111 citations · 148 across the 10 of their papers we have counts for
3 papers · 1 filter
Aspen Open Jets: Unlocking LHC Data for Foundation Models in Particle Physics
Oz Amram, Luca Anzalone, Joschka Birk +7
Foundation models are deep learning models pre-trained on large amounts of data which are capable of generalizing to multiple datasets and/or downstream tasks. This work demonstrat…
Universal New Physics Latent Space
Anna Hallin, Gregor Kasieczka, Sabine Kraml +4
We develop a machine learning method for mapping data originating from both Standard Model processes and various theories beyond the Standard Model into a unified representation (l…
OmniJet-: The first cross-task foundation model for particle physics
Joschka Birk, Anna Hallin, Gregor Kasieczka
Foundation models are multi-dataset and multi-task machine learning methods that once pre-trained can be fine-tuned for a large variety of downstream applications. The successful d…