Deep Divide-and-Reduce in Symbolic Regression

תקציר מקורי באנגליתarXiv:2608.02628v2 Announce Type: replace Abstract: Symbolic regression (SR) is the task of discovering underlying patterns from data and representing them using mathematical expressions. Current machine learning approaches to SR often lack a profound understanding of the intrinsic mathematical and physical principles governing these expressions. While the pioneering AI Feynman method leverages the mathematical properties underlying the data, its expression decomposition mechanism suffers from a narrow scope of applicability and is prone to failure on complex equations. Furthermore, its underlying mechanisms rely heavily on brute-force searches for sub-expressions, severely limiting its practical utility. Building on AI Feynman, we propose Deep Divide-and-Reduce in Symbolic Regression (DDR