Shortcut learning in geometric knot classification
Classifying the topology of closed curves is a central problem in low dimensional topology with applications beyond mathematics spanning protein folding, polymer physics and even magnetohydrodynamics. The central problem is how to determine whether two embeddings of a closed arc are equivalent under ambient isotopy. Given the striking ability of neural networks to solve complex classification tasks, it is therefore natural to ask if the knot classification problem can be tackled using Machine Learning (ML). In this paper, we investigate generic shortcut methods employed by ML to solve the knot classification challenge and specifically discover hidden non-topological features in training d...
| Authors | Djordje Mihajlovic, Davide Michieletto |
|---|---|
| Primary category | cs.LG |
| Published | 19 February 2026 |
| Last updated at source | 19 February 2026 |
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