Open Access
Issue
A&A
Volume 668, December 2022
Article Number A28
Number of page(s) 21
Section Numerical methods and codes
DOI https://doi.org/10.1051/0004-6361/202243478
Published online 01 December 2022
  1. Astropy Collaboration (Price-Whelan, A. M., et al.) 2018, AJ, 156, 123 [Google Scholar]
  2. Alegre, L., Sabater, J., Best, R., et al. 2022, MNRAS, 516, 4716 [NASA ADS] [CrossRef] [Google Scholar]
  3. Alexander, R., & Leahy, J. P. 1987, MNRAS, 225, 1 [NASA ADS] [CrossRef] [Google Scholar]
  4. Banfield, J. K., Wong, O. I., Willett, K. W., et al. 2015, MNRAS, 453, 2326 [Google Scholar]
  5. Barkus, B., Croston, J. H., Piotrowska, J., et al. 2022, MNRAS, 509, 1 [Google Scholar]
  6. Belkin, M., Hsu, D., Ma, S., & Mandal, S. 2019, Proc. Natl. Acad. Sci. U.S.A., 116, 15849 [Google Scholar]
  7. Bonaldi, A., & Braun, R. 2018, ArXiv e-prints [arXiv: 1811.10454] [Google Scholar]
  8. Bonaldi, A., An, T., Brüggen, M., et al. 2021, MNRAS, 500, 3821 [Google Scholar]
  9. Braun, R., Bourke, T., Green, J. A., Keane, E., & Wagg, J. 2015, in Advancing Astrophysics with the Square Kilometre Array (AASKA14), 174 [Google Scholar]
  10. Dey, A., Schlegel, D. J., Lang, D., et al. 2019, AJ, 157, 168 [Google Scholar]
  11. Dollár, P., Appel, R., Belongie, S., & Perona, P. 2014, IEEE Trans. Pattern Anal. Mach. Intell., 36, 1532 [CrossRef] [Google Scholar]
  12. Dumoulin, V., & Visin, F. 2016, ArXiv e-prints [arXiv:1603.07285] [Google Scholar]
  13. Fan, D., Budavári, T., Norris, R. R., & Hopkins, A. M. 2015, MNRAS, 451, 1299 [NASA ADS] [CrossRef] [Google Scholar]
  14. Fanaroff, B. L., & Riley, J. M. 1974, MNRAS, 167, 31P [Google Scholar]
  15. Galvin, T. J., Huynh, M., Norris, R. R., et al. 2019, PASP, 131, 108009 [NASA ADS] [CrossRef] [Google Scholar]
  16. Galvin, T. J., Huynh, M. T., Norris, R. R., et al. 2020, MNRAS, 497, 2730 [NASA ADS] [CrossRef] [Google Scholar]
  17. Girshick, R. 2015, in Proceedings of the IEEE International Conference on Computer Vision, 1440 [Google Scholar]
  18. Goodfellow, I., Bengio, Y., & Courville, A. 2016, Deep Learning (MIT Press) [Google Scholar]
  19. Grobler, T. L., Nunhokee, C. D., Smirnov, O. M., van Zyl, A. J., & de Bruyn, A. G. 2014, MNRAS, 439, 4030 [CrossRef] [Google Scholar]
  20. Hancock, P. J., Murphy, T., Gaensler, B. M., Hopkins, A., & Curran, J. R. 2012, MNRAS, 422, 1812 [Google Scholar]
  21. Hancock, P. J., Trott, C. M., & Hurley-Walker, N. 2018, PASA, 35, e011 [Google Scholar]
  22. Hanson, S., & Pratt, L. 1988, in Proceedings of the Advances in Neural Information Processing Systems, 177 [Google Scholar]
  23. Hardcastle, M., & Croston, J. 2020, N. Astron. Rev., 88, 101539 [Google Scholar]
  24. Harwood, J. J., Hardcastle, M. J., Croston, J. H., & Goodger, J. L. 2013, MNRAS, 435, 3353 [NASA ADS] [CrossRef] [Google Scholar]
  25. He, K., Zhang, X., Ren, S., & Sun, J. 2016, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770 [Google Scholar]
  26. He, K., Gkioxari, G., Dollár, R., & Girshick, R. 2017, in Proceedings of the IEEE International Conference on Computer Vision, 2961 [Google Scholar]
  27. Heywood, I., Jarvis, M. J., Hale, C. L., et al. 2022, MNRAS, 509, 2150 [Google Scholar]
  28. Houlsby, N., Huszár, F., Ghahramani, Z., & Lengyel, M. 2011, ArXiv e-prints [arXiv: 1112.5745] [Google Scholar]
  29. Jarrett, T. H., Chester, T., Cutri, R., et al. 2000, AJ, 119, 2498 [Google Scholar]
  30. Kaiser, N., Burgett, W., Chambers, K., et al. 2010, in Ground-based and Airborne Telescopes III, Proc. SPIE, 7733, 159 [Google Scholar]
  31. Karpathy, A. 2015a, CS231n Convolutional Neural Networks for Visual Recognition, MIT course syllabus: https://cs231n.github.io/optimization-2/ [Google Scholar]
  32. Karpathy, A. 2015b, CS231n Convolutional Neural Networks for Visual Recognition, MIT course syllabus: https://cs231n.github.io/neural-networks-3/ [Google Scholar]
  33. Kondapally, R., Best, P. N., Hardcastle, M. J., et al. 2021, A&A, 648, A3 [EDP Sciences] [Google Scholar]
  34. Krizhevsky, A., Sutskever, I., & Hinton, G. E. 2012, in Advances in Neural Information Processing Systems, 25, 1097 [Google Scholar]
  35. Lin, T.-Y., Dollár, P., Girshick, R., et al. 2017, in Proceedings of the IEEE conference on computer vision and pattern recognition, 2117 [Google Scholar]
