Kai Arulkumaran
Impact in
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- Generative Adversarial Networks and Image Synthesis
- Advanced Image Processing Techniques
- Advanced Neural Network Applications
- Robotic Path Planning Algorithms
- Artificial Intelligence top 0.5%
- Reinforcement Learning in Robotics
- Anomaly Detection Techniques and Applications
Papers in
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- Gaze Tracking and Assistive Technology 2
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- Image and Signal Denoising Methods 2
- Generative Adversarial Networks and Image Synthesis 2
- Advanced Image Processing Techniques 2
- Multimodal Machine Learning Applications 2
- Co-authors
- Anil A. BharathMarc Peter DeisenrothMiles BrundageAntonia CreswellVincent DumoulinTom WhiteBiswa SenguptaJess Whittlestone
- Journals
- IEEE Signal Processing Magazine (2 papers)Journal of Artificial Intelligence Research (1 paper)Journal of the Intensive Care Society (1 paper)Computer Vision and Image Understanding (1 paper)Neurocomputing (1 paper)
- Partner nations
- United KingdomJapanUnited States
In The Last Decade
Kai Arulkumaran
14 papers receiving 5.4k citations
Hit Papers
Peers
Comparison fields: 5 of 181
- Computer Vision and Pattern Recognition 1.4k
- Artificial Intelligence 1.9k
- Computer Networks and Communications 812
- Signal Processing 344
- Control and Systems Engineering 706
Countries citing papers authored by Kai Arulkumaran
This map shows the geographic impact of Kai Arulkumaran'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 Kai Arulkumaran with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kai Arulkumaran more than expected).
Fields of papers citing papers by Kai Arulkumaran
This network shows the impact of papers produced by Kai Arulkumaran. 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 Kai Arulkumaran. The network helps show where Kai Arulkumaran may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Kai Arulkumaran, 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 | 2025 | 0 | |
| 2 | 2024 | 1 | |
| 3 | 2024 | 1 | |
| 4 | 2023 | 1 | |
| 5 | 2022 | 14 | |
| 6 | 2022 | 4 | |
| 7 | 2021 | 23 | |
| 8 | Generative Adversarial Networks: An Overview Hit paper breakdown → | 2018 | 2721 |
| 9 | 2018 | 9 | |
| 10 | Deep Reinforcement Learning for Subpixel Neural Tracking | 2018 | 7 |
| 11 | 2018 | 8 | |
| 12 | Image Synthesis with a Convolutional Capsule Generative Adversarial Network | 2018 | 10 |
| 13 | Deep Reinforcement Learning: A Brief Survey Hit paper breakdown → | 2017 | 2731 |
| 14 | 2016 | 4 | |
| 15 | Classifying options for deep reinforcement learning | 2016 | 3 |
About Kai Arulkumaran
Kai Arulkumaran is a scholar working on Human-Computer Interaction, Computer Vision and Pattern Recognition, Artificial Intelligence, Anesthesiology and Pain Medicine and Cognitive Neuroscience, having authored 15 papers that have together received 5.5k indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (5 papers), Explainable Artificial Intelligence (XAI) (3 papers), Image and Signal Denoising Methods (2 papers), Gaze Tracking and Assistive Technology (2 papers), Generative Adversarial Networks and Image Synthesis (2 papers), EEG and Brain-Computer Interfaces (2 papers), Advanced Image Processing Techniques (2 papers) and Multimodal Machine Learning Applications (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (1.4k citations), Artificial Intelligence (1.9k citations), Computer Networks and Communications (812 citations), Signal Processing (344 citations) and Control and Systems Engineering (706 citations). Kai Arulkumaran has collaborated with scholars based in United Kingdom, Japan and United States. Frequent co-authors include Anil A. Bharath, Marc Peter Deisenroth, Miles Brundage, Antonia Creswell, Vincent Dumoulin, Tom White, Biswa Sengupta, Jess Whittlestone, Matthew Crosby and Tianhong Dai. Their work appears in journals such as IEEE Signal Processing Magazine, Journal of Artificial Intelligence Research, Journal of the Intensive Care Society, Computer Vision and Image Understanding and Neurocomputing.
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.