| Marc Dymetman, Sriram Venkatapathy, Chunyang Xiao |
| EMNLP, Lisboa, Portugal, September 17-21, 2015 |
We restate the classical logical notion of generation/parsing reversibility in terms of feasible probabilistic sampling, and argue for an implementation based on finite-state factors.
We propose a modular decomposition that reconciles generation accuracy with parsing robustness and allows the introduction of dynamic contextual factors.
For a robot to be useful it must be able to represent its knowledge of the world, share what it learns and interact with other agents, in particular humans. Our research combines expertise in human-robot interaction, natural language processing, speech, information retrieval, data management and low code/no code programming to build AI components that will help next-generation robots perform complex real-world tasks. These components will help robots interact safely with humans and their physical environment, other robots and systems, represent and update their world knowledge and share it with the rest of the fleet. More details on our research can be found in the Explore section below.
Visual perception is a necessary part of any intelligent system that is meant to interact with the world. Robots need to perceive the structure, the objects, and people in their environment to better understand the world and perform the tasks they are assigned. Our research combines expertise in visual representation learning, self-supervised learning and human behaviour understanding to build AI components that help robots understand and navigate in their 3D environment, detect and interact with surrounding objects and people and continuously adapt themselves when deployed in new environments. More details on our research can be found in the Explore section below.
To make robots autonomous in real-world everyday spaces, they should be able to learn from their interactions within these spaces, how to best execute tasks specified by non-expert users in a safe and reliable way. To do so requires sequential decision-making skills that combine machine learning, adaptive planning and control in uncertain environments as well as solving hard combinatorial optimization problems. Our research combines expertise in reinforcement learning, computer vision, robotic control, sim2real transfer, large multimodal foundation models and neural combinatorial optimization to build AI-based architectures and algorithms to improve robot autonomy and robustness when completing everyday complex tasks in constantly changing environments. More details on our research can be found in the Explore section below.
The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.
Our work covers the spectrum from unsupervised to supervised approaches, and from very deep architectures to very compact ones. We’re excited about the promise of big data to bring big performance gains to our algorithms but also passionate about the challenge of working in data-scarce and low-power scenarios.
Furthermore, we believe that a modern computer vision system needs to be able to continuously adapt itself to its environment and to improve itself via lifelong learning. Our driving goal is to use our research to deliver embodied intelligence to our users in robotics, autonomous driving, via phone cameras and any other visual means to reach people wherever they may be.
The NAVER France (all entities combined) gender equality index score: 67/100. This score is based on 2025 data.
– Difference in female/male salary: 17/40 points
– Difference in salary increases female/male: 35/35 points
– Salary increases upon return from maternity leave: 15/15 points
– Number of employees in under-represented gender in 10 highest salaries: 0/10 points
Index NAVER France de l’égalité professionnelle entre les femmes et les hommes pour l’année 2025 au titre des données 2025 : 67/100
Détail des indicateurs :
– Les écarts de salaire entre les femmes et les hommes : 17/40 points
– Les écarts des augmentations individuelles entre les femmes et les hommes : 35/35 points
– Toutes les salariées augmentées revenant de congé maternité : 15/15 points
– Le nombre de salariés du sexe sous-représenté parmi les 10 plus hautes rémunérations : 0/10 points
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