Antonio D'Isanto
Citata da
Citata da
The VIPERS Multi-Lambda Survey-I. UV and near-IR observations, multi-colour catalogues, and photometric redshifts
T Moutard, S Arnouts, O Ilbert, J Coupon, P Hudelot, D Vibert, V Comte, ...
Astronomy & Astrophysics 590, A102, 2016
Photometric redshift estimation via deep learning-Generalized and pre-classification-less, image based, fully probabilistic redshifts
A D’Isanto, KL Polsterer
Astronomy & Astrophysics 609, A111, 2018
An analysis of feature relevance in the classification of astronomical transients with machine learning methods
A D'Isanto, S Cavuoti, M Brescia, C Donalek, G Longo, G Riccio, ...
Monthly Notices of the Royal Astronomical Society 457 (3), 3119-3132, 2016
Return of the features-Efficient feature selection and interpretation for photometric redshifts
A D’Isanto, S Cavuoti, F Gieseke, KL Polsterer
Astronomy & Astrophysics 616, A97, 2018
DCMDN: Deep Convolutional Mixture Density Network
A D'Isanto, KL Polsterer
Astrophysics Source Code Library, 2017
Uncertain photometric redshifts via combining deep convolutional and mixture density networks.
A D'Isanto, KL Polsterer
ESANN, 2017
Uncertain Photometric Redshifts with Deep Learning Methods
A D’Isanto
Proceedings of the International Astronomical Union 12 (S325), 209-212, 2016
Probabilistic photometric redshift estimation in massive digital sky surveys via machine learning
A D'Isanto
Return of the features
A D’Isanto, S Cavuoti, F Gieseke, KL Polsterer
VizieR Online Data Catalog: SDSS QSO DR7 and DR9 (D'Isanto+, 2018)
A D'Isanto, S Cavuoti, F Gieseke, KL Polsterer
VizieR Online Data Catalog 361, 2018
Il sistema al momento non può eseguire l'operazione. Riprova più tardi.
Articoli 1–10