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Dec 13, 2022, 1:25:31 PM12/13/22

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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

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

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