Stéphane Mallat

This lecture will move towards this world by beginning from well relatively understood maximum entropy modelization. We first show that non-Gaussian and non-Markovian stationary processes requires to separate scales and measure scale interactions, which can be done with a deep neural network. Applications to turbulence models in physics and cosmology will be shown.

We shall review deep Generative networks such as GAN and Variational Encoders, which can synthesize realizations of non-stationary processes or highly complex processes such as speech or music. We show that they can be considerably simplified by defining the estimation as an inverse problem. This will build a bridge with  maximum entropy estimation. Applications will be shown on images, speech and music generation.

Stéphane Mallat was Professor at the Courant Institute of Mathematical Sciences from 1988 to 1994. In 1995, he became Professor in Applied Mathematics at Ecole Polytechnique, Paris and Department Chair in 2001. From 2001 to 2007 he was co-founder and CEO of a semiconductor start-up company. From 2012 to 2017 he was Professor in the Computer Science Department of Ecole Normale Supérieure, in Paris. Since 2017, he holds the “Data Sciences” chair at the Collège de France.

Stéphane Mallat’s research interests include machine learning, signal processing, and harmonic analysis. He is a member of the French Academy of sciences, a foreign member of the US National Academy of Engineering, an IEEE Fellow and a EUSIPCO Fellow. In 1997, he received the Outstanding Achievement Award from the SPIE Society and was a plenary lecturer at the International Congress of Mathematicians in 1998. He also received the 2004 European IST Grand prize, the 2004 INIST-CNRS prize for most cited French researcher in engineering and computer science, the 2007 EADS grand prize of the French Academy of Sciences, the 2013 Innovation medal of the CNRS, and the 2015 IEEE Signal Processing best sustaining paper award.