EPJ Web Conf.
Volume 150, 2017Connecting The Dots/Intelligent Trackers 2017 (CTD/WIT 2017)
|Number of page(s)||12|
|Published online||08 August 2017|
- Rapid analytics and model prototyping (2017), http://ramp.studio/
- S. van der Walt, S.C. Colbert, G. Varoquaux, The numpy array: A structure for efficient numerical computation, computing in science & engineering (2011), http://www.numpy.org/
- W. McKinney, pandas : powerful python data analysis toolkit (2011), http://pandas.sourceforge.net/
- F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg et al., Journal of Machine Learning Research 12, 2825 (2011)
- M. Ester, H.P. Kriegel, J. Sander, X. Xu, A density-based algorithm for discovering clusters in large spatial databases with noise (AAAI Press, 1996), pp. 226–231 [EDP Sciences]
- The ATLAS Collaboration, CERN-LHCC-2015-020, “ATLAS Phase-II Upgrade Scoping Document” (2015)
- The CMS Collaboration, CERN-LHCC-2015-019, “CMS Phase-II Upgrade Scoping Document” (2015)
- The Tracking Machine Learning Challenge (2017), https://github.com/trackml
- TrackMLRamp Simulation (2017), https://github.com/ramp-kits/HEP_tracking/tree/ramp_ctd_2017/simulation
- T. Abe et al. (Belle-II) (2010), arXiv:1011.0352
- S. Farrell, The HEP.TrkX Project: deep neural networks for HL-LHC online and offline tracking, in Connecting The Dots / Intelligent Trackers (2017)
- S. Hochreiter, J. Schmidhuber, Neural Comput. 9, 1735 (1997) [CrossRef] [PubMed]
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