Chair objectives

The chair focuses on the training part of neural networks, “intelligence acquisition”, and on the mathematics of the learning phase.

We seek for adequate mathematical tools to assess efficiency in training, learning and forms of robustness.

Our angles for achieving such goals range from the consideration of structural properties of neural networks, mathematical analysis, to algorithm design.

The three lines along which we address these issues are:

  • the geometry of neural networks
  • the tools for nonsmooth optimization (in particular, the mathematics of algorithmic differentiation)
  • the geometry/study of algorithms for the training phase

Our scientific approach mixes: mathematics (optimization, geometry, analysis), computer science, and applications to signal processing.

Program Certifiable AI
Themes: AI and physical models, Optimization and game theory for AI

Chair holder:
Jérôme Bolte, Université Toulouse Capitole

F. de Gournay (Insa)
F. Malgouyres (UT3),
E. Pauwels (UT3),
P. Weiss (CNRS),


Know more

In artificial intelligence, there is no point in running, you have to take the right path

In the mathematics family, optimization is a discipline in its own right that is essential for research in artificial intelligence. Explanations with researcher Jérôme Bolte, holder of the "Large-scale optimization for AI" chair.

Read the article in French


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