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.
International Conference on Machine Learning , pages=
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
SAGE-Fit improves symbolic regression evaluation by exploiting structural and semantic priors to enhance parameter optimization in non-convex inner-loop fitting.
citing papers explorer
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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.
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When Good Equations Get Bad Scores: Improving Symbolic Regression Through Better Parameter Optimization
SAGE-Fit improves symbolic regression evaluation by exploiting structural and semantic priors to enhance parameter optimization in non-convex inner-loop fitting.