Deep architectures, such as neural networks with two or more hidden layers of units, are a class of machines that comprise several levels of non-linear operations, each expressed in terms of parameters that can be learned. In this project we investigate various aspects of deep networks, such as their training via the use of different cost functions, their reusability, and their application to the analysis of biomedical data. We also aim to use larger datasets using GPU processing.
This project is financed by FEDER funds through the Programa Operacional Factores de Competitividade COMPETE and by Portuguese funds through FCT Fundação para a Ciência e a Tecnologia in the framework.