Name: 3rd IML Machine Learning Workshop
Start: 2019-04-15T09:00:00+02:00
End: 2019-04-18T20:00:00+02:00
Location: CERN

3rd IML Machine Learning Workshop

from Monday, April 15, 2019 (9:00 AM) to Thursday, April 18, 2019 (8:00 PM)

Monday, April 15, 2019
9:00 AM
9:00 AM - 10:50 AM
Room: 500/1-001 - Main Auditorium
Contributions

9:00 AM Welcome - Steven Randolph Schramm (Universite de Geneve (CH))

9:20 AM Conceptual overview of ML in HEP - Sergei Gleyzer (University of Florida (US))

10:05 AM Future areas of focus for ML in particle physics - Kyle Stuart Cranmer (New York University (US))
10:50 AM Coffee break
Coffee break
10:50 AM - 11:15 AM
Room: 500/1-001 - Main Auditorium
11:15 AM IML + PHYSTAT + CERN-DS
IML + PHYSTAT + CERN-DS
11:15 AM - 12:15 PM
Room: 500/1-001 - Main Auditorium
Contributions

11:15 AM On the Statistical Mechanics and Information Theory of Deep Learning for Particle Physicists - Naftali Tishby (Hebrew University of Jerusalem)
12:30 PM Lunch
Lunch
12:30 PM - 2:00 PM
Room: 500/1-001 - Main Auditorium
2:00 PM
2:00 PM - 6:00 PM
Room: 500/1-001 - Main Auditorium
Contributions

2:00 PM Introduction - Markus Stoye (CERN)

2:20 PM Amazon AI: Tensor and Higher-Order Generalizations of the GSVD with Applications to Personalized Cancer Medicine - Priya Ponnapalli (Amazon AI)

3:05 PM Google DeepMind: Compressing neural networks - Tim Genewein (DeepMind)

4:30 PM B12 Consulting: Big value out of small data - Michel Herquet (B12)

5:05 PM Industry panel - Michel Herquet (B12) Tim Genewein (DeepMind)
6:00 PM Welcome reception
Welcome reception
6:00 PM - 8:00 PM
Room: 500/1-001 - Main Auditorium
Tuesday, April 16, 2019
9:00 AM Session 1
Session 1
9:00 AM - 10:45 AM
Room: 500/1-001 - Main Auditorium
Contributions

9:00 AM Daily announcements

9:05 AM Machine Learning Uncertainties with Adversarial Neural Networks - Peter Galler (University of Glasgow)

9:25 AM Decoding Physics Information in DNNs - Taoli Cheng (University of Montreal)

9:45 AM Learning Invariant Representations using Mutual Information Regularization - Justin Tan (University of Melbourne)

10:05 AM Neural networks for the abstraction of the physical symmetries in the nature - Wonsang Cho (Seoul National University)

10:25 AM Containers for Machine Learning in HEP - Matthew Feickert (Southern Methodist University (US))
10:45 AM Coffee break
Coffee break
10:45 AM - 11:15 AM
Room: 500/1-001 - Main Auditorium
11:15 AM
11:15 AM - 12:30 PM
Room: 500/1-001 - Main Auditorium
Contributions

11:15 AM The information theory of Deep Learning - Naftali Tishby (Hebrew University of Jerusalem)
12:30 PM Lunch
Lunch
12:30 PM - 2:00 PM
Room: 500/1-001 - Main Auditorium
1:59 PM
1:59 PM - 3:00 PM
Room: 31/3-004 - IT Amphitheatre
Contributions

2:15 PM Q&A with Prof. Naftali Tishby - Naftali Tishby (Hebrew University of Jerusalem)
2:00 PM Session 2
Session 2
2:00 PM - 4:00 PM
Room: 500/1-001 - Main Auditorium
Contributions

2:00 PM Novelty Detection Meets Collider Physics - Ying-Ying Li (HKUST)

2:30 PM Uncertain Networks - Jennifer Thompson (ITP Heidelberg)

3:00 PM Exploring SMEFT in VH channel with Machine Learning - Charanjit Kaur Khosa

3:30 PM The Tracking Machine Learning challenge - David Rousseau (LAL-Orsay, FR)
4:00 PM Coffee break
Coffee break
4:00 PM - 4:30 PM
Room: 500/1-001 - Main Auditorium
4:30 PM ML colloquium by Max Welling
ML colloquium by Max Welling
4:30 PM - 6:00 PM
Room: 500/1-001 - Main Auditorium
Contributions

