Machine-Learning Mathematical Structures. (arXiv:2101.06317v1 [cs.LG])

We review, for a general audience, a variety of recent experiments on
extracting structure from machine-learning mathematical data that have been
compiled over the years. Focusing on supervised machine-learning on labeled
data from different fields ranging from geometry to representation theory, from
combinatorics to number theory, we present a comparative study of the
accuracies on different problems. The paradigm should be useful for conjecture
formulation, finding more efficient methods of computation, as well as probing
into certain hierarchy of structures in mathematics.



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