Research
machine learning and optimization image

During the last two decades, research in machine learning has evolved from the status of promising science to industrial reality. Formalized as an optimization task under constraint or as a mathematical integration, solutions to problems now exist that were previously considered beyond reach. In this context, the disciplines of machine learning and optimization constitute a cornerstone of the conception and development of systems with the capabilities to adapt and enhance through time.

We propose innovative models to design algorithms and imagine new tasks that push the possibilities given by this incoming revolution. These will make our ambient intelligence vision a reality by bringing to life intelligent systems to supervise, enhance, secure and automate our everyday activities.

The team works across deep learning, autonomous indoor robotics, adversarial learning protocols, machine reading and optimization in large graphs. We contribute to the development of  the cutting age products of NAVER LABS and are very active in the scientific communities producing papers and being involved in conferences and workshops in a variety of ways.

Recent publications by the Machine Learning and Optimization team:

Julien Perez teaching Master's course in Deep learning for Machine Reading at @CentraleSupélec (5th March 2018)

Julien Perez will be speaker at PAISS, Artificial Intelligence Summer School co-organized by Inria and NAVER LABS Europe.

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Agents of Change. Blog article by Julien Perez