Jaesik Yoon

449 citations
5 papers · 86 indexed · h-index 3
Topics
Advanced Vision and Imaging (2 papers)Advanced Image and Video Retrieval Techniques (1 paper)Image Retrieval and Classification Techniques (1 paper)
Journals
arXiv (Cornell University)neural information processing systems

In The Last Decade

Jaesik Yoon

5 papers receiving 81 citations

Peers

Jaesik Yoon
Comparison fields: 5 of 32
  • Artificial Intelligence 73
  • Computer Vision and Pattern Recognition 36
  • Radiology, Nuclear Medicine and Imaging 8
  • Cancer Research 7
  • Control and Systems Engineering 6
Replace Ousmane Dia with:
Ousmane Dia United States
Orestis Plevrakis United States
Jake Snell Canada
Francesco Visin United Kingdom
Francis Dutil Canada
Shagun Sodhani Canada
Colin Wei United States
Yonatan Geifman Israel
Hippolyt Ritter United Kingdom
Emilie Morvant France
Jaesik Yoon relative to Ousmane Dia United States Ousmane Dia's profile →
Citations per field
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Ousmane Dia · 1×
Citations per year

Countries citing papers authored by Jaesik Yoon

Since Specialization
Citations

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

Fields of papers citing papers by Jaesik Yoon

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jaesik Yoon

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

All Works

5 of 5 papers shown
#WorkIndexed citations
1 2
2
Sequential Neural Processes
14
3
Bayesian Model-Agnostic Meta-Learning
65
4 4
5 1

About Jaesik Yoon

Jaesik Yoon is a scholar working on Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition and Artificial Intelligence, having authored 5 papers that have together received 86 indexed citations. Recurring topics across this work include Advanced Vision and Imaging (2 papers), Advanced Image and Video Retrieval Techniques (1 paper) and Image Retrieval and Classification Techniques (1 paper). The work is most often cited by research in Artificial Intelligence (73 citations), Computer Vision and Pattern Recognition (36 citations) and Signal Processing (5 citations). Jaesik Yoon has collaborated with scholars based in Netherlands, South Korea and Canada. Frequent co-authors include Sungjin Ahn, Taesup Kim, Sungwoong Kim, Yoshua Bengio, Ousmane Dia, Young-Sung Son, Chang D. Yoo and Jinho Choi. Their work appears in journals such as arXiv (Cornell University) and neural information processing systems.

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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