113 citations · 571 across the 40 of their papers we have counts for
4 papers · 2 filters
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…
Re-Simulation-based Self-Supervised Learning for Pre-Training Foundation Models
Philip Harris, Michael Kagan, Jeffrey Krupa +2
Self-Supervised Learning (SSL) is at the core of training modern large machine learning models, providing a scheme for learning powerful representations that can be used in a varie…
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…