutorial 2020
    Octavian Ganea 2020.

2. Core Methods and Geometry

2.1 Hyperbolic Shallow Models and Embeddings

  1. Poincaré Embeddings for Learning Hierarchical Representations, NeurIPS 2017
    Maximilian Nickel, Douwe Kiela

  2. Learning Continuous Hierarchies in the Lorentz Model of Hyperbolic Geometry, ICML 2018
    Maximilian Nickel, Douwe Kiela

  3. Hyperbolic Entailment Cones for Learning Hierarchical Embeddings, ICML 2018
    Octavian-Eugen Ganea, Gary Bécigneul, Thomas Hofmann

  4. Lorentzian Distance Learning for Hyperbolic Representations, ICML 2019
    Marc T. Law, Renjie Liao, Jake Snell, Richard S. Zemel

  5. Hyperbolic Disk Embeddings for Directed Acyclic Graphs, ICML 2019
    Ryota Suzuki, Ryusuke Takahama, Shun Onoda

  6. Learning Structured Representations with Hyperbolic Embeddings, NeurIPS 2024
    Aditya Sinha, Siqi Zeng, Makoto Yamada, Han Zhao

  7. Hyperbolic Embeddings of Supervised Models, NeurIPS 2024, Slides
    Richard Nock, Ehsan Amid, Frank Nielsen, Alexander Soen, Manfred Warmuth

2.2 Hyperbolic Neural Networks

  1. Hyperbolic Neural Networks, NeurIPS 2018
    Octavian-Eugen Ganea, Gary Bécigneul, Thomas Hofmann

  2. Hyperbolic Neural Network++, ICLR 2021
    Ryohei Shimizu, Yusuke Mukuta, Tatsuya Harada

  3. Fully Hyperbolic Neural Networks, ACL 2022
    Weize Chen, Xu Han, Yankai Lin, Hexu Zhao, Zhiyuan Liu, Peng Li, Maosong Sun, Jie Zhou

  4. Poincaré ResNet, arxiv 2023
    Max van Spengler, Erwin Berkhout, Pascal Mettes

  5. Riemannian Residual Neural Networks, arxiv 2023
    Isay Katsman, Eric Ming Chen, Sidhanth Holalkere, Anna Asch, Aaron Lou, Ser-Nam Lim, Christopher De Sa

  6. Nested Hyperbolic Spaces for Dimensionality Reduction and Hyperbolic NN Design, CVPR 2022
    Xiran Fan, Chun-Hao Yang, Baba C. Vemuri

  7. Fully Hyperbolic Convolutional Neural Networks for Computer Vision, ICLR 2024
    Ahmad Bdeir, Kristian Schwethelm, Niels Landwehr

  8. Lorentzian Residual Neural Networks, KDD 2025
    Neil He, Menglin Yang, Rex Ying

2.3 Hyperbolic Graph Neural Networks

  1. Hyperbolic Graph Convolutional Neural Networks, NeurIPS 2019
    Ines Chami*, Rex Ying*, Christopher Ré, Jure Leskovec

  2. Hyperbolic Graph Neural Network, NeurIPS 2019
    Qi Liu, Maximilian Nickel, Douwe Kiela

  3. Lorentzian Graph Convolutional Networks, WWW 2021
    Yiding Zhang, Xiao Wang, Chuan Shi, Nian Liu, Guojie Song

  4. A Hyperbolic-to-Hyperbolic Graph Convolutional Network, CVPR 2021
    Jindou Dai, Yuwei Wu, Zhi Gao, Yunde Jia

  5. Hyperbolic Graph Attention Network, Transcations on Big Data 2021
    Yiding Zhang, Xiao Wang, Xunqiang Jiang, Chuan Shi, Yanfang Ye

  6. Hyperbolic Kernel Convolution: A Generic Framework, LoG 2025
    Eric Qu, Lige Zhang, Habib Debaya, Yue Wu, Dongmian Zou

  7. Unsupervised Hyperbolic Representation Learning via Message Passing Auto-Encoders, CVPR 2021
    Jiwoong Park, Junho Cho, Hyung Jin Chang, Jin Young Choi

  8. $\kappa$HGCN: Tree-likeness Modeling via Continuous and Discrete Curvature Learning, KDD 2023
    Menglin Yang, Min Zhou, Lujia Pan, Irwin King

  9. Hyperbolic Graph Neural Networks at Scale: A Meta Learning Approach, NeurIPS 2023
    Nurendra Choudhary, Nikhil Rao, Chandan K. Reddy

  10. Residual Hyperbolic Graph Convolution Networks, AAAI 2024
    Yangkai Xue, Jindou Dai, Zhipeng Lu, Yuwei Wu, Yunde Jia

  11. Spectro-Riemannian Graph Neural Networks, ICLR 2025
    Karish Grover, Haiyang Yu, Xiang Song, Qi Zhu, Han Xie, Vassilis N. Ioannidis, Christos Faloutsos

  12. Hyperbolic-PDE GNN: Spectral Graph Neural Networks in the Perspective of A System of Hyperbolic Partial Differential Equations, ICML 2025
    Juwei Yue, Haikuo Li, Jiawei Sheng, Xiaodong Li, Taoyu Su, Tingwen Liu, Li Guo

  13. Curvature-aware Graph Attention for PDEs on Manifolds, ICML 2025
    Yunfeng Liao, Jiawen Guan, Xiucheng Li

  14. Understanding and Mitigating Hyperbolic Dimensional Collapse in Graph Contrastive Learning, KDD 2025
    Yifei Zhang, Hao Zhu, Menglin Yang, Jiahong Liu, Rex Ying, Irwin King, Piotr Koniusz

2.4 Attention, Transformers, and Sequence Models

  1. Hyperbolic Attention Networks, ICLR 2019
    Caglar Gulcehre, Misha Denil, Mateusz Malinowski, Ali Razavi, Razvan Pascanu, Karl Moritz Hermann, Peter Battaglia, Victor Bapst, David Raposo, Adam Santoro, Nando de Freitas

  2. Curve Your Attention: Mixed-Curvature Transformers for Graph Representation Learning, arxiv 2023
    Sungjun Cho, Seunghyuk Cho, Sungwoo Park, Hankook Lee, Honglak Lee, Moontae Lee

  3. Hypformer: Exploring Efficient Hyperbolic Transformer Fully in Hyperbolic Space, KDD 2024
    Menglin Yang, Harshit Verma, Delvin Ce Zhang, Jiahong Liu, Irwin King, Rex Ying

2.5 Theory, Analysis, and Numerical Stability

Theory and Generalization

  1. Representation Tradeoffs for Hyperbolic Embeddings, ICML 2018
    Christopher De Sa, Albert Gu, Christopher Ré, Frederic Sala

  2. Generalization Error Bound for Hyperbolic Ordinal Embedding, ICML 2021
    Atsushi Suzuki, Atsushi Nitanda, Jing Wang, Linchuan Xu, Marc Cavazza, Kenji Yamanishi

  3. Generalization Bounds for Graph Embedding Using Negative Sampling: Linear vs Hyperbolic, NeurIPS 2021
    Atsushi Suzuki, Atsushi Nitanda, jing wang, Linchuan Xu, Kenji Yamanishi, Marc Cavazza

