Can machine learning identify interesting mathematics? An exploration using empirically observed laws
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Abstract (arXiv)
We explore the possibility of using machine learning to identify interesting mathematical structures by using certain quantities that serve as fingerprints. In particular, we extract features from integer sequences using two empirical laws: Benford's law and Taylor's law and experiment with various classifiers to identify whether a sequence is, for example, nice, important, multiplicative, easy to compute or related to primes or palindromes.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 May 2018.
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Citing papers per year: 2022: 3; 2023: 0; 2024: 1.
4 papers.
- 2024A Triumvirate of AI Driven Theoretical Discovery30 May 2024
- 2022FACT: Learning Governing Abstractions Behind Integer Sequences20 Sep 2022
- 2022KitBit: A New AI Model for Solving Intelligence Tests and Numerical Series17 Jun 2022
- 2022Deep Symbolic Regression for Recurrent Sequences12 Jan 2022