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 15 April 2019 (09:00) to Thursday 18 April 2019 (20:00)

Monday 15 April 2019
09:00
09:00 - 10:50
Room: 500/1-001 - Main Auditorium
Contributions

09:00 Welcome - Steven Randolph Schramm (Universite de Geneve (CH))

09:20 Conceptual overview of ML in HEP - Sergei Gleyzer (University of Florida (US))

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

11:15 On the Statistical Mechanics and Information Theory of Deep Learning for Particle Physicists - Naftali Tishby (Hebrew University of Jerusalem)
12:30 Lunch
Lunch
12:30 - 14:00
Room: 500/1-001 - Main Auditorium
14:00
14:00 - 18:00
Room: 500/1-001 - Main Auditorium
Contributions

14:00 Introduction - Markus Stoye (CERN)

14:20 Amazon AI: Tensor and Higher-Order Generalizations of the GSVD with Applications to Personalized Cancer Medicine - Priya Ponnapalli (Amazon AI)

15:05 Google DeepMind: Compressing neural networks - Tim Genewein (DeepMind)

16:30 B12 Consulting: Big value out of small data - Michel Herquet (B12)

17:05 Industry panel - Michel Herquet (B12) Tim Genewein (DeepMind)
18:00 Welcome reception
Welcome reception
18:00 - 20:00
Room: 500/1-001 - Main Auditorium
Tuesday 16 April 2019
09:00 Session 1
Session 1
09:00 - 10:45
Room: 500/1-001 - Main Auditorium
Contributions

09:00 Daily announcements

09:05 Machine Learning Uncertainties with Adversarial Neural Networks - Peter Galler (University of Glasgow)

09:25 Decoding Physics Information in DNNs - Taoli Cheng (University of Montreal)

09:45 Learning Invariant Representations using Mutual Information Regularization - Justin Tan (University of Melbourne)

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

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

11:15 The information theory of Deep Learning - Naftali Tishby (Hebrew University of Jerusalem)
12:30 Lunch
Lunch
12:30 - 14:00
Room: 500/1-001 - Main Auditorium
13:59
13:59 - 15:00
Room: 31/3-004 - IT Amphitheatre
Contributions

14:15 Q&A with Prof. Naftali Tishby - Naftali Tishby (Hebrew University of Jerusalem)
14:00 Session 2
Session 2
14:00 - 16:00
Room: 500/1-001 - Main Auditorium
Contributions

14:00 Novelty Detection Meets Collider Physics - Ying-Ying Li (HKUST)

14:30 Uncertain Networks - Jennifer Thompson (ITP Heidelberg)

15:00 Exploring SMEFT in VH channel with Machine Learning - Charanjit Kaur Khosa

15:30 The Tracking Machine Learning challenge - David Rousseau (LAL-Orsay, FR)
16:00 Coffee break
Coffee break
16:00 - 16:30
Room: 500/1-001 - Main Auditorium
16:30 ML colloquium by Max Welling
ML colloquium by Max Welling
16:30 - 18:00
Room: 500/1-001 - Main Auditorium
Contributions

16:30 Gauge Fields in Deep Learning
Wednesday 17 April 2019
09:00 Session 3
Session 3
09:00 - 12:30
Room: 500/1-001 - Main Auditorium
Contributions

09:00 Daily announcements

09:05 Applying Generative Models to Scientific Research - Fedor Ratnikov (Yandex School of Data Analysis (RU))

09:35 DijetGAN: A Generative-Adversarial Network Approach for the Simulation of QCD Dijet Events at the LHC - Serena Palazzo (The University of Edinburgh (GB))

09:55 High Granularity Calorimeter Simulation using Generative Adversarial Networks - Gul Rukh Khattak (University of Peshawar (PK))

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

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

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

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

12:05 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 Lunch
Lunch
12:30 - 14:00
Room: 500/1-001 - Main Auditorium
14:00 Session 4
Session 4
14:00 - 18:40
Room: 500/1-001 - Main Auditorium
Contributions

14:00 A hybrid deep learning approach to vertexing - Henry Fredrick Schreiner (University of Cincinnati (US))

14:20 Feature ranking based on subtraction methods - Paul Glaysher (DESY)

14:40 ML Techniques for heavy flavour identification in CMS - Emil Sorensen Bols (Vrije Universiteit Brussel (BE))

15:10 ParticleNet: Jet Tagging via Particle Clouds - Huilin Qu (Univ. of California Santa Barbara (US))

15:30 Learning representations of irregular particle-detector geometry with distance-weighted graph networks - Jan Kieseler (CERN)

16:30 GroomRL: jet grooming through reinforcement learning - Frederic Alexandre Dreyer (Oxford)

16:50 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))

17:10 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))

17:30 Close-out - Steven Randolph Schramm (Universite de Geneve (CH))
Thursday 18 April 2019
09:00 Part 1
Part 1
09:00 - 13:10
Room: 500/1-001 - Main Auditorium
Contributions

09:00 Introduction to the basics of deep learning - Yannik Alexander Rath (RWTH Aachen University (DE))

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

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

12:30 Differentiable architecture search - Andrey Ustyuzhanin (Yandex School of Data Analysis (RU))
13:10 Lunch
Lunch
13:10 - 15:00
Room: 500/1-001 - Main Auditorium
15:00 Part 2
Part 2
15:00 - 18:30
Room: 500/1-001 - Main Auditorium
Contributions

15:00 Bayesian approach to network design - Andrey Ustyuzhanin (Yandex School of Data Analysis (RU))

15:30 Bayesian dropout explanation and examples - Andrey Ustyuzhanin (Yandex School of Data Analysis (RU))

16:45 Advanced Generative Adversarial Network Techniques - Jonas Glombitza (Rheinisch-Westfaelische Tech. Hoch. (DE))

Choose timezone