Jaehong Yoon
- Artificial Intelligence
- Computer Vision and Pattern Recognition
- Computational Mechanics
- Electrical and Electronic Engineering
- Radiology, Nuclear Medicine and Imaging
- Co-authors
- Sung Ju HwangEunho YangHuaxiu YaoZhang Xiao-huiMohit BansalZiyang WangGedas BertasiusFeng Cheng
- Topics
- Multimodal Machine Learning Applications (2 papers)Natural Language Processing Techniques (2 papers)Speech and dialogue systems (1 paper)
- Journals
- Scholarworks@UNIST (Ulsan National Institute of Science and Technology)arXiv (Cornell University)
- Partner nations
- South KoreaUnited States
In The Last Decade
Jaehong Yoon
6 papers receiving 109 citations
Peers
Comparison fields: 5 of 44
- Artificial Intelligence 81
- Computer Vision and Pattern Recognition 56
- Computational Mechanics 12
- Electrical and Electronic Engineering 8
- Radiology, Nuclear Medicine and Imaging 7
Countries citing papers authored by Jaehong Yoon
This map shows the geographic impact of Jaehong 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 Jaehong Yoon with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jaehong Yoon more than expected).
Fields of papers citing papers by Jaehong Yoon
This network shows the impact of papers produced by Jaehong 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 Jaehong Yoon. The network helps show where Jaehong Yoon may publish in the future.
Co-authorship network of co-authors of Jaehong Yoon
This figure shows the co-authorship network connecting the top 25 collaborators of Jaehong Yoon. A scholar is included among the top collaborators of Jaehong 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 Jaehong Yoon. Jaehong Yoon is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 5 | |
| 4 | 0 | |
| 5 | 6 | |
| 6 | 11 | |
| 7 | Federated Continual Learning with Weighted Inter-client Transfer | 29 |
| 8 | Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learning | 9 |
| 9 | Combined Group and Exclusive Sparsity for Deep Neural Networks | 55 |
About Jaehong Yoon
Jaehong Yoon is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Aerospace Engineering, having authored 9 papers that have together received 115 indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (2 papers), Natural Language Processing Techniques (2 papers) and Speech and dialogue systems (1 paper). The work is most often cited by research in Computational Mathematics (2 citations), Computer Vision and Pattern Recognition (56 citations) and Artificial Intelligence (81 citations). Jaehong Yoon has collaborated with scholars based in South Korea and United States. Frequent co-authors include Sung Ju Hwang, Eunho Yang, Huaxiu Yao, Zhang Xiao-hui, Mohit Bansal, Ziyang Wang, Gedas Bertasius, Feng Cheng, Ziyang Wang and Jiyoon Lee. Their work appears in journals such as Scholarworks@UNIST (Ulsan National Institute of Science and Technology) and arXiv (Cornell University).
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.