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Rigor with Machine Learning from Field Theory to the Poincaré Conjecture
Machine learning techniques are increasingly powerful, leading to many breakthroughs in the natural sciences, but they are often stochastic, error-prone, and blackbox.
Searching for ribbons with machine learning
We apply Bayesian optimization and reinforcement learning to a problem in topology: the question of when a knot bounds a ribbon disk.
Learning to Unknot
We introduce natural language processing into the study of knot theory, as made natural by the braid word representation of knots.
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