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National Repository of Grey Literature 1 records found  Search took 0.01 seconds. 
Deep Learning for Symbolic Regression
 Vastl, Martin ; Pilát, Martin (advisor) ; Neruda, Roman (referee)
Symbolic regression is a task of finding mathematical equation based on the observed data. Historically, genetic programming was the main tool to tackle the symbolic regres- sion, however, recently, new neural network based approaches emerged. In this work, we propose transformer based approach which predicts the expression as a whole without the need of finding the expression coefficients in post-processing step. We also use a local gradient search to further improve the expression coefficients. We compare our so- lution to previous approaches on several benchmarks and demonstrate, that our solution is comparable in terms of performance while outperforming them in terms of speed of the prediction in the average case. 1
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