Jihoon Tack
Impact in
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- Adversarial Robustness in Machine Learning
- Anomaly Detection Techniques and Applications
- Domain Adaptation and Few-Shot Learning
- Topic Modeling
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- Advanced Neural Network Applications
Papers in
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- Adversarial Robustness in Machine Learning 3
- Anomaly Detection Techniques and Applications 2
- Domain Adaptation and Few-Shot Learning 2
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- Advanced Neural Network Applications 1
- Co-authors
- Jinwoo Shin (5 shared papers)Jongheon Jeong (2 shared papers)Sangwoo Mo (1 shared paper)Sung Ju Hwang (3 shared papers)Minseon Kim (2 shared papers)Kyuyoung Kim (1 shared paper)Jaehyung Kim (1 shared paper)Jae Hyun Nam (1 shared paper)
- Journals
- arXiv (Cornell University) (1 paper)Proceedings of the AAAI Conference on Artificial Intelligence (1 paper)International Conference on Machine Learning (1 paper)
- Partner nations
- South KoreaCanada
In The Last Decade
Jihoon Tack
4 papers receiving 45 citations
Peers
Comparison fields: 5 of 29
- Artificial Intelligence 38
- Computer Vision and Pattern Recognition 18
- Health Informatics 1
- Biophysics 2
- Industrial and Manufacturing Engineering 3
Countries citing papers authored by Jihoon Tack
This map shows the geographic impact of Jihoon Tack'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 Jihoon Tack with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jihoon Tack more than expected).
Fields of papers citing papers by Jihoon Tack
This network shows the impact of papers produced by Jihoon Tack. 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 Jihoon Tack. The network helps show where Jihoon Tack may publish in the future.
Co-authors
The 8 scholars most cited alongside Jihoon Tack, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2022 | 25 | |
| 2 | CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances | 2020 | 19 |
| 3 | Entropy Weighted Adversarial Training | 2021 | 2 |
| 4 | 2023 | 1 | |
| 5 | 2024 | 0 |
About Jihoon Tack
Jihoon Tack is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Instrumentation, Epidemiology and Infectious Diseases, having authored 5 papers that have together received 47 indexed citations. Recurring topics across this work include Adversarial Robustness in Machine Learning (3 papers), Anomaly Detection Techniques and Applications (2 papers), Domain Adaptation and Few-Shot Learning (2 papers), Advanced Optical Sensing Technologies (1 paper), Data-Driven Disease Surveillance (1 paper) and Advanced Neural Network Applications (1 paper). The work is most often cited by research in Artificial Intelligence (38 citations), Computer Vision and Pattern Recognition (18 citations), Health Informatics (1 citation), Biophysics (2 citations) and Industrial and Manufacturing Engineering (3 citations). Jihoon Tack has collaborated with scholars based in South Korea and Canada. Frequent co-authors include Jinwoo Shin, Jongheon Jeong, Sangwoo Mo, Sung Ju Hwang, Minseon Kim, Kyuyoung Kim, Jaehyung Kim and Jae Hyun Nam. Their work appears in journals such as arXiv (Cornell University), Proceedings of the AAAI Conference on Artificial Intelligence and International Conference on Machine Learning.
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.