IML Machine Learning Working Group

Europe/Zurich
Virtual (everywhere and nowhere)

Virtual (everywhere and nowhere)

Description

Meeting will be by video only on Zoom.

Join Zoom Meeting

https://cern.zoom.us/j/91836626922?pwd=UzBmeThoWU9mWEU5SzVsUVJTRDhoUT09

Meeting ID: 918 3662 6922
Password:  check email

Recording available at: https://videos.cern.ch/record/2745739

Videoconference
IML Machine Learning Working Group
Zoom Meeting ID
96543252431
Host
Simon Akar
Alternative hosts
Riccardo Torre, Fabio Catalano
Useful links
Join via phone
Zoom URL
    • 1
      News
      Speakers: Andrea Wulzer (CERN and EPFL), David Rousseau (LAL-Orsay, FR), Gian Michele Innocenti (CERN), Lorenzo Moneta (CERN), Loukas Gouskos (CERN), Dr Pietro Vischia (Universite Catholique de Louvain (UCL) (BE)), Riccardo Torre (CERN), Simon Akar (University of Cincinnati (US))
    • 2
      Introduction to workshop series on “AI in business” by CERN Knowledge Transfer
      Speakers: CERN Knowledge Transfer, Paul Hientz
    • 15:15
      Question time
    • 3
      FastCaloGAN: a tool for fast simulation of the ATLAS calorimeter system with Generative Adversarial Networks
      Speaker: Michele Faucci Giannelli (INFN e Universita Roma Tor Vergata (IT))
    • 15:45
      Question time
    • 4
      Pre-Learning a Geometry Using Machine Learning to Accelerate High Energy Physics Detector Simulations
      Speaker: Evangelos Kourlitis (Argonne National Laboratory (US))
    • 16:15
      Question time
    • 5
      High Fidelity Simulation of High Granularity Calorimeters with High Speed
      Speakers: Engin Eren, Engin Eren (Deutsches Elektronen-Synchrotron DESY), Sascha Daniel Diefenbacher (Hamburg University (DE))
    • 16:45
      Question time
    • 6
      Estimating Support Size of Distribution Learnt by Generative Adversarial Networks for Particle Detector Simulation
      Speakers: Kristina Jaruskova (Czech Technical University in Prague), Kristina Jarůšková, Dr Sofia Vallecorsa (CERN)
    • 17:15
      Question time
    • 7
      Sparse data from graph GANs
      Speaker: Raghav Kansal (Univ. of California San Diego (US))
    • 17:32
      Question time
    • 8
      Sparse data from Variational autoencoders
      Speakers: Breno Orzari (UNESP - Universidade Estadual Paulista (BR)), Mary Touranakou (National and Kapodistrian University of Athens (GR))
    • 17:47
      Question time