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CERN Document Server 8 záznamov nájdených  Hľadanie trvalo 0.63 sekúnd. 
1.
Fast Inference for Machine Learning in ROOT/TMVA / Albertsson, Kim (CERN ; Lulea U.) ; An, Sitong (CERN ; Carnegie Mellon U.) ; Moneta, Lorenzo (CERN) ; Wunsch, Stefan (CERN ; KIT, Karlsruhe) ; Zampieri, Luca (Ecole Polytechnique, Lausanne)
ROOT provides, through TMVA, machine learning tools for data analysis at HEP experiments and beyond. However, with the rapidly evolving ecosystem for machine learning, the focus of TMVA is shifting. [...]
2020 - 8 p. - Published in : EPJ Web Conf. 245 (2020) 06008 Fulltext: PDF;
In : 24th International Conference on Computing in High Energy and Nuclear Physics, Adelaide, Australia, 4 - 8 Nov 2019, pp.06008
Úplný záznam - Podobné záznamy
2.
Machine Learning with ROOT/TMVA / Albertsson, Kim (CERN ; Lulea U.) ; An, Sitong (CERN ; Carnegie Mellon U.) ; Gleyzer, Sergei (Alabama U.) ; Moneta, Lorenzo (CERN) ; Niermann, Joana (CERN) ; Wunsch, Stefan (CERN ; KIT, Karlsruhe) ; Zampieri, Luca (CERN) ; Mesa, Omar Andres Zapata (CERN)
ROOT provides, through TMVA, machine learning tools for data analysis at HEP experiments and beyond. We present recently included features in TMVA and the strategy for future developments in the diversified machine learning landscape. [...]
2020 - 7 p. - Published in : EPJ Web Conf. 245 (2020) 06019 Fulltext: PDF;
In : 24th International Conference on Computing in High Energy and Nuclear Physics, Adelaide, Australia, 4 - 8 Nov 2019, pp.06019
Úplný záznam - Podobné záznamy
3.
Towards Fast Displaced Vertex Finding / Albertsson, Kim (CERN ; Lulea U.) ; Meloni, Federico (DESY)
Many Standard Model extensions predict metastable massive particles that can be detected by looking for displaced decay vertices in the inner detector volume. [...]
arXiv:1910.10508 ; PROC-CTD19-014. - 4 p.
Fulltext
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4.
New machine learning developments in ROOT/TMVA / Albertsson, Kim (CERN ; Lulea U. Technol. (main)) ; Gleyzer, Sergei (U. Florida, Gainesville (main)) ; Huwiler, Marc (Ecole Polytechnique, Lausanne) ; Ilievski, Vladimir (Ecole Polytechnique, Lausanne) ; Moneta, Lorenzo (CERN) ; Shekar, Saurav (ETH, Zurich (main)) ; Estrade, Victor (CERN) ; Vashistha, Akshay (CERN) ; Wunsch, Stefan (CERN ; KIT, Karlsruhe) ; Zapata Mesa, Omar Andres (Antioquia U. ; Inst. Tech. Metro., Medellin)
The Toolkit for Multivariate Analysis, TMVA, the machine learning package integrated into the ROOT data analysis framework, has recently seen improvements to its deep learning module, parallelisation of multivariate methods and cross validation. Performance benchmarks on datasets from high-energy physics are presented with a particular focus on the new deep learning module which contains robust fully-connected, convolutional and recurrent deep neural networks implemented on CPU and GPU architectures. [...]
2019 - 8 p. - Published in : EPJ Web Conf. 214 (2019) 06014 Fulltext from publisher: PDF;
In : 23rd International Conference on Computing in High Energy and Nuclear Physics, CHEP 2018, Sofia, Bulgaria, 9 - 13 Jul 2018, pp.06014
Úplný záznam - Podobné záznamy
5.
