SymbolNet: Neural Symbolic Regression with Adaptive Dynamic Pruning

Contrary to genetic programming, the neural network approach to symbolic regression can efficiently handle high-dimensional inputs and leverage gradient methods for faster equation searching. Common ways of constraining expression complexity often involve multistage pruning with fine-tuning, which c...

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Veröffentlicht in:	arXiv.org 2024-08
Hauptverfasser:	Ho Fung Tsoi, Loncar, Vladimir, Dasu, Sridhara, Harris, Philip
Format:	Artikel
Sprache:	eng
Schlagworte:	Complexity Genetic algorithms Neural networks Operators (mathematics) Pruning Regression Regularization Sparsity Training
Online-Zugang:	Volltext
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