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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 introduc
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