Open Access
Issue |
A&A
Volume 651, July 2021
|
|
---|---|---|
Article Number | A65 | |
Number of page(s) | 18 | |
Section | Numerical methods and codes | |
DOI | https://doi.org/10.1051/0004-6361/202040152 | |
Published online | 14 July 2021 |
- Aharon, M., Elad, M., & Bruckstein, A. 2006, IEEE Trans. Signal Process., 54, 4311 [NASA ADS] [CrossRef] [Google Scholar]
- Allys, E., Levrier, F., Zhang, S., et al. 2019, A&A, 629, A115 [CrossRef] [EDP Sciences] [Google Scholar]
- Armitage-Caplan, C., & Wandelt, B. D. 2009, ApJS, 181, 533 [NASA ADS] [CrossRef] [Google Scholar]
- Baldi, P., & Hornik, K. 1989, Neural Networks, 2, 53 [CrossRef] [Google Scholar]
- Bengio, Y., Courville, A., & Vincent, P. 2013, IEEE Trans. Pattern Anal. Mach. Intell., 35, 1798 [CrossRef] [Google Scholar]
- Böhme, T. J., Fletcher, I., & Cox, C. S. 1999, e&i Elektrotechnik und Informationstechnik, 116, 375 [CrossRef] [Google Scholar]
- Bojanowski, P., Joulin, A., Lopez-Pas, D., & Szlam, A. 2018, International Conference on Machine Learning, 599 [Google Scholar]
- Bouakkaz, M., & Harkat, M. F. 2012, Proc. 4th International Joint Conference on Computational Intelligence (NCTA-2012), 483 [Google Scholar]
- Bourlard, H., & Kamp, Y. 1988, Biol. Cybern., 59, 291 [CrossRef] [Google Scholar]
- Bruna, J., Mallat, S., Bacry, E., Muzy, J.-F., et al. 2015, Ann. Stat., 43, 323 [CrossRef] [Google Scholar]
- Choi, S., Cichocki, A., Park, H.-M., & Lee, S.-Y. 2005, Neural Inf. Process.-Lett. Rev., 6, 1 [Google Scholar]
- de Gasperis, G., Balbi, A., Cabella, P., Natoli, P., & Vittorio, N. 2005, A&A, 436, 1159 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Delouis, J.-M., Pagano, L., Mottet, S., Puget, J.-L., & Vibert, L. 2019, A&A, 629, A38 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- DeMers, D., & Cottrell, G. W. 1993, Advances in Neural Information Processing Systems, 580 [Google Scholar]
- Denton, E. L., Chintala, S., Fergus, R., et al. 2015, Advances in Neural Information Processing Systems, 1486 [Google Scholar]
- Doré, O., Teyssier, R., Bouchet, F., Vibert, D., & Prunet, S. 2001, A&A, 374, 358 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Dumoulin, V., & Visin, F. 2016, ArXiv e-prints [arXiv:1603.07285] [Google Scholar]
- Erguo, Y., & Jinshou, Y. 2002, in Proceedings of the 4th World Congress on Intelligent Control and Automation (Cat. No.02EX527), 4, 2755 [CrossRef] [Google Scholar]
- Erichson, N. B., Muehlebach, M., & Mahoney, M. W. 2019, ArXiv e-prints [arXiv:1905.10866] [Google Scholar]
- Fan, J., & Cheng, J. 2018, Neural Networks, 98, 34 [CrossRef] [Google Scholar]
- Geng, Z., & Zhu, Q. 2005, Ind. Eng. Chem. Res., 44, 3585 [CrossRef] [Google Scholar]
- Goodfellow, I., Pouget-Abadie, J., Mirza, M., et al. 2014, Advances in Neural Information Processing Systems, 2672 [Google Scholar]
- Górski, K. M., Hivon, E., Banday, A. J., et al. 2005, ApJ, 622, 759 [NASA ADS] [CrossRef] [Google Scholar]
- Hassoun, M. H., & Sudjianto, A. 1997, in Workshop on Advances in Autoencoder/Autoassociator-Based Computations at the NIPS, 97, 605 [Google Scholar]
- Hinton, G. E., & Salakhutdinov, R. R. 2006, Science, 313, 504 [NASA ADS] [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
- Jia, F., Martin, E., & Morris, A. 1998, Comput. Chem. Eng., 22, S851 [CrossRef] [Google Scholar]
- Karpatne, A., Atluri, G., Faghmous, J. H., et al. 2017, IEEE Trans. Knowl. Data Eng., 29, 2318 [CrossRef] [Google Scholar]
- Keihänen, E., Kurki-Suonio, H., & Poutanen, T. 2005, MNRAS, 360, 390 [NASA ADS] [CrossRef] [Google Scholar]
- Keihänen, E., Keskitalo, R., Kurki-Suonio, H., Poutanen, T., & Sirviö, A. 2010, A&A, 510, A57 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Kramer, M. A. 1991, AIChE J., 37, 233 [CrossRef] [Google Scholar]
- LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. 1998, Proc. IEEE, 86, 2278 [Google Scholar]
- Lee, J. A., & Verleysen, M. 2007, Nonlinear Dimensionality Reduction (Springer Science& Business Media) [CrossRef] [Google Scholar]
- Liu, F., & Zhao, Z. 2004, in Advances in Neural Networks - ISNN 2004, eds. F. L. Yin, J. Wang, & C. Guo (Berlin, Heidelberg: Springer, Berlin Heidelberg), 798 [Google Scholar]
- Lusch, B., Kutz, J. N., & Brunton, S. L. 2018, Nat. Commun., 9, 4950 [CrossRef] [Google Scholar]
- Lutter, M., Ritter, C., & Peters, J. 2019, in International Conference on Learning Representations [Google Scholar]
