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 | 162 | 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 | 58 | 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 | 38 | 2018 |
Astronomy & Astrophysics, 609 A D’Isanto, KL Polsterer A111, 2018 | 18 | 2018 |
From Photometric Redshifts to Improved Weather Forecasts: machine learning and proper scoring rules as a basis for interdisciplinary work KL Polsterer, A D'Isanto, S Lerch arXiv preprint arXiv:2103.03780, 2021 | 2 | 2021 |
Comparison of outlier detection methods on astronomical image data L Doorenbos, S Cavuoti, M Brescia, A D’Isanto, G Longo Intelligent Astrophysics, 197-223, 2021 | 2 | 2021 |
DCMDN: Deep Convolutional Mixture Density Network A D'Isanto, KL Polsterer Astrophysics Source Code Library, ascl: 1709.006, 2017 | 1 | 2017 |
Uncertain photometric redshifts via combining deep convolutional and mixture density networks. A D'Isanto, KL Polsterer ESANN, 2017 | 1 | 2017 |
Uncertain photometric redshifts with deep learning Methods A D’Isanto Proceedings of the International Astronomical Union 12 (S325), 209-212, 2016 | 1 | 2016 |
Probabilistic photometric redshift estimation in massive digital sky surveys via machine learning A D'Isanto | | 2019 |
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, J/A+ A/616/A97, 2018 | | 2018 |