Understanding Concentration and Separation in Deep Neural Networks

12 November 2020
16:00
Stéphane Mallat

Further Information: 

Abstract

Deep convolutional networks have spectacular performances that remain mostly not understood. Numerical experiments show that they classify by progressively concentrating each class in separate regions of a low-dimensional space. To explain these properties, we introduce a concentration and separation mechanism with multiscale tight frame contractions. Applications are shown for image classification and statistical physics models of cosmological structures and turbulent fluids.

The join button will be published on the right (Above the view all button) 30 minutes before the seminar starts (login required).