Free Access
Issue
A&A
Volume 624, April 2019
Article Number A102
Number of page(s) 17
Section Extragalactic astronomy
DOI https://doi.org/10.1051/0004-6361/201834575
Published online 18 April 2019
  1. Acquaviva, V., Gawiser, E., & Guaita, L. 2011, ApJ, 737, 47 [NASA ADS] [CrossRef] [Google Scholar]
  2. Alam, S., Albareti, F. D., Allende Prieto, C., et al. 2015, ApJS, 219, 12 [NASA ADS] [CrossRef] [Google Scholar]
  3. Alger, M. J., Banfield, J. K., Ong, C. S., et al. 2018, MNRAS, 478, 5556 [NASA ADS] [CrossRef] [Google Scholar]
  4. Baldry, I. K., Glazebrook, K., & Driver, S. P. 2008, MNRAS, 388, 945 [NASA ADS] [Google Scholar]
  5. Bell, E. F., & de Jong, R. S. 2001, ApJ, 550, 212 [NASA ADS] [CrossRef] [Google Scholar]
  6. Bertin, E., Mellier, Y., Radovich, M., et al. 2002, in Astronomical Data Analysis Software and Systems XI, eds. D. A. Bohlender, D. Durand, T. H. Handley, ASP Conf. Ser., 281, 228 [NASA ADS] [Google Scholar]
  7. Bilicki, M., Hoekstra, H., Brown, M. J. I., et al. 2018, A&A, 616, A69 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  8. Boquien, M., Burgarella, D., Roehlly, Y., et al. 2018, A&A, 622, A103 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  9. Breiman, L. 1996, Mach. Learn., 24, 123 [Google Scholar]
  10. Brinchmann, J., Charlot, S., White, S. D. M., et al. 2004, MNRAS, 351, 1151 [NASA ADS] [CrossRef] [Google Scholar]
  11. Bruzual, G., & Charlot, S. 2003, MNRAS, 344, 1000 [NASA ADS] [CrossRef] [Google Scholar]
  12. Camps, P., & Baes, M. 2015, Astron. Comput., 9, 20 [NASA ADS] [CrossRef] [Google Scholar]
  13. Camps, P., Trčka, A., Trayford, J., et al. 2018, ApJS, 234, 20 [NASA ADS] [CrossRef] [Google Scholar]
  14. Carnall, A. C., McLure, R. J., Dunlop, J. S., & Davé, R. 2018, MNRAS, 480, 4379 [NASA ADS] [CrossRef] [Google Scholar]
  15. Chabrier, G. 2003, Publ. Astron. Soc. Pac., 115, 763 [NASA ADS] [CrossRef] [Google Scholar]
  16. Chevallard, J., & Charlot, S. 2016, MNRAS, 462, 1415 [NASA ADS] [CrossRef] [Google Scholar]
  17. Chevallard, J., Charlot, S., Senchyna, P., et al. 2018, MNRAS, 479, 3264 [NASA ADS] [CrossRef] [Google Scholar]
  18. Chollet, F. 2017, ArXiv e-prints [arXiv:1610.02357] [Google Scholar]
  19. Conroy, C. 2013, ARA&A, 51, 393 [NASA ADS] [CrossRef] [Google Scholar]
  20. Cortese, L., Ciesla, L., Boselli, A., et al. 2012, A&A, 540, A52 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  21. da Cunha, E., Charlot, S., & Elbaz, D. 2008, MNRAS, 388, 1595 [NASA ADS] [CrossRef] [Google Scholar]
  22. Dai, J.-M., & Tong, J. 2018, ArXiv e-prints [arXiv:1807.10406] [Google Scholar]
  23. Deng, J., Dong, W., Socher, R., et al. 2009, Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on (IEEE), 248 [CrossRef] [Google Scholar]
  24. Dieleman, S., Willett, K. W., & Dambre, J. 2015, MNRAS, 450, 1441 [NASA ADS] [CrossRef] [Google Scholar]
  25. Dietterich, T. G. 2000, Mach. Learn., 40, 139 [CrossRef] [Google Scholar]
  26. Domínguez Sánchez, H., Huertas-Company, M., Bernardi, M., Tuccillo, D., & Fischer, J. L. 2018, MNRAS, 476, 3661 [NASA ADS] [CrossRef] [Google Scholar]
  27. Dozat, T. 2016, Incorporating Nesterov Momentum into Adam [Google Scholar]
  28. Friedman, J. H. 2001, Ann. stat., 1189 [CrossRef] [Google Scholar]
  29. Friedman, J. H. 2002, Comput. Stat. Data Anal., 38, 367 [CrossRef] [Google Scholar]
  30. Gallazzi, A., Charlot, S., Brinchmann, J., White, S. D. M., & Tremonti, C. A. 2005, MNRAS, 362, 41 [NASA ADS] [CrossRef] [Google Scholar]
