Phase Collapse in Neural Networks

1 code implementation • ICLR 2022 • Florentin Guth, John Zarka, Stéphane Mallat

Spatial variability is therefore transformed into variability along channels.

Tight Frame Contractions in Deep Networks

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.

Separation and Concentration in Deep Networks

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.

General Classification Image Classification

Deep Network Classification by Scattering and Homotopy Dictionary Learning

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.

Classification Dictionary Learning +2

Kymatio: Scattering Transforms in Python

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.

BIG-bench Machine Learning