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Found 3 papers, 1 papers with code
SymbolFit: Automatic Parametric Modeling with Symbolic Regression
We introduce SymbolFit, a framework that automates parametric modeling by using symbolic regression to perform a machine-search for functions that fit the data, while simultaneously providing uncertainty estimates in a single run.
SymbolNet: Neural Symbolic Regression with Adaptive Dynamic Pruning
Unlike most existing symbolic regression methods that struggle with datasets containing more than $\mathcal{O}(10)$ inputs, we demonstrate the effectiveness of our model on the LHC jet tagging task (16 inputs), MNIST (784 inputs), and SVHN (3072 inputs).
Symbolic Regression on FPGAs for Fast Machine Learning Inference
The high-energy physics community is investigating the potential of deploying machine-learning-based solutions on Field-Programmable Gate Arrays (FPGAs) to enhance physics sensitivity while still meeting data processing time constraints.
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