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All issues Volume 251 (2021) EPJ Web Conf., 251 (2021) 03059 References
Open Access
Issue
EPJ Web Conf.
Volume 251, 2021
25th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2021)
Article Number 03059
Number of page(s) 13
Section Offline Computing
DOI https://doi.org/10.1051/epjconf/202125103059
Published online 23 August 2021
  1. T. Sjöstrand, S. Ask, J.R. Christiansen, R. Corke, N. Desai, P. Ilten, S. Mrenna, S. Prestel, C.O. Rasmussen, P.Z. Skands, Comput. Phys. Commun. 191, 159 (2015), 1410.3012 [Google Scholar]
  2. J. Bellm et al., Eur. Phys. J. C 76, 196(2016), 1512.01178 [Google Scholar]
  3. T. Gleisberg, S. Hoeche, F. Krauss, M. Schonherr, S. Schumann, F. Siegert, J. Winter, JHEP 02, 007 (2009), 0811.4622 [Google Scholar]
  4. J. Brehmer, K. Cranmer, G. Louppe, J. Pavez, Phys. Rev. D 98, 052004 (2018), 1805.00020 [Google Scholar]
  5. K. Cranmer, J. Brehmer, G. Louppe, The frontier of simulation-based inference (National Academy of Sciences, 2020), ISSN 0027-8424, https://www.pnas.org/content/early/2020/05/28/1912789117.full.pdf [Google Scholar]
  6. M. Cacciari, G.P. Salam, G. Soyez, JHEP 04, 063 (2008), 0802.1189 [Google Scholar]
  7. S. Catani, Y.L. Dokshitzer, M.H. Seymour, B.R. Webber, Nucl. Phys. B 406, 187 (1993) [Google Scholar]
  8. Y.L. Dokshitzer, G.D. Leder, S. Moretti, B.R. Webber, JHEP 08, 1 (1997), 9707323 [Google Scholar]
  9. S.D. Ellis, D.E. Soper, Phys. Rev. D 48, 3160 (1993), 9305266 [Google Scholar]
  10. A. Buckley, H. Hoeth, H. Lacker, H. Schulz, J.E. von Seggern, Eur. Phys. J. C 65, 331 (2010), 0907.2973 [Google Scholar]
  11. J. Brehmer, K. Cranmer, G. Louppe, J. Pavez, Phys. Rev. Lett. 121, 111801 (2018), 1805.00013 [Google Scholar]
  12. J. Brehmer, G. Louppe, J. Pavez, K. Cranmer (2018), 1805.12244 [Google Scholar]
  13. A. Andreassen, B. Nachman, Phys. Rev. D 101, 091901 (2020), 1907.08209 [Google Scholar]
  14. G. Louppe, J. Hermans, K. Cranmer (2017), 1707.07113 [Google Scholar]
  15. A.G. Baydin et al., Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale (2019), 1907.03382 [Google Scholar]
  16. A.G. Baydin et al. (2018), 1807.07706 [Google Scholar]
  17. S. Catani, F. Krauss, R. Kuhn, B. Webber, JHEP 11, 063 (2001), hep-ph/0109231 [Google Scholar]
  18. L. Lonnblad, JHEP 05, 046 (2002), hep-ph/0112284 [Google Scholar]
  19. D.E. Soper, M. Spannowsky, Phys. Rev. D 84, 074002 (2011), 1102.3480 [Google Scholar]
  20. D.E. Soper, M. Spannowsky, Phys. Rev. D 87, 054012 (2013), 1211.3140 [Google Scholar]
  21. D. Ferreira de Lima, P. Petrov, D. Soper, M. Spannowsky, Phys. Rev. D 95, 034001 (2017), 1607.06031 [Google Scholar]
  22. S. Höche, S. Prestel, H. Schulz, Phys. Rev. D 100, 014024 (2019), 1905.05120 [Google Scholar]
  23. C.S. Greenberg, S. Macaluso, N. Monath, J.A. Lee, P. Flaherty, K. Cranmer, A. McGregor, A. McCallum (2020), 2002.11661 [Google Scholar]
  24. K. Cranmer, S. Macaluso, D. Pappadopulo, Toy Generative Model for Jets Package (2019), https://github.com/SebastianMacaluso/ToyJetsShower [Google Scholar]
