EDP Sciences logo
Subscriber Authentication Point
Search
Menu
Home All issues Volume 657 (January 2022) A&A, 657 (2022) A98 References
Open Access
Issue
A&A
Volume 657, January 2022
Article Number A98
Number of page(s) 9
Section Numerical methods and codes
DOI https://doi.org/10.1051/0004-6361/202141166
Published online 18 January 2022
  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. Arcelin, B., Doux, C., Aubourg, E., Roucelle, C., & LSST Dark Energy Science Collaboration 2021, MNRAS, 500, 531 [Google Scholar]
  3. Awang Iskandar, D. N. F., Zijlstra, A. A., McDonald, I., et al. 2020, Galaxies, 8, 88 [NASA ADS] [CrossRef] [Google Scholar]
  4. Barbary, K. 2016, J. Open Source Softw., 1, 58 [Google Scholar]
  5. Bertin, E., & Arnouts, S. 1996, A&AS, 117, 393 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  6. Bosch, J., Armstrong, R., Bickerton, S., et al. 2018, PASJ, 70, S5 [Google Scholar]
  7. Chollet, F., et al. 2015, Keras https://keras.io. [Google Scholar]
  8. Cropper, M., Cole, R., James, A., et al. 2012, in Space Telescopes and Instrumentation 2012: Optical, Infrared, and Millimeter Wave, eds. M. C. Clampin, G. G. Fazio, H. A. MacEwen, J. Oschmann, & M. Jacobus, SPIE Conf. Ser., 8442, 84420V [NASA ADS] [CrossRef] [Google Scholar]
  9. de Vaucouleurs, G. 1948, Ann. Astrophys., 11, 247 [Google Scholar]
  10. Deng, J., Dong, W., Socher, R., et al. 2009, Proc. CVPR [Google Scholar]
  11. Duchi, J., Hazan, E., & Singer, Y. 2011, J. Mach. Learn. Res., 12, 2121 [Google Scholar]
  12. Euclid Collaboration (Martinet, N., et al. 2019, A&A, 627, A59 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  13. George, D., Shen, H., & Huerta, E. A. 2018, Phys. Rev. D, 97 [Google Scholar]
  14. Guinot, A., Kilbinger, M., Farrens, S., et al. 2021, A&A, submitted [Google Scholar]
  15. Hartlap, J., Hilbert, S., Schneider, P., & Hildebrandt, H. 2011, A&A, 528, A51 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  16. Hoekstra, H., Viola, M., & Herbonnet, R. 2017, MNRAS, 468, 3295 [NASA ADS] [CrossRef] [Google Scholar]
  17. Joseph, R., Courbin, F., & Starck, J.-L. 2016, A&A, 589, A2 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  18. Kingma, D. P., & Ba, J. 2014, Adam: A Method for Stochastic Optimization, cite arxiv:1412.6980 Comment: Published as a conference paper at the 3rd International Conference for Learning Representations, San Diego, 2015 [Google Scholar]
  19. Kotsiantis, S. 2007, Super. Mach. Learn.: Rev. Class. Tech., 31, 249 [Google Scholar]
  20. Lecun, Y., Bengio, Y., & Hinton, G. 2015, Nature, 521, 436 [CrossRef] [PubMed] [Google Scholar]
  21. MacCrann, N., Becker, M. R., McCullough, J., et al. 2022, MNRAS, 509, 3371 [Google Scholar]
  22. Mandelbaum, R. 2018, ARA&A, 56, 393 [Google Scholar]
  23. Mandelbaum, R., Hirata, C. M., Leauthaud, A., Massey, R. J., & Rhodes, J. 2012, MNRAS, 420, 1518 [NASA ADS] [CrossRef] [Google Scholar]
  24. Mandelbaum, R., Rowe, B., Bosch, J., et al. 2014, ApJS, 212, 5 [Google Scholar]
  25. Melchior, P., Moolekamp, F., Jerdee, M., et al. 2018, Astron. Comput., 24, 129 [Google Scholar]
  26. Reiman, D. M., & Göhre, B. E. 2019, MNRAS, 485, 2617 [NASA ADS] [CrossRef] [Google Scholar]
  27. Rowe, B. T. P., Jarvis, M., Mandelbaum, R., et al. 2015, Astron. Comput., 10, 121 [Google Scholar]
  28. Russakovsky, O., Deng, J., Su, H., et al. 2015, Int. J. Comput. Vis., 115, 211 [Google Scholar]
  29. Samuroff, S., Bridle, S. L., Zuntz, J., et al. 2018, MNRAS, 475, 4524 [NASA ADS] [CrossRef] [Google Scholar]
  30. Sanchez, J., Mendoza, I., Kirkby, D. P., Burchat, P. R., & LSST Dark Energy Science Collaboration 2021, JCAP, 2021, 043 [Google Scholar]
  31. Scoville, N., Aussel, H., Brusa, M., et al. 2007, ApJS, 172, 1 [Google Scholar]
  32. Shan, J.-H., Feng, L., Yuan, H.-Q., et al. 2020, Chin. Astron. Astrophys., 44, 507 [Google Scholar]
  33. Simonyan, K., & Zisserman, A. 2014, CoRR [arXiv:1409.1556] [Google Scholar]
  34. Srinivas, S., Sarvadevabhatla, R. K., Mopuri, K. R., et al. 2016, Front. Robot. AI, 2, 36 [CrossRef] [Google Scholar]
  35. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. 2014, J. Mach. Learn. Res., 15, 1929 [Google Scholar]
  36. Tanoglidis, D., Ćiprijanović, A., & Drlica-Wagner, A. 2021, Astron. Comput., 35 [Google Scholar]
  37. Tatarski, V. I. 2016, Wave propagation in a turbulent medium (Courier Dover Publications) [Google Scholar]
  38. Tieleman, T., & Hinton, G. 2012, Lecture 6.5–RmsProp: Divide the gradient by a running average of its recent magnitude (COURSERA: Neural Networks for Machine Learning) [Google Scholar]
  39. Trujillo, I., Aguerri, J., Cepa, J., & Gutiérrez, C. 2001, MNRAS, 328, 977 [NASA ADS] [CrossRef] [Google Scholar]
  40. Wei, W., Huerta, E. A., Whitmore, B. C., et al. 2020, MNRAS, 493, 3178 [NASA ADS] [CrossRef] [Google Scholar]
  41. Wu, C., Wong, O. I., Rudnick, L., et al. 2019, MNRAS, 482, 1211 [NASA ADS] [CrossRef] [Google Scholar]
  42. Zhu, X.-P., Dai, J.-M., Bian, C.-J., et al. 2019, Ap&SS, 364, 55 [NASA ADS] [CrossRef] [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.