PhD Seminar • Machine Learning | Natural Language Processing • SymbolicGPT: A Generative Transformer Model for Symbolic Regression

Please note: This seminar will take place online.

Mojtaba Valipour, PhD candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Ali Ghodsi

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.

Link to the paper: https://arxiv.org/abs/2106.14131

To attend this PhD seminar on Google Meet, please go https://meet.google.com/ask-fndk-khj.