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Cited article:

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Research in Astronomy and Astrophysics 19 (12) 175 (2019)
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Probabilistic Random Forest: A Machine Learning Algorithm for Noisy Data Sets

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The Astronomical Journal 157 (1) 16 (2019)
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Astronomy & Astrophysics 621 A26 (2019)
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Using convolutional neural networks to predict galaxy metallicity from three-colour images

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Monthly Notices of the Royal Astronomical Society 484 (4) 4683 (2019)
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Deblending and classifying astronomical sources with Mask R-CNN deep learning

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Monthly Notices of the Royal Astronomical Society 490 (3) 3952 (2019)
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The Extremely Luminous Quasar Survey in the Pan-STARRS 1 Footprint (PS-ELQS)

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The Astrophysical Journal Supplement Series 243 (1) 5 (2019)
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Statistical analysis of probability density functions for photometric redshifts through the KiDS-ESO-DR3 galaxies

V Amaro, S Cavuoti, M Brescia, et al.
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The High Cadence Transit Survey (HiTS): Compilation and Characterization of Light-curve Catalogs

Jorge Martínez-Palomera, Francisco Förster, Pavlos Protopapas, Juan Carlos Maureira, Paulina Lira, Guillermo Cabrera-Vives, Pablo Huijse, Lluis Galbany, Thomas de Jaeger, Santiago González-Gaitán, Gustavo Medina, Giuliano Pignata, Jaime San Martín, Mario Hamuy and Ricardo R. Muñoz
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https://doi.org/10.3847/1538-3881/aadfd8