no code implementations • 13 Jun 2020 • Randall Balestriero, Herve Glotin, Richard G. Baraniuk
We develop an interpretable and learnable Wigner-Ville distribution that produces a super-resolved quadratic signal representation for time-series analysis.
no code implementations • ICML 2018 • Randall Balestriero, Romain Cosentino, Herve Glotin, Richard Baraniuk
We propose to tackle the problem of end-to-end learning for raw waveform signals by introducing learnable continuous time-frequency atoms.
no code implementations • 27 Feb 2018 • Randall Balestriero, Herve Glotin, Richard Baraniuk
Deep Neural Networks (DNNs) provide state-of-the-art solutions in several difficult machine perceptual tasks.
no code implementations • 18 Jul 2017 • Randall Balestriero, Herve Glotin
In this paper we propose a scalable version of a state-of-the-art deterministic time-invariant feature extraction approach based on consecutive changes of basis and nonlinearities, namely, the scattering network.