  4. The Dark Side of the Hyperbolic Moon, ICLR 2024
    Tao Yu, Toni J.B. Liu, Albert Tseng, Christopher De Sa

  5. Tempered Calculus for ML: Application to Hyperbolic Model Embedding, arxiv 2024
    Richard Nock, Ehsan Amid, Frank Nielsen, Alexander Soen, Manfred K. Warmuth

Analysis and Empirical Studies

  1. Understanding and Improving Hyperbolic Deep Reinforcement Learning, ICLR 2026 Poster
    Timo Klein, Thomas Lang, Andrii Shkabrii, Alexander Sturm, Kevin Sidak, Lukas Miklautz, Claudia Plant, Yllka Velaj, Sebastian Tschiatschek

  2. Riemannian-Geometric Fingerprints of Generative Models, arXiv 2024
    Hae Jin Song, Laurent Itti

  3. Hyperbolicity Measures "Democracy" in Real-World Networks, Phys. Rev. E 2015
    Michele Borassi, Alessandro Chessa, and Guido Caldarelli

Numerical Stability and Optimization

  1. The Numerical Stability of Hyperbolic Representation Learning, ICML 2023
    Gal Mishne, Zhengchao Wan, Yusu Wang, Sheng Yang

  2. Hyperbolic Optimizer as a Dynamical System, ICML 2024
    Nico Alvarado, Hans Lobel

  3. Sparse Spectral Training and Inference on Euclidean and Hyperbolic Neural Networks, ICML 2025
    Jialin Zhao, Yingtao Zhang, Xinghang Li, Huaping Liu, Carlo Cannistraci

2.6 Hierarchy and Tree Modeling

  1. Fitting trees to $\ell_1$-hyperbolic distances, NeurIPS 2023
    Joon-Hyeok Yim, Anna Gilbert

  2. From Trees to Continuous Embeddings and Back: Hyperbolic Hierarchical Clustering
    Ines Chami, Albert Gu, Vaggos Chatziafratis, Christopher Ré

  3. Tree-Wasserstein Distance for High Dimensional Data with a Latent Feature Hierarchy, ICLR 2025
    Ya-Wei Eileen Lin, Ronald R. Coifman, Gal Mishne, Ronen Talmon

  4. Shadow Cones: A Generalized Framework for Partial Order Embeddings, ICLR 2024
    Tao Yu, Toni J.B. Liu, Albert Tseng, Christopher De Sa

  5. Hyperboloid GPLVM for Discovering Continuous Hierarchies via Nonparametric Estimation, AISTATS 2025
    Koshi Watanabe, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama

  6. Low-distortion and GPU-compatible Tree Embeddings in Hyperbolic Space, ICML 2025
    Max van Spengler, Pascal Mettes

2.7 Mixed-Curvature and Alternative Geometries

Mixed-curvature and product spaces

  1. Learning mixed-curvature representations in product spaces, ICLR 2019
    Albert Gu, Frederic Sala, Beliz Gunel, Christopher Ré

  2. Mixed-curvature Variational Autoencoders, ICLR 2020
    Skopek, Ondrej, Octavian-Eugen Ganea, and Gary Bécigneul

  3. Constant Curvature Graph Convolutional Networks, ICML 2020
    Gregor Bachmann, Gary Bécigneul, Octavian-Eugen Ganea

  4. Geometry Interaction Learning, NeurIPS 2020
    Shichao Zhu, Shirui Pan, Chuan Zhou, Jia Wu, Yanan Cao, Bin Wang

  5. Mixed-curvature multi-relational graph neural network for knowledge graph completion, WWW 2021
    Wang, Shen, Xiaokai Wei, Cicero Nogueira Nogueira dos Santos, Zhiguo Wang, Ramesh Nallapati, Andrew Arnold, Bing Xiang, Philip S. Yu, and Isabel F. Cruz.

  6. Enhancing Hyperbolic Graph Embeddings via Contrastive Learning, NeurIPS 2021 SSL Workshop
    Jiahong Liu, Menglin Yang, Min Zhou, Shanshan Feng, Philippe Fournier-Viger

  7. A Self-supervised Mixed-curvature Graph Neural Network, AAAI 2022
    Li Sun, Zhongbao Zhang, Junda Ye, Hao Peng, Jiawei Zhang, Sen Su, Philip S. Yu

  8. A Mixed-Curvature based Pre-training Paradigm for Multi-Task Vehicle Routing Solver, ICML 2025
    Suyu Liu, Zhiguang Cao, Shanshan Feng, Yew Soon Ong

  9. Principal Component Analysis in Space Forms, arXiv 2022
    Puoya Tabaghi, Michael Khanzadeh, Yusu Wang, Sivash Mirarab

  10. FMGNN: Fused Manifold Graph Neural Network, TKDD 2023
    Cheng Deng, Fan Xu, Jiaxing Ding, Luoyi Fu, Weinan Zhang, Xinbing Wang

  11. Matrix Manifold Neural Networks++, ICLR 2024
    Xuan Son Nguyen, Shuo Yang, Aymeric Histace

  12. Linear Classifiers in Product Space Forms, arXiv 2022
    Puoya Tabaghi, Chao Pan, Eli Chien, Jianhao Peng, Olgica Milenkovic

Semi-Riemannian and pseudo-Riemannian models

  1. Directed Graph Embeddings in Pseudo-Riemannian Manifolds, ICML 2021
    Aaron Sim, Maciej Wiatrak, Angus Brayne, Páidí Creed, Saee Paliwal

  2. Semi-Riemannian Graph Convolutional Networks, NeurIPS 2022
    Bo Xiong, Shichao Zhu, Nico Potyka, Shirui Pan, Chuan Zhou, Steffen Staab

  3. Ultrahyperbolic Neural Networks, NeurIPS 2021
    Marc T Law

  4. Ultrahyperbolic Representation Learning, NeurIPS 2020
    Marc T. Law, Jos Stam

2.8 Hyperbolic Generative Models

  1. Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders, NeurIPS 2019
    Emile Mathieu, Charline Le Lan, Chris J. Maddison, Ryota Tomioka, Yee Whye Teh

  2. Latent Variable Modelling with Hyperbolic Normalizing Flows, ICML 2020
    Avishek Joey Bose, Ariella Smofsky, Renjie Liao, Prakash Panangaden, William L. Hamilton

  3. Lorentzian fully hyperbolic generative adversarial network, arxiv 2022
    Eric Qu, Dongmian Zou

  4. Hyperbolic VAE via Latent Gaussian Distributions, NeurIPS 2023
    Seunghyuk Cho, Juyong Lee, Dongwoo Kim

  5. Hyperbolic Graph Diffusion Model, AAAI 2024
    Lingfeng Wen, Xuan Tang, Mingjie Ouyang, Xiangxiang Shen, Jian Yang, Daxin Zhu, Mingsong Chen, Xian Wei

