Topological Deep Learning
Differentiable persistence layers and topology-aware neural networks.
Topological Deep Learning. Differentiable persistence layers and topology-aware neural networks.
Recent technical contributions
A handful of recent papers carry the methodological frontier of topological deep learning forward. Topological Deep Learning: Going Beyond Graph Data (Hajij et al., 2023) is a primary reference for this area and develops new techniques or results that downstream work builds on.
Open methodological questions for topological 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 · 2023hajij-2023, zamzmi-2023, papamarkou-2023, maroulas-2023
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