4:30 PM Gauge Fields in Deep Learning
Wednesday, April 17, 2019
9:00 AM Session 3
Session 3
9:00 AM - 12:30 PM
Room: 500/1-001 - Main Auditorium
Contributions

9:00 AM Daily announcements

9:05 AM Applying Generative Models to Scientific Research - Fedor Ratnikov (Yandex School of Data Analysis (RU))

9:35 AM DijetGAN: A Generative-Adversarial Network Approach for the Simulation of QCD Dijet Events at the LHC - Serena Palazzo (The University of Edinburgh (GB))

9:55 AM High Granularity Calorimeter Simulation using Generative Adversarial Networks - Gul Rukh Khattak (University of Peshawar (PK))

10:15 AM Deep generative models for fast shower simulation in ATLAS - Aishik Ghosh (Centre National de la Recherche Scientifique (FR))

11:05 AM Fast Simulation Using Generative Adversarial Network in LHCB - Artem Maevskiy (National Research University Higher School of Economics (RU))

11:25 AM Model-Assisted GANs for the optimisation of simulation parameters and as an algorithm for fast Monte Carlo production - Saul Alonso Monsalve (CERN)

11:45 AM Event Generation and Statistical Sampling with Deep Generative Models - Sydney Otten (Radboud Universiteit Nijmegen)

12:05 PM LUMIN - a deep learning and data science ecosystem for high-energy physics - Giles Chatham Strong (LIP Laboratorio de Instrumentacao e Fisica Experimental de Part)
12:30 PM Lunch
Lunch
12:30 PM - 2:00 PM
Room: 500/1-001 - Main Auditorium
2:00 PM Session 4
Session 4
2:00 PM - 6:40 PM
Room: 500/1-001 - Main Auditorium
Contributions

2:00 PM A hybrid deep learning approach to vertexing - Henry Fredrick Schreiner (University of Cincinnati (US))

2:20 PM Feature ranking based on subtraction methods - Paul Glaysher (DESY)

2:40 PM ML Techniques for heavy flavour identification in CMS - Emil Sorensen Bols (Vrije Universiteit Brussel (BE))

3:10 PM ParticleNet: Jet Tagging via Particle Clouds - Huilin Qu (Univ. of California Santa Barbara (US))

3:30 PM Learning representations of irregular particle-detector geometry with distance-weighted graph networks - Jan Kieseler (CERN)

4:30 PM GroomRL: jet grooming through reinforcement learning - Frederic Alexandre Dreyer (Oxford)

4:50 PM NeuralRinger: An Ensemble of Neural Networks Fed from Calorimeter Ring Sums for Triggering on Electrons - Werner Spolidoro Freund (Federal University of of Rio de Janeiro (BR))

5:10 PM Fast Deep Learning on FPGAs for the Phase-II L0 Muon Barrel Trigger of the ATLAS Experiment - Luigi Sabetta (Sapienza Universita e INFN, Roma I (IT))

5:30 PM Close-out - Steven Randolph Schramm (Universite de Geneve (CH))
Thursday, April 18, 2019
9:00 AM Part 1
Part 1
9:00 AM - 1:10 PM
Room: 500/1-001 - Main Auditorium
Contributions

9:00 AM Introduction to the basics of deep learning - Yannik Alexander Rath (RWTH Aachen University (DE))

11:00 AM Physics inspired Autonomous Feature Engineering - Marcel Rieger (RWTH Aachen University (DE))

12:00 PM Traditional approach to network architecture optimisation - Andrey Ustyuzhanin (Yandex School of Data Analysis (RU))

12:30 PM Differentiable architecture search - Andrey Ustyuzhanin (Yandex School of Data Analysis (RU))
1:10 PM Lunch
Lunch
1:10 PM - 3:00 PM
Room: 500/1-001 - Main Auditorium
3:00 PM Part 2
Part 2
3:00 PM - 6:30 PM
Room: 500/1-001 - Main Auditorium
Contributions

3:00 PM Bayesian approach to network design - Andrey Ustyuzhanin (Yandex School of Data Analysis (RU))

3:30 PM Bayesian dropout explanation and examples - Andrey Ustyuzhanin (Yandex School of Data Analysis (RU))

4:45 PM Advanced Generative Adversarial Network Techniques - Jonas Glombitza (Rheinisch-Westfaelische Tech. Hoch. (DE))

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