16 citations · 29 across the 5 of their papers we have counts for
6 papers
A Rainbow in Deep Network Black Boxes
Florentin Guth, Brice Ménard, Gaspar Rochette +1
A central question in deep learning is to understand the functions learned by deep networks. What is their approximation class? Do the learned weights and representations depend on…
An Empirical Analysis on the Vulnerabilities of End-to-End Speech Segregation Models
Rahil Parikh, Gaspar Rochette, Carol Espy-Wilson +1
End-to-end learning models have demonstrated a remarkable capability in performing speech segregation. Despite their wide-scope of real-world applications, little is known about th…
Scale Dependencies and Self-Similar Models with Wavelet Scattering Spectra
Rudy Morel, Gaspar Rochette, Roberto Leonarduzzi +2
We introduce the wavelet scattering spectra which provide non-Gaussian models of time-series having stationary increments. A complex wavelet transform computes signal variations at…
Efficient Per-Example Gradient Computations in Convolutional Neural Networks
Gaspar Rochette, Andre Manoel, Eric W. Tramel
Deep learning frameworks leverage GPUs to perform massively-parallel computations over batches of many training examples efficiently. However, for certain tasks, one may be interes…
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…
Phase Harmonic Correlations and Convolutional Neural Networks
Stéphane Mallat, Sixin Zhang, Gaspar Rochette
A major issue in harmonic analysis is to capture the phase dependence of frequency representations, which carries important signal properties. It seems that convolutional neural ne…