  36. Liu, L., Ouyang, W., Wang, X., et al. 2020, Int. J. Comput. Vis., 128, 261 [CrossRef] [Google Scholar]
  37. Lonsdale, C. J., Smith, H. E., Rowan-Robinson, M., et al. 2003, PASP, 115, 897 [Google Scholar]
  38. Marshall, P. J., Verma, A., More, A., et al. 2016, MNRAS, 455, 1171 [NASA ADS] [CrossRef] [Google Scholar]
  39. Martí-Vidal, I., & Marcaide, J. M. 2008, A&A, 480, 289 [CrossRef] [EDP Sciences] [Google Scholar]
  40. Meisner, A. M., Lang, D., & Schlegel, D. J. 2018, RNAAS, 2, 1 [Google Scholar]
  41. Miley, G. 1980, ARA&A, 18, 165 [Google Scholar]
  42. Mohan, N., & Rafferty, D. 2015, PyBDSF: Python Blob Detection and Source Finder, Astrophysics Source Code Library [record ascl:1107.013] [Google Scholar]
  43. Mostert, R. I. J., Duncan, K. J., Röttgering, H. J. A., et al. 2021, A&A, 645, A89 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  44. Murphy, K. P. 2012, Machine Learning: A Probabilistic Perspective (MIT press) [Google Scholar]
  45. Nakkiran, P., Kaplun, G., Bansal, Y., et al. 2021, J. Stat. Mech.: Theory Exp. 2021, 124003 [Google Scholar]
  46. Norris, R. P., Afonso, J., Appleton, P. N., et al. 2006, AJ, 132, 2409 [NASA ADS] [CrossRef] [Google Scholar]
  47. Northcutt, C. G., Athalye, A., & Mueller, J. 2021, ArXiv e-prints [arXiv: 2103.14749] [Google Scholar]
  48. Polsterer, K., Gieseke, F. C., Igel, C., Doser, B., & Gianniotis, N. 2016, ESANN 2016 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning [Google Scholar]
  49. Ralph, N. O., Norris, R. P., Fang, G., et al. 2019, PASP, 131, 108011 [Google Scholar]
  50. Ren, S., He, K., Girshick, R., & Sun, J. 2015, in Advances in Neural Information Processing Systems, 91 [Google Scholar]
  51. Robotham, A. S. G., Davies, L. J. M., Driver, S. P., et al. 2018, MNRAS, 476, 3137 [NASA ADS] [CrossRef] [Google Scholar]
  52. Settles, B. 2009, Active Learning Literature Survey, Tech. rep. 1648, University of Wisconsin-Madison, Department of Computer Sciences [Google Scholar]
  53. Shimwell, T. W., Röttgering, H. J. A., Best, P. N., et al. 2017, A&A, 598, A104 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  54. Shimwell, T. W., Hardcastle, M. J., Tasse, C., et al. 2022, A&A, 659, A1 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  55. Skrutskie, M. F., Cutri, R. M., Stiening, R., et al. 2006, AJ, 131, 1163 [NASA ADS] [CrossRef] [Google Scholar]
  56. Sun, C., Shrivastava, A., Singh, S., & Gupta, A. 2017, in Proceedings of the IEEE International Conference on Computer Vision, 843 [Google Scholar]
  57. Sutskever, I., Martens, J., Dahl, G., & Hinton, G. 2013, in International Conference on Machine Learning, PMLR, 1139 [Google Scholar]
  58. Uijlings, J. R., Van De Sande, K. E., Gevers, T., & Smeulders, A. W. 2013, Int. J. Comput. Vis., 104, 154 [CrossRef] [Google Scholar]
  59. van Haarlem, M. P., Wise, M. W., Gunst, A. W., et al. 2013, A&A, 556, A2 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  60. van Velzen, S., Falcke, H., & Körding, E. 2015, MNRAS, 446, 2985 [Google Scholar]
  61. Walmsley, M., Smith, L., Lintott, C., et al. 2020, MNRAS, 491, 1554 [Google Scholar]
  62. White, R. L., Becker, R. H., Helfand, D. J., & Gregg, M. D. 1997, ApJ, 475, 479 [Google Scholar]
  63. Willett, K. W., Lintott, C. J., Bamford, S. P., et al. 2013, MNRAS, 435, 2835 [Google Scholar]
  64. Williams, W. L., Hardcastle, M. J., Best, P. N., et al. 2019, A&A, 622, A2 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  65. Wilman, R. J., Miller, L., Jarvis, M. J., et al. 2008, MNRAS, 388, 1335 [NASA ADS] [Google Scholar]
  66. Wright, E. L., Eisenhardt, P. R. M., Mainzer, A. K., et al. 2010, AJ, 140, 1868 [Google Scholar]
  67. Wu, C., Wong, O. I., Rudnick, L., et al. 2019a, MNRAS, 482, 1211 [NASA ADS] [CrossRef] [Google Scholar]
  68. Wu, Y., Kirillov, A., Massa, F., Lo, W.-Y., & Girshick, R. 2019b, Detectron2, https://github.com/facebookresearch/detectron2 [Google Scholar]
  69. Xie, S., Girshick, R., Dollár, P., Tu, Z., & He, K. 2017, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1492 [Google Scholar]
  70. Zhang, C., Bengio, S., & Singer, Y. 2019, ArXiv e-prints [arXiv: 1902.01996] [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.