AI RESEARCH
FePySR: A Neural Feature Extraction Framework for Efficient and Scalable Symbolic Regression
arXiv CS.AI
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ArXi:2605.12704v1 Announce Type: cross A fundamental challenge in symbolic regression (SR) is efficiently recovering complex mathematical expressions from observational data. Although this problem is NP-hard, many expressions of practical interest decompose naturally into combinations of nonlinear feature modules, concentrating structural complexity into a small number of reusable components. Here, we