Open Access
Issue
EPJ Web Conf.
Volume 214, 2019
23rd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2018)
Article Number 06017
Number of page(s) 8
Section T6 - Machine learning & analysis
DOI https://doi.org/10.1051/epjconf/201921406017
Published online 17 September 2019
  1. Y. LeCun, Y. Bengio, G. Hinton, Nature 521, 436 (2015) [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
  2. P. Baldi, P. Sadowski, D. Whiteson, Nature Communications 5 (2014) [Google Scholar]
  3. B.P. Roe, H.J. Yang, J. Zhu, Y. Liu, I. Stancu, G. McGregor, Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 543, 577 (2005) [CrossRef] [Google Scholar]
  4. H. Kolanoski, Application of Artificial Neural Networks in Particle Physics (Springer Berlin Heidelberg, Berlin, Heidelberg, 1996), pp. 1–14, ISBN 978-3-540-68684-2, https://doi.org/10.1007/3-540-61510-5_1 [Google Scholar]
  5. C. collaboration, Journal of Physics G: Nuclear and Particle Physics 34 (2007) [Google Scholar]
  6. Y. Bengio, A. Courville, P. Vincent, Representation learning: A review and new perspectives (2012), arXiv:1206.5538 [Google Scholar]
  7. I. Heredia, Large-Scale Plant Classification with Deep Neural Networks, in Proceedings of the Computing Frontiers Conference (ACM, New York, NY, USA, 2017), CF’17, pp. 259–262, ISBN 978-1-4503-4487-6, http://doi.acm.org/10.1145/3075564. 3075590 [CrossRef] [Google Scholar]
  8. K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition (2015), arXiv:1512.03385 [Google Scholar]
  9. O. Russakovskyet al., International Journal of Computer Vision (IJCV) 115, 211 (2015) [CrossRef] [Google Scholar]
  10. S. Dielemanet al., Lasagne: First release. (2015), http://dx.doi.org/10.5281/zenodo.27878 [Google Scholar]
  11. J. Bergstraet al., Theano: a CPU and GPU Math Expression Compiler, in Proceedings of the Python for Scientific Computing Conference (SciPy) (2010), oral Presentation [Google Scholar]
  12. F. Bastienet al., >Theano: new features and speed improvements, Deep Learning and Unsupervised Feature Learning NIPS 2012 Workshop (2012) [Google Scholar]
  13. D. Kingma, J. Ba, Adam: A method for stochastic optimization (2014), arXiv:1412.6980 [Google Scholar]
  14. CMS Collaboration, Journal of Instrumentation 3, S08004 (2008) [Google Scholar]
  15. CMS Collaboration, Simulated dataset dyjetstoll_tunez2_m-50_7tev-madgraph-tauola in aodsim format for 2011 collision data (sm inclusive) (2016), DOI: 10.7483/opendata.cms.txt4.4rrp, http://opendata.cern.ch/record/ 1395 [Google Scholar]
  16. CMS Collaboration, Simulated dataset wjetstolnu_tunez2_7tev-madgraph-tauola in aodsim format for 2011 collision data (sm inclusive) (2016), DOI: 10.7483/opendata.cms.u7p6.ckvb, http://opendata.cern.ch/record/ 1633 [Google Scholar]
  17. CMS Collaboration, Simulated dataset ttjets_tunez2_7tev-madgraph-tauola in aodsim format for 2011 collision data (sm inclusive) (2016), DOI: 10.7483/opendata.cms.zbgf.h543, http://opendata.cern.ch/record/1544 [Google Scholar]
  18. N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, R. Salakhutdinov, Journal of Machine Learning Research 15, 1929 (2014) [Google Scholar]
  19. T.Q. Nguyen, D. Weitekamp III, D. Anderson, R. Castello, O. Cerri, M. Pierini, M. Spiropulu, J.R. Vlimant, Topology classification with deep learning to improve realtime event selection at the lhc (2018) [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.