SymbolicGPT: A Generative Transformer Model for Symbolic Regression

Autor:	Valipour, Mojtaba, You, Bowen, Panju, Maysum, Ghodsi, Ali
Rok vydání:	2021
Předmět:	Computer Science - Machine Learning Computer Science - Computation and Language Computer Science - Symbolic Computation
Druh dokumentu:	Working Paper
Popis:	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. Comment: 11 pages, 4 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2106.14131 Zobrazit plný text záznamu View this record from Arxiv