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Home All issues Volume 653 (September 2021) A&A, 653 (2021) A22 References
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Issue
A&A
Volume 653, September 2021
Article Number A22
Number of page(s) 16
Section Numerical methods and codes
DOI https://doi.org/10.1051/0004-6361/202140369
Published online 03 September 2021
  1. Ackermann, S., Schawinski, K., Zhang, C., Weigel, A. K., & Turp, M. D. 2018, MNRAS, 479, 415 [NASA ADS] [CrossRef] [Google Scholar]
  2. Agatonovic-Kustrin, S., & Beresford, R. 2000, J. Pharm. Biomed. Anal., 22, 717 [Google Scholar]
  3. Amacker, J., Balunas, W., Beresford, L., et al. 2020, J. High Energy Phys., 2020, 115 [Google Scholar]
  4. Bailer-Jones, C. A. L., Fouesneau, M., & Andrae, R. 2019, MNRAS, 490, 5615 [CrossRef] [Google Scholar]
  5. Bellm, E. C., Kulkarni, S. R., Graham, M. J., et al. 2019, PASP, 131, 018002 [NASA ADS] [CrossRef] [Google Scholar]
  6. Boughorbel, S., Jarray, F., & El-Anbari, M. 2017, PLoS ONE, 12, e0177678 [NASA ADS] [CrossRef] [Google Scholar]
  7. Breiman, L. 2001, Mach. Learn., 45, 5 [CrossRef] [Google Scholar]
  8. Byra, M., Wu, M., Zhang, X., et al. 2020, Magn. Reson. Med., 83, 1109 [Google Scholar]
  9. Chambers, K. C., Magnier, E. A., Metcalfe, N., et al. 2016, ArXiv e-prints [arXiv:1612.05560] [Google Scholar]
  10. Chawla, N., Bowyer, K., Hall, L., & Kegelmeyer, W. 2002, J. Artif. Intell. Res. (JAIR), 16, 321 [Google Scholar]
  11. Chicco, D. 2017, BioData Mining, 10, 35 [Google Scholar]
  12. Chicco, D., & Jurman, G. 2020, BMC Genom., 21, 6 [Google Scholar]
  13. Cowan, J., Tesauro, G., & Alspector, J. 1994, Advances in Neural Information Processing Systems 6 (Morgan Kaufmann) [Google Scholar]
  14. Di, S., Zhang, H., Li, C., et al. 2018, IEEE Trans. Intell. Transp. Syst., 19, 745 [Google Scholar]
  15. D’Isanto, A., & Polsterer, K. L. 2018, A&A, 609, A111 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  16. Domínguez Sánchez, H., Huertas-Company, M., Bernardi, M., et al. 2019, MNRAS, 484, 93 [NASA ADS] [CrossRef] [Google Scholar]
  17. Dubath, P., Rimoldini, L., Süveges, M., et al. 2011, MNRAS, 414, 2602 [NASA ADS] [CrossRef] [Google Scholar]
  18. Duchi, J., Hazan, E., & Singer, Y. 2011, J. Mach. Learn. Res., 12, 2121 [Google Scholar]
  19. Duev, D. A., Mahabal, A., Ye, Q., et al. 2019, MNRAS, 486, 4158 [CrossRef] [Google Scholar]
  20. Ellaway, P. 1978, Electroencephalogr. Clin. Neurophysiol., 45, 302 [Google Scholar]
  21. Elorrieta, F., Eyheramendy, S., Jordán, A., et al. 2016, A&A, 595, A82 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  22. Fernandes, J. A., Irigoien, X., Goikoetxea, N., et al. 2010, Ecol. Model., 221, 338 [Google Scholar]
  23. Gaia Collaboration (Prusti, T., et al.) 2016, A&A, 595, A1 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  24. Goodfellow, I., Pouget-Abadie, J., Mirza, M., et al. 2014, in Advances in Neural Information Processing Systems 27, eds. Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, & K. Q. Weinberger (Curran Associates, Inc.), 2672 [Google Scholar]
  25. Gorodkin, J. 2004, Comput. Biol. Chem., 28, 367 [Google Scholar]
  26. Graczyk, D., Soszyński, I., Poleski, R., et al. 2011, Acta Astron., 61, 103 [Google Scholar]
  27. Graves, A., Mohamed, A. R., & Hinton, G. 2013, ArXiv e-prints [arXiv:1303.5778] [Google Scholar]
  28. Grison, P. 1994, A&A, 289, 404 [NASA ADS] [Google Scholar]
