Open Access
| Issue |
EPJ Web Conf.
Volume 245, 2020
24th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2019)
|
|
|---|---|---|
| Article Number | 02019 | |
| Number of page(s) | 7 | |
| Section | 2 - Offline Computing | |
| DOI | https://doi.org/10.1051/epjconf/202024502019 | |
| Published online | 16 November 2020 | |
- LHCb Collaboration, Expression of Interest for a Phase-II LHCb Upgrade: Opportunities in flavour physics, and beyond, in the HL-LHC era, CERN report CERN-LHCC-2017003 (2017) [Google Scholar]
- S. Agostinelli et al. GEANT4: A Simulation toolkit. Nucl. Instrum. Meth., A506: 250–303, 2003 [Google Scholar]
- The LHCb Detector at the LHC LHCb Collaboration (Alves, A. AugustoJr., et al.) JINST 3 (2008) S08005 [Google Scholar]
- D. Belayneh, F. Carminati, A. Farbin et al, [arXiv:1912.06794 [physics.ins-det]]. [Google Scholar]
- L. de Oliveira, M. Paganini and B. Nachman, Comput. Softw. Big Sci. 1 (2017) no.1, 4 doi:10.1007/s41781-017-0004-6 [arXiv:1701.05927 [stat.ML]]. [Google Scholar]
- V. Chekalina, E. Orlova, F. Ratnikov, D. Ulyanov, A. Ustyuzhanin and E. Zakharov, EPJ Web Conf. 214 (2019), 02034 doi:10.1051/epjconf/201921402034 [arXiv:1812.01319 [physics.data-an]]. [Google Scholar]
- F. Ratnikov, JINST 15 (2020) no.05, C05032 doi:10.1088/1748-0221/15/05/C05032 [arXiv:2003.12440 [physics.ins-det]]. [CrossRef] [Google Scholar]
- SciPy 1.0 – Fundamental Algorithms for Scientific Computing in Python arXiv:1907.10121v1 [cs.MS], Jul 2019 [Google Scholar]
- XGBoost: A Scalable Tree Boosting System. arXiv:1603.02754 [cs.LG], March 2016 [Google Scholar]
- Bergstra, James, and Yoshua Bengio. “Random search for hyper-parameter optimization.” Journal of machine learning research, 281-305, Feb 2012 [Google Scholar]
- Head T, MechCoder, Louppe G, Shcherbatyi I, fcharras, et al. 2020 Scikit-optimize [software] version 0.7.1 Zenodo https://doi.org/10.5281/zenodo.1207017 [Google Scholar]
- Bergstra J, Yamins D, Cox D D, Making a Science of Model Search: Hyperparameter optimisation in Hundreds of Dimensions for Vision Architectures. Proc. of the 30th International Conference on Machine Learning (ICML 2013) [Google Scholar]
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