Machine learning of the well known things
High Energy Physics - Theory
2023-04-05 v1 Machine Learning
Abstract
Machine learning (ML) in its current form implies that an answer to any problem can be well approximated by a function of a very peculiar form: a specially adjusted iteration of Heavyside theta-functions. It is natural to ask if the answers to the questions, which we already know, can be naturally represented in this form. We provide elementary, still non-evident examples that this is indeed possible, and suggest to look for a systematic reformulation of existing knowledge in a ML-consistent way. Success or a failure of these attempts can shed light on a variety of problems, both scientific and epistemological.
Keywords
Cite
@article{arxiv.2204.11613,
title = {Machine learning of the well known things},
author = {V. Dolotin and A. Morozov and A. Popolitov},
journal= {arXiv preprint arXiv:2204.11613},
year = {2023}
}