Open Access
Issue |
A&A
Volume 669, January 2023
|
|
---|---|---|
Article Number | A120 | |
Number of page(s) | 23 | |
Section | Numerical methods and codes | |
DOI | https://doi.org/10.1051/0004-6361/202244103 | |
Published online | 20 January 2023 |
- Alzahrani, Y., & Boufama, B. 2021, SN Comput. Sci., 2, 1 [CrossRef] [Google Scholar]
- André, P., Men’shchikov, A., Bontemps, S., et al. 2010, A&A, 518, A102 [Google Scholar]
- André, P., Di Francesco, J., Ward-Thompson, D., et al. 2014, in Protostars and Planets VI, eds. H. Beuther, R. S. Klessen, C. P. Dullemond, & T. Henning, 27 [Google Scholar]
- Aragon-Calvo, M. A. 2019, MNRAS, 484, 5771 [CrossRef] [Google Scholar]
- Arzoumanian, D., André, P., Didelon, P., et al. 2011, A&A, 529, A6 [Google Scholar]
- Arzoumanian, D., André, P., Könyves, V., et al. 2019, A&A, 621, A42 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Batson, J., & Royer, L. 2019, in International Conference on Machine Learning, PMLR, 524 [Google Scholar]
- Bekki, K. 2021, A&A, 647, A120 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Bernard, J. P., Paradis, D., Marshall, D. J., et al. 2010, A&A, 518, A88 [Google Scholar]
- Bracco, A., Bresnahan, D., Palmeirim, P., et al. 2020, A&A, 644, A5 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Bianco, M., Giri, S. K., Iliev, I. T., & Mellema, G. 2021, MNRAS, 505, 3982 [NASA ADS] [CrossRef] [Google Scholar]
- Chen, M. C.-Y., Di Francesco, J., Rosolowsky, E., et al. 2020, ApJ, 891, 84 [NASA ADS] [CrossRef] [Google Scholar]
- Christy, C. T., Jayasinghe, T., Stanek, K. Z., et al. 2022, PASP, 134, 024201 [NASA ADS] [CrossRef] [Google Scholar]
- Clarke, S. D., Williams, G. M., & Walch, S. 2020, MNRAS, 497, 4390 [NASA ADS] [CrossRef] [Google Scholar]
- Elia, D., Molinari, S., Fukui, Y., et al. 2013, ApJ, 772, 45 [NASA ADS] [CrossRef] [Google Scholar]
- Fiorio, C., & Gustedt, J. 1996, Theor. Comput. Sci., 154, 165 [Google Scholar]
- Fu, K.-S., & Mui, J. 1981, Pattern Recognit., 13, 3 [NASA ADS] [CrossRef] [Google Scholar]
- Fukui, Y., Tokuda, K., Saigo, K., et al. 2019, ApJ, 886, 14 [NASA ADS] [CrossRef] [Google Scholar]
- Goodfellow, I., Bengio, Y., & Courville, A. 2016, Deep Learning (MIT Press) [Google Scholar]
- Gu, W., Bai, S., & Kong, L. 2022, Image Vis. Comput., 120, 104401 [CrossRef] [Google Scholar]
- Hacar, A., Tafalla, M., Forbrich, J., et al. 2018, A&A, 610, A77 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Hacar, A., Bosman, A. D., & van Dishoeck, E. F. 2020, A&A, 635, A4 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Hacar, A., Clark, S., Heitsch, F., et al. 2022, ArXiv e-prints [arXiv: 2203.09562] [Google Scholar]
- Hastie, T., Tibshirani, R., & Friedman, J. 2001, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer series in Statistics (Springer) [Google Scholar]
- Hausen, R., & Robertson, B. E. 2020, ApJS, 248, 20 [CrossRef] [Google Scholar]
- He, K., Zhang, X., Ren, S., & Sun, J. 2016, in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770 [Google Scholar]
- Hoemann, E., Heigl, S., & Burkert, A. 2021, MNRAS, 507, 3486 [NASA ADS] [CrossRef] [Google Scholar]
- Hsieh, C.-H., Arce, H. G., Mardones, D., Kong, S., & Plunkett, A. 2021, ApJ, 908, 92 [CrossRef] [Google Scholar]
- Jadon, S. 2020, 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 1 [Google Scholar]
- Kingma, D. P., & Ba, J. 2015, ArXiv e-prints [arXiv: 1412.6980] [Google Scholar]
- Koch, E. W., & Rosolowsky, E. W. 2015, MNRAS, 452, 3435 [Google Scholar]
- Könyves, V., André, P., Arzoumanian, D., et al. 2020, A&A, 635, A34 [Google Scholar]
- Krizhevsky, A., Sutskever, I., & Hinton, G. E. 2012, Commun. ACM, 60, 84 [Google Scholar]
