Subject: QC770-798 | High Energy Physics - Phenomenology | Physics - Data Analysis, Statistics and Probability | Statistics - Machine Learning | Nuclear and particle physics. Atomic energy. Radioactivity | Jets
arxiv: Computer Science::Machine Learning
Abstract As machine learning algorithms become increasingly sophisticated to exploit subtle features of the data, they often become more dependent on simulations. This paper presents a new approach called weakly supervised classification in which class proportions are t... View more
 F. Chollet, Keras, https://github.com/fchollet/keras (2015).
 CMS collaboration, V tagging observables and correlations, CMS-PAS-JME-14-002 (2014).  B. Bhattacherjee, S. Mukhopadhyay, M.M. Nojiri, Y. Sakaki and B.R. Webber, Quark-gluon  J. Alwall et al., The automated computation of tree-level and next-to-leading order  S. Alioli, P. Nason, C. Oleari and E. Re, A general framework for implementing NLO  T. Sjostrand, S. Mrenna and P.Z. Skands, PYTHIA 6.4 physics and manual, JHEP 05  M. Bahr et al., HERWIG++ physics and manual, Eur. Phys. J. C 58 (2008) 639  T. Gleisberg et al., Event generation with SHERPA 1.1, JHEP 02 (2009) 007  A.J. Larkoski, J. Thaler and W.J. Waalewijn, Gaining (mutual) information about