E-resources
PDF-
Mallat, Stéphane
Philosophical transactions of the Royal Society of London. Series A: Mathematical, physical, and engineering sciences, 04/2016, Volume: 374, Issue: 2065Journal Article
Deep convolutional networks provide state-of-the-art classifications and regressions results over many high-dimensional problems. We review their architecture, which scatters data with a cascade of linear filter weights and nonlinearities. A mathematical framework is introduced to analyse their properties. Computations of invariants involve multiscale contractions with wavelets, the linearization of hierarchical symmetries and sparse separations. Applications are discussed.
