2020/10/28 by Sergei Gukov, James Halverson, Gukov, Sergei +5 · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · #Artificial Intelligence in Games #Biochemical and Structural Characterization #FOS: Computer and information sciences #FOS: Mathematics #FOS: Physical sciences #Geometric Topology (math.GT) #Geometric and Algebraic Topology #High Energy Physics - Theory (hep-th) #Machine Learning (cs.LG)
paper · pdf · doi:10.48550/arxiv.2010.16263
openalex publication_date 2020/10/28 · openalex created_date 2022/10/05 · openalex updated_date 2026/07/28
We introduce natural language processing into the study of knot theory, as made natural by the braid word representation of knots. We study the UNKNOT problem of determining whether or not a given knot is the unknot. After describing an algorithm to randomly generate N-crossing braids and their knot closures and discussing the induced prior on the distribution of knots, we apply binary classification to the UNKNOT decision problem. We find that the Reformer and shared-QK Transformer network architectures outperform fully-connected networks, though all perform well. Perhaps surprisingly, we find that accuracy increases with the length of the braid word, and that the networks learn a direct correlation between the confidence of their predictions and the degree of the Jones polynomial. Finally, we utilize reinforcement learning (RL) to find sequences of Markov moves and braid relations that simplify knots and can identify unknots by explicitly giving the sequence of unknotting actions. Trust region policy optimization (TRPO) performs consistently well for a wide range of crossing numbers and thoroughly outperformed other RL algorithms and random walkers. Studying these actions, we find that braid relations are more useful in simplifying to the unknot than one of the Markov moves.