1 code implementation • ICLR 2022 • Florentin Guth, John Zarka, Stéphane Mallat
Spatial variability is therefore transformed into variability along channels.
no code implementations • ICLR 2021 • John Zarka, Florentin Guth, Stéphane Mallat
Numerical experiments demonstrate that deep neural networks classifiers progressively separate class distributions around their mean, achieving linear separability.
2 code implementations • 18 Dec 2020 • John Zarka, Florentin Guth, Stéphane Mallat
On the opposite, a soft-thresholding on tight frames can reduce within-class variabilities while preserving class means.
1 code implementation • ICLR 2020 • John Zarka, Louis Thiry, Tomás Angles, Stéphane Mallat
It is implemented in a deep convolutional network with a homotopy algorithm having an exponential convergence.
2 code implementations • 28 Dec 2018 • Mathieu Andreux, Tomás Angles, Georgios Exarchakis, Roberto Leonarduzzi, Gaspar Rochette, Louis Thiry, John Zarka, Stéphane Mallat, Joakim andén, Eugene Belilovsky, Joan Bruna, Vincent Lostanlen, Muawiz Chaudhary, Matthew J. Hirn, Edouard Oyallon, Sixin Zhang, Carmine Cella, Michael Eickenberg
The wavelet scattering transform is an invariant signal representation suitable for many signal processing and machine learning applications.