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Hyperbolic Deep Neural Networks: A Survey.

Wei Peng, Tuomas Varanka, Abdelrahman Mostafa

IEEE Transactions on Pattern Analysis and Machine Intelligence

|December 21, 2021

Summary

This summary is machine-generated.

Hyperbolic deep neural networks (HDNNs) offer compact, interpretable models for hierarchical data. This review explores their components, applications, and future research directions in various scientific fields.

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Area of Science:

Artificial Intelligence
Machine Learning
Data Science

Background:

Hyperbolic deep neural networks (HDNNs) leverage hyperbolic space for high-fidelity, low-dimensional embeddings, particularly effective for hierarchical data structures.
HDNNs show promise across diverse fields like NLP, genomics, graph analysis, finance, and computer vision, outperforming Euclidean counterparts in capability and interpretability.

Approach:

This paper provides a comprehensive literature review of neural components essential for constructing HDNNs.
It examines the adaptation of leading deep learning approaches to hyperbolic geometry.

Key Points:

HDNNs offer significant model compactness and enhanced physical interpretability compared to Euclidean models.
Applications span natural language processing, single-cell RNA-sequence analysis, graph embedding, financial analysis, and computer vision.

Conclusions:

The review highlights the growing momentum and superior performance of HDNNs for specific data types.
It identifies open research questions and outlines promising future directions for hyperbolic deep learning.