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A machine learning based software pipeline to pick the variable ordering
  for algorithms with polynomial inputs

A machine learning based software pipeline to pick the variable ordering for algorithms with polynomial inputs

22 May 2020
Dorian Florescu
Matthew England
ArXiv (abs)PDFHTML

Papers citing "A machine learning based software pipeline to pick the variable ordering for algorithms with polynomial inputs"

3 / 3 papers shown
Title
Constrained Neural Networks for Interpretable Heuristic Creation to
  Optimise Computer Algebra Systems
Constrained Neural Networks for Interpretable Heuristic Creation to Optimise Computer Algebra Systems
Dorian Florescu
Matthew England
39
1
0
26 Apr 2024
Data Augmentation for Mathematical Objects
Data Augmentation for Mathematical Objects
Tereso Del Rio Almajano
Matthew England
31
4
0
13 Jul 2023
Explainable AI Insights for Symbolic Computation: A case study on
  selecting the variable ordering for cylindrical algebraic decomposition
Explainable AI Insights for Symbolic Computation: A case study on selecting the variable ordering for cylindrical algebraic decomposition
Lynn Pickering
Tereso Del Rio Almajano
Matthew England
Kelly Cohen
42
13
0
24 Apr 2023
1