Petersen et al. (2019) proposed the Deep Symbolic Regression (DSR) method to recover mathematical expressions from data. The method proceeds by: (1) representing expressions as node sequences in symbolic expression trees containing mathematical operators and operands; (2) using a Recurrent Neural Network (RNN) to predict the next operator or operand based on the existing sequence; (3) calculating the degree of fit of the generated expression on a specific dataset; and (4) using the fit as feedback to guide the RNN's subsequent generation process.

Authors

• Person: Not available Organization: arXiv
  Neuro-Symbolic AI: Explainability, Challenges, and Future Trends

Sources

• Neuro-Symbolic AI: Explainability, Challenges, and Future Trends arxiv.org arXiv via serper

Referenced by nodes (2)

• Recurrent Neural Network concept
• Deep Symbolic Regression concept