Wilson, Paul;
Zanasi, Fabio;
(2023)
An axiomatic approach to differentiation of polynomial circuits.
Journal of Logical and Algebraic Methods in Programming
, 135
, Article 100892. 10.1016/j.jlamp.2023.100892.
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Abstract
Reverse derivative categories (RDCs) have recently been shown to be a suitable semantic framework for studying machine learning algorithms. Whereas emphasis has been put on training methodologies, less attention has been devoted to particular model classes: the concrete categories whose morphisms represent machine learning models. In this paper we study presentations by generators and equations of classes of RDCs. In particular, we propose polynomial circuits as a suitable machine learning model class. We give an axiomatisation for these circuits and prove a functional completeness result. Finally, we discuss the use of polynomial circuits over specific semirings to perform machine learning with discrete values.
| Type: | Article |
|---|---|
| Title: | An axiomatic approach to differentiation of polynomial circuits |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.jlamp.2023.100892 |
| Publisher version: | https://doi.org/10.1016/j.jlamp.2023.100892 |
| Language: | English |
| Additional information: | Copyright © 2023 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10174391 |
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