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There are many problems in physics, biology, and other natural sciences inwhich symbolic regression can provide valuable insights and discover new lawsof nature. A widespread Deep Neural Networks do not provide interpretablesolutions. Meanwhile, symbolic expressions give us a clear relation betweenobservations and the target variable. However, at the moment, there is nodominant solution for the symbolic regression task, and we aim to reduce thisgap with our algorithm. In this work, we propose a novel deep learningframework for symbolic expression generation via variational autoencoder (VAE).In a nutshell, we suggest using a VAE to generate mathematical expressions, andour training strategy forces generated formulas to fit a given dataset. Ourframework allows encoding apriori knowledge of the formulas into fast-checkpredicates that speed up the optimization process. We compare our method tomodern symbolic regression benchmarks and show that our method outperforms thecompetitors under noisy conditions. The recovery rate of SEGVAE is 65% on theNgyuen dataset with a noise level of 10%, which is better than the previouslyreported SOTA by 20%. We demonstrate that this value depends on the dataset andcan be even higher.

Symbolic expression generation via Variational Auto-Encoder