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A deep learning approach for focal-plane wavefront sensing using vortex phase diversity

Autor: Quesnel, M., de Xivry, G. Orban, Louppe, G., Absil, O.

Publikováno v: A&A 668, A36 (2022)

The performance of high-contrast imaging instruments is limited by wavefront errors, in particular by non-common path aberrations (NCPAs). Focal-plane wavefront sensing (FPWFS) is appropriate to handle NCPAs because it measures the aberration where i

Externí odkaz: http://arxiv.org/abs/2210.00632

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Focal Plane Wavefront Sensing using Machine Learning: Performance of Convolutional Neural Networks compared to Fundamental Limits

Autor: de Xivry, G. Orban, Quesnel, M., Vanberg, P. -O., Absil, O., Louppe, G.

Focal plane wavefront sensing (FPWFS) is appealing for several reasons. Notably, it offers high sensitivity and does not suffer from non-common path aberrations (NCPA). The price to pay is a high computational burden and the need for diversity to lif

Externí odkaz: http://arxiv.org/abs/2106.04456

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Periodical

Characterizing the performance of the SPHERE exoplanet imager at the Very Large Telescope using deep learning

Autor: Jackson, Kathryn J., Schmidt, Dirk, Vernet, Elise, Bissot, L., Milli, J., Choquet, E., Cantalloube, F., Delorme, P., Mouillet, D., Louppe, G., Absil, O.

Publikováno v: Proceedings of SPIE; August 2024, Vol. 13097 Issue: 1 p130976I-130976I-14, 12966639p

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Akademický článek

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Akademický článek

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Truncated Marginal Neural Ratio Estimation

Autor: Cole, A., Forre, P., Louppe, G., Miller, B.K., Weniger, C., Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P.S., Wortman Vaughan, J.

Publikováno v: 35th Conference on Neural Information Processing Systems (NeurIPS 2021): online, 6-14 December 2021, 1, 129-143

Parametric stochastic simulators are ubiquitous in science, often featuring high-dimensional input parameters and/or an intractable likelihood. Performing Bayesian parameter inference in this context can be challenging. We present a neural simulation

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=narcis______::952aededcc57e16d949a557c3e575561
https://dare.uva.nl/personal/pure/en/publications/truncated-marginal-neural-ratio-estimation(e6f23068-5d5b-492f-8318-25195a90abdb).html

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Segmentation of photovoltaic panels in aerial photography using group equivariant FCNs

Autor: Bokkers, L.A.R., Ambrogioni, L., Güçlü, U., Beuls, K., Bogaerts, B., Bontempi, G., Geurts, P., Harley, N., Lebichot, B., Lenaerts, T., Louppe, G., Eecke, P. van

Publikováno v: Beuls, K.; Bogaerts, B.; Bontempi, G. (ed.), Proceedings of the 31st Benelux Conference on Artificial Intelligence (BNAIC 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), pp. 1-11
Beuls, K.; Bogaerts, B.; Bontempi, G. (ed.), Proceedings of the 31st Benelux Conference on Artificial Intelligence (BNAIC 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), 1-11. [S.l.] : [S.n.]
STARTPAGE=1;ENDPAGE=11;TITLE=Beuls, K.; Bogaerts, B.; Bontempi, G. (ed.), Proceedings of the 31st Benelux Conference on Artificial Intelligence (BNAIC 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019)

Item does not contain fulltext Previous research has shown the benefits of group equivariant convolutions for image recognition tasks. With this work we apply group equivariance to the segmentation of photovoltaic (PV) panel installations in aerial p

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::11655e91a428524e8294690a03529962
https://hdl.handle.net/2066/215842

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Temporal factorization of 3D convolutional kernels

Autor: Ras, G.E.H., Ambrogioni, L., Güçlü, U., Gerven, M.A.J. van, Beuls, K., Bogaerts, B., Bontempi, G., Geurts, P., Harley, N., Lebichot, B., Lenaerts, T., Louppe, G., Eecke, P. van

Publikováno v: Beuls, K.; Bogaerts, B.; Bontempi, G. (ed.), Proceedings of the 31st Benelux Conference on Artificial Intelligence (BNAIC 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), pp. 1-9
Beuls, K.; Bogaerts, B.; Bontempi, G. (ed.), Proceedings of the 31st Benelux Conference on Artificial Intelligence (BNAIC 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), 1-9
STARTPAGE=1;ENDPAGE=9;TITLE=Beuls, K.; Bogaerts, B.; Bontempi, G. (ed.), Proceedings of the 31st Benelux Conference on Artificial Intelligence (BNAIC 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019)

Item does not contain fulltext 3D convolutional neural networks are difficult to train because they are parameter-expensive and data-hungry. To solve these problems we propose a simple technique for learning 3D convolutional kernels efficiently requi

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::285e1917322bd6f9dc44ce8bd8c51292
https://hdl.handle.net/2066/213443

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