Rectangular knot diagrams classification with deep learning

DOI10.1142/S0218216522500675MaRDI QIDQ5050613

Louis H. Kauffman, N. E. Russkikh, Iskander A. Taimanov

Publication date: 17 November 2022

Published in: Journal of Knot Theory and Its Ramifications (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2011.03498

zbMATH Keywords

neural networksknotmachine learningrectangular diagramsDynnikov movesunknotting problem

Mathematics Subject Classification ID

Computational learning theory (68Q32) Learning and adaptive systems in artificial intelligence (68T05) Invariants of 3-manifolds (including skein modules, character varieties) (57K31)

Uses Software

• Adam

Cites Work

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• Khovanov homology is an unknot-detector
• On Dehn's lemma and the asphericity of knots
• Theorie der Normalflächen. Ein Isotopiekriterium für den Kreisknoten
• A new algorithm for recognizing the unknot
• Deep learning the hyperbolic volume of a knot
• A combinatorial description of knot Floer homology
• Knottedness is in NP, modulo GRH
• An upper bound on Reidemeister moves
• The computational complexity of knot and link problems
• A neural network approach to predicting and computing knot invariants
• Arc-presentations of links: Monotonic simplification
• Algorithmic topology and classification of 3-manifolds

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