Can machine learning identify interesting mathematics? An exploration using empirically observed laws
This work addresses the challenge of automating the discovery of interesting patterns in mathematics, but it appears incremental as it applies existing empirical laws and classifiers to a new domain without claiming major breakthroughs.
The authors tackled the problem of identifying interesting mathematical structures by using machine learning with features derived from Benford's and Taylor's laws, and they experimented with various classifiers to categorize sequences based on properties like importance or primality.
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.