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Home All issues Volume 643 (November 2020) A&A, 643 (2020) A122 References
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Issue
A&A
Volume 643, November 2020
Article Number A122
Number of page(s) 14
Section Catalogs and data
DOI https://doi.org/10.1051/0004-6361/201936090
Published online 11 November 2020
  1. Abadi, M., Agarwal, A., Barham, P., et al. 2015, TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems, Software available from tensorflow.org [Google Scholar]
  2. Alger, M. J., Banfield, J. K., Ong, C. S., et al. 2018, MNRAS, 478, 5547 [NASA ADS] [CrossRef] [Google Scholar]
  3. Alhassan, W., Taylor, A. R., & Vaccari, M. 2018, MNRAS, 480, 2085 [NASA ADS] [CrossRef] [Google Scholar]
  4. Aniyan, A. K., & Thorat, K. 2017, ApJS, 230, 20 [NASA ADS] [CrossRef] [Google Scholar]
  5. Bonnarel, F., Fernique, P., Bienaymé, O., et al. 2000, A&AS, 143, 33 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  6. Bromová, P., Škoda, P., & Vážný, J. 2014, Int. J. Autom. Comput., 11, 265 [CrossRef] [Google Scholar]
  7. Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. 2002, J. Artif. Intell. Res., 16, 321 [CrossRef] [Google Scholar]
  8. Chollet, F. 2015, Keras, https://keras.io Accessed: 2 July 2020 [Google Scholar]
  9. de la Calleja, J., Benitez, A., Medina, M. A., & Fuentes, O. 2011, 2011 International Conference of Soft Computing and Pattern Recognition, 435 [CrossRef] [Google Scholar]
  10. Demleitner, M., Neves, M. C., Rothmaier, F., & Wambsganss, J. 2014, Astron. Comput., 7–8, 27 [NASA ADS] [CrossRef] [Google Scholar]
  11. Domínguez Sánchez, H., Huertas-Company, M., Bernardi, M., Tuccillo, D., & Fischer, J. L. 2018, MNRAS, 476, 3661 [NASA ADS] [CrossRef] [Google Scholar]
  12. Fernique, P., Allen, M. G., Boch, T., et al. 2015, A&A, 578, A114 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  13. George, D., & Huerta, E. A. 2018, Phys. Rev. D, 97, 044039 [CrossRef] [Google Scholar]
  14. Glorot, X., Bordes, A., & Bengio, Y. 2011, in 28th International Conference on Machine Learning, eds. L. Getoor, & T. Scheffer (New York, USA: ACM), 513 [Google Scholar]
  15. Goodfellow, I., Bengio, Y., & Courville, A. 2016, Deep Learning (MIT Press), http://www.deeplearningbook.org, Accessed: 2 July 2020 [Google Scholar]
  16. Gray, R. O., & Corbally, C. J. 2009, Stellar Spectral Classification (Princeton University Press) [Google Scholar]
  17. Gupta, K. D., Pampana, R., Vilalta, R., Ishida, E. E. O., & de Souza, R. S. 2016, in 2016 IEEE Symposium Series on Computational Intelligence (SSCI), Athens, 1 [Google Scholar]
  18. Hanuschik, R. W., Kozok, J. R., & Kaiser, D. 1988, A&A, 189, 147 [NASA ADS] [Google Scholar]
  19. Heiter, U. 2014, Air-to-vacuum conversion, http://www.astro.uu.se/valdwiki/Air-to-vacuum20conversion, Accessed: 2 July 2020 [Google Scholar]
  20. Hou, W., Luo, A.-L., Hu, J.-Y., et al. 2016, Res. Astron. Astrophys., 16, 138 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  21. Ishida, E. E. O., Beck, R., González-Gaitán, S., et al. 2019a, MNRAS, 483, 2 [NASA ADS] [CrossRef] [Google Scholar]
