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DTSTAMP:20190719T085754Z
LOCATION:HG E 3
DTSTART;TZID=Europe/Stockholm:20190614T103000
DTEND;TZID=Europe/Stockholm:20190614T123000
UID:submissions.pasc-conference.org_PASC19_sess165@linklings.com
SUMMARY:MS44 - Cutting Edge Machine Learning for High Energy Physics Appli
 cations
DESCRIPTION:Minisymposium\nComputer Science and Applied Mathematics, Physi
 cs\n\nAdvanced Reconstruction and Pattern Recognition in HEP\n\nSalzburger
 \n\nMachine learning (ML) and Data science has seen a recent boost with gl
 obal players, such as Google, Facebook or Amazon have surpassed in many as
 pects, e.g. data quantity and algorithmic complexity the requirements of l
 arge scale HEP experiments. On the other hand, the upcoming challenges for
  the hig...\n\n---------------------\nMachine Learning and Inference for P
 article Physics with HPC and the Cloud\n\nCranmer, Heinrich\n\nThere is an
  explosion of interest in machine learning in particle physics, which has 
 significant impact on the data analysis strategies used by the large exper
 iments and theoretical efforts like lattice QCD. Simultaneously, there is 
 pressure on the particle physics community to adapt our computing m...\n\n
 ---------------------\nDeep Learning for Fast Detector Simulation\n\nValle
 corsa\n\nThe High Energy Physics (HEP) community has a long tradition of u
 sing Machine Learning methods to solve tasks related, mostly, to the selec
 tion of interesting events over the overwhelming background produced at co
 lliders. In recent years, several studies, in different fields of science,
  industry and...\n\n---------------------\nAutoencoders for Anomaly Detect
 ion in HEP\n\nThompson\n\nMachine learning methods are being increasingly 
 and successfully applied to many different physics problems. In this talk 
 I will address how autoencoders can be employed in model-independent, data
 -driven searches for new physics at the LHC. Autoencoders are an unsupervi
 sed machine learning method, w...\n
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