@inproceedings{
jenne2023can,
title={Can We Count on Deep Learning: Exploring and Characterizing Combinatorial Structures Using Machine Learning},
author={Helen Jenne and Herman Chau and Davis Brown and Jackson Warley and Timothy Doster and Henry Kvinge},
booktitle={The 3rd Workshop on Mathematical Reasoning and AI at NeurIPS'23},
year={2023},
url={https://openreview.net/forum?id=oX1ANuWuIn}
}
With its exceptional pattern matching ability, deep learning has proven to be a powerful tool in a range of scientific domains. This is increasingly true in research mathematics, where recent work has demonstrated deep learning's ability to highlight subtle connections between mathematical objects that might escape a human expert. In this work we describe a simple method to help domain experts characterize a set of mathematical objects using deep learning. Such *characterization problems* often occur when some particular class of function, space, linear representation, etc. naturally emerges in calculations or other means but lacks a simple description. The goal is to find simple rules that also ideally shed light on the underlying mathematics. Our method, which we call *Feature Attribution Clustering for Exploration (FACE)*, clusters the feature attribution representations extracted from a trained model, arriving at a short list of prototype attributions that the domain expert can then try to convert into formal and rigorous rules. As a case study, we use our method to derive a new result in combinatorics by characterizing a subset of 0-1 matrices that corresponds to certain representations of permutations known as two-sided ordered words.
Can We Count on Deep Learning: Exploring and Characterizing Combinatorial Structures Using Machine Learning
Helen Jenne Herman Chau Davis Brown Jackson Warley Timothy Doster Henry Kvinge