  22. Ishida, E. E. O., Kornilov, M. V., Malanchev, K. L., et al. 2019b, Active Anomaly Detection for Time-domain Discoveries [Google Scholar]
  23. Kang, W., & Lee, S.-G. 2012, MNRAS, 425, 3162 [Google Scholar]
  24. Kingma, D. P., & Ba, J. 2015, in 3rd International Conference on Learning Representations, eds. Y. Bengio, & Y. LeCun [Google Scholar]
  25. Kogure, T., & Leung, K. C. 2007, in The Astrophysics of Emission-Line Stars (Springer), Astrophys. Space Sci. Libr., 342 [CrossRef] [Google Scholar]
  26. Krizhevsky, A., Sutskever, I., & Hinton, G. E. 2012, in Advances in Neural Information Processing Systems, eds. F. Pereira, C. J. C. Burges, L. Bottou, & K. Q. Weinberger (Curran Associates, Inc.), 25, 1097 [Google Scholar]
  27. Kurosawa, R., Harries, T. J., & Symington, N. H. 2006, MNRAS, 370, 580 [NASA ADS] [CrossRef] [Google Scholar]
  28. LeCun, Y., Boser, B., Denker, J. S., et al. 1989, Neural Comput., 1, 541 [CrossRef] [Google Scholar]
  29. LeCun, Y., Bengio, Y., & Hinton, G. 2015, Nature, 521, 436 [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
  30. Lee, Y. S., Beers, T. C., Sivarani, T., et al. 2008, AJ, 136, 2022 [NASA ADS] [CrossRef] [Google Scholar]
  31. Lee, Y. S., Beers, T. C., Carlin, J. L., et al. 2015, AJ, 150, 187 [NASA ADS] [CrossRef] [Google Scholar]
  32. Lin, C.-C., Hou, J.-L., Chen, L., et al. 2015, Res. Astron. Astrophys., 15, 1325 [CrossRef] [Google Scholar]
  33. Liu, P., Zhang, H., & Eom, K. B. 2017, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 10, 712 [CrossRef] [Google Scholar]
  34. Luo, A.-L., Zhao, Y.-H., Zhao, G., et al. 2015, Res. Astron. Astrophys., 15, 1095 [NASA ADS] [CrossRef] [Google Scholar]
  35. Luo, A. L., Zhao, Y. H., Zhao, G., et al. 2016, VizieR Online Data Catalog: V/149 [Google Scholar]
  36. Lyon, R. J., Stappers, B. W., Cooper, S., Brooke, J. M., & Knowles, J. D. 2016, MNRAS, 459, 1104 [NASA ADS] [CrossRef] [Google Scholar]
  37. Nandrekar-Heinis, D., Michel, L., Louys, M., & Bonnarel, F. 2014, Astron. Comput., 7–8, 37 [CrossRef] [Google Scholar]
  38. Pan, S. J., & Yang, Q. 2010, IEEE Trans. Knowl. Data Eng., 22, 1345 [Google Scholar]
  39. Parimucha, V., & Škoda, P. 2006, in Binary Stars as Critical Tools& Tests in Contemporary Astrophysics, eds. W. I. Hartkopf, E. F. Guinan, & P. Harmanec (Cambridge University Press), Proc. IAU Symp., 240, 486 [Google Scholar]
  40. Podsztavek, O. 2017, Bachelors Thesis, Czech Technical University in Prague, Faculty of Information Technology, https://dspace.cvut.cz/handle/10467/69666?locale-attribute=en, Accessed: 2 July 2020 [Google Scholar]
  41. Porter, J. M., & Rivinius, T. 2003, PASP, 115, 1153 [Google Scholar]
  42. Prati, R. C., Batista, G. E., & Monard, M. C. 2009, 4th Indian International Conference on Artificial Intelligence, 359 [Google Scholar]
  43. Rastgoo, M., Lemaitre, G., Massich, J., et al. 2016, 3rd International Conference on Bioimaging [Google Scholar]
  44. Reipurth, B., Pedrosa, A., & Lago, M. T. V. T. 1996, A&A, 120, 229 [Google Scholar]
  45. Richards, J. W., Starr, D. L., Brink, H., et al. 2012, ApJ, 744, 192 [NASA ADS] [CrossRef] [Google Scholar]
