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Home All issues Volume 613 (May 2018) A&A, 613 (2018) A71 References
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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]

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