Better Representations for Artificial Intelligence (BRAIn) is a research team hosted at IMT Atlantique and part of the Mathematical and Electrical Engineering Department. It is also part of the CNRS laboratory Lab-STICC.
The purpose of BRAIn is to investigate key questions at the crossbreed of Artificial Intelligence, Deep Learning and Signal Processing, with applications using images, sounds, text and more complex domains including neuroimaging data.
As of beginning of 2021, these questions include:
- Few-shot learning
- BRAIn is ranked #1 on miniImageNet (c.f. PEME-BMS method: link)
- Continual (or incremental) learning
- BRAIn won the NIC (New Instances and Classes) continual learning competition at CVPR 2020
- Predicting generalization in deep learning
- BRAIn ranked #3 at the PGDL competition at NeurIPS 2020 (link)
- Compression of Deep Neural Networks
- BRAIn collaborates on this subject with Mila (lab directed by Yoshua Bengio) in Montréal, Polytechnique Montréal
- Some specific targets: autonomous vehicles, drones, microcontrollers, smartphones
- Graph Neural Networks and Graph Signal Processing
- BRAIn collaborates with Mila, University of Southern California, University of Rochester
- Associative memories
- BRAIn collaborates with University of Münster, University of Brest
- Robustness and Neural Network optimization
- BRAIn collaborates with University of Southern California
- Encoding and Decoding models for Neuroimaging
- BRAIn collaborates with University of Montréal
- Artificial Intelligence and Ethics
BRAIn was created in the continuation of the NEUCOD project (led by Prof. Emeritus Claude Berrou) funded by the European Research Council (ERC FP7 290901), as well as the Neural Coding and Neural Communications project funded by the Britanny Region and the CominLabs LabEx.
BRAIn is currently led by Pr. Vincent Gripon, Pr. Giulia Lioi and Pr. Nicolas Farrugia.