Philippe Pasquier

135 papers receiving 1.4k citations

Peers

Philippe Pasquier
Comparison fields: 5 of 125
  • Human-Computer Interaction 207
  • Signal Processing 288
  • Computer Vision and Pattern Recognition 534
  • Cognitive Neuroscience 327
  • Artificial Intelligence 417
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Simon Holland United Kingdom
Suranga Nanayakkara Singapore
Bill Kapralos Canada
Dan Morris United States
Akhil Mathur United Kingdom
Eiman Kanjo United Kingdom
Albert Ali Salah Türkiye
Maja Matarić United States
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Citations per year

Countries citing papers authored by Philippe Pasquier

Since Specialization
Citations

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

Fields of papers citing papers by Philippe Pasquier

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Philippe Pasquier, 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 Philippe Pasquier Line = papers co-authored together Philippe Pasquier links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 145 papers — load more, or switch the sort, to bring in the rest.

#Work
1 1998184
2 201187
3 200354
4 201151
5 201344
6 202044
7 201040
8 202040
9 201836
10 201833
11 201928
12 201428
13 201628
14 201926
15 201725
16
Argumentation and Persuasion in the Cognitive Coherence Theory
200623
17
Realtime Generation of Harmonic Progressions Using Constrained Markov Selection.
201022
18 201922
19
Scuddle: Generating Movement Catalysts for Computer-Aided Choreography.
201121
20 201621

About Philippe Pasquier

Philippe Pasquier is a scholar working on Computer Vision and Pattern Recognition, Signal Processing, Cognitive Neuroscience, Artificial Intelligence and Control and Systems Engineering, having authored 145 papers that have together received 1.5k indexed citations. Recurring topics across this work include Music Technology and Sound Studies (50 papers), Music and Audio Processing (50 papers), Neuroscience and Music Perception (28 papers), Multi-Agent Systems and Negotiation (18 papers), Human Motion and Animation (12 papers), Video Analysis and Summarization (9 papers), Human Pose and Action Recognition (9 papers) and Artificial Intelligence in Games (8 papers). The work is most often cited by research in Human-Computer Interaction (207 citations), Signal Processing (288 citations), Computer Vision and Pattern Recognition (534 citations), Cognitive Neuroscience (327 citations) and Artificial Intelligence (417 citations). Philippe Pasquier has collaborated with scholars based in Canada, United States and Australia. Frequent co-authors include Arne Eigenfeldt, Hélène Bertrand, John Wong-Chung, Thecla Schiphorst, Bernhard E. Riecke, Liz Sonenberg, Ekaterina R. Stepanova, Iyad Rahwan, Frank Dignum and Brahim Chaib-draa. Their work appears in journals such as Journal of New Music Research, Autonomous Agents and Multi-Agent Systems, Journal of Pediatric Orthopaedics, Leonardo and IEEE Transactions on Computational Intelligence and AI in Games.

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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