Speaker
Kristina Jarůšková
(FNSPE CTU in Prague)
Description
Fast detector simulations have been of interest in the high energy physics community because of the increasing data intake. Among different deep learning techniques, transformer networks stand out thanks to the lack of inductive bias and their potential to learn complex data structures. We present a study on representation learning of transformers using the calorimeter showers and an image completion task with data preprocessing inspired by the Vision Transformer.
Primary author
Kristina Jarůšková
(FNSPE CTU in Prague)
