353 citations · 1.8k across the 69 of their papers we have counts for
9 papers · 1 filter
Learning a Complete Image Indexing Pipeline
Himalaya Jain, Joaquin Zepeda, Patrick Pérez +1
To work at scale, a complete image indexing system comprises two components: An inverted file index to restrict the actual search to only a subset that should contain most of the i…
Sketched Clustering via Hybrid Approximate Message Passing
Evan Byrne, Antoine Chatalic, Remi Gribonval +1
In sketched clustering, a dataset of samples is first sketched down to a vector of modest size, from which the centroids are subsequently extracted. Advantages include i) reduc…
A modeling and algorithmic framework for (non)social (co)sparse audio restoration
Clément Gaultier, Nancy Bertin, Srđan Kitić +1
We propose a unified modeling and algorithmic framework for audio restoration problem. It encompasses analysis sparse priors as well as more classical synthesis sparse priors, and…
Analyzing the Approximation Error of the Fast Graph Fourier Transform
Luc LeMagoarou, Nicolas Tremblay, Rémi Gribonval
The graph Fourier transform (GFT) is in general dense and requires O(n^2) time to compute and O(n^2) memory space to store. In this paper, we pursue our previous work on the approx…
SUBIC: A supervised, structured binary code for image search
Himalaya Jain, Joaquin Zepeda, Patrick Pérez +1
For large-scale visual search, highly compressed yet meaningful representations of images are essential. Structured vector quantizers based on product quantization and its variants…
Beyond Moore-Penrose Part II: The Sparse Pseudoinverse
Ivan Dokmanić, Rémi Gribonval
This is the second part of a two-paper series on generalized inverses that minimize matrix norms. In Part II we focus on generalized inverses that are minimizers of entrywise p nor…
Beyond Moore-Penrose Part I: Generalized Inverses that Minimize Matrix Norms
Ivan Dokmanić, Rémi Gribonval
This is the first paper of a two-long series in which we study linear generalized inverses that minimize matrix norms. Such generalized inverses are famously represented by the Moo…
Compressive Statistical Learning with Random Feature Moments
Rémi Gribonval, Gilles Blanchard, Nicolas Keriven +1
We describe a general framework -- compressive statistical learning -- for resource-efficient large-scale learning: the training collection is compressed in one pass into a low-dim…
Compressed sensing in Hilbert spaces
Yann Traonmilin, Gilles Puy, Rémi Gribonval +1
In many linear inverse problems, we want to estimate an unknown vector belonging to a high-dimensional (or infinite-dimensional) space from few linear measurements. To overcome the…