Geometric Deep Learning
Equivariant networks on graphs, meshes, and manifolds.
Geometric Deep Learning. Equivariant networks on graphs, meshes, and manifolds.
Recent technical contributions
A handful of recent papers carry the methodological frontier of geometric deep learning forward. Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges (Bronstein et al., 2021) is a primary reference for this area and develops new techniques or results that downstream work builds on.
Open methodological questions for geometric deep learning include sharpening the bridges between foundational theory and computational practice, extending classical results to broader or more structured settings, and integrating the techniques surveyed above with adjacent mathematical disciplines. The references listed in this page are the entry points that current work builds on.
Prerequisites
Sources
- paper · primary · 2021bronstein-2021, bruna-2021, cohen-2021, velickovic-2021
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