Neuro-Symbolic Regression with Applications

Discovering symbolic models is growing in popularity with the increasing interest in interpretable machine learning. Symbolic regression is the task of learning an analytical form of underlying models in data. Two machine learning techniques have proven their effectiveness: reinforce trick and transformer neural network. This paper discusses in detail the two techniques and presents the application of symbolic regression on a simulated data set that describes a high-energy physics process.