  29. He, H., Bai, Y., Garcia, E. A., & Li, S. 2008, IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), 1322 [Google Scholar]
  30. Hinton, G., Deng, l., Yu, D., et al. 2012, Sign. Process. Mag. IEEE, 29, 82 [Google Scholar]
  31. Hinton, G., Vinyals, O., & Dean, J. 2015, ArXiv e-prints [arXiv:1503.02531] [Google Scholar]
  32. Hon, M., Stello, D., & Yu, J. 2017, MNRAS, 469, 4578 [NASA ADS] [CrossRef] [Google Scholar]
  33. Hosenie, Z., Lyon, R. J., Stappers, B. W., & Mootoovaloo, A. 2019, MNRAS, 488, 4858 [NASA ADS] [CrossRef] [Google Scholar]
  34. Hu, G., Zhang, Y., & Yang, Q. 2019, ArXiv e-prints [arXiv:1901.07199] [Google Scholar]
  35. Ioffe, S., & Szegedy, C. 2015, ArXiv e-prints [arXiv:1502.03167] [Google Scholar]
  36. Jayasinghe, T., Kochanek, C. S., Stanek, K. Z., et al. 2018, MNRAS, 477, 3145 [Google Scholar]
  37. Jayasinghe, T., Stanek, K. Z., Kochanek, C. S., et al. 2019, MNRAS, 485, 961 [NASA ADS] [CrossRef] [Google Scholar]
  38. Kains, N., Calamida, A., Rejkuba, M., et al. 2019, MNRAS, 482, 3058 [NASA ADS] [Google Scholar]
  39. Keller, S. C., Schmidt, B. P., Bessell, M. S., et al. 2007, PASA, 24, 1 [NASA ADS] [CrossRef] [Google Scholar]
  40. Kim, D.-W., & Bailer-Jones, C. A. L. 2016, A&A, 587, A18 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  41. Kim, E. J., & Brunner, R. J. 2017, MNRAS, 464, 4463 [NASA ADS] [CrossRef] [Google Scholar]
  42. Kim, D.-W., Protopapas, P., Byun, Y.-I., et al. 2011, ApJ, 735, 68 [NASA ADS] [CrossRef] [Google Scholar]
  43. Kim, D.-W., Protopapas, P., Trichas, M., et al. 2012, ApJ, 747, 107 [NASA ADS] [CrossRef] [Google Scholar]
  44. Kim, D.-W., Protopapas, P., Bailer-Jones, C. A. L., et al. 2014, A&A, 566, A43 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  45. Kingma, D. P., & Ba, J. 2014, ArXiv e-prints [arXiv:1412.6980] [Google Scholar]
  46. Krizhevsky, A., Sutskever, I., & Hinton, G. E. 2012, Commun. ACM, 60, 84 [Google Scholar]
  47. Lanusse, F., Ma, Q., Li, N., et al. 2018, MNRAS, 473, 3895 [NASA ADS] [CrossRef] [Google Scholar]
  48. Lecun, Y., Bengio, Y., & Hinton, G. 2015, Nature, 521, 436 [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
  49. Li, R., Zhao, Z., Chen, X., Palicot, J., & Zhang, H. 2014, IEEE Trans. Wirel. Commun., 13, 2000 [Google Scholar]
  50. Lieu, M., Conversi, L., Altieri, B., & Carry, B. 2019, MNRAS, 485, 5831 [CrossRef] [Google Scholar]
  51. Lomb, N. R. 1976, Ap&SS, 39, 447 [Google Scholar]
  52. Long, J. P., El Karoui, N., Rice, J. A., Richards, J. W., & Bloom, J. S. 2012, PASP, 124, 280 [NASA ADS] [CrossRef] [Google Scholar]
  53. Lu, C., Hu, F., Cao, D., et al. 2019, IEEE Trans. Intell. Transp. Syst., 1 [Google Scholar]
  54. Lundberg, S. M., & Lee, S. I. 2017, in Advances in Neural Information Processing Systems 30, eds. I. Guyon, U. V. Luxburg, S. Bengio, et al. (Curran Associates, Inc.), 4765 [Google Scholar]
  55. Maqsood, M., Nazir, F., Khan, U., et al. 2019, Sensors (Basel), 19, 2645 [NASA ADS] [CrossRef] [Google Scholar]
  56. Matthews, B. 1975, Biochim. Biophys. Acta (BBA) – Protein Struct., 405, 442 [Google Scholar]
  57. Nair, V., & Hinton, G. E. 2010, Proceedings of the 27th International Conference on Machine Learning (ICML-10), 807 [Google Scholar]
  58. Pan, S. J., & Yang, Q. 2010, IEEE Trans. Knowl. Data Eng., 22, 1345 [Google Scholar]
  59. Pan, W., Xiang, E. W., & Yang, Q. 2012, Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, AAAI’12 (AAAI Press), 662 [Google Scholar]
