16 papers
Unifying Generative Models with Path Integrals
Ramon Winterhalder
We formulate generative modeling as a path integral in which flow-based, diffusion-based, variational, and adversarial models arise as different evaluation principles for a single…
The Living Guide of Machine Learning for Particle Physics
Claudius Krause, Ramon Winterhalder, Matthew Feickert +1
We started the Living Review of Machine Learning for Particle Physics (HEP-ML Living Review) in 2020 as a community-maintained, near-comprehensive bibliography of machine learning…
Neural Control Variates at LO and NLO
Theo Heimel, Tilman Plehn, Rebecca Revelli +2
We employ neural control variates to minimize the range of event weights and avoid negative weights for phase-space integration and event generation. A signed control variate, buil…
Interpreting Parton Distributions with Shapley Values
Raphaël Bonnet-Guerrini, Stefano Carrazza, Stefano Forte +3
We show that Shapley values can be used to trace how individual parton distributions (PDFs) shape the theory predictions for high-energy observables computed from them. This provid…
The Monte Carlo Ecosystem in High-Energy Physics: A Primer
Melissa van Beekveld, Enrico Bothmann, Andy Buckley +3
Monte Carlo event generators are the central interface between theoretical calculations and experimental measurements in collider physics. Over several decades, a comprehensive and…
Uncertainty in Physics and AI: Taxonomy, Quantification, and Validation
Manuel HauÃmann, Ramon Winterhalder, Maria Ubiali
Reliable uncertainty quantification is essential for the use of machine learning in physics, where scientific discoveries depend on validated probabilistic statements. We provide a…