Mathematical Foundations of Geometric Deep Learning
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Abstract (arXiv)
We review the key mathematical concepts necessary for studying Geometric Deep Learning.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 1 Aug 2025.
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Citing papers per year: 2025: 1; 2026: 3.
4 papers.
- 2026Every Feedforward Neural Network Definable in an o-Minimal Structure Has Finite Sample Complexitycited by 3
- 2025VDW-GNNs: Vector diffusion wavelets for geometric graph neural networkscited by 1
- 2026A graph neural network based chemical mechanism reduction method for combustion applicationscited by 0
- 2026GUIDED Network-Agnostic Feature Initialization for Spatial Transferability in GNN-based Modelscited by 0
- 2026GUIDED Network-Agnostic Feature Initialization for Spatial Transferability in GNN-based Models21 Jul 2026
- 2026Every Feedforward Neural Network Definable in an o-Minimal Structure Has Finite Sample Complexity8 May 2026
- 2026A graph neural network based chemical mechanism reduction method for combustion applications20 Mar 2026
- 2025VDW-GNNs: Vector diffusion wavelets for geometric graph neural networks1 Oct 2025