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.
1 code implementation • 12 Dec 2019 • Gaspar Rochette, Andre Manoel, Eric W. Tramel
One notable application comes from the field of differential privacy, where per-example gradients must be norm-bounded in order to limit the impact of each example on the aggregated batch gradient.
3 code implementations • 19 Apr 2022 • Rudy Morel, Gaspar Rochette, Roberto Leonarduzzi, Jean-Philippe Bouchaud, Stéphane Mallat
We introduce the wavelet scattering spectra which provide non-Gaussian models of time-series having stationary increments.
1 code implementation • 29 Oct 2018 • Stéphane Mallat, Sixin Zhang, Gaspar Rochette
For wavelet filters, we show numerically that signals having sparse wavelet coefficients can be recovered from few phase harmonic correlations, which provide a compressive representation
no code implementations • 20 Jun 2022 • Rahil Parikh, Gaspar Rochette, Carol Espy-Wilson, Shihab Shamma
Knowing that harmonicity is a critical cue for these networks to group sources, in this work, we perform a thorough investigation on ConvTasnet and DPT-Net to analyze how they perform a harmonic analysis of the input mixture.
no code implementations • 29 May 2023 • Florentin Guth, Brice Ménard, Gaspar Rochette, Stéphane Mallat
Gaussian rainbow networks are defined with Gaussian weight distributions.