Open Access
EPJ Web Conf.
Volume 245, 2020
24th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2019)
Article Number 06019
Number of page(s) 7
Section 6 - Physics Analysis
Published online 16 November 2020
  1. R. Brun, F. Rademakers, ROOT An object oriented data analysis framework (1997) [Google Scholar]
  2. A. Hoecker, P. Speckmayer, J. Stelzer, J. Therhaag, E. von Toerne, H. Voss, M. Backes, T. Carli, O. Cohen, A. Christov et al., TMVA Toolkit for Multivariate Data Analysis (2007), physics/0703039 [Google Scholar]
  3. S. Chatrchyan et al. (CMS), Phys. Lett. B710, 403 (2012), 1202.1487 [CrossRef] [Google Scholar]
  4. The ATLAS Collaboration, Evidence for Higgs Boson Decays to the τ+τ Final State with the ATLAS Detector (2013) [Google Scholar]
  5. S. Chatrchyan, V. Khachatryan, A. Sirunyan, A. Tumasyan, W. Adam, E. Aguilo, T. Bergauer, M. Dragicevic, J. Erö, C. Fabjan et al., Physics Letters B 716, 30–61 (2012) [NASA ADS] [CrossRef] [Google Scholar]
  6. G. Aad, T. Abajyan, B. Abbott, J. Abdallah, S. Abdel Khalek, A. Abdelalim, O. Abdinov, R. Aben, B. Abi, M. Abolins et al., Physics Letters B 716, 1–29 (2012) [NASA ADS] [CrossRef] [Google Scholar]
  7. O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein et al., International Journal of Computer Vision (IJCV) 115, 211 (2015) [CrossRef] [Google Scholar]
  8. M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard et al., TensorFlow: A system for large-scale machine learning (2016) [Google Scholar]
  9. A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga et al., PyTorch: An Imperative Style, High-Performance Deep Learning Library (2019) [Google Scholar]
  10. T. Chen, M. Li, Y. Li, M. Lin, N. Wang, M. Wang, T. Xiao, B. Xu, C. Zhang, Z. Zhang, MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems (2015), 1512.01274 [Google Scholar]
  11. P. Baldi, P. Sadowski, D. Whiteson, Nature Communications 5 (2014) [Google Scholar]
  12. M.S. and, Journal of Physics: Conference Series 1085, 042029 (2018) [CrossRef] [Google Scholar]
  13. Quark versus Gluon Jet Tagging Using Jet Images with the ATLAS Detector (2017), [Google Scholar]
  14. S. Van Der Walt, S.C. Colbert, G. Varoquaux, Computing in Science & Engineering 13, 22 (2011) [Google Scholar]
  15. D. Piparo, P. Canal, E. Guiraud, X. Pla, G. Ganis, G. Amadio, A. Naumann, E. Tejedor, EPJ Web of Conferences 214, 06029 (2019) [EDP Sciences] [Google Scholar]
  16. W. McKinney, Data structures for statistical computing in python (2010) [Google Scholar]
  17. E.T. Saavedra, S. Wunsch, M. Galli, A new PyROOT: Modern, Interoperable and more Pythonic, Proceedings CHEP 2019 (2019) [Google Scholar]
  18. L. Buitinck, G. Louppe, M. Blondel, F. Pedregosa, A. Mueller, O. Grisel, V. Niculae, P. Prettenhofer, A. Gramfort, J. Grobler et al., API design for machine learning software: experiences from the scikit-learn project, in ECML PKDD Workshop: Languages for Data Mining and Machine Learning (2013), pp. 108–122 [Google Scholar]
  19. The Standard C++ Foundation, [Google Scholar]
  20. T. Chen, C. Guestrin, XGBoost: A Scalable Tree Boosting System, in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM, New York, NY, USA, 2016), KDD ’16, pp. 785–794, ISBN 978-1-45034232-2, [Google Scholar]
  21. V. Vasilev, P. Canal, A. Naumann, P. Russo, Journal of Physics: Conference Series 396, 052071 (2012) [CrossRef] [Google Scholar]
  22. K. Albertsson, L. Moneta, S. An, S. Wunsch, Fast Inference for Machine Learning in ROOT/TMVA, Proceedings CHEP 2019 (2019) [Google Scholar]
  23. K. Albertsson, S. Gleyzer, M. Huwiler, V. Ilievski, L. Moneta, S. Shekar, V. Estrade, A. Vashistha, S. Wunsch, O. Mesa, EPJ Web of Conferences 214, 06014 (2019) [EDP Sciences] [Google Scholar]
  24. F. Chollet et al., Keras, (2015) [Google Scholar]
  25. S. Chetlur, C. Woolley, P. Vandermersch, J. Cohen, J. Tran, B. Catanzaro, E. Shelhamer, CoRR abs/1410.0759 (2014), 1410.0759 [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.