Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Performance measurement in blind audio source separation
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).
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.
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
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
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
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 →
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
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.