  25. Cranmer, Kyle and Macaluso, Sebastian and Pappadopulo, Duccio, Greedy and Beam Search clustering algorithms for jet physics (2019), https://github.com/ SebastianMacaluso/StandardHC [Google Scholar]
  26. Cranmer, Kyle and Macaluso, Sebastian and Pappadopulo, Duccio, Visualize Binary Trees Package (2019), https://github.com/SebastianMacaluso/ VisualizeBinaryTrees [Google Scholar]
  27. A. Kraskov, H. Stögbauer, R.G. Andrzejak, P. Grassberger, EPL (Europhysics Letters) 70, 278 (2005) [Google Scholar]
  28. P. Cimiano, S. Staab, Learning concept hierarchies from text with a guided agglomerative clustering algorithm, in Proceedings of the ICML 2005 Workshop on Learning and Extending Lexical Ontologies with Machine Learning Methods (2005) [Google Scholar]
  29. T. Sorlie, C.M. Perou, R. Tibshirani, T. Aas, S. Geisler, H. Johnsen, T. Hastie, M.B. Eisen, M. Van De Rijn, S.S. Jeffrey et al., Proceedings of the National Academy of Sciences 98, 10869 (2001) [Google Scholar]
  30. D. Callan, A combinatorial survey of identities for the double factorial (2009), 0906.1317 [Google Scholar]
  31. E. Dale, J. Moon, The permuted analogues of three Catalan sets (1993), 0378-3758 [Google Scholar]
  32. J. Brehmer, S. Macaluso, D. Pappadopulo, K. Cranmer, Hierarchical clustering in particle physics through reinforcement learning, in 34th Conference on Neural Information Processing Systems (2020), 2011.08191 [Google Scholar]
  33. D. Silver, A. Huang, C.J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrit-Twieser, I. Antonoglou, V. Panneershelvam, M. Lanctot et al., nature 529, 484 (2016) [Google Scholar]
  34. D. Silver, J. Schrittwieser, K. Simonyan, I. Antonoglou, A. Huang, A. Guez, T. Hubert, L. Baker, M. Lai, A. Bolton et al., Nature 550, 354 (2017) [Google Scholar]
  35. D. Silver, T. Hubert, J. Schrittwieser, I. Antonoglou, M. Lai, A. Guez, M. Lanctot, L. Sifre, D. Kumaran, T. Graepel et al., arXiv:1712.01815 (2017) [Google Scholar]
  36. S. Carrazza, F.A. Dreyer, Phys. Rev. D100, 014014 (2019), 1903.09644 [Google Scholar]
  37. A.D. Gordon, T.A. Henzinger, A.V. Nori, S.K. Rajamani, in Proceedings of the on Future of Software Engineering (2014), pp. 167–181 [Google Scholar]
  38. T.A. Le, A.G. Baydin, F. Wood, Inference compilation and universal probabilistic programming, in Artificial Intelligence and Statistics (2017), pp. 1338–1348 [Google Scholar]
  39. A.G. Baydin, L. Shao, W. Bhimji, L. Heinrich, S. Naderiparizi, A. Munk, J. Liu, B. GramHansen, G. Louppe, L. Meadows et al., Efficient probabilistic inference in the quest for physics beyond the standard model, in Advances in neural information processing systems (2019), pp. 5459–5472 [Google Scholar]
  40. A.G. Baydin, L. Shao, W. Bhimji, L. Heinrich, L. Meadows, J. Liu, A. Munk, S. Naderiparizi, B. Gram-Hansen, G. Louppe et al., Etalumis: Bringing probabilistic programming to scientific simulators at scale, in Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (2019), pp. 1–24 [Google Scholar]

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