Core Concepts
Foundation models in physics data can revolutionize performance and efficiency.
Abstract
Foundation models are multi-dataset and multi-task machine learning methods.
They aim to improve physics performance while reducing training time and data.
OmniJet-α model demonstrates successful transfer learning between unsupervised and supervised tasks.
Transformer architectures are crucial for building foundation models in particle physics.
Tokenization strategies impact the quality of data representation.
Generative models for jet physics show promising results.
Transfer learning from generation to classification is effective.
Fine-tuning pre-trained models outperform training from scratch.
The potential benefits of foundation models are significant for physics data.
Stats
Foundation models aim to reduce training time and data required.
OmniJet-α model demonstrates successful transfer learning.
Tokenization strategies impact data representation quality.
Quotes
"Foundation models for physics data are an enticing promise."
"The potential benefits in physics performance and compute efficiency glimpsed at in this and other works makes this a worthy endeavor."