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Constrained Neural Networks for Interpretable Heuristic Creation to Optimise Computer Algebra Systems
We present a new methodology for utilising machine learning technology in symbolic computation research.
A Generalized Approach for Recovering Time Encoded Signals with Finite Rate of Innovation
In this paper, we consider the problem of recovering a sum of filtered Diracs, representing an input with finite rate of innovation (FRI), from its corresponding time encoding machine (TEM) measurements.
A machine learning based software pipeline to pick the variable ordering for algorithms with polynomial inputs
It may seem that the probabilistic nature of ML tools would invalidate the exact results prized by such software, however, the algorithms which underpin the software often come with a range of choices which are good candidates for ML application.
Improved cross-validation for classifiers that make algorithmic choices to minimise runtime without compromising output correctness
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 output.
Algorithmically generating new algebraic features of polynomial systems for machine learning
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 mathematical correctness.
Comparing machine learning models to choose the variable ordering for cylindrical algebraic decomposition
Prior work to apply ML on this problem implemented a Support Vector Machine (SVM) to select between three existing human-made heuristics, which did better than anyone heuristic alone.
