SymbolicGPT: A Generative Transformer Model for Symbolic Regression
This work addresses the problem of finding mathematical expressions from data for researchers and practitioners, representing an incremental improvement in deep learning-based methods for symbolic regression.
The authors tackled symbolic regression by proposing SymbolicGPT, a transformer-based language model, and demonstrated strong performance in accuracy, running time, and data efficiency compared to existing models.
Symbolic regression is the task of identifying a mathematical expression that best fits a provided dataset of input and output values. Due to the richness of the space of mathematical expressions, symbolic regression is generally a challenging problem. While conventional approaches based on genetic evolution algorithms have been used for decades, deep learning-based methods are relatively new and an active research area. In this work, we present SymbolicGPT, a novel transformer-based language model for symbolic regression. This model exploits the advantages of probabilistic language models like GPT, including strength in performance and flexibility. Through comprehensive experiments, we show that our model performs strongly compared to competing models with respect to the accuracy, running time, and data efficiency.