  46. Russakovsky, O., Deng, J., Su, H., et al. 2015, Int. J. Comput. Vision, 115, 211 [Google Scholar]
  47. Settles, B. 2009, Active Learning Literature Survet, Computer Sciences Technical Report 1648 (Madison: University of Wisconsin) [Google Scholar]
  48. Silaj, J., Jones, C. E., Tycner, C., Sigut, T. A. A., & Smith, A. D. 2010, ApJS, 187, 228 [NASA ADS] [CrossRef] [Google Scholar]
  49. Simonyan, K., & Zisserman, A. 2015, in 3rd International Conference on Learning Representations, eds. Y. Bengio, & Y. LeCun [Google Scholar]
  50. Škoda, P. 2019, https://doi.org/10.5281/zenodo.3242658 [Google Scholar]
  51. Škoda, P., & Šlechta, M. 2002, Publ. Astron. Inst. Czech. Acad. Sci., 90, 22 [Google Scholar]
  52. Škoda, P., & Vážný, J. 2012, ASP Conf. Ser., 461, 573 [Google Scholar]
  53. Škoda, P., Draper, P. W., Neves, M. C., Andrešič, D., & Jenness, T. 2014, Astron. Comput., 7–8, 108 [Google Scholar]
  54. Škoda, P., Bromová, P., Lopatovský, L., Palička, A., & Vážný, J. 2015, ASP Conf. Ser., 495, 87 [Google Scholar]
  55. Škoda, P., Palička, A., Koza, J., & Shakurova, K. 2016, Proc. IAU Symp., 325, 180 [Google Scholar]
  56. Škoda, P., Podsztavek, O., & Tvrdík, P. 2019, https://doi.org/10.5281/zenodo.3241520 [Google Scholar]
  57. Solorio, T., Fuentes, O., Terlevich, R., & Terlevich, E. 2005, MNRAS, 363, 543 [CrossRef] [Google Scholar]
  58. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. 2014, J. Mach. Learn. Res., 15, 1929 [Google Scholar]
  59. Taigman, Y., Polyak, A., & Wolf, L. 2017, 5th International Conference on Learning Representations OpenReview.net [Google Scholar]
  60. Taylor, M. B. 2005, ASP Conf. Ser., 347, 29 [Google Scholar]
  61. Taylor, M. B., Boch, T., & Taylor, J. 2015, Astron. Comput., 11, 81 [CrossRef] [Google Scholar]
  62. Tody, D., Dolensky, M., McDowell, J., et al. 2012, Simple Spectral Access Protocol Version 1.1, IVOA Recommendation 10 February 2012 [Google Scholar]
  63. Traven, G., Zwitter, T., Van Eck, S., et al. 2015, A&A, 581, A52 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  64. van der Marel, R. P., & Guhathakurta, P. 2008, ApJ, 678, 187 [NASA ADS] [CrossRef] [Google Scholar]
  65. Vilalta, R., Gupta, K. D., Boumber, D., & Meskhi, M. M. 2019, PASP, 131 [Google Scholar]
  66. Walborn, N. R., & Fitzpatrick, E. L. 2000, PASP, 112, 50 [NASA ADS] [CrossRef] [Google Scholar]
  67. Waters, C. Z., & Hollek, J. K. 2013, PASP, 125, 1164 [NASA ADS] [CrossRef] [Google Scholar]
  68. Wu, Y., Luo, A.-L., Li, H.-N., et al. 2011, Res. Astron. Astrophys., 11, 924 [NASA ADS] [CrossRef] [Google Scholar]
  69. Yang, L., MacEachren, A., Mitra, P., & Onorati, T. 2018, ISPRS Int. J. Geo-Inf., 7 [Google Scholar]
  70. Zeiler, M. D., & Fergus, R. 2014, in Computer Vision - ECCV 2014, eds. D. Fleet, T. Pajdla, B. Schiele, & T. Tuytelaars (Cham: Springer International Publishing), 818 [CrossRef] [Google Scholar]
  71. Zheng, Z., Carlin, J. L., Beers, T. C., et al. 2014, ApJ, 785, L23 [NASA ADS] [CrossRef] [Google Scholar]
  72. Zickgraf, F.-J. 2003, A&A, 408, 257 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]

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