Gérard Biau is a full professor at the Probability, Statistics, and Modeling Laboratory (LPSM) of Sorbonne University, Paris. His research is mainly focused in developing new methodologies and rigorous mathematical theory in statistical learning and artificial intelligence, whilst trying to find connections between statistics and algorithms. He was a member of the Institut Universitaire de France from 2012 to 2017 and served from 2015 to 2018 as the president of the French Statistical Society. In 2018, he was awarded the Michel Monpetit - Inria prize by the French Academy of Sciences. He is currently director of Sorbonne Center for Artificial Intelligence (SCAI).
He will lead the January session of the DATAIA Seminars on the theme: Recurrent Neural Networks and Ordinary Differential Equations*.
Deep learning has become a prominent method for many applications, for instance computer vision or neural language processing. Mathematical understanding of these methods is yet incomplete. A recent approach has been to view a neural network as a discretized version of an ordinary differential equation. I will start by providing an overview of this emerging field and discuss new results regarding Recurrent Neural Networks, a common type of neural networks for time series.
*Joint work with Adeline Fermanian (Sorbonne University), Pierre Marion (Sorbonne University) and Jean-Philippe Vert (Google Research).
Practical information
The seminar will be held in hybrid:
- in person at the Centre Inria Saclay, 1 rue Honoré d'Estienne d'Orves, 91120 Palaiseau - Grace Hopper room. The number of places is limited to 20 people. Please inform us of your wish to attend the seminar by email at: com-dataia@inria.fr
- online by logging in using the following link (please enter your name and email first to log in as a participant): https://inria.webex.com/inria/j.php?MTID=m0dac4cf7159b02be6548701291f5f37e