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arXiv Machine Learning By Giorgio Morales, Anik\'o Ek\'art

Exhaustive Canonical Skeletons Generation: A Formal Dataset for Decomposable Symbolic Regression

Read the original on arXiv Machine Learning →

The paper introduces an exhaustive dataset of univariate canonical skeletons for neural symbolic regression, generated deterministically via a constrained context-free grammar. These skeletons serve as generic structural representations that eliminate redundant expressions and provide uniform operator distribution, thereby avoiding simplicity bias. The authors also present a sampling strategy for clean numerical data and benchmark 150 SR problems across three tiers, demonstrating challenges for current state‑of‑the‑art methods.

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