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NeCOL: An Innovative Methodology for Building Better Deep Learning Tools for Real Word Applications

Objective

Nowadays, intelligent systems based on deep learning (DL) are latent in many aspects of our society. But the use of inadequate neural networks (NNs) architectures and the high computational costs required by DL limit its widespread use. Thus, advanced optimization methods (such as metaheuristics) may be applied to improve common DL methodologies, which in general use gradient based methods and apply complex engineering by hand.
This project aims to define an efficient DL methodology, which is named Neural CO-evolutionary Learning (NeCOL), based on the marriage between co-evolutionary algorithms (CEAs) and recurrent NNs (RNNs). NeCOL will be used to automatically define RNNs of high (unseen) efficiency and efficacy, which will be adapted to explicit needs. It will be applied in two use cases of the highest value and relevance in EU: cybersecurity and Smart City. We focus on RNNs because they are applied to non-stationary data streams, as in our use cases.
Despite EU efforts, China and the USA are the most productive countries in DL. Thus, EU must try harder to lead this compelling domain. This MSCA will support the candidate to master this new cutting-edge world-wide research, which will contribute to EU excellence and competitiveness. It will allow the candidate to get exceptional trainings from world class experts at the prestigious MIT that will be exploited at UMA and the priceless supervision of Prof. Alba (UMA) and Prof. O’Reilly (MIT).
The applicant is the appropriate choice to successfully accomplish this research because he has a valuable expertise in modeling hard-to-solve real-world problems (as it is the case of RNNs optimization) and addressing them by using metaheuristics. The expected early high scientific impact of this research in the EU will open up the best possible career opportunities for him, preparing him to overwhelmingly compete for a solid permanent position at UMA and other possible destinations (even industry).

Field of science

  • /natural sciences/computer and information sciences/artificial intelligence/computational intelligence
  • /engineering and technology/civil engineering/architecture engineering/smart city
  • /natural sciences/computer and information sciences/artificial intelligence/machine learning/deep learning

Call for proposal

H2020-MSCA-IF-2017
See other projects for this call

Funding Scheme

MSCA-IF-GF - Global Fellowships

Coordinator

UNIVERSIDAD DE MALAGA
Address
Avda Cervantes, Num. 2
29016 Malaga
Spain
Activity type
Higher or Secondary Education Establishments
EU contribution
€ 239 191,20

Partners (1)

MASSACHUSETTS INSTITUTE OF TECHNOLOGY
United States
Address
Massachusetts Avenue 77
02139 Cambridge
Activity type
Higher or Secondary Education Establishments