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Found 16 papers, 0 papers with code
Lessons on Datasets and Paradigms in Machine Learning for Symbolic Computation: A Case Study on CAD
Symbolic Computation algorithms and their implementation in computer algebra systems often contain choices which do not affect the correctness of the output but can significantly impact the resources required: such choices can benefit from having them made separately for each problem via a machine learning model.
Data Augmentation for Mathematical Objects
This paper discusses and evaluates ideas of data balancing and data augmentation in the context of mathematical objects: an important topic for both the symbolic computation and satisfiability checking communities, when they are making use of machine learning techniques to optimise their tools.
Generating Elementary Integrable Expressions
There has been an increasing number of applications of machine learning to the field of Computer Algebra in recent years, including to the prominent sub-field of Symbolic Integration.
Explainable AI Insights for Symbolic Computation: A case study on selecting the variable ordering for cylindrical algebraic decomposition
In recent years there has been increased use of machine learning (ML) techniques within mathematics, including symbolic computation where it may be applied safely to optimise or select algorithms.
SC-Square: Future Progress with Machine Learning?
The algorithms employed by our communities are often underspecified, and thus have multiple implementation choices, which do not effect the correctness of the output, but do impact the efficiency or even tractability of its production.
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.
Polynomial Superlevel Set Representation of the Multistationarity Region of Chemical Reaction Networks
In this paper we introduce a new representation for the multistationarity region of a reaction network, using polynomial superlevel sets.
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.
A Combined CNN and LSTM Model for Arabic Sentiment Analysis
Deep neural networks have shown good data modelling capabilities when dealing with challenging and large datasets from a wide range of application areas.
Using Machine Learning to Improve Cylindrical Algebraic Decomposition
We investigate the use of machine learning (specifically support vector machines) to make such choices instead.
Improving Sentiment Analysis in Arabic Using Word Representation
The complexities of Arabic language in morphology, orthography and dialects makes sentiment analysis for Arabic more challenging.
Arabic Language Sentiment Analysis on Health Services
While there has been a lot of research on sentiment analysis in English, the amount of researches and datasets in Arabic language is limited.
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.