- Maino, D., Burigana, C., Górski, K. M., Mandolesi, N., & Bersanelli, M. 2002, A&A, 387, 356 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- McCann, M. T., Jin, K. H., & Unser, M. 2017, IEEE Signal Process. Mag., 34, 85 [CrossRef] [Google Scholar]
- Mordvintsev, A., Olah, C., & Tyka, M. 2015, Google Research, 2 [Google Scholar]
- Nabian, M. A., & Meidani, H. 2018, J. Comput. Inf. Sci. Eng., 20, 1 [Google Scholar]
- Nandi, S., Mukherjee, P., Tambe, S. S., Kumar, R., & Kulkarni, B. D. 2002, Ind. Eng. Chem. Res., 41, 2159 [CrossRef] [Google Scholar]
- Natoli, P., de Gasperis, G., Gheller, C., & Vittorio, N. 2001, A&A, 372, 346 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Pan, S. J., & Yang, Q. 2009, IEEE Trans. Knowl. Data Eng., 22, 1345 [CrossRef] [Google Scholar]
- Park, J. J., Florence, P., Straub, J., Newcombe, R., & Lovegrove, S. 2019, ArXiv e-prints [arXiv:1901.05103] [Google Scholar]
- Planck Collaboration VIII. 2014, A&A, 571, A8 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Planck Collaboration III. 2016, A&A, 594, A3 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Planck Collaboration VII. 2016, A&A, 594, A7 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Planck Collaboration VIII. 2016, A&A, 594, A8 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Planck Collaboration ES 2018, The Legacy Explanatory Supplement (ESI), http://wiki.cosmos.esa.int/planck-legacy-archive [Google Scholar]
- Planck Collaboration III. 2020, A&A, 641, A3 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Poutanen, T., de Gasperis, G., Hivon, E., et al. 2006, A&A, 449, 1311 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Prunet, S., Ade, P. A. R., Bock, J. J., et al. 2001, ArXiv e-prints [arXiv:astro-ph/0101073] [Google Scholar]
- Radford, A., Metz, L., & Chintala, S. 2015, ArXiv e-prints [arXiv:1511.06434] [Google Scholar]
- Raissi, M., & Karniadakis, G. E. 2018, J. Comput. Phys., 357, 125 [CrossRef] [Google Scholar]
- Raissi, M., Perdikaris, P., & Karniadakis, G. E. 2017a, ArXiv e-prints [arXiv:1711.10561] [Google Scholar]
- Raissi, M., Perdikaris, P., & Karniadakis, G. E. 2017b, ArXiv e-prints [arXiv:1711.10566] [Google Scholar]
- Raissi, M., Yazdani, A., & Karniadakis, G.E. 2018, ArXiv e-prints [arXiv:1808.04327] [Google Scholar]
- Reddy, V., & Mavrovouniotis, M. 1998, Chem. Eng. Res. Des., 76, 478 [CrossRef] [Google Scholar]
- Reddy, V. N., Riley, P. M., & Mavrovouniotis, M. L. 1996, Comput. Chem. Eng., 20, S889 [CrossRef] [Google Scholar]
- Roscher, R., Bohn, B., Duarte, M. F., & Garcke, J. 2020, IEEE Access, 8, 42200 [CrossRef] [Google Scholar]
- Roweis, S. T., & Saul, L. K. 2000, Science, 290, 2323 [CrossRef] [Google Scholar]
- Saul, L. K., & Roweis, S. T. 2003, J. Mach. Learn. Res., 4, 119 [Google Scholar]
- Schölkopf, B., Smola, A., & Müller, K.-R. 1998, Neural Comput., 10, 1299 [CrossRef] [Google Scholar]
- Scholz, M. 2002, PhD Thesis, Master’s Thesis, Dep. of Computer Science, Humboldt-University, Berlin, Germany [Google Scholar]
- Scholz, M., Kaplan, F., Guy, C. L., Kopka, J., & Selbig, J. 2005, Bioinformatics, 21, 3887 [CrossRef] [Google Scholar]
- Schryver, J. C., Brandt, C. C., Pfiffner, S. M., et al. 2006, Microb. Ecol., 51, 177 [CrossRef] [Google Scholar]
- Seo, S., & Liu, Y. 2019, ArXiv e-prints [arXiv:1902.02950] [Google Scholar]
- Tan, S., & Mayrovouniotis, M. L. 1995, AIChE J., 41, 1471 [CrossRef] [Google Scholar]
- Tang, H., Scaife, A. M. M., & Leahy, J. P. 2019, MNRAS, 488, 3358 [Google Scholar]
- Tauber, J. A., Norgaard-Nielsen, H. U., Ade, P. A. R., et al. 2010a, A&A, 520, A2 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Tauber, J. A., Mandolesi, N., Puget, J., et al. 2010b, A&A, 520, A1 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Tenenbaum, J. B., Silva, V. D., & Langford, J. C. 2000, Science, 290, 2319 [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
- Van Der Maaten, L., Postma, E., & Van den Herik, J. 2009, J. Mach. Learn. Res., 10, 13 [Google Scholar]
- Yang, Y., & Perdikaris, P. 2018, ArXiv e-prints [arXiv:1812.03511] [Google Scholar]
- Yang, Y., & Perdikaris, P. 2019, J. Comput. Phys., 394, 136 [CrossRef] [Google Scholar]
- Zhu, Q., & Li, C. 2006, Chin. J. Chem. Eng., 14, 597 [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.