28 citations · 64 across the 9 of their papers we have counts for
4 papers · 1 filter
Flow Annealed Importance Sampling Bootstrap meets Differentiable Particle Physics
Annalena Kofler, Vincent Stimper, Mikhail Mikhasenko +2
High-energy physics requires the generation of large numbers of simulated data samples from complex but analytically tractable distributions called matrix elements. Surrogate model…
Is Tokenization Needed for Masked Particle Modelling?
Matthew Leigh, Samuel Klein, François Charton +5
In this work, we significantly enhance masked particle modeling (MPM), a self-supervised learning scheme for constructing highly expressive representations of unordered sets releva…
Masked Particle Modeling on Sets: Towards Self-Supervised High Energy Physics Foundation Models
Tobias Golling, Lukas Heinrich, Michael Kagan +4
We propose masked particle modeling (MPM) as a self-supervised method for learning generic, transferable, and reusable representations on unordered sets of inputs for use in high e…
Differentiable Matrix Elements with MadJax
Lukas Heinrich, Michael Kagan
MadJax is a tool for generating and evaluating differentiable matrix elements of high energy scattering processes. As such, it is a step towards a differentiable programming paradi…