Lessons on datasets and paradigms in machine learning for symbolic computation: a case study on CAD
From MaRDI portal
Publication:6653101
DOI10.1007/S11786-024-00591-0MaRDI QIDQ6653101
Tereso del Río, Matthew England
Publication date: 16 December 2024
Published in: Mathematics in Computer Science (Search for Journal in Brave)
classificationsymbolic computationregressioncylindrical algebraic decompositionmachine learningdata augmentation
Symbolic computation and algebraic computation (68W30) Learning and adaptive systems in artificial intelligence (68T05)
Cites Work
- Title not available (Why is that?)
- Title not available (Why is that?)
- Title not available (Why is that?)
- Title not available (Why is that?)
- Real quantifier elimination is doubly exponential
- Using machine learning to improve cylindrical algebraic decomposition
- Neurons on amoebae
- New heuristic to choose a cylindrical algebraic decomposition variable ordering motivated by complexity analysis
- Good pivots for small sparse matrices
- Identifying the parametric occurrence of multiple steady states for some biological networks
- Comparing machine learning models to choose the variable ordering for cylindrical algebraic decomposition
- Machine learning the real discriminant locus
- Optimising Problem Formulation for Cylindrical Algebraic Decomposition
- Applied Predictive Modeling
- Cylindrical Algebraic Decomposition in the RegularChains Library
- MetiTarski: Past and Future
- Satisfiability Modulo Theories
- Efficient projection orders for CAD
- Improved Cross-Validation for Classifiers that Make Algorithmic Choices to Minimise Runtime Without Compromising Output Correctness
- Advancing mathematics by guiding human intuition with AI
- Variable Ordering Selection for Cylindrical Algebraic Decomposition with Artificial Neural Networks
- Applying Machine Learning to Heuristics for Real Polynomial Constraint Solving
- Applying Machine Learning to the Problem of Choosing a Heuristic to Select the Variable Ordering for Cylindrical Algebraic Decomposition
- Improved projection for cylindrical algebraic decomposition
- An augmented MetiTarski dataset for real quantifier elimination using machine learning
- Explainable AI insights for symbolic computation: a case study on selecting the variable ordering for cylindrical algebraic decomposition
- Symbolic integration algorithm selection with machine learning: LSTMs vs tree LSTMs
This page was built for publication: Lessons on datasets and paradigms in machine learning for symbolic computation: a case study on CAD
Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6653101)