  60. Pearson, K. A., Palafox, L., & Griffith, C. A. 2018, MNRAS, 474, 478 [NASA ADS] [CrossRef] [Google Scholar]
  61. Petegrosso, R., Park, S., Hwang, T. H., & Kuang, R. 2016, Bioinformatics, 33, 529 [Google Scholar]
  62. Pham, C., Pham, V., & Dang, T. 2019, 2019 IEEE International Conference on Big Data (Big Data), 5844 [CrossRef] [Google Scholar]
  63. Pojmanski, G. 1997, Acta Astron., 47, 467 [Google Scholar]
  64. Poleski, R., Soszyñski, I., Udalski, A., et al. 2010, Acta Astron., 60, 1 [NASA ADS] [Google Scholar]
  65. Powers, D. M. W. 2011, J. Mach. Learn. Technol., 2, 37 [NASA ADS] [Google Scholar]
  66. Scillitoe, A., Seshadri, P., & Girolami, M. 2021, J. Comput. Phys., 430, 110116 [NASA ADS] [CrossRef] [Google Scholar]
  67. Shapiro, S. S., & Wilk, M. B. 1965, Biometrika, 52, 591 [Google Scholar]
  68. Shapley, L. S. 1953, Contributions to the Theory of Games (AM-28) (Princeton: Princeton University Press), 2 [Google Scholar]
  69. Shappee, B. J., Prieto, J. L., Grupe, D., et al. 2014, ApJ, 788, 48 [NASA ADS] [CrossRef] [Google Scholar]
  70. Shin, H. C., Roth, H. R., Gao, M., et al. 2016, ArXiv e-prints [arXiv:1602.03409] [Google Scholar]
  71. Silver, D., Huang, A., Maddison, C. J., et al. 2016, Nature, 529, 484 [CrossRef] [PubMed] [Google Scholar]
  72. Smith, L. N. 2015, ArXiv e-prints [arXiv:1506.01186] [Google Scholar]
  73. Soszynski, I., Poleski, R., Udalski, A., et al. 2008, Acta Astron., 58, 163 [NASA ADS] [Google Scholar]
  74. Soszyñski, I., Udalski, A., Szymañski, M. K., et al. 2009, Acta Astron., 59, 239 [Google Scholar]
  75. Soszyński, I., Udalski, A., Szymański, M. K., et al. 2008, Acta Astron., 58, 293 [NASA ADS] [Google Scholar]
  76. Soszyński, I., Udalski, A., Szymański, M. K., et al. 2009, Acta Astron., 59, 1 [NASA ADS] [Google Scholar]
  77. Stetson, P. B. 1996, PASP, 108, 851 [NASA ADS] [CrossRef] [Google Scholar]
  78. Szegedy, C., Liu, W., Jia, Y., et al. 2015, Computer Vision and Pattern Recognition (CVPR) (IEEE) [Google Scholar]
  79. Tang, H., Scaife, A. M. M., & Leahy, J. P. 2019, MNRAS, 488, 3358 [Google Scholar]
  80. Tisserand, P., Le Guillou, L., Afonso, C., et al. 2007, A&A, 469, 387 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  81. Udalski, A., Kubiak, M., & Szymanski, M. 1997, Acta Astron., 47, 319 [NASA ADS] [Google Scholar]
  82. Vafaei Sadr, A., Vos, E. E., Bassett, B. A., et al. 2019, MNRAS, 484, 2793 [NASA ADS] [CrossRef] [Google Scholar]
  83. Valverde-Albacete, F. J., & Peláez-Moreno, C. 2014, PLoS ONE, 9, e84217 [NASA ADS] [CrossRef] [Google Scholar]
  84. von Neumann, J. 1941, Ann. Math. Stat., 12, 367 [Google Scholar]
  85. Wang, J., Zheng, H., Huang, Y., & Ding, X. 2018, IEEE Trans. Intell. Transp. Syst., 19, 2913 [CrossRef] [Google Scholar]
  86. Xu, Q., Xiang, E. W., & Yang, Q. 2010, IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 62 [CrossRef] [Google Scholar]
  87. Yeo, D., Bae, J., Kim, N., et al. 2018, 2018 25th IEEE International Conference on Image Processing (ICIP), 674 [CrossRef] [Google Scholar]
  88. Yim, J., Joo, D., Bae, J., & Kim, J. 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 7130 [CrossRef] [Google Scholar]
  89. Zeiler, M. D. 2012, ArXiv e-prints [arXiv:1212.5701] [Google Scholar]
  90. Zhao, Q., & Grace, D. 2014, 1st International Conference on 5G for Ubiquitous Connectivity, 152 [Google Scholar]

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