5 papers
Gradient estimators for parameter inference in discrete stochastic kinetic models
Ludwig Burger, Annalena Kofler, Lukas Heinrich +1
Stochastic kinetic models are ubiquitous in physics, yet inferring their parameters from experimental data remains challenging. For deterministic models, parameter inference often…
It Just Takes Two: Scaling Amortized Inference to Large Sets
Antoine Wehenkel, Michael Kagan, Lukas Heinrich +1
Neural posterior estimation has emerged as a powerful tool for amortized inference, with growing adoption across scientific and applied domains. In many of these applications, the…
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…
Large Physics Models: Towards a collaborative approach with Large Language Models and Foundation Models
Kristian G. Barman, Sascha Caron, Emily Sullivan +19
This paper explores ideas and provides a potential roadmap for the development and evaluation of physics-specific large-scale AI models, which we call Large Physics Models (LPMs).…
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…