59 citations · 117 across the 10 of their papers we have counts for
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cs.SC2019★ 7 cited
Improved cross-validation for classifiers that make algorithmic choices to minimise runtime without compromising output correctness
Dorian Florescu, Matthew England
Our topic is the use of machine learning to improve software by making choices which do not compromise the correctness of the output, but do affect the time taken to produce such o…
cs.SC2019★ 9 cited
Algorithmically generating new algebraic features of polynomial systems for machine learning
Dorian Florescu, Matthew England
There are a variety of choices to be made in both computer algebra systems (CASs) and satisfiability modulo theory (SMT) solvers which can impact performance without affecting math…
cs.SC2019★ 18 cited
Comparing machine learning models to choose the variable ordering for cylindrical algebraic decomposition
Matthew England, Dorian Florescu
There has been recent interest in the use of machine learning (ML) approaches within mathematical software to make choices that impact on the computing performance without affectin…