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09:00
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Invited keynote talks
-
Steven Randolph Schramm
(Universite de Geneve (CH))
Paul Seyfert
(CERN)
(until 10:50)
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09:00
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Welcome
-
Steven Randolph Schramm
(Universite de Geneve (CH))
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09:20
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Conceptual overview of ML in HEP
- Dr
Sergei Gleyzer
(University of Florida (US))
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10:05
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Future areas of focus for ML in particle physics
-
Kyle Stuart Cranmer
(New York University (US))
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|
10:50
|
--- Coffee break ---
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11:15
|
Joint lecture/seminar
-
Olaf Behnke
(Deutsches Elektronen-Synchrotron (DE))
Louis Lyons
(Imperial College (GB))
(until 12:15)
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11:15
|
On the Statistical Mechanics and Information Theory of Deep Learning for Particle Physicists
- Prof.
Naftali Tishby
(Hebrew University of Jerusalem)
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09:00
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Submitted contributions
-
Paul Seyfert
(CERN)
Lorenzo Moneta
(CERN)
(until 10:45)
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09:00
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Daily announcements
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09:05
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Machine Learning Uncertainties with Adversarial Neural Networks
- Dr
Peter Galler
(University of Glasgow)
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09:25
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Decoding Physics Information in DNNs
-
Taoli Cheng
(University of Montreal)
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09:45
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Learning Invariant Representations using Mutual Information Regularization
- Mr
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 ---
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11:15
|
Invited plenary talk
(until 12:30)
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11:15
|
The information theory of Deep Learning
- Prof.
Naftali Tishby
(Hebrew University of Jerusalem)
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|
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09:00
|
Submitted contributions
-
Paul Seyfert
(CERN)
Lorenzo Moneta
(CERN)
(until 12:30)
|
|
09:00
|
Daily announcements
|
|
09:05
|
Applying Generative Models to Scientific Research
-
Fedor Ratnikov
(Yandex School of Data Analysis (RU))
|
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09:35
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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
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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))
|
|
10:35
|
--- Coffee break ---
|
|
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
- Mr
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)
|
|
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09:00
|
Tutorials
-
Lorenzo Moneta
(CERN)
Rudiger Haake
(Yale University (US))
(until 13:10)
|
|
09:00
|
Introduction to the basics of deep learning
-
Yannik Alexander Rath
(RWTH Aachen University (DE))
|
|
10:30
|
--- Coffee break ---
|
|
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))
|
|
|
12:30
|
--- Lunch ---
|
|
14:00
|
Industry talks and panel
-
Markus Stoye
(CERN)
David Rousseau
(LAL-Orsay, FR)
(until 18:00)
|
|
14:00
|
Introduction
-
Markus Stoye
(CERN)
|
|
14:20
|
Amazon AI: Tensor and Higher-Order Generalizations of the GSVD with Applications to Personalized Cancer Medicine
- Dr
Priya Ponnapalli
(Amazon AI)
|
|
15:05
|
Google DeepMind: Compressing neural networks
- Dr
Tim Genewein
(DeepMind)
|
|
16:00
|
--- Coffee break ---
|
|
16:30
|
B12 Consulting: Big value out of small data
- Dr
Michel Herquet
(B12)
|
|
17:05
|
Industry panel
- Dr
Michel Herquet
(B12) Dr
Tim Genewein
(DeepMind)
|
|
18:00
|
--- Welcome reception ---
|
|
|
12:30
|
--- Lunch ---
|
|
13:59
|
Q&A with Prof. Naftali Tishby
-
Louis Lyons
(Imperial College (GB))
(until 15:00)
(31/3-004 - IT Amphitheatre)
|
|
14:15
|
Q&A with Prof. Naftali Tishby
- Prof.
Naftali Tishby
(Hebrew University of Jerusalem)
|
|
14:00
|
Submitted contributions
-
David Rousseau
(LAL-Orsay, FR)
Steven Randolph Schramm
(Universite de Geneve (CH))
(until 16:00)
|
|
14:00
|
Novelty Detection Meets Collider Physics
- Ms
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 ---
|
|
16:30
|
CERN Colloquium
(until 18:00)
|
|
16:30
|
Gauge Fields in Deep Learning
|
|
|
12:30
|
--- Lunch ---
|
|
14:00
|
Submitted contributions
-
Rudiger Haake
(Yale University (US))
Steven Randolph Schramm
(Universite de Geneve (CH))
(until 18:40)
|
|
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)
|
|
16:00
|
--- Coffee break ---
|
|
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))
|
|
|
13:10
|
--- Lunch ---
|
|
15:00
|
Tutorials
-
Rudiger Haake
(Yale University (US))
David Rousseau
(LAL-Orsay, FR)
(until 18:30)
|
|
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))
|
|
16:15
|
--- Coffee break ---
|
|
16:45
|
Advanced Generative Adversarial Network Techniques
-
Jonas Glombitza
(Rheinisch-Westfaelische Tech. Hoch. (DE))
|
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