Section: Application Domains

Application Domains

Machine learning research can be conducted from two main perspectives: the first one, which has been dominant in the last 30 years, is to design learning algorithms and theories which are as generic as possible, the goal being to make as few assumptions as possible regarding the problems to be solved and to let data speak for themselves. This has led to many interesting methodological developments and successful applications. However, we believe that this strategy has reached its limit for many application domains, such as computer vision, bioinformatics, neuro-imaging, text and audio processing, which leads to the second perspective our team is built on: Research in machine learning theory and algorithms should be driven by interdisciplinary collaborations, so that specific prior knowledge may be properly introduced into the learning process, in particular with the following fields:

• Computer vision: object recognition, object detection, image segmentation, image/video processing, computational photography. In collaboration with the Willow project-team.

• Bioinformatics: cancer diagnosis, protein function prediction, virtual screening. In collaboration with Institut Curie.

• Text processing: document collection modeling, language models.

• Audio processing: source separation, speech/music processing. In collaboration with Telecom Paristech.

• Neuro-imaging: brain-computer interface (fMRI, EEG, MEG). In collaboration with the Parietal project-team.