  6. Hyperbolic Geometric Latent Diffusion Model for Graph Generation, ICML 2024
    Xingcheng Fu, Yisen Gao, Yuecen Wei, Qingyun Sun, Hao Peng, Jianxin Li, Xianxian Li

2.9 Hyperbolic Classifiers and Decision Models

Large-margin and kernel methods

  1. Large-Margin Classification in Hyperbolic Space, AISTATS 2019
    Hyunghoon Cho, Benjamin DeMeo, Jian Peng, Bonnie Berger

  2. Kernel Methods in Hyperbolic Spaces, ICCV 2021
    Pengfei Fang, Mehrtash Harandi, Lars Petersson

  3. Robust Large-Margin Learning in Hyperbolic Space, NeurIPS 2020
    Melanie Weber, Manzil Zaheer, Ankit Singh Rawat, Aditya Menon, Sanjiv Kumar

  4. Horospherical Decision Boundaries for Large Margin Classification in Hyperbolic Space, NeurIPS 2023
    Xiran Fan, Chun-Hao Yang, Baba C. Vemuri

  5. Clipped Hyperbolic Classifiers Are Super-Hyperbolic Classifiers, CVPR 2022
    Yunhui Guo, Xudong Wang, Yubei Chen, Stella X. Yu

Tree-based and random-forest models

  1. Hyperbolic Random Forests, TMLR 2024
    Lars Doorenbos, Pablo Márquez-Neila, Raphael Sznitman, Pascal Mettes

  2. Fast Hyperboloid Decision Tree Algorithms, ICLR 2024
    Philippe Chlenski, Ethan Turok, Antonio Moretti, Itsik Pe'er

  3. Mixed-Curvature Decision Trees and Random Forests, ICML 2025
    Philippe Chlenski, Quentin Chu, Raiyan R. Khan, Kaizhu Du, Antonio Khalil Moretti, Itsik Pe'er

  4. Even Faster Hyperbolic Random Forests: A Beltrami-Klein Wrapper Approach, arXiv 2025
    Philippe Chlenski, Itsik Pe'er

2.10 Hyperbolic Operations and Representation Utilities

  1. Mean Computation and BatchNorm
    Differentiating through the Fréchet Mean, ICML 2020
    Aaron Lou, Isay Katsman, Qingxuan Jiang, Serge Belongie, Ser-Nam Lim, Christopher De Sa

  2. BatchNorm
    Gyrogroup Batch Normalization, ICLR 2025
    Ziheng Chen, Yue Song, Xiaojun Wu, Nicu Sebe

  3. Sampling
    A Wrapped Normal Distribution on Hyperbolic Space for Gradient-Based Learning, ICML 2019
    Yoshihiro Nagano, Shoichiro Yamaguchi, Yasuhiro Fujita, Masanori Koyama

  4. Sampling
    Wrapped Distributions on homogeneous Riemannian manifolds, 2022
    Fernando Galaz-Garcia, Marios Papamichalis, Kathryn Turnbull, Simon Lunagomez, Edoardo Airoldi

  5. Sampling
    A Rotated Hyperbolic Wrapped Normal Distribution for Hierarchical Representation Learning, NeurIPS 2022 \

  6. MixUp and Data Augmentation
    HYPMIX: Hyperbolic Interpolative Data Augmentation, EMNLP 2021
    Ramit Sawhney, Megh Thakkar, Shivam Agarwal, Di Jin, Diyi Yang, Lucie Flek

  7. Dataset Distillation
    Hyperbolic Dataset Distillation, Code, NeurIPS 2025
    Wenyuan Li, Guang Li, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama

  8. Backward-compatible Representation Learning
    Learning Along the Arrow of Time: Hyperbolic Geometry for Backward-Compatible Representation Learning, ICML 2025
    Ngoc Bui, Menglin Yang, Runjin Chen, Leonardo Neves, Mingxuan Ju, Zhitao Ying, Neil Shah, Tong Zhao

  9. PCA
    HoroPCA: Hyperbolic Dimensionality Reduction via Horospherical Projections, ICML 2021
    Ines Chami*, Albert Gu*, Dat Nguyen, Christopher Ré*

  10. TSNE
    Accelerating hyperbolic t-SNE, arxiv 2024
    Martin Skrodzki, Hunter van Geffen, Nicolas F. Chaves-de-Plaza, Thomas Höllt, Elmar Eisemann, Klaus Hildebrandt

2.11 LLMs and Foundation Models

  1. Language Models as Hierarchy Encoders, NeurIPS 2024
    Yuan He, Zhangdie Yuan, Jiaoyan Chen, Ian Horrocks

  2. HELM: Hyperbolic Large Language Models via Mixture-of-Curvature Experts, NeurIPS 2025 🔥
    Neil He, Rishabh Anand, Hiren Madhu, Ali Maatouk, Smita Krishnaswamy, Leandros Tassiulas, Menglin Yang, Rex Ying

  3. Hyperbolic Fine-Tuning for Large Language Models, NeurIPS 2025 (Spotlight)
    Menglin Yang, Ram Samarth B B, Aosong Feng, Bo Xiong, Jiahong Liu, Irwin King, Rex Ying

  4. HyperET: Efficient Training in Hyperbolic Space for Multi-modal Large Language Models, NeurIPS 2025 (Oral)
    Zelin Peng, Zhengqin Xu, Qingyang Liu, Xiaokang Yang, Wei Shen

3. Domain Applications

These sections group papers by primary application domain. Cross-domain task settings such as few-shot learning or open-vocabulary learning are collected in Section 4.

3.1 Language and Text Applications

Representation, Semantics, and Hierarchy

  1. Poincare Glove: Hyperbolic Word Embeddings, ICLR 2019
    Alexandru Tifrea and Gary Becigneul and Octavian-Eugen Gane

  2. Skip-gram word embeddings in hyperbolic space, ACL 2018
    Matthias Leimeister, Benjamin J. Wilson

  3. Embedding text in hyperbolic spaces, ACL 2018
    Bhuwan Dhingra, Christopher J. Shallue, Mohammad Norouzi, Andrew M. Dai, George E. Dahl

  4. Inferring Concept Hierarchies from Text Corpora via Hyperbolic Embeddings
    Matt Le, Stephen Roller, Laetitia Papaxanthos, Douwe Kiela, Maximilian Nickel

  5. Cross-lingual Word Embeddings in Hyperbolic Space, arXiv 2022
    Chandni Saxena, Mudit Chaudhary, Helen Meng

  6. Probing BERT in Hyperbolic Spaces, ICLR 2021
    Boli Chen, Yao Fu, Guangwei Xu, Pengjun Xie, Chuanqi Tan, Mosha Chen, Liping Jing

Reasoning, Summarization, Classification, and Search

  1. Hyperbolic Representation Learning for Fast and Efficient Neural Question Answering, WSDM 2018
    Yi Tay, Luu Anh Tuan, Siu Cheung Hui

  2. Enhancing Multi-Hop Reasoning for Question Answering with Hyperbolic Representations, ACL Findings 2025
    Simon Welz, Lucie Flek, Akbar Karimi

  3. HISum: Hyperbolic Interaction Model for Extractive Multi-Document Summarization, WWW 2023
    Mingyang Song, Yi Feng, Liping Jing

