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 | |
- Y. LeCun, Y. Bengio, G. Hinton, Nature 521, 436 (2015) [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
- P. Baldi, P. Sadowski, D. Whiteson, Nature Communications 5 (2014) [Google Scholar]
- 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]
- 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]
- C. collaboration, Journal of Physics G: Nuclear and Particle Physics 34 (2007) [Google Scholar]
- Y. Bengio, A. Courville, P. Vincent, Representation learning: A review and new perspectives (2012), arXiv:1206.5538 [Google Scholar]
- 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]
- K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition (2015), arXiv:1512.03385 [Google Scholar]
- O. Russakovskyet al., International Journal of Computer Vision (IJCV) 115, 211 (2015) [Google Scholar]
- S. Dielemanet al., Lasagne: First release. (2015), http://dx.doi.org/10.5281/zenodo.27878 [Google Scholar]
- 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]
- F. Bastienet al., >Theano: new features and speed improvements, Deep Learning and Unsupervised Feature Learning NIPS 2012 Workshop (2012) [Google Scholar]
- D. Kingma, J. Ba, Adam: A method for stochastic optimization (2014), arXiv:1412.6980 [Google Scholar]
- CMS Collaboration, Journal of Instrumentation 3, S08004 (2008) [Google Scholar]
- 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]
- 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]
- 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]
- N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, R. Salakhutdinov, Journal of Machine Learning Research 15, 1929 (2014) [Google Scholar]
- 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]
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