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 | |
- 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]
- 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]
- J.E. Gerken, J. Aronsson, O. Carlsson, H. Linander, F. Ohlsson, C. Petersson, D. Persson (2021), 2105.13926 [Google Scholar]
- K. Zhou, G. Endrődi, L.G. Pang, H. Stöcker, Phys. Rev. D 100, 011501 (2019), 1810.12879 [Google Scholar]
- 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]
- S. Blücher, L. Kades, J.M. Pawlowski, N. Strodthoff, J.M. Urban, Phys. Rev. D 101, 094507 (2020)80003.01504 [Google Scholar]
- D. Bachtis, G. Aarts, B. Lucini, Phys. Rev. E 102, 053306 (2020), 2007.00355 [Google Scholar]
- S. Bulusu, M. Favoni, A. Ipp, D.I. Müller, D. Schuh, Phys. Rev. D 104, 074504 (2021), 2103.14686 [Google Scholar]
- D. Bachtis, G. Aarts, B. Lucini, Phys. Rev. D 103, 074510 (2021), 2102.09449 [Google Scholar]
- D. Bachtis, G. Aarts, F. Di Renzo, B. Lucini, Phys. Rev. Lett. 128, 081603 (2022), 2107.00466 [Google Scholar]
- 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]
- 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]
- A. Tomiya, Y. Nagai (2021), 2103.11965 [Google Scholar]
- D. Luo, G. Carleo, B.K. Clark, J. Stokes, Phys. Rev. Lett. 127, 276402 (2021), 2012.05232 [Google Scholar]
- R. Abbott et al., Phys. Rev. D 106, 074506 (2022), 2207.08945 [Google Scholar]
- 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]
- M. Favoni, A. Ipp, D.I. Müller, D. Schuh, Phys. Rev. Lett. 128, 032003 (2022), 2012.12901 [Google Scholar]
- P. de Haan, C. Rainone, M.C.N. Cheng, R. Bondesan (2021), 2110.02673 [Google Scholar]
- M. Gerdes, P. de Haan, C. Rainone, R. Bondesan, M.C.N. Cheng (2022), 2207.00283 [Google Scholar]
- 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]
- M. Lüscher, JHEP 08, 071 (2010), [Erratum: JHEP 03, 092 (2014)], 1006.4518 [CrossRef] [Google Scholar]
- K.G. Wilson, Phys. Rev. D 10, 2445 (1974) [Google Scholar]
- 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]
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