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Bulletin of the American Mathematical Society

The Bulletin publishes expository articles on contemporary mathematical research, written in a way that gives insight to mathematicians who may not be experts in the particular topic. The Bulletin also publishes reviews of selected books in mathematics and short articles in the Mathematical Perspectives section, both by invitation only.

ISSN 1088-9485 (online) ISSN 0273-0979 (print)

The 2020 MCQ for Bulletin of the American Mathematical Society is 0.84.

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Is deep learning a useful tool for the pure mathematician?
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by Geordie Williamson
Bull. Amer. Math. Soc. 61 (2024), 271-286
DOI: https://doi.org/10.1090/bull/1829
Published electronically: February 15, 2024

Abstract:

A personal and informal account of what a pure mathematician might expect when using tools from deep learning in their research.
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Bibliographic Information
  • Geordie Williamson
  • Affiliation: Sydney Mathematical Research Institute, University of Sydney, A14 - Quadrangle, NSW 2006, Sydney, Australia
  • MR Author ID: 845262
  • ORCID: 0000-0003-3672-5284
  • Email: g.williamson@sydney.edu.au
  • Received by editor(s): May 25, 2023
  • Published electronically: February 15, 2024
  • Additional Notes: I am a pure mathematician, working mostly in geometric representation theory and related fields. I began an ongoing collaboration with DeepMind in 2020 on possible interactions of machine learning and mathematics, and have been fascinated by the subject ever since.
  • © Copyright 2024 American Mathematical Society
  • Journal: Bull. Amer. Math. Soc. 61 (2024), 271-286
  • MSC (2020): Primary 68T07, 05E10
  • DOI: https://doi.org/10.1090/bull/1829
  • MathSciNet review: 4726992