Machine Learning - Optimization Research - NAVER LABS Europe

MACHINE LEARNING AND OPTIMIZATION

Innovative models to design algorithms and imagine new tasks that push the boundaries and bring to life intelligent systems in our everyday lives.

Highlights

2021

  • 2 papers at ICRA 2021 on robot task learning and robot navigation.
  • Paper at EACL 2021 on Globalizing BERT-based transformer architectures for long document summarization

2020

Related Content

3D vision illustrating 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 vision a reality by bringing to life intelligent systems to supervise, enhance, secure and automate our everyday activities.

We work across deep learning, autonomous indoor robotics, adversarial learning protocols, machine reading and optimization in large graphs. We contribute to the development of the cutting edge products of NAVER LABS and are very active in the scientific community where we produce papers, contribute code and datasets and organise conferences, workshops and challenges.

Risk Sensitive Robot Navigation
We explore how effectively a single policy learned by reinforcement learning can modulate robot behaviour, from risk-averse (cautious) to risk-neutral (maximizing the average reward) Blog article by Chris Dance et al.
JulienPerez Podcast
Robot learning workshop at NeurIPS2020 – podcast with Julien Perez
ICRA blog banner
Our navigation system enables robots to adapt to specific real-world environments and use cases with only small amounts of human preference data. Blog article by Christopher Dance

Recent publications

Machine Learning and Optimization team:

Approved
Michel Aractingi
PhD candidate
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Theo Cachet
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Darko Drakulic
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Thierry Jacquin
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Seungsu Kim
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Sofia Michel
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Michael Niemaz
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Julien Perez
Group lead
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Denys Proux
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Tomi Silander
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Arnaud Sors