Cognitive Computing

  • Applied Learning Methods

    This research line explores the use of Learning Techniques to different domains, from data center optimization to cancer genomics, leveraging different techniques from statistical Machine Learning to state of the art Deep Learning and Neural Networks, and different programming frameworks.

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  • Neural networks for data-streams

    Hoeffding Trees are an established method for classification; at the same time, gradient descent methods are becoming increasingly popular, owing in part to the successes of deep learning. We are investigating the benefits of using GPUs for data-stream learning due to their high scalability.

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  • Supercomputing for Artificial Intelligence

    We require a new development of Supercomputing  that enables the convergence of Artificial Intelligence and High Performance Computing systems driving new insights based on the massive amounts of available data.

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