6th – 12th December 2020. NeurIPS 2020 is a virtual-only conference.
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NeurIPS 2020 – Conference on Neural Information Processing Systems
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Accepted papers:
- “SuperLoss: A Generic Loss for Robust Curriculum Learning”
Thibault Castells, Philippe Weinzaepfel and Jérôme Revaud - “Hard Negative Mixing for Contrastive Learning”
Yannis Kalantidis, Mert Bulent Sariyildiz, Noe Pion, Philippe Weinzaepfel and Diane Larlus - “Deep Transformation-Invariant Clustering”
Tom Monnier, Thibault Groueix and Mathieu Aubry
3rd Robot Learning Workshop: Grounding Machine Learning Development in the Real World
Paper: Transformer-based meta-Imitation learning for robotic manipulation, Julien Perez, Theo Cachet, Seungsu Kim
NAVER LABS Europe is a co-organiser and supporter (best paper, complimentary registrations) of the workshop.
HAMLETS (Human And Machine in-the-Loop Evaluation and Learning Strategies) workshop
Paper: Robust active learning strategies for model variability, Jose Mena Roldan, Matthias Gallé
15th Women in Machine Learning Workshop (WiML 2020)
NAVER LABS Europe is a bronze sponsor of the WiML workshop.
Images from the joint NAVER LABS Europe and NAVER Clova booth at NeurIPS 2019.
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 optimisation problems. Our research combines expertise in reinforcement learning, computer vision, robotic control, sim2real transfer, large multimodal foundation models and neural combinatorial optimisation to build AI-based architectures and algorithms to improve robot autonomy and robustness when completing everyday complex tasks in constantly changing environments.
VISION
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.
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