Rémi Gribonval

15.6k total citations · 3 hit papers
168 papers, 8.5k citations indexed

About

Rémi Gribonval is a scholar working on Signal Processing, Computational Mechanics and Computer Vision and Pattern Recognition. According to data from OpenAlex, Rémi Gribonval has authored 168 papers receiving a total of 8.5k indexed citations (citations by other indexed papers that have themselves been cited), including 93 papers in Signal Processing, 86 papers in Computational Mechanics and 60 papers in Computer Vision and Pattern Recognition. Recurrent topics in Rémi Gribonval's work include Blind Source Separation Techniques (72 papers), Sparse and Compressive Sensing Techniques (69 papers) and Speech and Audio Processing (57 papers). Rémi Gribonval is often cited by papers focused on Blind Source Separation Techniques (72 papers), Sparse and Compressive Sensing Techniques (69 papers) and Speech and Audio Processing (57 papers). Rémi Gribonval collaborates with scholars based in France, Switzerland and United Kingdom. Rémi Gribonval's co-authors include Emmanuel Vincent, Cédric Févotte, Pierre Vandergheynst, David K. Hammond, Morten Nielsen, Frédéric Bimbot, Mike E. Davies, Ngoc Q. K. Duong, Emmanuel Bacry and Michael Elad and has published in prestigious journals such as PLoS ONE, NeuroImage and Proceedings of the IEEE.

In The Last Decade

Rémi Gribonval

159 papers receiving 7.9k citations

Hit Papers

Performance measurement i... 2003 2026 2010 2018 2006 2010 2003 500 1000 1.5k

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Rémi Gribonval 4.5k 3.9k 2.2k 1.8k 965 168 8.5k
Lieven De Lathauwer 4.3k 0.9× 3.3k 0.9× 1.7k 0.8× 1.8k 1.0× 558 0.6× 243 12.1k
Mike E. Davies 2.6k 0.6× 3.9k 1.0× 2.3k 1.1× 661 0.4× 1.8k 1.9× 181 7.2k
Pierre Comon 6.4k 1.4× 2.0k 0.5× 1.5k 0.7× 2.4k 1.3× 502 0.5× 193 10.8k
Jelena Kovačević 2.3k 0.5× 1.2k 0.3× 4.4k 2.0× 2.2k 1.2× 362 0.4× 153 8.6k
J.-F. Cardoso 9.1k 2.0× 2.1k 0.5× 899 0.4× 2.3k 1.2× 700 0.7× 97 11.9k
C.L. Nikias 4.1k 0.9× 2.2k 0.6× 1.1k 0.5× 1.2k 0.6× 374 0.4× 198 7.2k
Zhifeng Zhang 1.8k 0.4× 2.6k 0.7× 2.4k 1.1× 644 0.4× 1.0k 1.1× 43 6.8k
Arvind Ganesh 2.4k 0.5× 4.8k 1.2× 7.7k 3.5× 1.9k 1.0× 1.3k 1.3× 43 11.9k
James H. McClellan 3.0k 0.7× 2.0k 0.5× 1.7k 0.8× 769 0.4× 1.4k 1.5× 282 7.0k
Jae S. Lim 3.0k 0.7× 1.4k 0.4× 2.5k 1.2× 1.2k 0.7× 465 0.5× 90 6.1k

Countries citing papers authored by Rémi Gribonval

Since Specialization
Citations

This map shows the geographic impact of Rémi Gribonval's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Rémi Gribonval with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rémi Gribonval more than expected).

Fields of papers citing papers by Rémi Gribonval

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Rémi Gribonval. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Rémi Gribonval. The network helps show where Rémi Gribonval may publish in the future.

