Frédéric Guyard

1.1k total citations · 1 hit paper
13 papers, 655 citations indexed

About

Frédéric Guyard is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computer Networks and Communications. According to data from OpenAlex, Frédéric Guyard has authored 13 papers receiving a total of 655 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 6 papers in Computer Vision and Pattern Recognition and 3 papers in Computer Networks and Communications. Recurrent topics in Frédéric Guyard's work include Image and Signal Denoising Methods (2 papers), Time Series Analysis and Forecasting (2 papers) and AI in cancer detection (2 papers). Frédéric Guyard is often cited by papers focused on Image and Signal Denoising Methods (2 papers), Time Series Analysis and Forecasting (2 papers) and AI in cancer detection (2 papers). Frédéric Guyard collaborates with scholars based in France, Sweden and Austria. Frédéric Guyard's co-authors include María A. Zuluaga, Pietro Michiardi, Michel Barlaud, Violaine Foltz, Jérémie Sellam, Anna Moltó, Christophe Hudry, Bruno Fautrel, H. Servy and Laure Gossec and has published in prestigious journals such as Pattern Recognition, European Radiology and Arthritis Care & Research.

In The Last Decade

Frédéric Guyard

13 papers receiving 635 citations

Hit Papers

USAD 2020 2026 2022 2024 2020 100 200 300 400 500

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Frédéric Guyard France 7 469 334 268 114 59 13 655
Shilin Qiu China 12 243 0.5× 90 0.3× 105 0.4× 17 0.1× 7 0.1× 21 607
Enrique Puertas Spain 12 359 0.8× 152 0.5× 88 0.3× 59 0.5× 6 0.1× 52 794
Mohamed Maher Ben Ismail Saudi Arabia 13 127 0.3× 81 0.2× 70 0.3× 24 0.2× 41 0.7× 68 465
Johnny S. Wong United States 14 181 0.4× 478 1.4× 163 0.6× 33 0.3× 12 0.2× 47 652
Dimitris Mitropoulos Greece 16 114 0.2× 119 0.4× 159 0.6× 24 0.2× 27 0.5× 52 591
Hyeonseung Im South Korea 11 84 0.2× 137 0.4× 171 0.6× 15 0.1× 4 0.1× 35 489
Juan E. Rubio Spain 11 105 0.2× 185 0.6× 43 0.2× 160 1.4× 16 0.3× 12 406
Michael Galloway United States 10 137 0.3× 367 1.1× 46 0.2× 18 0.2× 18 0.3× 35 669
Marcus Chun Jin Tan Singapore 10 263 0.6× 185 0.6× 67 0.3× 144 1.3× 1 0.0× 21 712
Soma Bandyopadhyay India 14 207 0.4× 161 0.5× 76 0.3× 18 0.2× 8 0.1× 33 561

Countries citing papers authored by Frédéric Guyard

Since Specialization
Citations

This map shows the geographic impact of Frédéric Guyard'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 Frédéric Guyard with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Frédéric Guyard more than expected).

Fields of papers citing papers by Frédéric Guyard

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Frédéric Guyard. 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 Frédéric Guyard. The network helps show where Frédéric Guyard may publish in the future.

Co-authorship network of co-authors of Frédéric Guyard

This figure shows the co-authorship network connecting the top 25 collaborators of Frédéric Guyard. A scholar is included among the top collaborators of Frédéric Guyard 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 Frédéric Guyard. Frédéric Guyard is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

13 of 13 papers shown
1.
Guyard, Frédéric, et al.. (2023). Learning Sparse auto-Encoders for Green AI image coding. 5. 1–5. 1 indexed citations
2.
Guyard, Frédéric, et al.. (2023). A New Semi-Supervised Classification Method Using a Supervised Autoencoder for Biomedical Applications. SPIRE - Sciences Po Institutional REpository. 1–5. 4 indexed citations
3.
Michiardi, Pietro, et al.. (2022). Do deep neural networks contribute to multivariate time series anomaly detection?. Pattern Recognition. 132. 108945–108945. 1 indexed citations
4.
Barlaud, Michel & Frédéric Guyard. (2021). Learning sparse deep neural networks using efficient structured projections on convex constraints for green AI. 1566–1573. 9 indexed citations
5.
Barlaud, Michel & Frédéric Guyard. (2021). Learning a Sparse Generative Non-Parametric Supervised Autoencoder. 3315–3319. 4 indexed citations
6.
Michiardi, Pietro, et al.. (2020). USAD. 3395–3404. 500 indexed citations breakdown →
8.
Varela, Martı́n, Lea Skorin‐Kapov, Frédéric Guyard, & Markus Fiedler. (2014). Meta-Modeling QoE. PIK - Praxis der Informationsverarbeitung und Kommunikation. 37(4). 3 indexed citations
9.
Fiedler, Markus, et al.. (2013). Back to normal? Impact of temporally increasing network disturbances on QoE. 21. 1186–1191. 7 indexed citations
10.
Guyard, Frédéric & Sergio Beker. (2009). Towards real-time anomalies monitoring for QoE indicators. Annals of Telecommunications. 65(1-2). 59–71. 8 indexed citations
11.
Guyard, Frédéric, et al.. (2006). Detection and Comparison of RTP and Skype Traffic and Performance. Global Communications Conference. 9 indexed citations
12.
Guyard, Frédéric, et al.. (2006). QRP08-5: Detection and Comparison of RTP and Skype Traffic and Performance. Globecom. 1–5. 9 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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