marlin-codes/Awesome-Hyperbolic-Representation-and-Deep-Learning：关于双曲嵌入、双曲模型、双曲应用的论文列表

627

项目介绍

marlin-codes/Awesome-Hyperbolic-Representation-and-Deep-Learning 是一个托管在 GitHub 上的开源项目，目前获得 627 个 Star、40 次 Fork。

仓库全名: marlin-codes/Awesome-Hyperbolic-Representation-and-Deep-Learning
Star 数: 627
Fork 数: 40

README 摘要

Introduction Recently, hyperbolic spaces have emerged as a promising alternative for processing data with a tree-like structure or power-law distribution, owing to its exponential growth property and tree-likeness prior. Different from the Euclidean space, which expands polynomially, the hyperbolic space grows exponentially which makes it gain natural advantages in abstracting tree-like or scale-free data with…

完整内容见 GitHub 原始 README 或本页。

常见问题

如何获取 Awesome-Hyperbolic-Representation-and-Deep-Learning 的源码？

可以直接克隆仓库：git clone https://github.com/marlin-codes/Awesome-Hyperbolic-Representation-and-Deep-Learning.git，默认分支为 main；安装与运行步骤见仓库 README。

Awesome-Hyperbolic-Representation-and-Deep-Learning 最近还在维护吗？

仓库最近一次更新为 6 天前，当前没有未关闭的 Issue；Star 数为 627，可作为社区活跃度的参考。

关于

GitHub 原始描述（英文）

Paper list about hyperbolic embedding, hyperbolic models,hyperbolic applications

描述取自 GitHub 官方 API，保持仓库作者书写的原文。

简要分析

热度分

721

价值分

389

活跃状态

活跃

graph-embedding graph-neural-networks hyperbolic-geometry knowledge-graph network-embedding recommender-system representation-learning

GitHub

贡献者

marlin-codes

pchlenski

minzhou1900

1. Latest Update
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	3.8 Education and Learning Analytics
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

Awesome-Hyperbolic-Representation-and-Deep-Learning

项目介绍

README 摘要

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README

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