Co-authorship network of co-authors of Rémi Gribonval

This figure shows the co-authorship network connecting the top 25 collaborators of Rémi Gribonval. A scholar is included among the top collaborators of Rémi Gribonval based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Rémi Gribonval. Rémi Gribonval is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Gribonval, Rémi, et al.. (2021). Compressive statistical learning with random feature moments. arXiv (Cornell University). 3(2). 113–164.
2.
Fan, Angela, Pierre Stock, Benjamin Graham, et al.. (2020). Training with Quantization Noise for Extreme Fixed-Point Compression. arXiv (Cornell University). 3 indexed citations
3.
Flamary, Rémi, et al.. (2020). Learning with minibatch Wasserstein : asymptotic and gradient properties. HAL (Le Centre pour la Communication Scientifique Directe).
4.
Keriven, Nicolas & Rémi Gribonval. (2018). Instance Optimal Decoding and the Restricted Isometry Property. HAL (Le Centre pour la Communication Scientifique Directe). 2 indexed citations
5.
Magoarou, Luc Le, Rémi Gribonval, & Nicolas Tremblay. (2017). Approximate Fast Graph Fourier Transforms via Multilayer Sparse Approximations. IEEE Transactions on Signal and Information Processing over Networks. 4(2). 407–420. 42 indexed citations
6.
Puy, Gilles, Mike E. Davies, & Rémi Gribonval. (2017). Recipes for Stable Linear Embeddings From Hilbert Spaces to $ {\mathbb {R}}^{m}$. IEEE Transactions on Information Theory. 63(4). 2171–2187. 7 indexed citations
7.
Puy, Gilles, Nicolas Tremblay, Rémi Gribonval, & Pierre Vandergheynst. (2016). Random sampling of bandlimited signals on graphs. Applied and Computational Harmonic Analysis. 44(2). 446–475. 95 indexed citations
8.
Gribonval, Rémi, et al.. (2015). Stable recovery of low-dimensional cones in Hilbert spaces: One RIP to\n rule them all. arXiv (Cornell University). 18 indexed citations
9.
Gribonval, Rémi & Pierre Machart. (2013). Reconciling "priors" & "priors" without prejudice?. Neural Information Processing Systems. 26. 2193–2201. 7 indexed citations
10.
Kitić, Srđan, Nancy Bertin, & Rémi Gribonval. (2013). A review of cosparse signal recovery methods applied to sound source localization. HAL (Le Centre pour la Communication Scientifique Directe). 1 indexed citations
11.
Giryes, Raja, Sangwook Nam, Michael Elad, Rémi Gribonval, & Mike E. Davies. (2013). Greedy-like algorithms for the cosparse analysis model. Linear Algebra and its Applications. 441. 22–60. 61 indexed citations
12.
Bimbot, Frédéric, et al.. (2012). Well-posedness of the permutation problem in sparse filter estimation withpminimization. Applied and Computational Harmonic Analysis. 35(3). 394–406. 1 indexed citations
13.
Soussen, Charles, Rémi Gribonval, Jérôme Idier, & Cédric Herzet. (2011). Sparse recovery conditions for Orthogonal Least Squares. arXiv (Cornell University).
14.
Hammond, David K., Pierre Vandergheynst, & Rémi Gribonval. (2010). Wavelets on graphs via spectral graph theory. Applied and Computational Harmonic Analysis. 30(2). 129–150. 1360 indexed citations breakdown →
15.
Foucart, Simon & Rémi Gribonval. (2010). Real versus complex null space properties for sparse vector recovery. Comptes Rendus Mathématique. 348(15-16). 863–865. 10 indexed citations
16.
Ozerov, Alexey, Pierrick Philippe, Rémi Gribonval, & Frédéric Bimbot. (2007). Choix et adaptation de modèles statistiques pour la séparation de voix chantée à partir d'un seul microphone. Traitement du signal. 24(3). 211–224. 1 indexed citations
17.
Gribonval, Rémi & Morten Nielsen. (2006). Highly sparse representations from dictionaries are unique and independent of the sparseness measure. Applied and Computational Harmonic Analysis. 22(3). 335–355. 95 indexed citations
18.
Borup, Lasse, Rémi Gribonval, & Morten Nielsen. (2004). Bi-framelet systems with few vanishing moments characterize Besov spaces. Applied and Computational Harmonic Analysis. 17(1). 3–28. 37 indexed citations
19.
Gribonval, Rémi. (2001). A Counter-Example to the General Convergence of Partially Greedy Algorithms. Journal of Approximation Theory. 111(1). 128–138. 1 indexed citations
20.
Gribonval, Rémi, Philippe Depalle, Xavier Rodet, Emmanuel Bacry, & Stéphane Mallat. (1996). Sound signals decomposition using a high resolution matching pursuit. HAL (Le Centre pour la Communication Scientifique Directe). 1996. 293–296. 22 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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