  4. Public Wisdom Matters! Discourse-Aware Hyperbolic Fourier Co-Attention for Social-Text Classification, NeurIPS 2022 Oral (Spotlight)
    K Grover, SM Angara, M Akhtar, T Chakraborty

  5. Exploring Hyperbolic Hierarchical Structure for Multimodal Rumor Detection, Findings of EMNLP 2025
    Md Mahbubur Rahman, Shufeng Hao, Chongyang Shi, An Lao, Jinyan Liu

  6. Hyperbolic Relevance Matching for Neural Keyphrase Extraction, NAACL 2022
    Mingyang Song, Yi Feng, Liping Jing

  7. Medical Triage Chatbot Diagnosis Improvement via Multi-relational Hyperbolic Graph Neural Network, SIGIR short paper 2021
    Zheng Liu, Xiaohan Li, Zeyu You, Tao Yang, Wei Fan, Philip Yu

  8. ANTHEM: Attentive Hyperbolic Entity Model for Product Search, WSDM 2022
    Nurendra Choudhary, Nikhil Rao, Sumeet Katariya, Karthik Subbian, Chandan K. Reddy

3.2 Computer Vision, Graphics, and Robotics

Visual Recognition and Structured Perception

  1. HiTag: Hierarchical Image Tagging with Hyperbolic Vision-Language Modeling, CVPR 2026
    Yi Yang, Ziyue Peng, Kang Zhang, Xincheng Tan, Fan Lu, Jingting Ding, Kecheng Zheng, Minfeng Zhu, Wei Chen

  2. Improving Visual Recognition with Hyperbolical Visual Hierarchy Mapping, CVPR 2025
    Hyeongjun Kwon, Jinhyun Jang, Jin Kim, Kwonyoung Kim, Kwanghoon Sohn

  3. Hyperbolic Prototypical Entailment Cones for Image Classification, AISTATS 2025
    Samuele Fonio, Roberto Esposito, Marco Aldinucci

  4. Hyperbolic Image Embedding, CVPR 2020
    Valentin Khrulkov, Leyla Mirvakhabova, Evgeniya Ustinova, Ivan Oseledets, Victor Lempitsky

  5. Hyperbolic Image Segmentation, CVPR 2022
    Mina GhadimiAtigh, Julian Schoep, Erman Acar, Nanne van Noord, Pascal Mettes

  6. Hyperbolic Vision Transformers: Combining Improvements in Metric Learning, CVPR 2022
    Aleksandr Ermolov, Leyla Mirvakhabova, Valentin Khrulkov, Nicu Sebe, Ivan Oseledets

  7. Rethinking the compositionality of point clouds through regularization in the hyperbolic space, NeurIPS 2022
    Antonio Montanaro, Diego Valsesia, Enrico Magli

  8. HypLiLoc: Towards Effective LiDAR Pose Regression with Hyperbolic Fusion, CVPR 2023
    Sijie Wang, Qiyu Kang, Rui She, Wei Wang, Kai Zhao, Yang Song, Wee Peng Tay

  9. Hyperbolic Active Learning for Semantic Segmentation under Domain Shift, ICML 2024
    Luca Franco, Paolo Mandica, Konstantinos Kallidromitis, Devin Guillory, Yu-Teng Li, Trevor Darrell, Fabio Galasso

  10. Capturing implicit hierarchical structure in 3D biomedical images with self-supervised hyperbolic representations, NeurIPS 2021
    Joy Hsu, Jeffrey Gu, Gong-Her Wu, Wah Chiu, Serena Yeung

  11. Learning Hyperbolic Representations of Topological Features, ICLR 2021
    Panagiotis Kyriakis, Iordanis Fostiropoulos, Paul Bogdan

Vision-Language, Retrieval, and 3D Generation

  1. Enhancing Partially Relevant Video Retrieval with Hyperbolic Learning, Code, ICCV 2025
    Jun Li, Jinpeng Wang, Chaolei Tan, Niu Lian, Long Chen, Yaowei Wang, Min Zhang, Shu-Tao Xia, Bin Chen

  2. Learning Visual Hierarchies in Hyperbolic Space for Image Retrieval, ICCV 2025
    Ziwei Wang, Sameera Ramasinghe, Chenchen Xu, Julien Monteil, Loris Bazzani, Thalaiyasingam Ajanthan

  3. Geo-Sign: Hyperbolic Contrastive Regularisation for Geometrically Aware Sign Language Translation, Code, NeurIPS 2025
    Edward Fish, Richard Bowden

  4. Hyperbolic Hierarchical Alignment Reasoning Network for Text-3D Retrieval, AAAI 2026
    Wenrui Li, Yidan Lu, Yeyu Chai, Rui Zhao, Hengyu Man, Xiaopeng Fan

  5. HyperSDFusion: Bridging Hierarchical Structures in Language and Geometry for Enhanced 3D Text2Shape Generation, arXiv 2024
    Zhiying Leng, Tolga Birdal, Xiaohui Liang, Federico Tombari

  6. Hyperbolic Image-Text Representations, ICML 2023
    Karan Desai, Maximilian Nickel, Tanmay Rajpurohit, Justin Johnson, Ramakrishna Vedantam

  7. Accept the Modality Gap: An Exploration in the Hyperbolic Space, CVPR 2024
    Sameera Ramasinghe, Violetta Shevchenko, Gil Avraham, Ajanthan Thalaiyasingam

  8. G^3-LQ: Marrying Hyperbolic Alignment with Explicit Semantic-Geometric Modeling for 3D Visual Grounding, CVPR 2024
    Yuan Wang, Yali Li, Shengjin Wang

  9. Compositional Entailment Learning for Hyperbolic Vision-Language Models, ICLR 2025
    Avik Pal, Max van Spengler, Guido Maria D'Amely di Melendugno, Alessandro Flaborea, Fabio Galasso, Pascal Mettes

Motion, World Models, and Robotics

  1. GeoWorld: Geometric World Models, CVPR 2026
    Zeyu Zhang, Danning Li, Ian Reid, Richard Hartley

  2. HypeVPR: Exploring Hyperbolic Space for Perspective to Equirectangular Visual Place Recognition, CVPR 2026
    Suhan Woo, Seongwon Lee, Jinwoo Jang, Euntai Kim

  3. Searching for Actions on the Hyperbole, CVPR 2020
    Teng Long, Pascal Mettes, Heng Tao Shen, Cees Snoek

  4. Mix Dimension in Poincaré Geometry for 3D Skeleton-based Action Recognition, ACM MM 2020
    Wei Peng, Jingang Shi, Zhaoqiang Xia, Guoying Zhao

  5. Bringing Motion Taxonomies to Continuous Domains via GPLVM on Hyperbolic manifolds, ICML 2024
    Noemie Jaquier, Leonel Rozo, Miguel Gonzalez-Duque, Viacheslav Borovitskiy, Tamim Asfour

  6. Leveraging Hyperbolic Embeddings for Coarse-to-Fine Robot Design, ICLR 2024
    Heng Dong, Junyu Zhang, Chongjie Zhang

