Mathematical Foundations of Geometric Deep Learning
Haitz S\'aez de Oc\'ariz Borde, Michael Bronstein
TL;DR
This paper reviews the essential mathematical concepts underpinning Geometric Deep Learning, providing a foundational understanding for researchers in the field.
Contribution
It offers a comprehensive overview of the mathematical principles crucial for advancing Geometric Deep Learning research.
Findings
Identifies core mathematical tools used in geometric deep learning
Clarifies the theoretical basis for geometric neural networks
Serves as a foundational reference for future research
Abstract
We review the key mathematical concepts necessary for studying Geometric Deep Learning.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.