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Is deep learning a useful tool for the pure mathematician?

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One-sentence machine reading of the paper's core claim.

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arxiv 2304.12602 v2 pith:VWIR4SSS submitted 2023-04-25 math.RT cs.LGmath.AGmath.CO

classification math.RTcs.LGmath.AGmath.CO
keywords deeplearningmathematicianpureaccountexpectinformalmight
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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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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Neural Discovery in Mathematics: Do Machines Dream of Colored Planes?

    cs.LG 2025-01 conditional novelty 6.0 of 10

    A neural network relaxation of geometric coloring constraints produced new plane colorings, including an almost 5-coloring covering all but 3.74% of the plane, improving known bounds for Hadwiger-Nelson variants.

  2. A Hybrid Framework for Healing Semigroups with Machine Learning

    math.RA 2025-09 conditional novelty 5.0 of 10

    A hybrid random-forest-plus-deterministic method heals corrupted finite semigroup tables, restoring associativity in 95% of small cases and 60% at n=10.

  3. Explaining Deep Network Classification of Matrices: A Case Study on Monotonicity

    cs.LG 2025-07 conditional novelty 5.0 of 10

    For random 7x7 matrices with entries uniform in (-1,1), the ratio of the two lowest characteristic-polynomial coefficients, equal to 1/tr(A^{-1}) for monotone A, is empirically below 0.1755 for all 18,000 sampled mono...

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