Machine Learning in High Energy Physics Community White Paper / Albertsson, Kim (Lulea U.) ; Altoe, Piero (NVIDIA, Santa Clara) ; Anderson, Dustin (Caltech) ; Anderson, John ; Andrews, Michael (Carnegie Mellon U.) ; Araque Espinosa, Juan Pedro (LIP, Lisbon) ; Aurisano, Adam (Cincinnati U.) ; Basara, Laurent (INFN, Padua ; Padua U.) ; Bevan, Adrian (University Coll. London) ; Bhimji, Wahid (LBL, Berkeley) et al.
Machine learning has been applied to several problems in particle physics research, beginning with applications to high-level physics analysis in the 1990s and 2000s, followed by an explosion of applications in particle and event identification and reconstruction in the 2010s. In this document we discuss promising future research and development areas for machine learning in particle physics. [...]
arXiv:1807.02876; FERMILAB-PUB-18-318-CD-DI-PPD.- 2018-10-18 - 27 p. - Published in : J. Phys.: Conf. Ser. 1085 (2018) 022008 Fulltext: 1807.02876 - PDF; Albertsson_2018_J._Phys.__Conf._Ser._1085_022008 - PDF; fulltext1681439 - PDF; fermilab-pub-18-318-cd-di-ppd - PDF; Fulltext from Publisher: PDF; External link: Fermilab Library Server (fulltext available)
In : 18th International Workshop on Advanced Computing and Analysis Techniques in Physics Research, Seattle, WA, USA, 21 - 25 Aug 2017, pp.022008
Úplný záznam - Podobné záznamy
6.
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Formal Verification - Robust and Efficient code: Why Formal Verification / ALBERTSSON, Kim (speaker) (CERN)
LECTURE 2: In this lecture we will expand on the concepts of the previous lecture and establish formal methods in a broader context, ignoring implementation detail, and investigate how and where these methods are used today, and where they might be used tomorrow. As concrete examples we will be studying how FV can benefit static analysis and comp-cert, and verified C compiler. This talk aims to introduce the concepts of Formal Verification and how they can be used to the benefit of the programmer to produce robust and efficient code. [...]
2016 - 3977. inverted CSC; inverted CERN School of Computing 2016 External links: Talk details; Event details In : inverted CERN School of Computing 2016
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7.
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Formal verification - Robust and efficient code: Introduction to Formal Verification / ALBERTSSON, Kim (speaker) (CERN)
LECTURE 1: We will establish two general approaches to FV and where they are applicable: model checking and theorem proving. We will explore the latter in more details and have a brief look at the underlying theory, predicate logic [...]
2016 - 3655. inverted CSC; inverted CERN School of Computing 2016 External links: Talk details; Event details In : inverted CERN School of Computing 2016
Úplný záznam - Podobné záznamy
8.
A New Event Builder for CMS Run II / Albertsson, Kim (CERN) ; Andre, Jean-marc Olivier (Fermilab) ; Andronidis, Anastasios (Ioannina U.) ; Behrens, Ulf (DESY) ; Branson, James (UC, San Diego) ; Chaze, Olivier (CERN) ; Cittolin, Sergio (UC, San Diego) ; Darlea, Georgiana Lavinia (MIT) ; Deldicque, Christian (CERN) ; Dobson, Marc (CERN) et al.
The data acquisition system (DAQ) of the CMS experiment at the CERN Large Hadron Collider (LHC) assembles events at a rate of 100kHz, transporting event data at an aggregate throughput of 100GB/s to the high-level trigger (HLT) farm. The DAQ system has been redesigned during the LHC shutdown in 2013/14. [...]
CMS-CR-2015-063.- Geneva : CERN, 2015 - 9 p. - Published in : J. Phys.: Conf. Ser. 664 (2015) 082035 Fulltext: PDF; IOP Open Access article: PDF;
In : 21st International Conference on Computing in High Energy and Nuclear Physics, Okinawa, Japan, 13 - 17 Apr 2015, pp.082035
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