3.3 Graphs and Networked Data

General Network Representation

  1. Node2LV: Squared Lorentzian Representations for Node Proximity, ICDE 2021
    Shanshan Feng, Lisi Chen, Kaiqi Zhao, Wei Wei, Fan Li, Shuo Shang

  2. Non-Euclidean Mixture Model for Social Network Embedding, NeurIPS 2024
    Roshni G. Iyer, Yewen Wang, Wei Wang, Yizhou Sun

Directed, Signed, and Attributed Graphs

  1. Hyperbolic Disk Embeddings for Directed Acyclic Graphs, ICML 2019
    Ryota Suzuki, Ryusuke Takahama, Shun Onoda

  2. A hyperbolic Embedding Model for Directed Networks
    Zongning Wu, Zengru Di, Ying Fan (this paper includes many errors)

  3. Hyperbolic Node Embedding for Signed Networks, Neurocomputing 2021
    Wenzhuo Song, Hongxu Chen, Xueyan Liu, Hongzhe Jiang, Shengsheng Wang

  4. HEAT: Hyperbolic Embedding of Attributed Networks, IDEAL 2020
    David McDonald, Shan He

Heterogeneous, Multiplex, and Event Graphs

  1. Hyperbolic Heterogeneous Information Network Embedding, AAAI 2020
    Xiao Wang, Yiding Zhang, Chuan Shi

  2. Embedding Heterogeneous Information Network in Hyperbolic Spaces, TKDD 2022
    Yiding Zhang, Xiao Wang, Nian Liu, Chuan Shi

  3. Hyperbolic Multiplex Network Embedding with Maps of Random Walk
    Peiyuan Sun

  4. An Efficient Automatic Meta-Path Selection for Social Event Detection via Hyperbolic Space, WWW 2024
    Zitai Qiu, Congbo Ma, Jia Wu, Jian Yang

Dynamic and Temporal Graphs

  1. SINCERE: Sequential Interaction Networks representation learning on Co-Evolving RiEmannian manifolds, WWW 2023
    Junda Ye, Zhongbao Zhang, Li Sun, Yang Yan, Feiyang Wang, Fuxin Ren

  2. Discrete-time Temporal Network Embedding via Implicit Hierarchical Learning in Hyperbolic Space, KDD 2021
    Menglin Yang, Min Zhou, Marcus Kalander, Zengfeng Huang, Irwin King

  3. Hyperbolic Variational Graph Neural Network for Modeling Dynamic Graphs, AAAI 2021
    Li Sun, Zhongbao Zhang, Jiawei Zhang, Feiyang Wang, Hao Peng, Sen Su, Philip S. Yu

  4. Predicting Long-term Dynamics of Complex Networks via Identifying Skeleton in Hyperbolic Space, KDD 2024
    Ruikun Li, Huandong Wang, Jinghua Piao, Qingmin Liao, Yong Li

  5. Exploring the Scale-Free Nature of Stock Markets: Hyperbolic Graph Learning for Algorithmic Trading, WWW 2021
    Ramit Sawhney, Shivam Agarwal, Arnav Wadhwa, Rajiv Shah

3.4 Recommender Systems

General and Analytical Studies

  1. VoRec: Enhancing Recommendation with Voronoi Diagram in Hyperbolic Space, SIGIR 2025
    Yong Chen, Li Li, Wei Peng, Songzhi Su

  2. Large Language Models Enhanced Hyperbolic Space Recommender Systems, SIGIR 2025
    Wentao Cheng, Zhida Qin, Zexue Wu, Pengzhan Zhou, Tianyu Huang

  3. Review-Based Hyperbolic Cross-Domain Recommendation, WSDM 2025
    Yoonhyuk Choi, Jiho Choi, Taewook Ko

  4. Learning geometry-aware recommender systems with manifold regularization, RecSys 2025 (LBR)
    Zaira Zainulabidova, Julia Borisova, Alexander Hvatov

  5. Where are we in embedding spaces? A Comprehensive Analysis on Network Embedding Approaches for Recommender Systems, KDD 2021
    Sixiao Zhang, Hongxu Chen, Xiao Ming, Lizhen Cui, Hongzhi Yin, Guandong Xu

  6. Logical Relation Modeling and Mining in Hyperbolic Space for Recommendation, ICDE 2024
    Yanchao Tan, Hang Lv, Zihao Zhou, Wenzhong Guo, Bo Xiong, Weiming Liu, Chaochao Chen, Shiping Wang, Carl Yang

Collaborative Filtering and Ranking

  1. HyperML: A Boosting Metric Learning Approach in Hyperbolic Space for Recommender Systems, WSDM 2020
    Lucas Vinh Tran, Yi Tay, Shuai Zhang, Gao Cong, Xiaoli Li

  2. HICF: Hyperbolic Informative Collaborative Filtering, KDD 2022
    Menglin Yang, Zhihao Li, Min Zhou, Jiahong Liu, Irwin King

  3. HRCF: Enhancing Collaborative Filtering via Hyperbolic Geometric Regularization, WWW 2022
    Menglin Yang, Min Zhou, Jiahong Liu, Defu Lian, Irwin King

  4. HGCF: Hyperbolic Graph Convolution Networks for Collaborative Filtering, WWW 2021
    Jianing Sun, Zhaoyue Cheng, Saba Zuberi, Felipe Perez, Maksims Volkovs

  5. HGCC: Enhancing Hyperbolic Graph Convolution Networks on Heterogeneous Collaborative Graph for Recommendation, arXiv 2022
    Lu Zhang, Ning Wu

  6. Hyperbolic Neural Collaborative Recommender, TKDE 2022
    Anchen Li, Bo Yang, Huan Huo, Hongxu Chen, Guandong Xu, Zhen Wang

  7. Hyperbolic Graph Transformer for Collaborative Filtering, ICML 2025
    Yang Xin, Xingrun Li, Heng Chang, Yang Jinze, Xihong Yang, Shengyu Tao, Maiko Shigeno, Ningkang Chang, Junfeng Wang, Dawei Yin, Erxue Min

  8. Leveraging Geometric Insights in Hyperbolic Triplet Loss for Improved Recommendations, RecSys 2025 (LBR)
    Viacheslav Yusupov, Maxim Rakhuba, Evgeny Frolov

Knowledge-aware and KG-enhanced Recommendation

  1. HAKG: Hierarchy-Aware Knowledge Gated Network for Recommendation, SIGIR 2022
    Yuntao Du, Xinjun Zhu, Lu Chen, Baihua Zheng, Yunjun Gao

  2. Modeling Scale-free Graphs with Hyperbolic Geometry for Knowledge-aware Recommendation, WSDM 2022
    Yankai Chen, Menglin Yang, Yingxue Zhang, Mengchen Zhao, Ziqiao Meng, Jianye Hao, Irwin King

  3. Knowledge Based Hyperbolic Propagation, SIGIR short paper 2021
    Chang-You Tai, Chien-Kun Huang, Liang-Ying Huang, Lun-Wei Ku

  4. Lorentz Equivariant Model for Knowledge-Enhanced Collaborative Filtering, arXiv 2023
    Bosong Huang, Weihao Yu, Ruzhong Xie, Jing Xiao, Jin Huang

