3 papers
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-ex2026
Neural Scaling Laws for Boosted Jet Tagging
Matthias Vigl, Nicole Hartman, Michael Kagan +1
The success of Large Language Models (LLMs) has established that scaling compute, through joint increases in model capacity and dataset size, is the primary driver of performance i…
hep-ex2025
Double Descent and Overparameterization in Particle Physics Data
Matthias Vigl, Lukas Heinrich
Recently, the benefit of heavily overparameterized models has been observed in machine learning tasks: models with enough capacity to easily cross the \emph{interpolation threshold…