Research - NAVER LABS Europe
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RESEARCH

Our technical expertise is enriched with a deep understanding of how people interact with technology.

Some of the big cross-disciplinary challenges we address:

Representation learning: turning all data entities (text, images, videos, web pages, emails, …) into semantically-rich representations to that can be efficiently processed, mined and related to each other.

Interactive learning: including the human in the learning loop to benefit from the complementary capabilities of humans and machines

Lifelong learning: exploring how to learn and manage prediction systems, policies or processes in a sustainable manner, i.e. in a manner that makes maximum reuse of previous experiences.

Machine reasoning: connecting existing pieces of knowledge to create new knowledge, e.g. being able to answer complex questions that require multiple supporting facts.

Decision making: learning how to make actionable decisions in uncertainty, especially in ever-changing environments

Agility: we live in an ever-changing world which means the technology we use must evolve in synergy with the people and business processes that touch it.

User centric: an important challenge in any technology is to the adoption by people. Only then does science become innovation. Our solutions are grounded in a deep understanding of how people work and how technology can help them every day.

The AI for Robotics theme comprises work from multiple research groups.

Collaboration

Our scientists collaborate with national and international partners, academics and businesses to solve problems and invent technology and services that will have impact in the real world.

Discover how our research is recognized.

Research illustrating image woman and 2 man in front of computers
14 April 2020

ECIR 2020

14th - 17th April 2020
Stéphane Clinchant will present a paper.
1 March 2020

2020 Winter Conference on Applications of Computer Vision

1st - 5th March 2020
Nicolas Monet will present two papers.
13 January 2020

AI Seminar at the University of Copenhagen: What is the best atomic unit to represent text?

23rd January 2020
Speaker: Matthias Gallé, NAVER LABS Europe Natural Language Processing group lead
13 December 2019

Towards understanding human actions out of context with the Mimetics dataset

This article introduces our recent arxiv preprint on further understanding human actions out of context thanks to the newly introduced Mimetics dataset.
6 December 2019

Explainability Matters in Machine Learning Pipelines

Workshop on methods and measures for explainability and trustworthiness of machine learning pipelines
4 December 2019

R2D2: Repeatable and Reliable Detector and Descriptor

Combining keypoint reliability in an image as part of the keypoint detection problem significantly improves feature matching.

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