Free Access
Issue
A&A
Volume 614, June 2018
Article Number A5
Number of page(s) 13
Section The Sun
DOI https://doi.org/10.1051/0004-6361/201731344
Published online 06 June 2018
  1. Asensio Ramos, A., & de la Cruz Rodríguez, J. 2015, A&A, 577, A140 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  2. Asensio Ramos, A., & Socas-Navarro, H. 2005, A&A, 438, 1021 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  3. Asensio Ramos, A., Requerey, I. S., & Vitas, N. 2017, A&A, 604, A11 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  4. Bamba, Y., Kusano, K., Imada, S., & Iida, Y. 2014, PASJ, 66, S16 [NASA ADS] [CrossRef] [Google Scholar]
  5. Bello González,N., Yelles Chaouche, L., Okunev, O., & Kneer, F. 2009, A&A, 494, 1091 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  6. Bishop, C. M. 1996, Neural Networks for Pattern Recognition (Oxford: Oxford University Press) [Google Scholar]
  7. Borman, S., & Stevenson, R. L. 1998, Proc. Midwest Symp. Circ. Syst., 374-378 [Google Scholar]
  8. Carroll, T. A., & Kopf, M. 2008, A&A, 481, L37 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  9. Cheung, M. C. M., Rempel, M., Title, A. M., & Schüssler, M. 2010, ApJ, 720, 233 [NASA ADS] [CrossRef] [Google Scholar]
  10. Ciuca, R., Hernández, O. F., & Wolman, M. 2017, ArXiv e-prints [arXiv:1708.08878] [Google Scholar]
  11. Colak, T., & Qahwaji, R. 2008, Sol. Phys., 248, 277 [NASA ADS] [CrossRef] [Google Scholar]
  12. Couvidat, S., Schou, J., Hoeksema, J. T., et al. 2016, Sol. Phys., 291, 1887 [NASA ADS] [CrossRef] [Google Scholar]
  13. Danilovic, S., Gandorfer, A., Lagg, A., et al. 2008, A&A, 484, L17 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  14. Danilovic, S., Schüssler, M., & Solanki, S. K. 2010, A&A, 513, A1 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  15. DeRosa, M. L., Wheatland, M. S., Leka, K. D., et al. 2015, ApJ, 811, 107 [NASA ADS] [CrossRef] [Google Scholar]
  16. Dong, C., Change Loy, C., He, K., & Tang, X. 2015, ArXiv e-prints [arXiv:1501.00092] [Google Scholar]
  17. Dong, C., Change Loy, C., & Tang, X. 2016, ArXiv e-prints [arXiv:1608.00367] [Google Scholar]
  18. Hayat, K. 2017, ArXiv e-prints [arXiv:1706.09077] [Google Scholar]
  19. He, K., Zhang, X., Ren, S., & Sun, J. 2015, ArXiv e-prints [arXiv:1512.03385] [Google Scholar]
  20. Ichimoto, K., Lites, B., Elmore, D., et al. 2008, Sol. Phys., 249, 233 [NASA ADS] [CrossRef] [Google Scholar]
  21. Ioffe, S., & Szegedy, C. 2015, in Proceedings of the 32nd International Conference on Machine Learning (ICML-15), eds. D. Blei, & F. Bach, JMLR Workshop and Conference Proceeding, 448 [Google Scholar]
  22. Kim, J., Lee, J. K., & Lee, K. M. 2015, ArXiv e-prints [arXiv:1511.04491] [Google Scholar]
  23. Kingma, D. P., & Ba, J. 2014, ArXiv e-prints [arXiv:1412.6980] [Google Scholar]
  24. Kosugi, T., Matsuzaki, K., Sakao, T., et al. 2007, Sol. Phys., 243, 3 [NASA ADS] [CrossRef] [Google Scholar]
  25. Krivova, N. A., & Solanki, S. K. 2004, A&A, 417, 1125 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  26. LeCun, Y., & Bengio, Y. 1998, in The Handbook of Brain Theory and Neural Networks, ed. M. A. Arbib (Cambridge, MA: MIT Press), 255 [Google Scholar]
  27. LeCun, Y., Bottou, L., Orr, G. B., & Müller, K.-R. 1998, in Neural Networks: Tricks of the Trade, This Book is an Outgrowth of a 1996 NIPS Workshop (London, UK: Springer-Verlag), 9 [Google Scholar]
  28. Ledig, C., Theis, L., Huszar, F., et al. 2016, ArXiv e-prints [arXiv:1609.04802] [Google Scholar]
