SymbolNet: Neural Symbolic Regression with Adaptive Dynamic Pruning

no code implementations • 18 Jan 2024 • Ho Fung Tsoi, Vladimir Loncar, Sridhara Dasu, Philip Harris

Contrary to the use of genetic programming, the neural network approach to symbolic regression can scale well with high input dimension and leverage gradient methods for faster equation searching.

Jet Tagging regression +1

Symbolic Regression on FPGAs for Fast Machine Learning Inference

no code implementations • 6 May 2023 • Ho Fung Tsoi, Adrian Alan Pol, Vladimir Loncar, Ekaterina Govorkova, Miles Cranmer, Sridhara Dasu, Peter Elmer, Philip Harris, Isobel Ojalvo, Maurizio Pierini

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.

Neural Architecture Search regression +1