The ELLIS Unit Paris will co-organize GSI'21 with SCAI Sorbonne and SEE, including a session on "Geometric Deep Learning" and a keynote given by Max Welling. As for GSI’13, GSI’15, GSI’17 and GSI’19, the objective of this SEE GSI’21 conference, hosted in PARIS, is to bring together pure/applied mathematicians and engineers, with common interest for Geometric tools and their applications for Information analysis. It emphasizes an active participation of young researchers to discuss emerging areas of collaborative research on “Geometric Science of Information and their Applications”. Current and ongoing uses of Information Geometry Manifolds in applied mathematics are the following: Advanced Signal/Image/Video Processing, Complex Data Modeling and Analysis, Information Ranking and Retrieval, Coding, Cognitive Systems, Optimal Control, Statistics on Manifolds, Topology/Machine/Deep Learning, Artificial Intelligence, Speech/sound recognition, natural language treatment, Big Data Analytics, Learning for Robotics, etc., which are substantially relevant for industry.
The Conference will be therefore held in areas of topics of mutual interest with the aim to:
- Provide an overview on the most recent state-of-the-art
- Exchange mathematical information/knowledge/expertise in the area
- Identify research areas/applications for future collaboration
Four keynotes have been scheduled:
- Max Welling (University of Amsterdam) on "Exploring Quantum Statistics for Machine Learning"
- Michel Broniatowski (Sorbonne University) on "Some insights on statistical divergences and choice of models"
- Maurice de Gosson (University of Vienna) on "Gaussian states from a symplectic geometry point of view"
- Jean Petitot (EHESS Paris) on "The primary visual system as a Cartan engine"
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