# Can machine learning identify interesting mathematics? An exploration using empirically observed laws

arXiv: https://arxiv.org/abs/1805.07431 · HTML: https://mlsanity.com/paper/1805.07431/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 May 2018.

## Built on by (most cited first, 4 of 4)

- [Deep Symbolic Regression for Recurrent Sequences](https://mlsanity.com/paper/2201.04600/) (2022)
- [FACT: Learning Governing Abstractions Behind Integer Sequences](https://mlsanity.com/paper/2209.09543/) (2022)
- [A Triumvirate of AI Driven Theoretical Discovery](https://mlsanity.com/paper/2405.19973/) (2024)
- [KitBit: A New AI Model for Solving Intelligence Tests and Numerical Series](https://mlsanity.com/paper/2206.08965/) (2022)

## 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.