Exhaustive Canonical Skeletons Generation: A Formal Dataset for Decomposable Symbolic Regression
Neural symbolic regression (SR) models require diverse datasets of mathematical expressions for robust training. However, existing datasets rely predominantly on stochastic sampling methods, which suffer from high structural redundancy and incomplete functional coverage. To address this, we introduce an exhaustive dataset of univariate canonical skeletons generated via a deterministic constrained context-free grammar. A canonical skeleton acts as a generic structural representation that subsumes more specific functional forms, eliminating redundant expressions and avoiding repeated computational efforts. Although our dataset consists of univariate skeletons, it is designed to support the ...
| Authors | Giorgio Morales, Anikó Ekárt |
|---|---|
| Primary category | cs.LG |
| Published | 14 October 2026 |
| Last updated at source | 15 October 2026 |
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