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hep-ex2026
How to scale your HEP ML models: A recipe for robust architecture comparisons at scale
Matthias Vigl, Nikita Pond, Jackson Barr +5
Much of the recent progress in machine learning domains such as language models has come from scaling laws that predict performance as a function of training effort. In high-energy…
hep-ex2024★ 3 cited
Finetuning Foundation Models for Joint Analysis Optimization
Matthias Vigl, Nicole Hartman, Lukas Heinrich
In this work we demonstrate that significant gains in performance and data efficiency can be achieved in High Energy Physics (HEP) by moving beyond the standard paradigm of sequent…