Kevin Robert Canini

26 total papers · 1.0k total citations
16 papers, 414 citations indexed

About

Kevin Robert Canini is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Cultural Studies. According to data from OpenAlex, Kevin Robert Canini has authored 16 papers receiving a total of 414 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Artificial Intelligence, 3 papers in Computer Vision and Pattern Recognition and 3 papers in Cultural Studies. Recurrent topics in Kevin Robert Canini's work include Bayesian Methods and Mixture Models (5 papers), Bayesian Modeling and Causal Inference (3 papers) and Child and Animal Learning Development (3 papers). Kevin Robert Canini is often cited by papers focused on Bayesian Methods and Mixture Models (5 papers), Bayesian Modeling and Causal Inference (3 papers) and Child and Animal Learning Development (3 papers). Kevin Robert Canini collaborates with scholars based in United States, Belgium and Canada. Kevin Robert Canini's co-authors include Thomas L. Griffiths, Lei Shi, Bongwon Suh, Peter Pirolli, Maya R. Gupta, Andrew Cotter, Jan Pfeifer, Michael Armbrust, Armando Fox and Peter Bodík and has published in prestigious journals such as Journal of Machine Learning Research, Psychonomic Bulletin & Review and Cognitive Science.

In The Last Decade

Kevin Robert Canini

16 papers receiving 390 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Kevin Robert Canini 221 118 88 65 59 16 414
Shuiqiao Yang 324 1.5× 90 0.8× 89 1.0× 61 0.9× 51 0.9× 26 488
Daniel Krause 163 0.7× 197 1.7× 37 0.4× 71 1.1× 50 0.8× 33 385
Matevž Kunaver 153 0.7× 297 2.5× 69 0.8× 77 1.2× 28 0.5× 17 447
Antonio González-Pardo 121 0.5× 95 0.8× 79 0.9× 47 0.7× 44 0.7× 33 443
Wan Shiou Yang 104 0.5× 257 2.2× 113 1.3× 73 1.1× 61 1.0× 15 446
Simon Dooms 156 0.7× 355 3.0× 62 0.7× 125 1.9× 40 0.7× 25 424
Mark Levene 184 0.8× 151 1.3× 21 0.2× 52 0.8× 31 0.5× 25 466
Kumaripaba Athukorala 180 0.8× 259 2.2× 39 0.4× 112 1.7× 18 0.3× 23 488
Denis Kotkov 167 0.8× 328 2.8× 78 0.9× 100 1.5× 17 0.3× 20 452
Levente Kocsis 217 1.0× 81 0.7× 45 0.5× 40 0.6× 22 0.4× 24 371

Countries citing papers authored by Kevin Robert Canini

Since Specialization
Citations

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

Fields of papers citing papers by Kevin Robert Canini

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kevin Robert Canini. 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 Kevin Robert Canini. The network helps show where Kevin Robert Canini may publish in the future.

Co-authorship network of co-authors of Kevin Robert Canini

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

All Works

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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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