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The application of deep learning techniques using convolutional neural networks to the classification of particle collisions in High Energy Physics is explored.
Learning representations by back-propagating errors
David E. Rumelhart, Geoffrey E. Hinton, and Ronald J. Williams · 1986
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Application of Artificial Neural Networks in Particle Physics
Hermann Kolanoski · 1996
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Boosted decision trees as an alternative to artificial neural networks for particle identification
Byron P. Roe, Hai-Jun Yang, Ji Zhu, Yong Liu, Ion Stancu, and Gordon McGregor · 2005
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CMS technical design report, volume II: Physics performance
CMS collaboration · 2007
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The CMS experiment at the CERN LHC
CMS Collaboration · 2008
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Theano: a CPU and GPU math expression compiler
James Bergstra et al · 2010
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Representation learning: A review and new perspectives, 2012
Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2012
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Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Olga Russakovsky et al · 2015
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Sander Dieleman et al · 2015
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Simulated dataset dyjetstoll_tunez2_m-50_7tev-madgraph-tauola in aodsim format for 2011 collision data (sm inclusive), 2016
CMS Collaboration · 2016
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Simulated dataset wjetstolnu_tunez2_7tev-madgraph-tauola in aodsim format for 2011 collision data (sm inclusive), 2016
CMS Collaboration · 2016
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Simulated dataset ttjets_tunez2_7tev-madgraph-tauola in aodsim format for 2011 collision data (sm inclusive), 2016
CMS Collaboration · 2016
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Large-scale plant classification with deep neural networks
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Ignacio Heredia · 2017
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