Open Access
Issue |
A&A
Volume 662, June 2022
|
|
---|---|---|
Article Number | A36 | |
Number of page(s) | 28 | |
Section | Numerical methods and codes | |
DOI | https://doi.org/10.1051/0004-6361/202142751 | |
Published online | 14 June 2022 |
- Alam, S., Albareti, F. D., Allende Prieto, C., et al. 2015, ApJS, 219, 12 [Google Scholar]
- Alarcon, A., Sánchez, C., Bernstein, G. M., & Gaztañaga, E. 2020, MNRAS, 498, 2614 [NASA ADS] [CrossRef] [Google Scholar]
- Alibert, Y., & Venturini, J. 2019, A&A, 626, A21 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Ansari, Z., Agnello, A., & Gall, C. 2021, A&A, 650, A90 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Arjovsky, M., & Bottou, L. 2017, ArXiv e-prints [arXiv:1701.04862] [Google Scholar]
- Armitage, T.J., Kay, S.T., & Barnes, D.J. 2019, MNRAS, 484, 1526 [NASA ADS] [CrossRef] [Google Scholar]
- Arnouts, S., Cristiani, S., Moscardini, L., et al. 1999, MNRAS, 310, 540 [Google Scholar]
- Baldry, I. K., Liske, J., Brown, M. J. I., et al. 2018, MNRAS, 474, 3875 [Google Scholar]
- Beck, R., Dobos, L., Budavári, T., Szalay, A. S., & Csabai, I. 2016, MNRAS, 460, 1371 [Google Scholar]
- Bengio, Y., Courville, A. C., & Vincent, P. 2013, IEEE Trans. Pattern Analy. Mach. Intell., 35, 1798 [CrossRef] [Google Scholar]
- Bhagyashree, Kushwaha, V., & Nandi, G.C. 2020, in 2020 IEEE 4th Conference on Information Communication Technology (CICT), 1–6 [Google Scholar]
- Bonjean, V., Aghanim, N., Salomé, P., et al. 2019, A&A, 622, A137 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Bonnett, C. 2015, MNRAS, 449, 1043 [NASA ADS] [CrossRef] [Google Scholar]
- Bradshaw, E. J., Almaini, O., Hartley, W. G., et al. 2013, MNRAS, 433, 194 Article number, page 18 of 28 [NASA ADS] [CrossRef] [Google Scholar]
- Buchs, R., Davis, C., Gruen, D., et al. 2019, MNRAS, 489, 820 [Google Scholar]
- Buda, M., Maki, A., & Mazurowski, M. A. 2018, Neural Netw., 106, 249 [CrossRef] [Google Scholar]
- Burhanudin, U. F., Maund, J. R., Killestein, T., et al. 2021, MNRAS, 505, 4345 [NASA ADS] [CrossRef] [Google Scholar]
- Cao, K., Wei, C., Gaidon, A., Arechiga, N., & Ma, T. 2019, in Advances in Neural Information Processing Systems, eds. H. Wallach, H. Larochelle, A. Beygelzimer, et al. (USA: Curran Associates, Inc.), 32 [Google Scholar]
- Carrasco Kind, M., & Brunner, R. J. 2013, MNRAS, 432, 1483 [Google Scholar]
- Carrasco Kind, M., & Brunner, R. J. 2014, MNRAS, 438, 3409 [NASA ADS] [CrossRef] [Google Scholar]
- Cavuoti, S., Tortora, C., Brescia, M., et al. 2016, MNRAS, 466, 2039 [Google Scholar]
- Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. 2002, J. Artif. Intell. Res., 16, 321 [CrossRef] [Google Scholar]
- Chong, P., Ruff, L., Kloft, M., & Binder, A. 2020, in 2020 International Joint Conference on Neural Networks (IJCNN), IEEE, 1–9 [Google Scholar]
- Coil, A. L., Blanton, M. R., Burles, S. M., et al. 2011, ApJ, 741, 8 [Google Scholar]
- Collister, A. A., & Lahav, O. 2004, PASP, 116, 345 [NASA ADS] [CrossRef] [Google Scholar]
- Cool, R. J., Moustakas, J., Blanton, M. R., et al. 2013, ApJ, 767, 118 [NASA ADS] [CrossRef] [Google Scholar]
- Cranmer, M., Tamayo, D., Rein, H., et al. 2021, Proc. Natl. Acad. Sci., 118, 2026053118 [NASA ADS] [CrossRef] [Google Scholar]
