IML Machine Learning Working Group

Name: IML Machine Learning Working Group
Start: 2020-11-23T15:00:00+01:00
End: 2020-11-23T18:00:00+01:00
Location: Virtual (everywhere and nowhere)

Monday 23 Nov 2020, 15:00 → 18:00 Europe/Zurich

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

- 15:00 → 15:05
  
  News 5m
  
  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))
  
  2020-11-23_IMLMeetingIntroduction_vischia.pdf
- 15:05 → 15:15
  
  Introduction to workshop series on “AI in business” by CERN Knowledge Transfer 10m
  
  Speakers: CERN Knowledge Transfer, Paul Hientz
  
  CERN Knowledge Transfer - Introduction AI in business workshops.pdf
- 15:15 → 15:20
  
  Question time 5m
- 15:20 → 15:45
  
  FastCaloGAN: a tool for fast simulation of the ATLAS calorimeter system with Generative Adversarial Networks 25m
  
  Speaker: Michele Faucci Giannelli (INFN e Universita Roma Tor Vergata (IT))
  
  2020-11-23, FastCaloGAN IML long talk.pptx
- 15:45 → 15:50
  
  Question time 5m
- 15:50 → 16:15
  
  Pre-Learning a Geometry Using Machine Learning to Accelerate High Energy Physics Detector Simulations 25m
  
  Speaker: Evangelos Kourlitis (Argonne National Laboratory (US))
  
  ML4G4_23Nov20_IML.pdf
- 16:15 → 16:20
  
  Question time 5m
- 16:20 → 16:45
  
  High Fidelity Simulation of High Granularity Calorimeters with High Speed 25m
  
  Speakers: Engin Eren, Engin Eren (Deutsches Elektronen-Synchrotron DESY), Sascha Daniel Diefenbacher (Hamburg University (DE))
  
  IML_getting_high_long.pdf
- 16:45 → 16:50
  
  Question time 5m
- 16:50 → 17:15
  
  Estimating Support Size of Distribution Learnt by Generative Adversarial Networks for Particle Detector Simulation 25m
  
  Speakers: Kristina Jaruskova (Czech Technical University in Prague), Kristina Jarůšková, Dr Sofia Vallecorsa (CERN)
  
  3DGAN_support_kristina.pdf
- 17:15 → 17:20
  
  Question time 5m
- 17:20 → 17:32
  
  Sparse data from graph GANs 12m
  
  Speaker: Raghav Kansal (Univ. of California San Diego (US))
  
  Sparse Data Generation IML Meeting 23:11.pdf
- 17:32 → 17:35
  
  Question time 3m
- 17:35 → 17:47
  
  Sparse data from Variational autoencoders 12m
  
  Speakers: Breno Orzari (UNESP - Universidade Estadual Paulista (BR)), Mary Touranakou (National and Kapodistrian University of Athens (GR))
  
  vae_sparse_data_generation_IML_meeting_231120_MT_BO.pdf
- 17:47 → 17:50
  
  Question time 3m