no code implementations • 15 Aug 2023 • Guillermo Cabrera-Vives, César Bolivar, Francisco Förster, Alejandra M. Muñoz Arancibia, Manuel Pérez-Carrasco, Esteban Reyes
Time domain astronomy is advancing towards the analysis of multiple massive datasets in real time, prompting the development of multi-stream machine learning models.
1 code implementation • 9 Aug 2023 • Manuel Pérez-Carrasco, Guillermo Cabrera-Vives, Lorena Hernández-García, Francisco Forster, Paula Sánchez-Sáez, Alejandra Muñoz Arancibia, Nicolás Astorga, Franz Bauer, Amelia Bayo, Martina Cádiz-Leyton, Marcio Catelan
Our results demonstrate the efficacy of MCDSVDD in detecting anomalous sources while leveraging the presence of different inlier categories.
1 code implementation • 4 Apr 2022 • Manuel Pérez-Carrasco, Pavlos Protopapas, Guillermo Cabrera-Vives
We use different loss functions to enforce consistency between the feature representations of associated data pairs of samples.
no code implementations • 7 Aug 2020 • Rodrigo Carrasco-Davis, Esteban Reyes, Camilo Valenzuela, Francisco Förster, Pablo A. Estévez, Giuliano Pignata, Franz E. Bauer, Ignacio Reyes, Paula Sánchez-Sáez, Guillermo Cabrera-Vives, Susana Eyheramendy, Márcio Catelan, Javier Arredondo, Ernesto Castillo-Navarrete, Diego Rodríguez-Mancini, Daniela Ruz-Mieres, Alberto Moya, Luis Sabatini-Gacitúa, Cristóbal Sepúlveda-Cobo, Ashish A. Mahabal, Javier Silva-Farfán, Ernesto Camacho-Iñiquez, Lluís Galbany
We present a real-time stamp classifier of astronomical events for the ALeRCE (Automatic Learning for the Rapid Classification of Events) broker.
no code implementations • 25 Sep 2019 • Manuel Pérez-Carrasco, Guillermo Cabrera-Vives, Pavlos Protopapas, Nicolás Astorga, Marouan Belhaj
In this work we address the problem of transferring knowledge obtained from a vast annotated source domain to a low labeled target domain.
1 code implementation • 2 Jan 2017 • Guillermo Cabrera-Vives, Ignacio Reyes, Francisco Förster, Pablo A. Estévez, Juan-Carlos Maureira
We introduce Deep-HiTS, a rotation invariant convolutional neural network (CNN) model for classifying images of transients candidates into artifacts or real sources for the High cadence Transient Survey (HiTS).