Is deep learning a useful tool for the pure mathematician?

DOI10.1090/bull/1829arXiv2304.12602OpenAlexW4391839309MaRDI QIDQ6130526

Geordie Williamson

Publication date: 3 April 2024

Published in: Bulletin of the American Mathematical Society (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2304.12602

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07)

Cites Work

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• Kazhdan-Lusztig polynomials: history, problems, and combinatorial invariance.
• Deep learning Gauss-Manin connections
• Hilbert series, machine learning, and applications to physics
• Intelligent Machinery, A Heretical Theory*
• Advancing mathematics by guiding human intuition with AI
• Reconciling modern machine-learning practice and the classical bias–variance trade-off
• Learning representations by back-propagating errors
• A logical calculus of the ideas immanent in nervous activity
• Towards combinatorial invariance for Kazhdan-Lusztig polynomials
• Machine Learning Line Bundle Cohomology

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