  31. Glorot, X., & Bengio, Y. 2010, J. Mach. Learn. Res., 9, 249 [Google Scholar]
  32. Goodfellow, I., Bengio, Y., Courville, A., & Bengio, Y. 2016, Deep Learning (Cambridge: MIT press), 1 [Google Scholar]
  33. Hart, R. E., Bamford, S. P., Willett, K. W., et al. 2016, MNRAS, 461, 3663 [NASA ADS] [CrossRef] [Google Scholar]
  34. He, K., Zhang, X., Ren, S., & Sun, J. 2016, Proceedings of the IEEE conference on computer vision and pattern recognition, 770 [Google Scholar]
  35. Holmberg, E. 1958, Meddelanden fran Lunds Astronomiska Observatorium Serie II, 1 [Google Scholar]
  36. Hoo-Chang, S., Roth, H. R., Gao, M., et al. 2016, IEEE Trans. Med. Imaging, 35, 1285 [CrossRef] [Google Scholar]
  37. Hornik, K., Stinchcombe, M., & White, H. 1989, Neural Networks, 2, 359 [CrossRef] [Google Scholar]
  38. Hoyle, B. 2016, Astron. Comput., 16, 34 [NASA ADS] [CrossRef] [Google Scholar]
  39. Huertas-Company, M., Primack, J. R., Dekel, A., et al. 2018, ApJ, 858, 114 [NASA ADS] [CrossRef] [Google Scholar]
  40. Ioffe, S., & Szegedy, C. 2015, ArXiv e-prints [arXiv:1502.03167] [Google Scholar]
  41. Ivezić, Z, Kahn, S. M., Tyson, J. A., et al. 2019, ApJ, 873, 111 [NASA ADS] [CrossRef] [Google Scholar]
  42. Jahnke, K., Bongiorno, A., Brusa, M., et al. 2009, ApJ, 706, L215 [NASA ADS] [CrossRef] [Google Scholar]
  43. Joseph, R. D., & Wright, G. S. 1985, MNRAS, 214, 87 [NASA ADS] [Google Scholar]
  44. Ke, G., Meng, Q., Finley, T., et al. 2017, Adv. Neural Inf. Proc. Syst., 3149 [Google Scholar]
  45. Kingma, D. P., & Ba, J. 2014, ArXiv e-prints [arXiv:1412.6980] [Google Scholar]
  46. Kravtsov, A. V., Vikhlinin, A. A., & Meshcheryakov, A. V. 2018, Astron. Lett., 44, 8 [NASA ADS] [CrossRef] [Google Scholar]
  47. Kriek, M., van Dokkum, P. G., Labbé, I., et al. 2009, ApJ, 700, 221 [NASA ADS] [CrossRef] [Google Scholar]
  48. Krizhevsky, A., Sutskever, I., & Hinton, G. E. 2012, Adv. Proc. Syst. Neural Inf., 1097 [Google Scholar]
  49. Lara-López, M. A., Cepa, J., Bongiovanni, A., et al. 2010, A&A, 521, L53 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  50. Laureijs, R., Gondoin, P., Duvet, L., et al. 2012, in Space Telescopes and Instrumentation 2012: Optical, Infrared, and Millimeter Wave, International Society for Optics and Photonics, 8442, 84420T [CrossRef] [Google Scholar]
  51. LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. 1998, Proc. IEEE, 86, 2278 [CrossRef] [Google Scholar]
  52. Leja, J., Johnson, B. D., Conroy, C., van Dokkum, P. G., & Byler, N. 2017, ApJ, 837, 170 [NASA ADS] [CrossRef] [Google Scholar]
  53. Makarov, D., Prugniel, P., Terekhova, N., Courtois, H., & Vauglin, I. 2014, A&A, 570, A13 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  54. Mannucci, F., Cresci, G., Maiolino, R., Marconi, A., & Gnerucci, A. 2010, MNRAS, 408, 2115 [NASA ADS] [CrossRef] [Google Scholar]
  55. Maraston, C. 2005, MNRAS, 362, 799 [NASA ADS] [CrossRef] [Google Scholar]
  56. Marmanis, D., Datcu, M., Esch, T., & Stilla, U. 2016, IEEE Geosci. Remote Sens. Lett., 13, 105 [NASA ADS] [CrossRef] [Google Scholar]
  57. Mason, L., Baxter, J., Bartlett, P. L., & Frean, M. R. 2000, Adv. Proc. Syst. Neural Inf., 512 [Google Scholar]
  58. McGaugh, S. S., & Schombert, J. M. 2014, AJ, 148, 77 [NASA ADS] [CrossRef] [Google Scholar]
  59. Meidt, S. E., Schinnerer, E., Van De Ven, G., et al. 2014, ApJ, 788, 144 [NASA ADS] [CrossRef] [Google Scholar]
