Free Access
Volume 614, June 2018
Article Number A5
Number of page(s) 13
Section The Sun
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 [Google Scholar]
  12. Couvidat, S., Schou, J., Hoeksema, J. T., et al. 2016, Sol. Phys., 291, 1887 [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 [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] [PubMed] [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 [Google Scholar]
  41. Schmidhuber, J. 2015, Neural Networks, 61, 85 [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 [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 [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]

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.