Hyunjik Kim
- Computer Vision and Pattern Recognition top 10%
- Artificial Intelligence
- Signal Processing
- Statistical and Nonlinear Physics
- Radiology, Nuclear Medicine and Imaging
- Co-authors
- Andriy MnihYee Whye TehAdam R. KosiorekIngmar PosnerMatthias BauerLucas TheisJin XuEmilien Dupont
- Topics
- Anomaly Detection Techniques and Applications (2 papers)Gaussian Processes and Bayesian Inference (2 papers)Advanced Image Processing Techniques (2 papers)
- Journals
- Oxford University Research Archive (ORA) (University of Oxford)arXiv (Cornell University)International Conference on Machine Learning
- Partner nations
- United KingdomUnited States
In The Last Decade
Hyunjik Kim
8 papers receiving 137 citations
Peers
Comparison fields: 5 of 38
- Computer Vision and Pattern Recognition 92
- Artificial Intelligence 89
- Signal Processing 25
- Statistical and Nonlinear Physics 7
- Radiology, Nuclear Medicine and Imaging 6
Countries citing papers authored by Hyunjik Kim
This map shows the geographic impact of Hyunjik Kim'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 Hyunjik Kim with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hyunjik Kim more than expected).
Fields of papers citing papers by Hyunjik Kim
This network shows the impact of papers produced by Hyunjik Kim. 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 Hyunjik Kim. The network helps show where Hyunjik Kim may publish in the future.
Co-authorship network of co-authors of Hyunjik Kim
This figure shows the co-authorship network connecting the top 25 collaborators of Hyunjik Kim. A scholar is included among the top collaborators of Hyunjik Kim 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 Hyunjik Kim. Hyunjik Kim 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 | 11 | |
| 3 | LieTransformer: Equivariant Self-Attention for Lie Groups | 1 |
| 4 | The Lipschitz Constant of Self-Attention | 1 |
| 5 | MetaFun: Meta-Learning with Iterative Functional Updates | 11 |
| 6 | Scaling up the Automatic Statistician: Scalable Structure Discovery using Gaussian Processes | 6 |
| 7 | Disentangling by Factorising | 93 |
| 8 | 21 | |
| 9 | Scalable Structure Discovery in Regression using Gaussian Processes | 1 |
About Hyunjik Kim
Hyunjik Kim is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Signal Processing, having authored 9 papers that have together received 145 indexed citations. Recurring topics across this work include Anomaly Detection Techniques and Applications (2 papers), Gaussian Processes and Bayesian Inference (2 papers) and Advanced Image Processing Techniques (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (92 citations), Artificial Intelligence (89 citations) and Signal Processing (25 citations). Hyunjik Kim has collaborated with scholars based in United Kingdom and United States. Frequent co-authors include Andriy Mnih, Yee Whye Teh, Adam R. Kosiorek, Ingmar Posner, Matthias Bauer, Lucas Theis, Jin Xu, Emilien Dupont, Luca Versari and Michael Hutchinson. Their work appears in journals such as Oxford University Research Archive (ORA) (University of Oxford), arXiv (Cornell University) 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.