Machine Learning-Robotics
- Domaine
- Machine Learning-Robotics
- Domain - extra
- applications in particle physics
- Année
- 2013
- Starting
- 01/10/2013
- État
- Closed
- Sujet
- Learning to discover: supervised discrimination and unsupervised representation learning with applications in particle physics
- Thesis advisor
- KÉGL Balázs
- Co-advisors
- GERMAIN Cécile
- Laboratory
- Collaborations
- Laboratoire de l'Accélérateur Linéaire
- Abstract
- Today, machine learning methods are routinely used in high-energy particle physics to accelerate the discovery of new phenomena. Typically, standard classification algorithms are used for signal/background separation both in the online selection (trigger) step, and in the offline analysis. The first goal of this thesis to answer some of the theoretical questions raised by these unorthodox machine learning applications, and to design new algorithms that improve the analyses. In the second theme we propose to go beyond the standard setup of ``manual'' feature extraction followed by
classification and to investigate the applicability of recently developed techniques on unsupervised representation learning. Both themes are motivated by concrete particle and astroparticle
physics exeperiments (ATLAS@CERN), the future International Linear Collider (ILC), and the Pierre Auger Experiment (Auger). - Context
- Objectives
- Work program
- Extra information
- Prerequisite
- Détails
learningToDiscover.pdf- Expected funding
- Institutional funding
- Status of funding
- Expected
- Candidates
- DUBARD Franck
- Utilisateur
- balazs.kegl
- Créé
- Jeudi 28 février 2013 17:17:34 CET
- dernière modif.
- Jeudi 20 février 2014 10:27:30 CET
Connexion
Ecole Doctorale Informatique Paris-Sud
Directrice
Nicole Bidoit
Assistante
Stéphanie Druetta
Conseiller aux thèses
Dominique Gouyou-Beauchamps
ED 427 - Université Paris-Sud
UFR Sciences Orsay
Bat 650 - aile nord - 417
Tel : 01 69 15 63 19
Fax : 01 69 15 63 87
courriel: ed-info à lri.fr