  60. Nair, V., & Hinton, G. E. 2010, Proc. 27th Int. Conf. Mach. Learn. (ICML-10), 807 [Google Scholar]
  61. Narula, S. C., & Wellington, J. F. 1982, Int. Stat. Rev., 317 [CrossRef] [Google Scholar]
  62. Noll, S., Burgarella, D., Giovannoli, E., et al. 2009, A&A, 507, 1793 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  63. Ntampaka, M., Eisenstein, D., ZuHone, J., et al. 2018, ApJ, submitted [arXiv:1810.07703] [Google Scholar]
  64. Opitz, D., & Maclin, R. 1999, J. Artif. Intell. Res., 11, 169 [CrossRef] [Google Scholar]
  65. Pasquet, J., Bertin, E., Treyer, M., Arnouts, S., & Fouchez, D. 2018, A&A, 621, A26 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  66. Pillepich, A., Nelson, D., Hernquist, L., et al. 2018, MNRAS, 475, 648 [NASA ADS] [CrossRef] [Google Scholar]
  67. Raschka, S. 2015, Python Machine Learning (Packt Publishing Ltd) [Google Scholar]
  68. Rhoads, J. E. 1998, AJ, 115, 472 [NASA ADS] [CrossRef] [Google Scholar]
  69. Rix, H.-W., & Rieke, M. J. 1993, ApJ, 418, 123 [NASA ADS] [CrossRef] [Google Scholar]
  70. Roberts, M. S., & Haynes, M. P. 1994, ARA&A, 32, 115 [NASA ADS] [CrossRef] [Google Scholar]
  71. Salim, S., Lee, J. C., Janowiecki, S., et al. 2016, ApJS, 227, 2 [NASA ADS] [CrossRef] [Google Scholar]
  72. Salim, S., Boquien, M., & Lee, J. C. 2018, ApJ, 859, 11 [NASA ADS] [CrossRef] [Google Scholar]
  73. Salpeter, E. E. 1955, ApJ, 121, 161 [NASA ADS] [CrossRef] [Google Scholar]
  74. Sancisi, R., Fraternali, F., Oosterloo, T., & Van Der Hulst, T. 2008, A&ARv, 15, 189 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  75. Schaye, J., Crain, R. A., Bower, R. G., et al. 2015, MNRAS, 446, 521 [NASA ADS] [CrossRef] [Google Scholar]
  76. Simard, P. Y., Steinkraus, D., & Platt, J. C. 2003, Proceedings of the Seventh International Conference on Document Analysis and Recognition (IEEE), 958 [CrossRef] [Google Scholar]
  77. Simonyan, K., & Zisserman, A. 2014, ArXiv e-prints [arXiv:1409.1556] [Google Scholar]
  78. Somerville, R. S., & Davé, R. 2015, ARA&A, 53, 51 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  79. Sorba, R., & Sawicki, M. 2015, MNRAS, 452, 235 [NASA ADS] [CrossRef] [Google Scholar]
  80. Sorba, R., & Sawicki, M. 2018, MNRAS, 476, 1532 [NASA ADS] [CrossRef] [Google Scholar]
  81. Spergel, D., Gehrels, N., Baltay, C., et al. 2015, ArXiv e-prints [arXiv:1503.03757] [Google Scholar]
  82. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. 2014, J. Mach. Learn. Res., 15, 1929 [Google Scholar]
  83. Strobl, C., Boulesteix, A.-L., Zeileis, A., & Hothorn, T. 2007, BMC Bioinf., 8, 25 [CrossRef] [Google Scholar]
  84. Szegedy, C., Liu, W., Jia, Y., et al. 2015, ArXiv e-prints [arXiv:1409.4842] [Google Scholar]
  85. Tremonti, C. A., Heckman, T. M., Kauffmann, G., et al. 2004, ApJ, 613, 898 [NASA ADS] [CrossRef] [Google Scholar]
  86. Vafaei Sadr, A., Vos, E. E., Bassett, B. A., et al. 2019, MNRAS, 484, 2793 [NASA ADS] [CrossRef] [Google Scholar]
  87. Walcher, J., Groves, B., Budavári, T., & Dale, D. 2011, Ap&SS, 331, 1 [NASA ADS] [CrossRef] [Google Scholar]
  88. Willett, K. W., Lintott, C. J., Bamford, S. P., et al. 2013, MNRAS, 435, 2835 [NASA ADS] [CrossRef] [Google Scholar]
  89. Zibetti, S., Charlot, S., & Rix, H.-W. 2009, MNRAS, 400, 1181 [NASA ADS] [CrossRef] [Google Scholar]
  90. Zoph, B., & Le, Q. V. 2016, ArXiv e-prints [arXiv:1611.01578] [Google Scholar]

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