@FilmorBroadcast{ArantesFo:2021:GrPaAn,
                 cast = "Instituto Nacional de Pesquisas Espaciais (INPE)",
         datereleased = "13-17 set. 2021",
             director = "Arantes Filho, Lu{\'{\i}}s Ricardo",
                  ibi = "8JMKD3MGPDW34P/4627AP2",
                  url = "http://urlib.net/ibi/8JMKD3MGPDW34P/4627AP2",
       seriesdirector = "Santos, Rafael Duarte Coelho dos and Queiroz, Gilberto Ribeiro de 
                         and Shiguemori, Elcio Hideiti",
          seriestitle = "Workshop dos Cursos de Computa{\c{c}}{\~a}o Aplicada do INPE, 21 
                         (WORCAP)",
             synopsis = "The adoption of Machine Learning and Deep Learning techniques to 
                         build classification and regression models is an important trend 
                         in the current scientific scenario. In this sense, focusing only 
                         on the final performance of these models as a good classification 
                         or approximation of functions does not allow us to observe how 
                         features are extracted, processed, and separated. In this sense, 
                         we approach a feature extraction methodology for Deep Learning and 
                         Artificial Neural Networks models. We present Deep GPA which is a 
                         combination of Gradient Pattern Analysis and fully connected 
                         neural networks. To test and validate this approach, we address 
                         Deep GPA as an alternative to features extracted by CNN 
                         convolutional neural network models for the classification of 
                         supernova spectral data.",
           targetfile = "Arantes Depp-1.mp4",
                title = "Deep GPA: Gradient Pattern Analysis as feature extractor in deep 
                         neural networks for supernovae spectral data",
         yearreleased = "2021",
        urlaccessdate = "15 jun. 2024"
}