Open Access
Issue
EPJ Web Conf.
Volume 274, 2022
XVth Quark Confinement and the Hadron Spectrum Conference (ConfXV)
Article Number 09001
Number of page(s) 8
Section 9 - Parallel Track H
DOI https://doi.org/10.1051/epjconf/202227409001
Published online 22 December 2022
  1. T.S. Cohen, M. Welling, Group Equivariant Convolutional Networks, in Proceedings of The 33rd International Conference on Machine Learning (JMLR, 2016), Vol. 48, pp. 2990–2999, 1602.07576 [Google Scholar]
  2. T.S. Cohen, M. Weiler, B. Kicanaoglu, M. Welling, Gauge Equivariant Convolutional Networks and. the Icosahedral CNN, in Proceedings of the 36th International Conference on Machine Learning (JMLR, 2019), Vol. 97, pp. 1321–1330, 1902.04615 [Google Scholar]
  3. J.E. Gerken, J. Aronsson, O. Carlsson, H. Linander, F. Ohlsson, C. Petersson, D. Persson (2021), 2105.13926 [Google Scholar]
  4. K. Zhou, G. Endrődi, L.G. Pang, H. Stöcker, Phys. Rev. D 100, 011501 (2019), 1810.12879 [Google Scholar]
  5. D.L. Boyda, M.N. Chemodub, N.V. Gerasimeniuk, V.A. Goy, S.D. Liubimov, A.V. Molochkov, Phys. Rev. D 103, 014509 (2021), 2009.10971 [Google Scholar]
  6. S. Blücher, L. Kades, J.M. Pawlowski, N. Strodthoff, J.M. Urban, Phys. Rev. D 101, 094507 (2020)80003.01504 [Google Scholar]
  7. D. Bachtis, G. Aarts, B. Lucini, Phys. Rev. E 102, 053306 (2020), 2007.00355 [Google Scholar]
  8. S. Bulusu, M. Favoni, A. Ipp, D.I. Müller, D. Schuh, Phys. Rev. D 104, 074504 (2021), 2103.14686 [Google Scholar]
  9. D. Bachtis, G. Aarts, B. Lucini, Phys. Rev. D 103, 074510 (2021), 2102.09449 [Google Scholar]
  10. D. Bachtis, G. Aarts, F. Di Renzo, B. Lucini, Phys. Rev. Lett. 128, 081603 (2022), 2107.00466 [Google Scholar]
  11. G. Kanwar, M.S. Albergo, D. Boyda, K. Cranmer, D.C. Hackett, S. Racanière, D.J. Rezende, P.E. Shanahan, Phys. Rev. Lett. 125, 121601 (2020), 2003.06413 [Google Scholar]
  12. D. Boyda, G. Kanwar, S. Racanière, D.J. Rezende, M.S. Albergo, K. Cranmer, D.C. Hackett, PE. Shanahan, Phys. Rev. D 103, 074504 (2021), 2008.05456 [Google Scholar]
  13. A. Tomiya, Y. Nagai (2021), 2103.11965 [Google Scholar]
  14. D. Luo, G. Carleo, B.K. Clark, J. Stokes, Phys. Rev. Lett. 127, 276402 (2021), 2012.05232 [Google Scholar]
  15. R. Abbott et al., Phys. Rev. D 106, 074506 (2022), 2207.08945 [Google Scholar]
  16. M.S. Albergo, D. Boyda, D.C. Hackett, G. Kanwar, K. Cranmer, S. Racanière, D.J. Rezende, P.E. Shanahan (2021), 2101.08176 [Google Scholar]
  17. M. Favoni, A. Ipp, D.I. Müller, D. Schuh, Phys. Rev. Lett. 128, 032003 (2022), 2012.12901 [Google Scholar]
  18. P. de Haan, C. Rainone, M.C.N. Cheng, R. Bondesan (2021), 2110.02673 [Google Scholar]
  19. M. Gerdes, P. de Haan, C. Rainone, R. Bondesan, M.C.N. Cheng (2022), 2207.00283 [Google Scholar]
  20. R.T.Q. Chen, Y. Rubanova, J. Bettencourt, D.K. Duvenaud, Neural Ordinary Differential Equations, in Advances in Neural Information Processing Systems, edited by S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, R. Garnett (Curran Associates, Inc., 2018), Vol. 31, 1806.07366 [Google Scholar]
  21. M. Lüscher, JHEP 08, 071 (2010), [Erratum: JHEP 03, 092 (2014)], 1006.4518 [CrossRef] [Google Scholar]
  22. K.G. Wilson, Phys. Rev. D 10, 2445 (1974) [Google Scholar]
  23. K. He, X. Zhang, S. Ren, J. Sun, Deep Residual Learning for Image Recognition, in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016), pp.770–778 [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.