The End-to-End Deep Learning (E2E) project focuses on the development of particle and event reconstruction and identification tasks with end-to-end deep learning approaches.
Discovering and exploiting abstract symmetry elements obtained from low-level data within the CMS experiment
Masked Auto-Encoders for Efficient End-to-End Particle Reconstruction and Compression for the CMS Experiment
Self-Supervised Learning for End-to-End Particle Reconstruction for the CMS Experiment
End-to-End Deep Learning Regression for Measurements with the CMS Experiment
Diffusion models for Fast and accurate simulations of the low level CMS experiment data.