UniSymNet: A Unified Symbolic Network Guided by Transformer
Xinxin Li, Juan Zhang, Da Li, Xingyu Liu, Jin Xu, Junping Yin

TL;DR
UniSymNet introduces a unified symbolic network guided by a Transformer, effectively reducing complexity and overfitting in symbolic regression, and demonstrates high accuracy and symbolic solution rates on benchmark datasets.
Contribution
It unifies binary operators into nested unary operators and employs a Transformer-guided approach for structural selection, advancing symbolic regression methods.
Findings
High fitting accuracy on benchmarks
Excellent symbolic solution rate
Relatively low expression complexity
Abstract
Symbolic Regression (SR) is a powerful technique for automatically discovering mathematical expressions from input data. Mainstream SR algorithms search for the optimal symbolic tree in a vast function space, but the increasing complexity of the tree structure limits their performance. Inspired by neural networks, symbolic networks have emerged as a promising new paradigm. However, most existing symbolic networks still face certain challenges: binary nonlinear operators cannot be naturally extended to multivariate operators, and training with fixed architecture often leads to higher complexity and overfitting. In this work, we propose a Unified Symbolic Network that unifies nonlinear binary operators into nested unary operators and define the conditions under which UniSymNet can reduce complexity. Moreover, we pre-train a Transformer model with a novel label encoding…
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Taxonomy
TopicsEvolutionary Algorithms and Applications · Machine Learning in Materials Science · Mathematics, Computing, and Information Processing
MethodsLinear Layer · Multi-Head Attention · Dense Connections · Attention Is All You Need · ADaptive gradient method with the OPTimal convergence rate · Dropout · Layer Normalization · Position-Wise Feed-Forward Layer · Byte Pair Encoding · Softmax
