Kibok Lee
- Computer Vision and Pattern Recognition top 10%
- Artificial Intelligence
- Media Technology
- Computational Mechanics
- Industrial and Manufacturing Engineering
- Topics
- Domain Adaptation and Few-Shot Learning (4 papers)Machine Learning and Data Classification (3 papers)Reinforcement Learning in Robotics (2 papers)
- Journals
- Pattern RecognitionarXiv (Cornell University)International Conference on Machine Learning
- Partner nations
- South KoreaUnited StatesCanada
In The Last Decade
Kibok Lee
8 papers receiving 97 citations
Peers
Comparison fields: 5 of 39
- Computer Vision and Pattern Recognition 62
- Artificial Intelligence 61
- Media Technology 11
- Computational Mechanics 7
- Industrial and Manufacturing Engineering 6
Countries citing papers authored by Kibok Lee
This map shows the geographic impact of Kibok Lee'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 Kibok Lee with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kibok Lee more than expected).
Fields of papers citing papers by Kibok Lee
This network shows the impact of papers produced by Kibok Lee. 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 Kibok Lee. The network helps show where Kibok Lee may publish in the future.
Co-authorship network of co-authors of Kibok Lee
This figure shows the co-authorship network connecting the top 25 collaborators of Kibok Lee. A scholar is included among the top collaborators of Kibok Lee 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 Kibok Lee. Kibok Lee is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Network Randomization: A Simple Technique for Generalization in Deep Reinforcement Learning | 12 |
| 2 | $i$-Mix: A Domain-Agnostic Strategy for Contrastive Representation Learning | 1 |
| 3 | A Simple Randomization Technique for Generalization in Deep Reinforcement Learning | 3 |
| 4 | 28 | |
| 5 | Robust Determinantal Generative Classifier for Noisy Labels and Adversarial Attacks | 2 |
| 6 | Augmenting supervised neural networks with unsupervised objectives for large-scale image classification | 34 |
| 7 | 5 | |
| 8 | 20 |
About Kibok Lee
Kibok Lee is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Statistics, Probability and Uncertainty, having authored 8 papers that have together received 105 indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (4 papers), Machine Learning and Data Classification (3 papers) and Reinforcement Learning in Robotics (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (62 citations), Artificial Intelligence (61 citations) and Media Technology (11 citations). Kibok Lee has collaborated with scholars based in South Korea, United States and Canada. Frequent co-authors include Honglak Lee, Jinwoo Shin, Kimin Lee, Sukmin Yun, Bo Li, Junmo Kim, Kihyuk Sohn, Chunliang Li and Yian Zhu. Their work appears in journals such as Pattern Recognition, 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.