  5. MetaHKG: Meta Hyperbolic Learning for Few-shot Temporal Reasonin, SIGIR 2024
    Ruijie Wang, Yutong Zhang, Jinyang Li, Shengzhong Liu, Dachun Sun, Tianchen Wang, Tianshi Wang, Yizhuo Chen, Denizhan Kara, Tarek Abdelzaher

Session-based, Sequential, and Next-Item Recommendation

  1. Enhancing Hierarchy-Aware Graph Networks with Deep Dual Clustering for Session-based Recommendation, WWW 2023
    Jiajie Su, Chaochao Chen, Weiming Liu, Fei Wu, Xiaolin Zheng, Haoming Lyu

  2. HCGR: Hyperbolic Contrastive Graph Representation Learning for Session-based Recommendation, arXiv 2021
    Naicheng Guo, Xiaolei Liu, Shaoshuai Li, Qiongxu Ma, Yunan Zhao, Bing Han, Lin Zheng, Kaixin Gao, Xiaobo Guo

  3. Hyperbolic Hypergraphs for Sequential Recommendation, CIKM 2021
    Yicong Li, Hongxu Chen, Xiangguo Sun, Zhenchao Sun, Lin Li, Lizhen Cui, Philip S. Yu, Guandong Xu

  4. A hyperbolic metric embedding approach for next-poi recommendation, SIGIR 2020
    Shanshan Feng, Lucas Vinh Tran, Gao Cong, Lisi Chen, Jing Li, Fan Li

Social-aware, Tag-aware, and Feature-interaction Models

  1. Hypersorec: Exploiting hyperbolic user and item representations with multiple aspects for social-aware recommendation, TOIS 2021
    Hao Wang, Defu Lian, Hanghang Tong, Qi Liu, Zhenya Huang, Enhong Chen

  2. HSR: hyperbolic social recommender, Information Sciences 2022
    Anchen Li, Bo Yang

  3. Learning Feature Interactions with Lorentzian Factorization Machine, AAAI 2020
    Canran Xu, Ming Wu

  4. Scalable Hyperbolic Recommender Systems, WSDM 2020
    Benjamin Paul Chamberlain, Stephen R. Hardwick, David R. Wardrope, Fabon Dzogang, Fabio Daolio, Saul Vargas

  5. Enhancing Recommendation with Automated Tag Taxonomy Construction in Hyperbolic Space, ICDE 2022
    Yanchao Tan, Carl Yang, Xiangyu Wei, Chaochao Chen, Longfei Li, Xiaolin Zheng

  6. Hyperbolic Personalized Tag Recommendation, DASFAA 2022
    Weibin Zhao, Aoran Zhang, Lin Shang, Yonghong Yu, Li Zhang, Can Wang, Jiajun Chen, Hongzhi Yin

Matrix Completion

  1. Geometric Inductive Matrix Completion: A Hyperbolic Approach with Unified Message Passing, WSDM 2022
    Chengkun Zhang, Hongxu Chen, Sixiao Zhang, Guandong Xu, Junbin Gao

Multi-criteria Recommendation

  1. Hyperbolic Multi-Criteria Rating Recommendation, SIGIR 2025
    Zhihao Guo, Ting Han, Peng Song, Chenjiao Feng, Kaixuan Yao, Jiye Liang

3.5 Knowledge Graphs

Embedding and Completion

  1. Low-Dimensional Hyperbolic Knowledge Graph Embeddings, ACL 2019
    Ines Chami, Adva Wolf, Da-Cheng Juan, Frederic Sala, Sujith Ravi, Christopher Ré

  2. Multi-relational Poincaré Graph Embeddings, NeurIPS 2019
    Ivana Balažević, Carl Allen, Timothy Hospedales

  3. Knowledge Association with Hyperbolic Knowledge Graph Embeddings, EMNLP 2020
    Zequn Sun, Muhao Chen, Wei Hu, Chengming Wang, Jian Dai, Wei Zhang

  4. Knowledge Graph Completion with Mixed Geometry Tensor Factorization, AISTATS 2025
    Viacheslav Yusupov, Maxim Rakhuba, Evgeny Frolov

  5. HyperKG: Hyperbolic Knowledge Graph Embeddings for Knowledge Base Completion, arXiv
    Prodromos Kolyvakis, Alexandros Kalousis, Dimitris Kiritsis

  6. FFHR: Fully and Flexible Hyperbolic Representation for Knowledge Graph Completion, arXiv 2023
    Wentao Shi, Junkang Wu, Xuezhi Cao, Jiawei Chen, Wenqiang Lei, Wei Wu, Xiangnan He

  7. Mixed Geometry Message and Trainable Convolutional Attention Network for Knowledge Graph Completion, AAAI 2024
    Bin Shang, Yinliang Zhao, Jun Liu, Di Wang

Temporal and Logical Reasoning

  1. HyperKGR: Knowledge Graph Reasoning in Hyperbolic Space with Graph Neural Network Encoding Symbolic Path, EMNLP 2025
    Lihui Liu

  2. Hyperbolic Temporal Knowledge Graph Embeddings with Relational and Time Curvatures, ACL 2021
    Sebastien Montella, Lina Rojas-Barahona, Johannes Heinecke

  3. Self-supervised hyperboloid representations from logical queries over knowledge graphs, WWW 2021
    Nurendra Choudhary, Nikhil Rao, Sumeet Katariya, Karthik Subbian, Chandan K. Reddy

Hierarchy-aware Modeling

  1. Knowledge Graph Representation via Hierarchical Hyperbolic Neural Graph Embedding, IEEE Big Data
    Shen Wang, Xiaokai Wei, Cicero Nogueira Dos Santos, Zhiguo Wang, Ramesh Nallapati, Andrew Arnold, Philip S. Yu

  2. Mixed-Curvature Multi-relational Graph Neural Network for Knowledge Graph Completion, WWW 2021
    Shen Wang, Xiaokai Wei, Cicero Nogueira dos Santos, Zhiguo Wang, Ramesh Nallapati, Andrew Arnold, Bing Xiang, Philip S. Yu, Isabel F. Cruz

  3. Geometry Interaction Knowledge Graph Embeddings, AAAI 2022
    Zongsheng Cao, Qianqian Xu, Zhiyong Yang, Xiaochun Cao, Qingming Huang

  4. Modeling Heterogeneous Hierarchies with Relation-specific Hyperbolic Cones, NeurIPS 2021
    Yushi Bai, Rex Ying, Hongyu Ren, Jure Leskovec

  5. Hyperbolic Hierarchy-Aware Knowledge Graph Embedding for Link Prediction, EMNLP Findings 2021
    Zhe Pan, Peng Wang

3.6 Biology and Molecular Learning

Molecular and Drug Discovery

  1. PoinnCARE: Hyperbolic Multi-Modal Learning for Enzyme Classification, ICLR 2026 Poster
    Kun Xie, Peng Zhou, Xingyi Zhang, Wei Liu, Peilin Zhao, Sibo Wang, Biaobin Jiang

