Jinsung Yoon
About
In The Last Decade
Jinsung Yoon
65 papers receiving 2.7k citations
Hit Papers
Peers
Comparison fields: 5 of 172
- Artificial Intelligence 1.3k
- Cardiology and Cardiovascular Medicine 408
- Computer Vision and Pattern Recognition 375
- Radiology, Nuclear Medicine and Imaging 305
- Signal Processing 193
Countries citing papers authored by Jinsung Yoon
This map shows the geographic impact of Jinsung 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 Jinsung Yoon with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jinsung Yoon more than expected).
Fields of papers citing papers by Jinsung Yoon
This network shows the impact of papers produced by Jinsung 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 Jinsung Yoon. The network helps show where Jinsung Yoon may publish in the future.
Co-authorship network of co-authors of Jinsung Yoon
This figure shows the co-authorship network connecting the top 25 collaborators of Jinsung Yoon. A scholar is included among the top collaborators of Jinsung 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 Jinsung Yoon. Jinsung 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 | 2 | |
| 3 | 2 | |
| 4 | 7 | |
| 5 | 8 | |
| 6 | 6 | |
| 7 | CutPaste: Self-Supervised Learning for Anomaly Detection and Localization breakdown → | 502 |
| 8 | 9 | |
| 9 | Interpretable sequence learning for COVID-19 forecasting | 9 |
| 10 | Data Valuation using Reinforcement Learning | 9 |
| 11 | VIME: Extending the Success of Self- and Semi-supervised Learning to Tabular Domain | 64 |
| 12 | Differentially Private Bagging: Improved utility and cheaper privacy than subsample-and-aggregate | 4 |
| 13 | Time-series Generative Adversarial Networks breakdown → | 352 |
| 14 | GANITE: Estimation of Individualized Treatment Effects using Generative Adversarial Nets | 69 |
| 15 | KnockoffGAN: Generating Knockoffs for Feature Selection using Generative Adversarial Networks | 19 |
| 16 | INVASE: Instance-wise Variable Selection using Neural Networks | 38 |
| 17 | PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees | 145 |
| 18 | GAIN: Missing Data Imputation using Generative Adversarial Nets | 61 |
| 19 | Abstract 14882: Interpretable Machine Learning Identifies Risk Predictors in Patients With Heart Failure | 1 |
| 20 | ForecastICU: a prognostic decision support system for timely prediction of intensive care unit admission | 18 |
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.