A complex-weight extension to the Equation Learner enables stable recovery of symbolic expressions containing real-domain poles and unconstrained use of singular operators such as division and logarithm.
Proceedings of the 2020 genetic and evolutionary computation conference companion , pages=
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
SAGE-Fit improves symbolic regression by exploiting structure and semantic priors to optimize parameters in non-convex inner loops, reducing under-scoring of correct equation structures.
citing papers explorer
-
Complex Equation Learner: Rational Symbolic Regression with Gradient Descent in Complex Domain
A complex-weight extension to the Equation Learner enables stable recovery of symbolic expressions containing real-domain poles and unconstrained use of singular operators such as division and logarithm.
-
When Good Equations Get Bad Scores: Improving Symbolic Regression Through Better Parameter Optimization
SAGE-Fit improves symbolic regression by exploiting structure and semantic priors to optimize parameters in non-convex inner loops, reducing under-scoring of correct equation structures.