  2. Semi-supervised hierarchical drug embedding in hyperbolic space, Journal of Chemical Information and Modeling 2020
    Ke Yu, Shyam Visweswaran, Kayhan Batmanghelich

  3. Hyperbolic relational graph convolution networks plus: a simple but highly efficient QSAR-modeling method, Briefings in Bioinformatics 2021
    Zhenxing Wu, Dejun Jiang, Chang-Yu Hsieh, Guangyong Chen, Ben Liao, Dongsheng Cao, Tingjun Hou

  4. Hyperbolic Graph Diffusion Model, AAAI 2024
    Lingfeng Wen, Xuan Tang, Mingjie Ouyang, Xiangxiang Shen, Jian Yang, Daxin Zhu, Mingsong Chen, Xian Wei

  5. Hyperbolic Geometric Latent Diffusion Model for Graph Generation, ICML 2024
    Xingcheng Fu, Yisen Gao, Yuecen Wei, Qingyun Sun, Hao Peng, Jianxin Li, Xianxian Li

Phylogenetics and Biological Taxonomies

  1. Hyperbolic Multimodal Representation Learning for Biological Taxonomies, NeurIPS 2025 Workshop on Imageomics
    ZeMing Gong, Chuanqi Tang, Xiaoliang Huo, Nicholas Pellegrino, Austin T. Wang, Graham W. Taylor, Angel X. Chang, Scott C. Lowe, Joakim Bruslund Haurum

  2. Visualising very large phylogenetic trees in three dimensional hyperbolic space, BMC Bioinformatics 2004
    Timothy Hughes, Young Hyun, David A Liberles

  3. Novel metric for hyperbolic phylogenetic tree embeddings, Biology Methods and Protocols 2021
    Hirotaka Matsumoto, Takahiro Mimori, Tsukasa Fukunaga

  4. Learning Hyperbolic Embedding for Phylogenetic Tree Placement and Updates, Biology 2022
    Yueyu Jiang, Puoya Tabaghi, Siavash Mirarab

  5. GeoPhy: Differentiable Phylogenetic Inference via Geometric Gradients of Tree Topologies, NeurIPS 2023
    Takahiro Mimori, Michiaki Hamada

  6. Differentiable Phylogenetics via Hyperbolic Embeddings with Dodonaphy, Bioinformatics Advances 2024
    Matthew Macaulay, Mathieu Fourment

  7. Variational Combinatorial Sequential Monte Carlo for Bayesian Phylogenetics in Hyperbolic Space, AISTATS 2025
    Alex Chen, Philippe Chlenski, Kenneth Munyuza, Antonio Khalil Moretti, Christian A. Naesseth, Itsik Pe'er

Genomics, Ontologies, and Single-Cell Biology

  1. HiG2Vec: hierarchical representations of Gene Ontology and genes in the Poincaré ball, Bioinformatics 2021
    Jaesik Kim, Dokyoon Kim, Kyung-Ah Sohn

  2. Neural Distance Embeddings for Biological Sequences, NeurIPS 2021
    Gabriele Corso, Rex Ying, Michal Pandy, Petar Velickovic, Jure Leskovec, Pietro Lio

  3. Poincare Maps for Analyzing Complex Hierarchies in Single-Cell Data, Nature Communications 2020
    Anna Klimovskaia, David Lopez-Paz, Leon Bottou, Maximilian Nickel

  4. Deep generative model embedding of single-cell RNA-Seq profiles on hyperspheres and hyperbolic spaces, Nature Communications 2021
    Jiarui Ding, Aviv Regev

  5. Contrastive Poincare Maps for single-cell data analysis, ICLR workshop 2024
    Nithya Bhasker, Hattie Chung, Louis Boucherie, Vladislav Kim, Stefanie Speidel, Melanie Weber

  6. Hyperbolic Genome Embeddings, ICLR 2025
    Raiyan R. Khan, Philippe Chlenski, Itsik Pe'er

3.7 Code Representation

  1. The Natural Geometry of Code: Hyperbolic Representation Learning for Program Reasoning, ICLR 2026 Poster
    Weilin Zhou

  2. Hyperbolic Representations of Source Code, AAAI 2022
    Raiyan Khan, Thanh V. Nguyen, Sengamedu H. Srinivasan

4. Task-Oriented Settings

These sections capture recurring problem settings that cut across multiple domains. Some papers are intentionally cross-listed with Section 3 when the task and the domain are both central.

4.1 Multi-label and Hierarchical Classification

  1. Hyperbolic interaction model for hierarchical multi-label classification, AAAI 2021
    Boli Chen, Xin Huang, Lin Xiao, Zixin Cai, Liping Jing

  2. Hyperbolic Capsule Networks for Multi-Label Classification, ACL 2020
    Boli Chen, Xin Huang, Lin Xiao, Liping Jing

  3. Joint Learning of Hyperbolic Label Embeddings for Hierarchical Multi-label Classification, EACL 2021
    Soumya Chatterjee, Ayush Maheshwari, Ganesh Ramakrishnan, Saketha Nath Jagaralpudi

  4. Hyperbolic Embeddings for Hierarchical Multi-label Classification, 2020
    Tomaz Stepisnik, Dragi Kocev

  5. A Fully Hyperbolic Neural Model for Hierarchical Multi-Class Classification, EMNLP Findings
    Federico Lopez, Michael Strube

  6. Hyperbolic Space with Hierarchical Margin Boosts Fine-Grained Learning from Coarse Labels, NeurIPS 2023
    Shu-Lin Xu, Yifan Sun, Faen Zhang, Anqi Xu, Xiu-Shen Wei, Yi Yang

4.2 Metric Learning, Retrieval, and Clustering

  1. Enhancing Partially Relevant Video Retrieval with Hyperbolic Learning, Code, ICCV 2025
    Jun Li, Jinpeng Wang, Chaolei Tan, Niu Lian, Long Chen, Yaowei Wang, Min Zhang, Shu-Tao Xia, Bin Chen

  2. Learning Visual Hierarchies in Hyperbolic Space for Image Retrieval, ICCV 2025
    Ziwei Wang, Sameera Ramasinghe, Chenchen Xu, Julien Monteil, Loris Bazzani, Thalaiyasingam Ajanthan

  3. Hyperbolic Hierarchical Alignment Reasoning Network for Text-3D Retrieval, AAAI 2026
    Wenrui Li, Yidan Lu, Yeyu Chai, Rui Zhao, Hengyu Man, Xiaopeng Fan

  4. Hyperbolic Image-Text Representations, ICML 2023
    Karan Desai, Maximilian Nickel, Tanmay Rajpurohit, Justin Johnson, Ramakrishna Vedantam

  5. Accept the Modality Gap: An Exploration in the Hyperbolic Space, CVPR 2024
    Sameera Ramasinghe, Violetta Shevchenko, Gil Avraham, Ajanthan Thalaiyasingam

  6. G^3-LQ: Marrying Hyperbolic Alignment with Explicit Semantic-Geometric Modeling for 3D Visual Grounding, CVPR 2024
    Yuan Wang, Yali Li, Shengjin Wang

