Jonathan Uesato
Impact in
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- Software Testing and Debugging Techniques
- Artificial Intelligence top 10%
- Adversarial Robustness in Machine Learning
- Anomaly Detection Techniques and Applications
- Machine Learning and Algorithms
Papers in
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- Adversarial Robustness in Machine Learning 5
- Anomaly Detection Techniques and Applications 2
- Explainable Artificial Intelligence (XAI) 2
- Machine Learning and Data Classification 1
- Reinforcement Learning in Robotics 1
- Machine Learning and Algorithms 1
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- Integrated Circuits and Semiconductor Failure Analysis 2
- Co-authors
- Pushmeet Kohli (6 shared papers)Robert Stanforth (4 shared papers)Krishnamurthy Dvijotham (3 shared papers)Sven Gowal (3 shared papers)Brendan O’Donoghue (1 shared paper)Aäron van den Oord (1 shared paper)Rudy Bunel (2 shared papers)Relja Arandjelović (1 shared paper)
- Journals
- International Conference on Learning Representations (1 paper)arXiv (Cornell University) (3 papers)International Conference on Machine Learning (1 paper)
- Partner nations
- United StatesUnited Kingdom
In The Last Decade
Jonathan Uesato
7 papers receiving 165 citations
Peers
Comparison fields: 5 of 31
- Software 19
- Artificial Intelligence 148
- Computer Vision and Pattern Recognition 43
- Hardware and Architecture 12
- Signal Processing 19
Countries citing papers authored by Jonathan Uesato
This map shows the geographic impact of Jonathan Uesato'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 Jonathan Uesato with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jonathan Uesato more than expected).
Fields of papers citing papers by Jonathan Uesato
This network shows the impact of papers produced by Jonathan Uesato. 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 Jonathan Uesato. The network helps show where Jonathan Uesato may publish in the future.
Co-authors
The 25 scholars most cited alongside Jonathan Uesato, 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 | 2019 | 64 | |
| 2 | 2017 | 35 | |
| 3 | Adversarial Risk and the Dangers of Evaluating Against Weak Attacks. | 2018 | 31 |
| 4 | Are Labels Required for Improving Adversarial Robustness | 2019 | 24 |
| 5 | Toward Evaluating Robustness of Deep Reinforcement Learning with Continuous Control | 2020 | 8 |
| 6 | Uncovering Surprising Behaviors in Reinforcement Learning via Worst-case Analysis | 2018 | 6 |
| 7 | 2021 | 2 |
About Jonathan Uesato
Jonathan Uesato is a scholar working on Artificial Intelligence, Electrical and Electronic Engineering, Computer Vision and Pattern Recognition, Hardware and Architecture and Infectious Diseases, having authored 7 papers that have together received 170 indexed citations. Recurring topics across this work include Adversarial Robustness in Machine Learning (5 papers), Anomaly Detection Techniques and Applications (2 papers), Explainable Artificial Intelligence (XAI) (2 papers), Integrated Circuits and Semiconductor Failure Analysis (2 papers), Machine Learning and Data Classification (1 paper), Reinforcement Learning in Robotics (1 paper), Machine Learning and Algorithms (1 paper) and Physical Unclonable Functions (PUFs) and Hardware Security (1 paper). The work is most often cited by research in Software (19 citations), Artificial Intelligence (148 citations), Computer Vision and Pattern Recognition (43 citations), Hardware and Architecture (12 citations) and Signal Processing (19 citations). Jonathan Uesato has collaborated with scholars based in United States and United Kingdom. Frequent co-authors include Pushmeet Kohli, Robert Stanforth, Krishnamurthy Dvijotham, Sven Gowal, Brendan O’Donoghue, Aäron van den Oord, Rudy Bunel, Relja Arandjelović, Timothy Mann and Chongli Qin. Their work appears in journals such as International Conference on Learning Representations, 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.