EditSR: Enhancing Neural Symbolic Regression via Edit-based Rectification
Quick Answer
EditSR introduces a two-layer framework that enhances neural symbolic regression by integrating a pre-trained edit-based Rectifier, significantly improving symbolic structure recovery in complex expressions while minimizing error accumulation.
Quick Take
Extensive experiments demonstrate substantial performance gains with limited additional costs.
Key Points
- EditSR combines neural symbolic regression with an edit-based Rectifier for improved efficiency.
- The rectification process is formulated as a step-by-step state-transition chain.
- Each edit action is restricted to ensure syntactic validity of expressions.
- Extensive experiments show substantial improvements in complex expression recovery.
- EditSR reduces error accumulation risk by allowing subsequent edits to rectify earlier mistakes.
Paper Resources
Source Excerpt
arXiv:2606. 07915v1 Announce Type: new Abstract: Neural symbolic regression models improve inference efficiency by shifting structural search to pretraining, but their one-pass autoregressive decoding is prone to error accumulation, which may lead to generating structurally incorrect expressions, especially in complex expression generation scenarios.
Existing rectification strategies can alleviate this issue, but they often depend on restarting global search, thereby weakening the efficiency advantage of neural models, and remain susceptible to error accumulation. …
Want this in your inbox every morning?
Daily brief at your local 8am — bilingual EN/中文, free.
More from arXiv cs.AI
See more →AINTMA: Agentic AI Architecture for Autonomous Test Management with Generative Intelligence, Secure Cloud Communication and Adaptive Quality Analytics
AINTMA, an autonomous test management architecture utilizing six specialized AI agents, achieves 88.4% test prioritization accuracy and reduces defect escape rates from 8.3% to 2.1%. The system demonstrates a 340% ROI within nine months, showcasing the potential of agentic AI in enhancing software quality management in cloud environments.