16 citations · 27 across the 4 of their papers we have counts for
4 papers
Phase Collapse in Neural Networks
Florentin Guth, John Zarka, Stéphane Mallat
Deep convolutional classifiers linearly separate image classes and improve accuracy as depth increases. They progressively reduce the spatial dimension whereas the number of channe…
Separation and Concentration in Deep Networks
John Zarka, Florentin Guth, Stéphane Mallat
Numerical experiments demonstrate that deep neural network classifiers progressively separate class distributions around their mean, achieving linear separability on the training s…
Deep Network Classification by Scattering and Homotopy Dictionary Learning
John Zarka, Louis Thiry, Tomás Angles +1
We introduce a sparse scattering deep convolutional neural network, which provides a simple model to analyze properties of deep representation learning for classification. Learning…
Kymatio: Scattering Transforms in Python
Mathieu Andreux, Tomás Angles, Georgios Exarchakis +15
The wavelet scattering transform is an invariant signal representation suitable for many signal processing and machine learning applications. We present the Kymatio software packag…