Jonathan Uesato
Impact in
-
- 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
-
- Adversarial Robustness in Machine Learning 5
- Anomaly Detection Techniques and Applications 2
- Explainable Artificial Intelligence (XAI) 2
- Machine Learning and Algorithms 1
- Domain Adaptation and Few-Shot Learning 1
- Reinforcement Learning in Robotics 1
-
- Integrated Circuits and Semiconductor Failure Analysis 2
- Co-authors
- Pushmeet Kohli (6 shared papers)Robert Stanforth (4 shared papers)Sven Gowal (3 shared papers)Krishnamurthy Dvijotham (3 shared papers)Relja Arandjelović (1 shared paper)Chongli Qin (1 shared paper)Rudy Bunel (2 shared papers)Timothy Mann (1 shared paper)
- Journals
- International Conference on Machine Learning (1 paper)International Conference on Learning Representations (1 paper)arXiv (Cornell University) (3 papers)
- Partner nations
- United StatesUnited Kingdom
In The Last Decade
Jonathan Uesato
7 papers receiving 190 citations
Peers
Comparison fields: 5 of 34
- Software 21
- Artificial Intelligence 169
- Signal Processing 26
- Computer Vision and Pattern Recognition 47
- Hardware and Architecture 13
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 | 79 | |
| 2 | 2017 | 39 | |
| 3 | Adversarial Risk and the Dangers of Evaluating Against Weak Attacks. | 2018 | 33 |
| 4 | Are Labels Required for Improving Adversarial Robustness | 2019 | 25 |
| 5 | Toward Evaluating Robustness of Deep Reinforcement Learning with Continuous Control | 2020 | 11 |
| 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 195 indexed citations. Recurring topics across this work include Adversarial Robustness in Machine Learning (5 papers), Anomaly Detection Techniques and Applications (2 papers), Integrated Circuits and Semiconductor Failure Analysis (2 papers), Explainable Artificial Intelligence (XAI) (2 papers), Machine Learning and Algorithms (1 paper), Domain Adaptation and Few-Shot Learning (1 paper), Reinforcement Learning in Robotics (1 paper) and Physical Unclonable Functions (PUFs) and Hardware Security (1 paper). The work is most often cited by research in Software (21 citations), Artificial Intelligence (169 citations), Signal Processing (26 citations), Computer Vision and Pattern Recognition (47 citations) and Hardware and Architecture (13 citations). Jonathan Uesato has collaborated with scholars based in United States and United Kingdom. Frequent co-authors include Pushmeet Kohli, Robert Stanforth, Sven Gowal, Krishnamurthy Dvijotham, Relja Arandjelović, Chongli Qin, Rudy Bunel, Timothy Mann, Aäron van den Oord and Brendan O’Donoghue. Their work appears in journals such as International Conference on Machine Learning, International Conference on Learning Representations and arXiv (Cornell University).
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.