Free Access
Issue
A&A
Volume 613, May 2018
Article Number A71
Number of page(s) 13
Section Numerical methods and codes
DOI https://doi.org/10.1051/0004-6361/201731961
Published online 01 June 2018
  1. Abadi, M., Agarwal, A., Barham, P., et al. 2015, ArXiv e-prints: [arXiv:1603.04467], software available from tensorflow.org [Google Scholar]
  2. Absil, O., Milli, J., Mawet, D., et al. 2013, A&A, 559, L12 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  3. Amara, A., & Quanz, S. P. 2012, MNRAS, 427, 948 [NASA ADS] [CrossRef] [Google Scholar]
  4. Ball, N. M., & Brunner, R. J. 2010, Int. J. Mod. Phys. D, 19, 1049 [NASA ADS] [CrossRef] [Google Scholar]
  5. Barrett, H. H., Myers, K. J., Devaney, N., Dainty, J. C., & Caucci, L. 2006, in Advances in Adaptive Optics II., eds. B. L. Ellerbroek, & D. Bonaccini Calia, Proc. SPIE, 6272, 1W [Google Scholar]
  6. Bertin, E., & Arnouts, S. 1996, A&AS, 117, 393 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  7. Beuzit, J.-L., Feldt, M., Dohlen, K., et al. 2008, in Ground-based and Airborne Instrumentation for Astronomy II, eds. I. S. McLean, & M. M. Casali, Proc. SPIE, 7014, 701418 [CrossRef] [Google Scholar]
  8. Boureau, Y.-L., Ponce, J., & LeCun, Y. 2010, in ICML, eds. J. Fürnkranz & T. Joachims (Madison, WI: Omnipress), 111 [Google Scholar]
  9. Bowler, B. P. 2016, PASP, 128, 102001 [NASA ADS] [CrossRef] [Google Scholar]
  10. Braham, M., & Van Droogenbroeck, M. 2016, Int. Conf. on Systems, Signals and Image Processing, held in Bratislava, Slovakia [Google Scholar]
  11. Breiman, L. 2001, Machine Learning, 45, 5 [CrossRef] [Google Scholar]
  12. Cantalloube, F., Mouillet, D., Mugnier, L. M., et al. 2015, A&A, 582, A89 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  13. Chollet, F. 2017, Deep Learning with Python (Shelter Island, NY: Manning Publications) [Google Scholar]
  14. Chollet, F., et al. 2015, Keras, https://github.com/fchollet/keras [Google Scholar]
  15. Dieleman, S., Willett, K. W., & Dambre, J. 2015, MNRAS, 450, 1441 [NASA ADS] [CrossRef] [Google Scholar]
  16. Dohlen, K., Langlois, M., Saisse, M., et al. 2008, in Ground-based and Airborne Instrumentation for Astronomy II, eds. I. S. McLean, & M. M. Casali, Proc. SPIE, 7014, 70143L [CrossRef] [Google Scholar]
  17. Fergus, R., Hogg, D. W., Oppenheimer, R., Brenner, D., & Pueyo, L. 2014, ApJ, 794, 161 [NASA ADS] [CrossRef] [Google Scholar]
  18. Flamary, R. 2016, ArXiv e-prints [arXiv:1612.04526] [Google Scholar]
  19. Frontera-Pons, J., Sureau, F., Bobin, J., & Le Floc’h, E. 2017, A&A 603, A60 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  20. Gomez Gonzalez, C. A., Absil, O., Absil, P.-A., et al. 2016, A&A, 589, A54 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  21. Gomez Gonzalez, C. A., Wertz, O., Absil, O., et al. 2017, AJ, 154, 7 [NASA ADS] [CrossRef] [Google Scholar]
  22. Goodfellow, I., Bengio, Y., & Courville, A. 2016, Deep Learning (Cambridge, MA: MIT Press), http://www.deeplearningbook.org [Google Scholar]
  23. Graham, J. R., Macintosh, B., Doyon, R., et al. 2007, ArXiv e-prints [arXiv:0704.1454] [Google Scholar]
  24. Halko, N., Martinsson, P.-G., & Tropp, J. A. 2011, SIAM Review, 53, 217 [CrossRef] [Google Scholar]
  25. Hardy, A., Schreiber, M. R., Parsons, S. G., et al. 2015, ApJ, 800, L24 [NASA ADS] [CrossRef] [Google Scholar]
  26. Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. 2012, ArXiv e-prints [arXiv:1207.0580] [Google Scholar]
  27. Hochreiter, S.,& Schmidhuber, J. 1997, Neural Comput., 9, 1735 [CrossRef] [PubMed] [Google Scholar]
  28. Hoyle, B. 2016, Astron. Comput., 16, 34 [NASA ADS] [CrossRef] [Google Scholar]
  29. Kenworthy, M. A., Codona, J. L., Hinz, P. M., et al. 2007, ApJ, 660, 762 [NASA ADS] [CrossRef] [Google Scholar]
  30. Kim, E. J., & Brunner, R. J., 2017, MNRAS, 464, 4463 [NASA ADS] [CrossRef] [Google Scholar]
