Explainable artificial intelligence (XAI) has received great interest in the recent decade, due
to its importance in critical application domains, such as self-driving cars, law, and …

Symbolic regression (SR) is a machine learning-based regression method based on genetic
programming principles that integrates techniques and processes from heterogeneous …

Symbolic regression, the task of predicting the mathematical expression of a function from
the observation of its values, is a difficult task which usually involves a two-step procedure …

Many promising approaches to symbolic regression have been presented in recent years,
yet progress in the field continues to suffer from a lack of uniform, robust, and transparent …

The last few years have witnessed a surge in methods for symbolic regression, from
advances in traditional evolutionary approaches to novel deep learning-based systems …

Symbolic regression (SR) is the study of algorithms that automate the search for analytic
expressions that fit data. While recent advances in deep learning have generated renewed …

We present an approach for using machine learning to automatically discover the governing
equations and unknown properties (in this case, masses) of real physical systems from …

Symbolic regression (SR) is the task of learning a model of data in the form of a
mathematical expression. By their nature, SR models have the potential to be accurate and …

Symbolic regression (SR) is the problem of learning a symbolic expression from numerical
data. Recently, deep neural models trained on procedurally-generated synthetic datasets …

Discovering a meaningful symbolic expression that explains experimental data is a
fundamental challenge in many scientific fields. We present a novel, open-source …