Speaker
Tobias Golling
(Universite de Geneve (CH))
Description
“If you can simulate it, you can learn it.” The concept of conditional generation is powerful and versatile. The heavy lifting is distributed over a generator of a latent distribution of interest and an embedding network to encode the information contained in the data. Concrete applications to the reconstruction of neutrino kinematics in LHC collisions and associated interpretability questions will be presented.
