268 citations · 1.4k across the 80 of their papers we have counts for
11 papers · 1 filter
Keep the phase! Signal recovery in phase-only compressive sensing
Laurent Jacques, Thomas Feuillen
We demonstrate that a sparse signal can be estimated from the phase of complex random measurements, in a "phase-only compressive sensing" (PO-CS) scenario. With high probability an…
Going Below and Beyond, Off-the-Grid Velocity Estimation from 1-bit Radar Measurements
Gilles Monnoyer de Galland, Thomas Feuillen, Luc Vandendorpe +1
In this paper we propose to bridge the gap between using extremely low resolution 1-bit measurements and estimating targets' parameters, such as their velocities, that exist in a c…
When compressive learning fails: blame the decoder or the sketch?
Vincent Schellekens, Laurent Jacques
In compressive learning, a mixture model (a set of centroids or a Gaussian mixture) is learned from a sketch vector, that serves as a highly compressed representation of the datase…
One Bit to Rule Them All : Binarizing the Reconstruction in 1-bit Compressive Sensing
Thomas Feuillen, Mike Davies, Luc Vandendorpe +1
This work focuses on the reconstruction of sparse signals from their 1-bit measurements. The context is the one of 1-bit compressive sensing where the measurements amount to quanti…
MAYONNAISE: a morphological components analysis pipeline for circumstellar disks and exoplanets imaging in the near infrared
Benoît Pairet, Faustine Cantalloube, Laurent Jacques
Imaging circumstellar disks in the near-infrared provides unprecedented information about the formation and evolution of planetary systems. However, current post-processing techniq…
Sketching Datasets for Large-Scale Learning (long version)
Rémi Gribonval, Antoine Chatalic, Nicolas Keriven +3
This article considers "compressive learning," an approach to large-scale machine learning where datasets are massively compressed before learning (e.g., clustering, classification…
Factorization over interpolation: A fast continuous orthogonal matching pursuit
Gilles Monnoyer de Galland, Luc Vandendorpe, Laurent Jacques
We propose a fast greedy algorithm to compute sparse representations of signals from continuous dictionaries that are factorizable, i.e., with atoms that can be separated as a prod…
Breaking the waves: asymmetric random periodic features for low-bitrate kernel machines
Vincent Schellekens, Laurent Jacques
Many signal processing and machine learning applications are built from evaluating a kernel on pairs of signals, e.g. to assess the similarity of an incoming query to a database of…
Compressive Learning of Generative Networks
Vincent Schellekens, Laurent Jacques
Generative networks implicitly approximate complex densities from their sampling with impressive accuracy. However, because of the enormous scale of modern datasets, this training…
The importance of phase in complex compressive sensing
Laurent Jacques, Thomas Feuillen
We consider the question of estimating a real low-complexity signal (such as a sparse vector or a low-rank matrix) from the phase of complex random measurements. We show that in th…
Sparsity-Driven Moving Target Detection in Distributed Multistatic FMCW Radars
Gilles Monnoyer de Galland, Thomas Feuillen, Laurent Jacques +1
We investigate the problem of sparse target detection from widely distributed multistatic \textit{Frequency Modulated Continuous Wave} (FMCW) radar systems (using chirp modulation)…