Generating Elementary Integrable Expressions
This addresses the need for large-scale, unbiased benchmarks in computer algebra for machine learning applications, though it is incremental as it builds on existing algorithmic methods.
The paper tackled the problem of generating data for machine learning in symbolic integration by using the Risch Algorithm to create a dataset of elementary integrable expressions, showing that this method alleviates flaws found in earlier approaches.
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.