  29. Linker, J. A., Caplan, R. M., Downs, C., et al. 2017, ApJ, 848, 70 [NASA ADS] [CrossRef] [Google Scholar]
  30. Lites, B. W., Akin, D. L., Card, G., et al. 2013, Sol. Phys., 283, 579 [NASA ADS] [CrossRef] [Google Scholar]
  31. Nair, V., & Hinton, G. E. 2010, in Proceedings of the 27th International Conference on Machine Learning (ICML-10), (Ha: ACM Digital Library), 21, 807 [Google Scholar]
  32. Pesnell, W. D., Thompson, B. J., & Chamberlin, P. C. 2012, Sol. Phys., 275, 3 [NASA ADS] [CrossRef] [Google Scholar]
  33. Peyrard, C., Mamalet, F., & Garcia, C. 2015, in VISAPP, eds. J. Braz, S. Battiato, & J. F. H. Imai (Setùbal: SciTePress), 1, 84 [Google Scholar]
  34. Pietarila, A., Bertello, L., Harvey, J. W., & Pevtsov, A. A. 2013, Sol. Phys., 282, 91 [NASA ADS] [CrossRef] [Google Scholar]
  35. Quintero Noda, C., Asensio Ramos, A., Orozco Suárez, D., & Ruiz Cobo B. 2015, A&A, 579, A3 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  36. Richardson, W. H. 1972, J. Opt. Soc. Am, 62, 55 [NASA ADS] [CrossRef] [Google Scholar]
  37. Ruiz Cobo, B., & Asensio Ramos A. 2013, A&A, 549, L4 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  38. Rumelhart, D. E., Hinton, G. E., & Williams, R. J. 1986, Learning representations by back-propagating errors, (Cambridge, MA: MIT Press), Nature, 323, 533 [NASA ADS] [CrossRef] [Google Scholar]
  39. Schawinski, K., Zhang, C., Zhang, H., Fowler, L., & Santhanam, G. K. 2017, MNRAS, 467, L110 [NASA ADS] [Google Scholar]
  40. Scherrer, P. H., Schou, J., Bush, R. I., et al. 2012, Sol. Phys., 275, 207 [NASA ADS] [CrossRef] [Google Scholar]
  41. Schmidhuber, J. 2015, Neural Networks, 61, 85 [CrossRef] [PubMed] [Google Scholar]
  42. Shi, W., Caballero, J., Huszár, F., et al. 2016, ArXiv e-prints [arXiv:1609.05158] [Google Scholar]
  43. Simonyan, K., & Zisserman, A. 2014, ArXiv e-prints [arXiv:1409.1556] [Google Scholar]
  44. Socas-Navarro, H. 2005, ApJ, 621, 545 [NASA ADS] [CrossRef] [Google Scholar]
  45. Stein, R. F. 2012, Liv. Rev. Sol. Phys., 9, 4 [Google Scholar]
  46. Stein, R. F., & Nordlund, Å. 2012, ApJ, 753, L13 [NASA ADS] [CrossRef] [Google Scholar]
  47. Tadesse, T., Wiegelmann, T., Inhester, B., et al. 2013, A&A, 550, A14 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  48. Tai, Y., Yang, J., & Liu, X. 2017, Proceeding of IEEE Computer Vision and Pattern Recognition [Google Scholar]
  49. Tipping, M. E., & Bishop, C. M. 2003, Advances in Neural Information Processing Systems (Cambridge, MA: MIT Press), 1303 [Google Scholar]
  50. Tsuneta, S., Ichimoto, K., Katsukawa, Y., et al. 2008, Sol. Phys., 249, 167 [NASA ADS] [CrossRef] [Google Scholar]
  51. van Noort, M. 2012, A&A, 548, A5 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  52. Vögler, A., Shelyag, S., Schüssler, M., et al. 2005, A&A, 429, 335 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  53. Wachter, R., Schou, J., Rabello-Soares, M. C., et al. 2012, Sol. Phys., 275, 261 [NASA ADS] [CrossRef] [Google Scholar]
  54. Xu, L., Ren, J. S. J., Liu, C., & Jia, J. 2014, in Proceedings of the 27th International Conference on Neural Information Processing Systems, NIPS’14 (Cambridge, MA: MIT Press), 1790 [Google Scholar]
  55. Yeo, K. L., Feller, A., Solanki, S. K., et al. 2014, A&A, 561, A22 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  56. Zhao, Y., Wang, R., Dong, W., et al. 2017, ArXiv e-prints [arXiv:1703.04244] [Google Scholar]

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