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1
Introduction
2
About CERN
3
Large Hadron Collider
4
Machine Learning Applications
5
Challenges
6
Jet Energy Corrections
7
Particle Cloud
8
Kubeflow
9
Training
10
Inference
11
Live Demo
12
Results
13
Conclusion
Description:
Explore a conference talk on utilizing Kubeflow for jet energy corrections in particle physics at CERN. Dive into the world of the Large Hadron Collider and learn how graph neural networks are applied to correct energy values for particle jets. Discover how Kubeflow's pipeline component and training operators enable structured, reproducible machine learning workflows and scalable training. Gain insights into the potential impact of this work on future Kubeflow adoption within the CERN physics community. Follow the journey from introduction to live demo, covering topics such as machine learning applications in particle physics, challenges in jet energy corrections, and the implementation of Kubeflow in this cutting-edge research.

Jet Energy Corrections with GNN Regression using Kubeflow at CERN

CNCF [Cloud Native Computing Foundation]
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