Listar por autor "Camero Unzueta, Andres"
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Comparing Deep Recurrent Networks Based on the MAE Random Sampling, a First Approach
Camero Unzueta, Andres; Toutouh-el-Alamin, Jamal
; Alba-Torres, Enrique (2018-11-26)
Recurrent neural networks have demonstrated to be good at tackling prediction problems, however due to their high sensitivity to hyper-parameter configuration, finding an appropriate network is a tough task. Automatic ... -
Deep Neuroevolution: Smart City Applications
(UMA Editorial, 2021-05-05)El interés por desarrollar redes neuronales artificiales ha resurgido de la mano del Aprendizaje Profundo. En términos simples, el aprendizaje profundo consiste en diseñar y entrenar una red neuronal de gran complejidad y ... -
Random Error Sampling-based Recurrent Neural Network Architecture Optimization.
Camero Unzueta, Andres; Toutouh-el-Alamin, Jamal; Alba-Torres, Enrique (Elsevier, 2020-09-15)
Recurrent neural networks are good at solving prediction problems. However, finding a network that suits a problem is quite hard because their performance is strongly affected by their architecture configuration. Automatic ... -
Waste generation prediction under uncertainty in smart cities through deep neuroevolution.
Camero Unzueta, Andres; Toutouh-el-Alamin, Jamal; Ferrer-Urbano, Francisco Javier; Alba-Torres, Enrique (Universidad de Antioquia, 2019-08-23)
The unsustainable development of countries has created a problem due to the unstoppable waste generation. Moreover, waste collection is carried out following a pre-defined route that does not take into account the actual ...