Yongchan Kwon
About
In The Last Decade
Yongchan Kwon
12 papers receiving 454 citations
Hit Papers
Peers
Comparison fields: 5 of 108
- Artificial Intelligence 168
- Computational Theory and Mathematics 96
- Materials Chemistry 95
- Computer Vision and Pattern Recognition 59
- Molecular Biology 56
Countries citing papers authored by Yongchan Kwon
This map shows the geographic impact of Yongchan Kwon'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 Yongchan Kwon with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yongchan Kwon more than expected).
Fields of papers citing papers by Yongchan Kwon
This network shows the impact of papers produced by Yongchan Kwon. 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 Yongchan Kwon. The network helps show where Yongchan Kwon may publish in the future.
Co-authorship network of co-authors of Yongchan Kwon
This figure shows the co-authorship network connecting the top 25 collaborators of Yongchan Kwon. A scholar is included among the top collaborators of Yongchan Kwon 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 Yongchan Kwon. Yongchan Kwon is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Efficient Computation and Analysis of Distributional Shapley Values | 1 |
| 2 | Lipschitz Continuous Autoencoders in Application to Anomaly Detection. | 2 |
| 3 | 17 | |
| 4 | 23 | |
| 5 | An analytic formulation for positive-unlabeled learning via weighted integral probability metric. | 1 |
| 6 | 99 | |
| 7 | Uncertainty quantification using Bayesian neural networks in classification: Application to biomedical image segmentation breakdown → | 255 |
| 8 | 7 | |
| 9 | 12 | |
| 10 | Uncertainty quantification using Bayesian neural networks in classification: Application to ischemic stroke lesion segmentation | 31 |
| 11 | 8 | |
| 12 | 6 |
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.