Deep Learning

The complexity of the information with which we currently operate, with large sets of attributes describing each of the data samples, implies the need to also have models capable of extracting the most important characteristics, identifying hidden relationships between them and generating higher level representations. These are aspects in which the different families of deep learning models, including convolutional networks, recurrent networks or auto-encoders, stand out for their high performance.

The main objectives are the design of appropriate models for each type of problem and the definition of new architectures that allow to face tasks from a general perspective. This will require facing challenges such as the enormous variability in these architectures, the high computational cost involved and the loss of interpretability, all aspects to be taken into account in current research.

Contact: Javier Ramirez Pérez de Inestrosa

Related Researchers:

Letra:

  Name Email Area Cat.
Charte Ojeda, Francisco fcharte@ujaetHFI336n.es Data Science and Big Data Area PhD
Damas Arroyo, Sergio sdamas@ug3.HzegTer.es DaSCI Technology Applications Area PhD
Giráldez Crú, Jesús jgiraldez@ugiJlgGJMmar.es DaSCI Technology Applications Area PhD - Juan de la Cierva
Luzón García, María Victoria luzon@ugrxQof26MR.es DaSCI Technology Applications Area PhD
Pérez Godoy, María Dolores lperez@ujaen.TYGwJyIFes Data Science and Big Data Area PhD
Rivera Rivas, Antonio Jesús arivera@uFiIjBOn0jaen.es Data Science and Big Data Area PhD
Sánchez Sánchez, Pedro José pedroj@oBva.R8Vg1SGujaen.es Computational Intelligence Area PhD
Tabik, Siham siham@ugr.Q8bGlR_Lldpes Data Science and Big Data Area PhD - Ramón y Cajal
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