Speaker
Description
Generative Machine Learning models are at the forefront of many recent developments in science, with groundbreaking implications. High Energy Physics is no exception, and a wide range of algorithms is already being used to speed-up and improve simulation, monitor data quality and perform anomaly detection. In this lecture, we’ll uncover the hidden mechanisms of these algorithms, show the common building blocks and the key differences, and provide an overview of how this type of machine learning application can pave the way for future physics discoveries.
Basic knowledge of machine learning is helpful but not required to follow the lecture.
Join us in the linked hands-on session to start applying what you’ve learned to the problem of particle jet simulation, and try to design the best performing model yourself!