- Kull, M., Silva Filho, T. M., & Flach, P. 2017, Electron. J. Stat., 11, 5052 [CrossRef] [Google Scholar]
- Kumar, M. S. N., Palmeirim, P., Arzoumanian, D., & Inutsuka, S. I. 2020, A&A, 642, A87 [EDP Sciences] [Google Scholar]
- LeCun, Y., Boser, B. E., Denker, J. S., et al. 1989, Neural Comput., 1, 541 [NASA ADS] [CrossRef] [Google Scholar]
- Lee, E. J., Murray, N., & Rahman, M. 2012, ApJ, 752, 146 [NASA ADS] [CrossRef] [Google Scholar]
- Lee, C.-Y., Xie, S., Gallagher, P., Zhang, Z., & Tu, Z. 2015, in Proceedings of Machine Learning Research, Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, eds. G. Lebanon, & S. V. N. Vishwanathan (San Diego, CA, USA: PMLR), 38, 562 [Google Scholar]
- Leurini, S., Schisano, E., Pillai, T., et al. 2019, A&A, 621, A130 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Li Causi, G., Schisano, E., Liu, S. J., Molinari, S., & Di Giorgio, A. 2016, SPIE Conf. Ser., 9904, 99045V [NASA ADS] [Google Scholar]
- Long, J., Shelhamer, E., & Darrell, T. 2015, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3431 [Google Scholar]
- Mattern, M., Kauffmann, J., Csengeri, T., et al. 2018, A&A, 619, A166 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Men’shchikov, A. 2021, A&A, 649, A89 [EDP Sciences] [Google Scholar]
- Milletari, F., Navab, N., & Ahmadi, S.-A. 2016, in 2016 fourth International Conference on 3D Vision (3DV), IEEE, 565 [CrossRef] [Google Scholar]
- Molinari, S., Swinyard, B., Bally, J., et al. 2010, A&A, 518, A100 [Google Scholar]
- Molinari, S., Schisano, E., Elia, D., et al. 2016, A&A, 591, A149 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Oktay, O., Schlemper, J., Folgoc, L. L., et al. 2018, MIDL’18, accepted [arXiv:1804.03999] [Google Scholar]
- Pielawski, N., & Wählby, C. 2020, PLoS One, 15, e0229839 [NASA ADS] [CrossRef] [Google Scholar]
- Priestley, F. D. & Whitworth, A. P. 2022, MNRAS, 512, 1407 [NASA ADS] [CrossRef] [Google Scholar]
- Robinson, K., & Whelan, P. F. 2004, Pattern Recognit. Lett., 25, 1759 [CrossRef] [Google Scholar]
- Robitaille, T., Deil, C., & Ginsburg, A. 2020, Astrophysics Source Code Library, [record ascl:2011.023] [Google Scholar]
- Ronneberger, O., Fischer, P., & Brox, T. 2015, in International Conference on Medical Image Computing and Computer-assisted Intervention (Springer), 234 [Google Scholar]
- Schisano, E., Rygl, K. L. J., Molinari, S., et al. 2014, ApJ, 791, 27 [Google Scholar]
- Schisano, E., Molinari, S., Elia, D., et al. 2020, MNRAS, 492, 5420 [Google Scholar]
- Shimajiri, Y., André, P., Ntormousi, E., et al. 2019, A&A, 632, A83 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Soille, P. 2003, Morphological Image Analysis: Principles and Applications, 2nd edn. (Berlin, Heidelberg: Springer-Verlag) [Google Scholar]
- Sousbie, T. 2011, MNRAS, 414, 350 [NASA ADS] [CrossRef] [Google Scholar]
- Traficante, A., Calzoletti, L., Veneziani, M., et al. 2011, MNRAS, 416, 2932 [Google Scholar]
- Vincent, L. 1993, IEEE Trans. Image Process., 2, 176 [CrossRef] [Google Scholar]
- Wells, A. I., & Norman, M. L. 2021, ApJS, 254, 41 [NASA ADS] [CrossRef] [Google Scholar]
- Xia, X., & Kulis, B. 2017, ArXiv e-prints [arXiv:1711.08506] [Google Scholar]
- Zavagno, A., André, P., Schuller, F., et al. 2020, A&A, 638, A7 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Zhou, Z., Siddiquee, M. M. R., Tajbakhsh, N., & Liang, J. 2019, IEEE Trans. Med. Imaging, 39, 1856 [Google Scholar]
- Zhu, G., Lin, G., Wang, D., Liu, S., & Yang, X. 2019, Solar Phys., 294, 117 [NASA ADS] [CrossRef] [Google Scholar]
- Zucker, C., & Chen, H. H.-H. 2018, ApJ, 864, 152 [NASA ADS] [CrossRef] [Google Scholar]
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