25 citations · 51 across the 8 of their papers we have counts for
5 papers · 1 filter
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…
Statistical learning of geometric characteristics of wireless networks
Antoine Brochard, Bartłomiej Błaszczyszyn, Stéphane Mallat +1
Motivated by the prediction of cell loads in cellular networks, we formulate the following new, fundamental problem of statistical learning of geometric marks of point processes: A…
Generative networks as inverse problems with Scattering transforms
Tomás Angles, Stéphane Mallat
Generative Adversarial Nets (GANs) and Variational Auto-Encoders (VAEs) provide impressive image generations from Gaussian white noise, but the underlying mathematics are not well…
Multiscale Hierarchical Convolutional Networks
Jörn-Henrik Jacobsen, Edouard Oyallon, Stéphane Mallat +1
Deep neural network algorithms are difficult to analyze because they lack structure allowing to understand the properties of underlying transforms and invariants. Multiscale hierar…