- Cui, Y., Jia, M., Lin, T.-Y., Song, Y., & Belongie, S. 2019, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) [Google Scholar]
- Davis, C., Gatti, M., Vielzeuf, P., et al. 2017, ArXiv e-prints [arXiv:1710.02517] [Google Scholar]
- D’Isanto, A., & Polsterer, K. L. 2018, A&A, 609, A111 [Google Scholar]
- Domínguez Sánchez, H., Huertas-Company, M., Bernardi, M., Tuccillo, D., & Fischer, J. L. 2018, MNRAS, 476, 3661 [Google Scholar]
- Drinkwater, M. J., Byrne, Z. J., Blake, C., et al. 2018, MNRAS, 474, 4151 [Google Scholar]
- Duarte, K., Rawat, Y., & Shah, M. 2021, in 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2733 [CrossRef] [Google Scholar]
- Euclid Collaboration ( Ilbert, O., et al.) 2021, A&A, 647, A117 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Feldmann, R., Carollo, C. M., Porciani, C., et al. 2006, MNRAS, 372, 565 [NASA ADS] [CrossRef] [Google Scholar]
- García, V., Sánchez, J., & Mollineda, R. 2012, Knowledge-Based Syst., 25, 13 [CrossRef] [Google Scholar]
- Garilli, B., McLure, R., Pentericci, L., et al. 2021, A&A, 647, A150 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Gatti, M., Giannini, G., Bernstein, G. M., et al. 2022, MNRAS, 510, 1223 [Google Scholar]
- Gerdes, D. W., Sypniewski, A. J., McKay, T. A., et al. 2010, ApJ, 715, 823 [Google Scholar]
- Greisel, N., Seitz, S., Drory, N., et al. 2015, MNRAS, 451, 1848 [NASA ADS] [CrossRef] [Google Scholar]
- Gupta, A., Zorrilla Matilla, J. M., Hsu, D., & Haiman, Z. 2018, Phys. Rev. D, 97, 103515 [NASA ADS] [CrossRef] [Google Scholar]
- Han, H., Wang, W.-Y., & Mao, B.-H. 2005, in Advances in Intelligent Computing, eds. D.-S. Huang, X.-P. Zhang, & G.-B. Huang (Berlin, Heidelberg: Springer), 878 [CrossRef] [Google Scholar]
- Hatfield, P. W., Almosallam, I. A., Jarvis, M. J., et al. 2020, MNRAS, 498, 5498 [NASA ADS] [CrossRef] [Google Scholar]
- Hayat, M., Khan, S., Zamir, W., Shen, J., & Shao, L. 2019, Max-margin Class Imbalanced Learning with Gaussian Affinity [Google Scholar]
- Hemmati, S., Capak, P., Masters, D., et al. 2019, ApJ, 877, 117 [NASA ADS] [CrossRef] [Google Scholar]
- Hosenie, Z., Lyon, R., Stappers, B., Mootoovaloo, A., & McBride, V. 2020, MNRAS, 493, 6050 [NASA ADS] [CrossRef] [Google Scholar]
- Hoyle, B. 2016, Astron. Comput., 16, 34 [NASA ADS] [CrossRef] [Google Scholar]
- Huang, C., Li, Y., Loy, C. C., & Tang, X. 2016, in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 5375 [CrossRef] [Google Scholar]
- Huang, C., Li, Y., Loy, C. C., & Tang, X. 2020, IEEE Trans. Pattern Anal. Mach. Intell., 42, 2781 [CrossRef] [Google Scholar]
- Hudelot, P., Cuillandre, J. C., Withington, K., et al. 2012, VizieR Online Data Catalog: II/317 [Google Scholar]
- Ilbert, O., Arnouts, S., McCracken, H. J., et al. 2006, A&A, 457, 841 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Jia, J., & Zhao, Q. 2019, in 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), 1 [Google Scholar]
- Jones, E., & Singal, J. 2017, A&A, 600, A113 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Kang, B., Xie, S., Rohrbach, M., et al. 2020, in International Conference on Learning Representations [Google Scholar]
- Khan, S. H., Hayat, M., Bennamoun, M., Sohel, F. A., & Togneri, R. 2018, IEEE Trans. Neural Netw. Learn. Syst., 29, 3573 [CrossRef] [Google Scholar]
