<ul><li>The ability to discover meaningful, accurate, and concise mathematical equations that describe datasets is valuable across various domains.</li><li>Equations offer explicit relationships between variables, enabling deeper insights into underlying data patterns.</li><li>In this work, a deep graph generative model called Graph-EQ is proposed for efficient equation discovery.</li><li>Graph-EQ uses a conditional variational autoencoder (CVAE) to learn a rich latent representation of the equation space and has shown success in discovering ground-truth equations for given datasets.</li></ul>

Graph-Eq: Discovering Mathematical Equations using Graph Generative Models

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