Search Results for author:
Found 5 papers, 1 papers with code
IsarStep: a Benchmark for High-level Mathematical Reasoning
In this paper, we present a benchmark for high-level mathematical reasoning and study the reasoning capabilities of neural sequence-to-sequence models.
Bayesian Optimisation with Gaussian Processes for Premise Selection
Heuristics in theorem provers are often parameterised.
Using Machine Learning to Improve Cylindrical Algebraic Decomposition
We investigate the use of machine learning (specifically support vector machines) to make such choices instead.
Using Machine Learning to Decide When to Precondition Cylindrical Algebraic Decomposition With Groebner Bases
Previous experiments have shown that while this can often be very beneficial to the CAD algorithm, for some problems it can significantly worsen the CAD performance.
Applying machine learning to the problem of choosing a heuristic to select the variable ordering for cylindrical algebraic decomposition
Cylindrical algebraic decomposition(CAD) is a key tool in computational algebraic geometry, particularly for quantifier elimination over real-closed fields.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
