Benjamin Ricaud
Impact in
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- Complex Network Analysis Techniques
- Artificial Intelligence top 5%
- Advanced Graph Neural Networks
Papers in
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- Image and Signal Denoising Methods 5
- Music Technology and Sound Studies 2
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- Complex Network Analysis Techniques 8
- Co-authors
- Pierre Vandergheynst (8 shared papers)David I Shuman (3 shared papers)Bruno Torrésani (4 shared papers)Nathanaël Perraudin (3 shared papers)Andreas Loukas (1 shared paper)Kirell Benzi (2 shared papers)Pierre Borgnat (1 shared paper)Paulo Gonçalvès (1 shared paper)
- Journals
- Advances in Computational Mathematics (2 papers)Scientific Reports (1 paper)Atmosphere (1 paper)NeuroImage (1 paper)Comptes Rendus Physique (1 paper)
- Partner nations
- SwitzerlandFranceNorway
In The Last Decade
Benjamin Ricaud
24 papers receiving 617 citations
Peers
Comparison fields: 5 of 92
- Statistical and Nonlinear Physics 218
- Artificial Intelligence 314
- Computational Mathematics 5
- Computer Vision and Pattern Recognition 141
- Signal Processing 64
Countries citing papers authored by Benjamin Ricaud
This map shows the geographic impact of Benjamin Ricaud'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 Benjamin Ricaud with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Benjamin Ricaud more than expected).
Fields of papers citing papers by Benjamin Ricaud
This network shows the impact of papers produced by Benjamin Ricaud. 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 Benjamin Ricaud. The network helps show where Benjamin Ricaud may publish in the future.
Co-authors
The 25 scholars most cited alongside Benjamin Ricaud, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 25 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2015 | 205 | |
| 2 | 2017 | 109 | |
| 3 | 2012 | 69 | |
| 4 | 2017 | 52 | |
| 5 | 2013 | 48 | |
| 6 | 2019 | 40 | |
| 7 | 2013 | 15 | |
| 8 | 2013 | 12 | |
| 9 | 2013 | 12 | |
| 10 | 2016 | 12 | |
| 11 | 2006 | 9 | |
| 12 | 2011 | 9 | |
| 13 | 2024 | 8 | |
| 14 | 2016 | 7 | |
| 15 | 2022 | 6 | |
| 16 | 2009 | 5 | |
| 17 | 2012 | 4 | |
| 18 | 2019 | 4 | |
| 19 | 2024 | 3 | |
| 20 | SpectroBank: A filter-bank convolutional layer for CNN-based audio applications | 2019 | 3 |
About Benjamin Ricaud
Benjamin Ricaud is a scholar working on Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics, Signal Processing, Artificial Intelligence and Applied Mathematics, having authored 25 papers that have together received 639 indexed citations. Recurring topics across this work include Complex Network Analysis Techniques (8 papers), Advanced Graph Neural Networks (7 papers), Image and Signal Denoising Methods (5 papers), Speech and Audio Processing (4 papers), Mathematical Analysis and Transform Methods (4 papers), Music and Audio Processing (3 papers), Sparse and Compressive Sensing Techniques (3 papers) and Music Technology and Sound Studies (2 papers). The work is most often cited by research in Statistical and Nonlinear Physics (218 citations), Artificial Intelligence (314 citations), Computational Mathematics (5 citations), Computer Vision and Pattern Recognition (141 citations) and Signal Processing (64 citations). Benjamin Ricaud has collaborated with scholars based in Switzerland, France and Norway. Frequent co-authors include Pierre Vandergheynst, David I Shuman, Bruno Torrésani, Nathanaël Perraudin, Andreas Loukas, Kirell Benzi, Pierre Borgnat, Paulo Gonçalvès, Nicolas Tremblay and Xavier Bresson. Their work appears in journals such as Advances in Computational Mathematics, Scientific Reports, Atmosphere, NeuroImage and Comptes Rendus Physique.
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.