- Khan, S., Hayat, M., Zamir, S. W., Shen, J., & Shao, L. 2019, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 103 [CrossRef] [Google Scholar]
- Kingma, D. P., & Ba, J. 2015, in 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, ed. Y. Bengio, & Y. LeCun [Google Scholar]
- Kodali, N., Abernethy, J., Hays, J., & Kira, Z. 2017, ArXiv e-prints [arXiv:1705.07215] [Google Scholar]
- Kovetz, E. D., Raccanelli, A., & Rahman, M. 2017, MNRAS, 468, 3650 [NASA ADS] [CrossRef] [Google Scholar]
- Laureijs, R., Amiaux, J., Arduini, S., et al. 2011, ArXiv e-prints [arXiv:1110.3193] [Google Scholar]
- Le Fèvre, O., Cassata, P., Cucciati, O., et al. 2013, A&A, 559, A14 [Google Scholar]
- Le Fèvre, O., Tasca, L. A. M., Cassata, P., et al. 2015, A&A, 576, A79 [Google Scholar]
- Lee, K.-G., Krolewski, A., White, M., et al. 2018, ApJS, 237, 31 [Google Scholar]
- Leistedt, B., Hogg, D. W., Wechsler, R. H., & DeRose, J. 2019, ApJ, 881, 80 [Google Scholar]
- Li, Y., Wang, Q., Zhang, J., Hu, L., & Ouyang, W. 2021, Neurocomputing, 435, 26 [CrossRef] [Google Scholar]
- Lilly, S. J., Le Fèvre, O., Renzini, A., et al. 2007, ApJS, 172, 70 [Google Scholar]
- Liu, Z., Miao, Z., Zhan, X., et al. 2019, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2537 [Google Scholar]
- Malz, A. I. 2021, Phys. Rev. D, 103, 083502 [NASA ADS] [CrossRef] [Google Scholar]
- Malz, A. I., & Hogg, D. W. 2020, ApJ, submitted, [arXiv:2007.12178] [Google Scholar]
- Mandelbaum, R., Seljak, U., Hirata, C. M., et al. 2008, MNRAS, 386, 781 [NASA ADS] [CrossRef] [Google Scholar]
- McLeod, M., Libeskind, N., Lahav, O., & Hoffman, Y. 2017, J. Cosmol. Astropart. Phys., 2017, 034 [CrossRef] [Google Scholar]
- McLure, R. J., Pearce, H. J., Dunlop, J. S., et al. 2013, MNRAS, 428, 1088 [NASA ADS] [CrossRef] [Google Scholar]
- Momcheva, I. G., Brammer, G. B., van Dokkum, P. G., et al. 2016, ApJS, 225, 27 [Google Scholar]
- Morrison, C. B., Hildebrandt, H., Schmidt, S. J., et al. 2017, MNRAS, 467, 3576 [Google Scholar]
- Mu, Y.-H., Qiu, B., Zhang, J.-N., Ma, J.-C., & Fan, X.-D. 2020, Res. Astron. Astrophys., 20, 089 [Google Scholar]
- Müller, R., Kornblith, S., & Hinton, G. E. 2019, in Advances in Neural Information Processing Systems, ed. H. Wallach, H. Larochelle, A. Beygelzimer, et al. (USA: Curran Associates, Inc.), 32 [Google Scholar]
- Newman, J. A. 2008, ApJ, 684, 88 [Google Scholar]
- Newman, J. A., Cooper, M. C., Davis, M., et al. 2013, ApJS, 208, 5 [Google Scholar]
- Nguyen, Q., Mukkamala, M. C., & Hein, M. 2018, in Proceedings of Machine Learning Research, Vol. 80, Proceedings of the 35th International Conference on Machine Learning, eds. J. Dy, & A. Krause (PMLR), 3740 [Google Scholar]
- Ntampaka, M., Trac, H., Sutherland, D. J., et al. 2015, ApJ, 803, 50 [NASA ADS] [CrossRef] [Google Scholar]
- Ntampaka, M., ZuHone, J., Eisenstein, D., et al. 2019, ApJ, 876, 82 [NASA ADS] [CrossRef] [Google Scholar]
- Okerinde, A., Hsu, W., Theis, T., Nafi, N., & Shamir, L. 2021, in Computer Analysis of Images and Patterns, eds. N. Tsapatsoulis, A. Panayides, T. Theocharides, et al. (Cham: Springer International Publishing), 322 [CrossRef] [Google Scholar]
- Pasquet, J., Bertin, E., Treyer, M., Arnouts, S., & Fouchez, D. 2019, A&A, 621, A26 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Rau, M. M., Seitz, S., Brimioulle, F., et al. 2015, MNRAS, 452, 3710 [NASA ADS] [CrossRef] [Google Scholar]
