Abstract
There has been an increasing number of applications of machine learning to the field of Computer Algebra in recent years, including to the prominent sub-field of Symbolic Integration. However, machine learning models require an abundance of data for them to be successful and there exist few benchmarks on the scale required. While methods to generate new data already exist, they are flawed in several ways which may lead to bias in machine learning models trained upon them. In this paper, we describe how to use the Risch Algorithm for symbolic integration to create a dataset of elementary integrable expressions. Further, we show that data generated this way alleviates some of the flaws found in earlier methods.
Original language | English |
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Title of host publication | Computer Algebra in Scientific Computing |
Subtitle of host publication | 25th International Workshop, CASC 2023, Havana, Cuba, August 28 – September 1, 2023, Proceedings |
Editors | François Boulier, Matthew England, Ilias Kotsireas, Timur M. Sadykov, Evgenii V. Vorozhtsov |
Publisher | Springer |
Chapter | 2 |
Pages | 21-38 |
Number of pages | 18 |
Edition | 1 |
ISBN (Electronic) | 978-3-031-41724-5 |
ISBN (Print) | 978-3-031-41723-8 |
DOIs | |
Publication status | Published - 24 Aug 2023 |
Event | 25th International Workshop on Computer Algebra in Scientific Computing - Havana, Cuba Duration: 28 Aug 2023 → 1 Sept 2023 http://www.casc-conference.org/ |
Publication series
Name | Lecture Notes in Computer Science |
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Volume | 14139 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Workshop
Workshop | 25th International Workshop on Computer Algebra in Scientific Computing |
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Abbreviated title | CASC 2023 |
Country/Territory | Cuba |
City | Havana |
Period | 28/08/23 → 1/09/23 |
Internet address |
Bibliographical note
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Keywords
- Computer algebra
- Symbolic integration
- Machine learning
- Data generation