Benoît Gérard

1.7k citations
51 papers · 1.2k · h-index 18

Impact in

Papers in

Benoît Gérard

45 papers receiving 1.1k citations

Peers

Benoît Gérard
Comparison fields: 5 of 72
  • Hardware and Architecture 319
  • Cellular and Molecular Neuroscience 401
  • Artificial Intelligence 653
  • Computer Vision and Pattern Recognition 363
  • Signal Processing 129
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Wenjie Xiong United States
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Yi‐Chen Lu United States
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Citations per field
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Citations per year

Countries citing papers authored by Benoît Gérard

Since Specialization
Citations

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

Fields of papers citing papers by Benoît Gérard

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Benoît Gérard, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Benoît Gérard Line = papers co-authored together Benoît Gérard links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 51 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2003199
2 2013111
3 2013102
4 201496
5 200591
6 201380
7 200247
8 201944
9 201342
10 201039
11 201039
12 200637
13 201529
14 201725
15 201424
16 201222
17 201421
18 200820
19 201813
20 201312

About Benoît Gérard

Benoît Gérard is a scholar working on Artificial Intelligence, Cellular and Molecular Neuroscience, Computer Vision and Pattern Recognition, Hardware and Architecture and Electrical and Electronic Engineering, having authored 51 papers that have together received 1.2k indexed citations. Recurring topics across this work include Cryptographic Implementations and Security (18 papers), Neuroscience and Neural Engineering (11 papers), Physical Unclonable Functions (PUFs) and Hardware Security (10 papers), Chaos-based Image/Signal Encryption (9 papers), Advanced Memory and Neural Computing (5 papers), Management and Organizational Studies (5 papers), Coding theory and cryptography (5 papers) and Social Sciences and Governance (4 papers). The work is most often cited by research in Hardware and Architecture (319 citations), Cellular and Molecular Neuroscience (401 citations), Artificial Intelligence (653 citations), Computer Vision and Pattern Recognition (363 citations) and Signal Processing (129 citations). Benoît Gérard has collaborated with scholars based in France, Belgium and Japan. Frequent co-authors include François‐Xavier Standaert, Nicolas Veyrat-Charvillon, Jean Delbeke, Claude Veraart, Annick Vanlierde, María Naya‐Plasencia, Vincent Grosso, Mårten Brelén, Mathieu Renauld and Pierre-Alain Fouque. Their work appears in journals such as Investigative Ophthalmology & Visual Science, Critical Perspectives on Accounting, Artificial Organs, Journal of Cryptographic Engineering and Lecture notes in computer science.

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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