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Graph Neural Networks for Particle Momentum Estimation in the CMS Trigger System
Learning Representation Through Self-Supervised Learning on Real Gravitational Lensing Images
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.
Deep Graph anomaly detection with contrastive learning for new physics searches
Learning Parametrization with Implicit Neural Representations
Continuous learning for high-energy physics data quality monitoring
Quantum Graph Neural Networks for High Energy Physics Analysis at the LHC
Learning quantum representations of classical high energy physics data with contrastive learning
Equivariant quantum neural networks for High Energy Physics Analysis at the LHC
Quantum transformer for High Energy Physics Analysis at the LHC
Transformer Models for Symbolic Calculations of Squared Amplitudes in HEP
State-space models for squared amplitude calculation in high-energy physics