fyi, talk about framework for generating integral expressions using machine learning.

[Nasser M. Abbasi]

some might find this talk interesting


Taken from link at this page

Maple Maple conference 2022 about half the page down

https://www.maplesoft.com/mapleconference/2022/full-program.aspx#schedule

Here is the direct link to the youtube

https://www.youtube.com/watch?v=AfFWyz8E5kw&list=PLlcD7K2JXjTB5YRCJP26ZYlYM_w10N398&index=10&t=820s

It starts at 13:40

"Rashid Barket (Coventry University)
Framework for Generating Integrable Expressions
Applications of machine learning are becoming more prominent in
the field of computer algebra. Examples of such applications
include selecting S-pairs in Buchberger’s algorithm or solving
integrals and differential equations directly. With many of
these applications, data must be generated to train a model.
Methods such as generating binary trees representing mathematical
expressions or created randomly in a recursive manner from a set
of available function symbols, variables and constants have been
discussed. However, these generated expressions do not represent
a realistic dataset that draws from the typical Maple user’s experience.

I propose a framework for generating valid mathematical expressions.
More precisely, the focus will be on integrable expressions. The
difference from other methods lies in the fact that the data
generation method will be based on a test suite of data generated
from Maple users. Thus, the new synthetic data will have properties
similar to integrable expressions that Maple users would typically try.
This data generation method will be used to train machine learning models
that make efficient choices algorithm selection problems."



--Nasser