  7. Contrastive Multi-view Hyperbolic Hierarchical Clustering, IJCAI 2022
    Fangfei Lin, Bing Bai, Kun Bai, Yazhou Ren, Peng Zhao, Zenglin Xu

  8. Hyperbolic Busemann Learning with Ideal Prototypes, NeurIPS 2021
    Mina Ghadimi Atigh, Martin Keller-Ressel, Pascal Mettes

  9. Unsupervised Hyperbolic Metric Learning, CVPR 2021
    Jiexi Yan, Lei Luo, Cheng Deng, Heng Huang

  10. Hyperbolic Contrastive Learning, arXiv
    Yun Yue, Fangzhou Lin, Kazunori D Yamada, Ziming Zhang

  11. A Quadtree for Hyperbolic Space, arXiv 2023
    Sandor Kisfaludi-Bak, Geert van Wordragen

  12. Alignment and Outer Shell Isotropy for Hyperbolic Graph Contrastive Learning, arXiv 2023
    Yifei Zhang, Hao Zhu, Jiahong Liu, Piotr Koniusz, Irwin King

4.3 Data-Driven Geometry Learning and Adaptation

  1. Dimensionality Selection for Hyperbolic Embeddings using Decomposed Normalized Maximum Likelihood Code-Length, arXiv 2023
    Ryo Yuki, Yuichi Ike, Kenji Yamanishi

  2. CO-SNE: Dimensionality Reduction and Visualization for Hyperbolic Data, CVPR 2022
    Yunhui Guo, Haoran Guo, Stella Yu

  3. HyperAid: Denoising in hyperbolic spaces for tree-fitting and hierarchical clustering, KDD 2022
    Eli Chien, Puoya Tabaghi, Olgica Milenkovic

  4. Hyperbolic Feature Augmentation via Distribution Estimation and Infinite Sampling on Manifolds, NeurIPS 2022
    Zhi Gao, Yuwei Wu, Yunde Jia, Mehrtash Harandi

  5. Exploring Data Geometry for Continual Learning, CVPR 2023
    Zhi Gao, Chen Xu, Feng Li, Yunde Jia, Mehrtash Harandi, Yuwei Wu

  6. Curvature-Adaptive Meta-Learning for Fast Adaptation to Manifold Data, TPAMI 2023
    Zhi Gao, Yuwei Wu, Mehrtash Harandi, Yunde Jia

4.4 Few-Shot and Low-Data Learning

  1. Hyperbolic Uncertainty-Aware Few-Shot Incremental Point Cloud Segmentation, CVPR 2025
    Tanuj Sur, Samrat Mukherjee, Kaizer Rahaman, Subhasis Chaudhuri, Muhammad Haris Khan, Biplab Banerjee

  2. Hyperbolic Few-Shot Learning for Taxonomic Plant Classification, NeurIPS 2025 Workshop on Imageomics
    Mithil Shah

  3. HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation, ICCV 2025
    Lingxiao Li, Kaixuan Fan, Boqing Gong, Xiangyu Yue

  4. Hyperbolic Dataset Distillation, Code, NeurIPS 2025
    Wenyuan Li, Guang Li, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama

  5. MetaHKG: Meta Hyperbolic Learning for Few-shot Temporal Reasonin, SIGIR 2024
    Ruijie Wang, Yutong Zhang, Jinyang Li, Shengzhong Liu, Dachun Sun, Tianchen Wang, Tianshi Wang, Yizhuo Chen, Denizhan Kara, Tarek Abdelzaher

  6. Hyperbolic vs Euclidean Embeddings in Few-Shot Learning: Two Sides of the Same Coin, WACV
    Gabriel Moreira, Manuel Marques, Joao Paulo Costeira, Alexander Hauptmann

  7. Curvature Generation in Curved Spaces for Few-Shot Learning, ICCV 2021
    Zhi Gao, Yuwei Wu, Yunde Jia, Mehrtash Harandi

4.5 Open-Vocabulary, Zero-Shot, and Category Discovery

  1. Parameter-efficient Fine-tuning in Hyperspherical Space for Open-vocabulary Semantic Segmentation, CVPR 2025
    Zelin Peng, Zhengqin Xu, Zhilin Zeng, Changsong Wen, Yu Huang, Menglin Yang, Feilong Tang, Wei Shen

  2. Hyperbolic Learning with Synthetic Captions for Open-World Detection, CVPR 2024
    Fanjie Kong, Yanbei Chen, Jiarui Cai, Davide Modolo

  3. Hyperbolic Category Discovery, CVPR 2025
    Yuanpei Liu, Zhenqi He, Kai Han

  4. Unsupervised Discovery of the Long-Tail in Instance Segmentation Using Hierarchical Self-Supervision, CVPR 2021
    Zhenzhen Weng, Mehmet Giray Ogut, Shai Limonchik, Serena Yeung

  5. Meta Hyperbolic Networks for Zero-Shot Learning, Neurocomputing
    Yan Xu, Lifu Mu, ZhongJi, Xiyao Liu, JungongHan

4.6 Safety, Robustness, and Privacy

  1. Hyperbolic Prototype Learning with Uncertainty-Aware Consistency for Continual Test-Time Segmentation, CVPR 2026
    Siddhant Gole, Akash Pal, Amit More, Srinivasa Divakar Bhat, Subhasis Chaudhuri, Biplab Banerjee

  2. Hyperbolic Safety-Aware Vision-Language Models, CVPR 2025
    Tobia Poppi, Tejaswi Kasarla, Pascal Mettes, Lorenzo Baraldi, Rita Cucchiara

  3. Rethinking Generalizable Face Anti-spoofing via Hierarchical Prototype-guided Distribution Refinement in Hyperbolic Space, CVPR 2024
    Chengyang Hu, Ke-Yue Zhang, Taiping Yao, Shouhong Ding, Lizhuang Ma

  4. Hyperbolic Anomaly Detection, CVPR 2024
    Huimin Li, Zhentao Chen, Yunhao Xu, Junlin Hu

  5. Improving Robustness of Hyperbolic Neural Networks by Lipschitz Analysis, KDD 2024
    Yuekang Li, Yidan Mao, Yifei Yang, Dongmian Zou

  6. Human-Inspired Obfuscation for Model Unlearning: Local and Global Strategies with Hyperbolic Representations, Findings of EMNLP 2025
    Zekun Wang, Jingjie Zeng, Yingxu Li, Liang Yang, Hongfei Lin

  7. Leveraging Hierarchical Representations for Preserving Privacy and Utility in Text, ICDM 2019
    Oluwaseyi Feyisetan, Tom Diethe, Thomas Drake

4.7 Environmental Monitoring

  1. Deep Change Monitoring: A Hyperbolic Representative Learning Framework and a Dataset for Long-term Fine-grained Tree Change Detection, CVPR 2025
    Yante Li, Hanwen Qi, Haoyu Chen, Liang Xinlian, Guoying Zhao

4.8 Multi-Criteria Learning

  1. Hyperbolic Multi-Criteria Rating Recommendation, SIGIR 2025
    Zhihao Guo, Ting Han, Peng Song, Chenjiao Feng, Kaixuan Yao, Jiye Liang