New Decomposable Neuro-Symbolic Regression Method Improves Interpretability of Complex Mathematical Models
Researchers have introduced EditSR, a two-layer framework that adds a pretrained edit-based Rectifier on top of neural symbolic regression models to correct structurally incorrect expressions after initial generation. Neural symbolic regression models are efficient but prone to error accumulation during one-pass autoregressive decoding, particularly for complex expressions. EditSR addresses this without restarting costly global searches, offering improved symbolic structure recovery with limited additional computational overhead.
Neural symbolic regression models achieve inference efficiency by moving structural search to a pretraining phase, but their one-pass autoregressive decoding can accumulate errors, leading to syntactically or structurally incorrect mathematical expressions—a problem that worsens with expression complexity. EditSR proposes a two-layer solution: the first layer retains the standard neural symbolic regression model for initial prediction, while the second layer applies a pretrained Rectifier that performs step-by-step edit operations to correct the output. The rectification process is formulated as a state-transition chain beginning from an incorrect expression, with a dedicated algorithm constructing supervised training data for the Rectifier. Crucially, each edit action is constrained to a syntactically valid space, ensuring every intermediate expression remains parseable throughout the correction process. Because each edit decision depends only on the current expression state rather than the full history, the Rectifier can recover from earlier mistakes in subsequent steps, mitigating error accumulation. Extensive experiments and ablation studies reported by the authors demonstrate substantial improvements in symbolic structure recovery, with the most pronounced gains on complex expressions where one-pass decoding is most vulnerable.
What's missing
The paper does not report concrete quantitative results (e.g., accuracy figures or runtime comparisons) in the abstract. The computational cost of pretraining the Rectifier relative to the savings from avoiding global search restarts is not quantified. Generalization to real-world noisy data or out-of-distribution expression types is not discussed.
What different sources said
- arXiv cs.AICenter
EditSR: Enhancing Neural Symbolic Regression via Edit-based Rectification
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