  31. Kingma, D. P., & Ba, J. 2014, ArXiv e-prints [arXiv:1412.6980] [Google Scholar]
  32. Krizhevsky, A., Sutskever, I., & Hinton, G. E. 2012, in Advances in Neural Information Processing Systems, 1097 [Google Scholar]
  33. Lafrenière, D., Marois, C., Doyon, R., Nadeau, D., & Artigau, É. 2007, ApJ, 660, 770 [NASA ADS] [CrossRef] [Google Scholar]
  34. Lagrange, A.-M., Bonnefoy, M., Chauvin, G., et al. 2010, Science, 329, 57 [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
  35. Lawson, P. R., Poyneer, L., Barrett, H., et al. 2012, in Adaptive Optics Systems III, Proc. SPIE, 8447, 844722 [CrossRef] [Google Scholar]
  36. LeCun, Y., Jackel, L. D., Boser, B., et al. 1989, IEEE Commun. Mag., 27, 41 [CrossRef] [Google Scholar]
  37. Louppe, G. 2014, Ph.D. Thesis, University of Li, Belgium, https://github.com/glouppe/phd-thesis [arXiv:1407.7502] [Google Scholar]
  38. Marois, C., Lafrenière, D., Doyon, R., Macintosh, B., & Nadeau, D. 2006, ApJ, 641, 556 [NASA ADS] [CrossRef] [Google Scholar]
  39. Marois, C., Macintosh, B., Barman, T., et al. 2008, Science, 322, 1348 [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
  40. Marois, C., Zuckerman, B., Konopacky, Q. M., Macintosh, B., & Barman, T. 2010, Nature, 468, 1080 [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
  41. Masias, M., Freixenet, J., Lladó, X., & Peracaula, M. 2012, MNRAS, 422, 1674 [NASA ADS] [CrossRef] [Google Scholar]
  42. Mawet, D., Riaud, P., Absil, O., & Surdej, J. 2005, ApJ, 633, 1191 [NASA ADS] [CrossRef] [Google Scholar]
  43. Mawet, D., Milli, J., Wahhaj, Z., et al. 2014, ApJ, 792, 97 [NASA ADS] [CrossRef] [Google Scholar]
  44. Milli, J., Mawet, D., Mouillet, D., Kasper, M., & Girard, J. H. 2016, in Astronomy at High Angular Resolution, (Springer) 439, 17 [NASA ADS] [CrossRef] [Google Scholar]
  45. Mugnier, L. M., Cornia, A., Sauvage, J.-F., et al. 2009, J. Opt. Soc. Am. A, 26, 1326 [NASA ADS] [CrossRef] [Google Scholar]
  46. Nair, V., & Hinton, G. E. 2010, in ICML, eds. J. Fürnkranz & T. Joachims (Madison, WI: Omnipress), 807 [Google Scholar]
  47. Odewahn, S. C., Stockwell, E. B., Pennington, R. L., Humphreys, R. M., & Zumach, W. A. 1992, AJ, 103, 318 [NASA ADS] [CrossRef] [Google Scholar]
  48. Rouan, D., Riaud, P., Boccaletti, A., Clénet, Y., & Labeyrie, A. 2000, PASP, 112, 1479 [NASA ADS] [CrossRef] [Google Scholar]
  49. Ruffio, J.-B., Macintosh, B., Wang, J. J., et al. 2017, ApJ, 842, 14 [NASA ADS] [CrossRef] [Google Scholar]
  50. Rumelhart, D. E., Hinton, G. E., & Williams, R. J. 1986, in Parallel Distributed Processing, eds. D. E. Rumelhart & J. L. Mcclelland, (Cambridge, MA: MIT Press), 1, 318 [Google Scholar]
  51. Schawinski, K., Zhang, C., Zhang, H., Fowler, L., & Santhanam, G. K. 2017, MNRAS, 467, L110 [NASA ADS] [Google Scholar]
  52. Shi, X., Chen, Z., Wang, H., et al. 2015, in NIPS (Cambridge, MA: MIT Press), 802 [Google Scholar]
  53. Soummer, R. 2005, ApJ, 618, L161 [NASA ADS] [CrossRef] [Google Scholar]
  54. Soummer, R., Pueyo, L., & Larkin, J. 2012, ApJ, 755, L28 [NASA ADS] [CrossRef] [Google Scholar]
  55. Sparks, W. B.& Ford, H. C. 2002, ApJ, 578, 543 [NASA ADS] [CrossRef] [Google Scholar]
  56. Spergel, D., & Kasdin, J. 2001, in BAAS, 33, 1431 [NASA ADS] [Google Scholar]
  57. Srivastava, N., Hinton, G. E., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. 2014, J. Mach. Learn. Res., 15, 1929 [Google Scholar]
  58. Tagliaferri, R., Longo, G., Milano, L., et al. 2003, Neural Networks, 16, 297 [CrossRef] [Google Scholar]
  59. Tran, D., Bourdev, L. D., Fergus, R., Torresani, L., & Paluri, M. 2015, in ICCV (IEEE Computer Society), 4489 [Google Scholar]
  60. Xie, D., Zhang, L., & Bai, L. 2017, Appl. Comp. Int. Soft Comput., 2017, 13, 1320780 [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.