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SymbolFit: Automatic Parametric Modeling with Symbolic Regression / Tsoi, Ho Fung (UPenn, Philadelphia) ; Rankin, Dylan (UPenn, Philadelphia) ; Caillol, Cecile (CERN) ; Cranmer, Miles (Cambridge U. (main)) ; Dasu, Sridhara (U. Wisconsin, Madison (main)) ; Duarte, Javier (UC, San Diego (main)) ; Harris, Philip (MIT ; Harvard U. (main) ; IAIFI, Cambridge) ; Lipeles, Elliot (UPenn, Philadelphia) ; Loncar, Vladimir (MIT ; Belgrade, Inst. Phys.)
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. [...]
arXiv:2411.09851. - 53.
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Symbolic Regression on FPGAs for Fast Machine Learning Inference / Tsoi, Ho Fung (Wisconsin U., Madison) ; Pol, Adrian Alan (Princeton U.) ; Loncar, Vladimir (MIT ; Belgrade, Inst. Phys.) ; Govorkova, Ekaterina (MIT) ; Cranmer, Miles (Princeton U. ; Flatiron Inst., New York) ; Dasu, Sridhara (Wisconsin U., Madison) ; Elmer, Peter (Princeton U.) ; Harris, Philip (MIT) ; Ojalvo, Isobel (Princeton U.) ; Pierini, Maurizio (CERN)
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. In this contribution, we introduce a novel end-to-end procedure that utilizes a machine learning technique called symbolic regression (SR). [...]
arXiv:2305.04099.- 2024 - 9 p. - Published in : EPJ Web Conf. 295 (2024) 09036 Fulltext: 2305.04099 - PDF; document - PDF;
In : 26th International Conference on Computing in High Energy & Nuclear Physics, Norfolk, Virginia, Us, 8 - 12 May 2023, pp.09036

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