- Rau, M. M., Morrison, C. B., Schmidt, S. J., et al. 2022, MNRAS, 509, 4886 [Google Scholar]
- Ravanbakhsh, S., Oliva, J., Fromenteau, S., et al. 2016, in Proceedings of the 33rd International Conference on International Conference on Machine Learning - 48, ICML’16 (JMLR.org), 2407 [Google Scholar]
- Ribli, D., Pataki, B. A., & Csabai, I. 2019, Nat. Astron., 3, 93 [NASA ADS] [CrossRef] [Google Scholar]
- Ruff, L., Vandermeulen, R., Goernitz, N., et al. 2018, in Proceedings of Machine Learning Research, Vol. 80, Proceedings of the 35th International Conference on Machine Learning, eds. J. Dy, & A. Krause (PMLR), 4393 [Google Scholar]
- Salvato, M., Ilbert, O., & Hoyle, B. 2019, Nat. Astron., 3, 212 [NASA ADS] [CrossRef] [Google Scholar]
- Sanchez, C., & Bernstein, G. M. 2018, MNRAS, 483, 2801 [Google Scholar]
- Santurkar, S., Schmidt, L., & Madry, A. 2018, in International Conference on Machine Learning, PMLR, 4480 [Google Scholar]
- Schuldt, S., Suyu, S. H., Cañameras, R., et al. 2021, A&A, 651, A55 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Scodeggio, M., Guzzo, L., Garilli, B., et al. 2018, A&A, 609, A84 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Shuntov, M., Pasquet, J., Arnouts, S., et al. 2020, A&A, 636, A90 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Skelton, R. E., Whitaker, K. E., Momcheva, I. G., et al. 2014, ApJS, 214, 24 [Google Scholar]
- Soo, J. Y. H., Joachimi, B., Eriksen, M., et al. 2021, MNRAS, 503, 4118 [NASA ADS] [CrossRef] [Google Scholar]
- Speagle, J. S., & Eisenstein, D. J. 2017, MNRAS, 469, 1205 [NASA ADS] [CrossRef] [Google Scholar]
- Srivastava, A., Valkov, L., Russell, C., Gutmann, M. U., & Sutton, C. 2017, in Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS’17 (Red Hook, NY, USA: Curran Associates Inc.), 3310–3320 [Google Scholar]
- Szegedy, C., Liu, W., Jia, Y., et al. 2015, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) [Google Scholar]
- Thanh-Tung, H., & Tran, T. 2020, in 2020 International Joint Conference on Neural Networks (IJCNN), IEEE, 1–10 [Google Scholar]
- Tong, H., Liu, B., Wang, S., & Li, Q. 2019, ArXiv e-prints [arXiv:1901.08429] [Google Scholar]
- Voigt, T., Fried, R., Backes, M., & Rhode, W. 2014, Adv. Data Anal. Classification, 8, 195 [CrossRef] [Google Scholar]
- Way, M. J., & Klose, C. D. 2012, PASP, 124, 274 [NASA ADS] [CrossRef] [Google Scholar]
- Wilson, D., Nayyeri, H., Cooray, A., & Häußler, B. 2020, ApJ, 888, 83 [NASA ADS] [CrossRef] [Google Scholar]
- Wu, J. F., & Boada, S. 2019, MNRAS, 484, 4683 [NASA ADS] [CrossRef] [Google Scholar]
- Wu, T., Liu, Z., Huang, Q., Wang, Y., & Lin, D. 2021, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 8659 [Google Scholar]
- Yan, Z., Mead, A. J., Van Waerbeke, L., Hinshaw, G., & McCarthy, I. G. 2020, MNRAS, 499, 3445 [NASA ADS] [CrossRef] [Google Scholar]
- Yin, X., Yu, X., Sohn, K., Liu, X., & Chandraker, M. 2019, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) [Google Scholar]
- Zhang, J., Zhang, Y., & Zhao, Y. 2018a, AJ, 155, 108 [NASA ADS] [CrossRef] [Google Scholar]
- Zhang, Z., Li, M., & Yu, J. 2018b, in SIGGRAPH Asia 2018 Technical Briefs, SA ’18 (New York, NY, USA: Association for Computing Machinery) [Google Scholar]
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