A Seq2Seq approach to Symbolic Regression
Deep neural networks have proved to be powerful function approximators. The large hypothesis space they implicitly model allows them to fit very complicated black-box functions to the training data. However, often the data generating process is characterized by a concise and relatively simple functional form. This is especially true in natural sciences, where elegant physical laws govern the behaviour of the quantities of interest. In this work, we address this dichotomy from the perspective of Symbolic Regression (SR). In particular, we apply a fully-convolutional seq2seq model to map numerical data to the corresponding symbolic equations. We demonstrate the effectiveness of our approach on a large set of mathematical expressions by providing both a qualitative and a quantitative analysis of our results. Additionally, we release our new equation-generator Python library in order to facilitate benchmarking and stimulate new research on SR.
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