Robert Stanforth
Impact in
- Artificial Intelligence top 5%
- Adversarial Robustness in Machine Learning
- Topic Modeling
- Anomaly Detection Techniques and Applications
- Natural Language Processing Techniques
- Explainable Artificial Intelligence (XAI)
Papers in
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- Adversarial Robustness in Machine Learning 8
- Topic Modeling 3
- Anomaly Detection Techniques and Applications 3
- Explainable Artificial Intelligence (XAI) 2
- Sentiment Analysis and Opinion Mining 1
-
- Integrated Circuits and Semiconductor Failure Analysis 3
- Co-authors
- Pushmeet Kohli (9 shared papers)Sven Gowal (8 shared papers)Krishnamurthy Dvijotham (8 shared papers)Po-Sen Huang (4 shared papers)Johannes Welbl (3 shared papers)Dani Yogatama (2 shared papers)Jonathan Uesato (4 shared papers)Chongli Qin (3 shared papers)
- Journals
- SAR and QSAR in environmental research (1 paper)Uncertainty in Artificial Intelligence (1 paper)QSAR & Combinatorial Science (1 paper)Neural Information Processing Systems (1 paper)International Conference on Learning Representations (2 papers)
- Partner nations
- United StatesUnited Kingdom
In The Last Decade
Robert Stanforth
12 papers receiving 326 citations
Peers
Comparison fields: 5 of 78
- Artificial Intelligence 261
- Health Informatics 5
- General Social Sciences 10
- Signal Processing 29
- Computer Vision and Pattern Recognition 53
Countries citing papers authored by Robert Stanforth
This map shows the geographic impact of Robert Stanforth'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 Robert Stanforth with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Robert Stanforth more than expected).
Fields of papers citing papers by Robert Stanforth
This network shows the impact of papers produced by Robert Stanforth. 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 Robert Stanforth. The network helps show where Robert Stanforth may publish in the future.
Co-authors
The 25 scholars most cited alongside Robert Stanforth, 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 | 2020 | 78 | |
| 2 | 2019 | 66 | |
| 3 | 2019 | 65 | |
| 4 | 2007 | 28 | |
| 5 | 2007 | 28 | |
| 6 | Adversarial Robustness through Local Linearization | 2019 | 27 |
| 7 | Are Labels Required for Improving Adversarial Robustness | 2019 | 24 |
| 8 | Efficient Neural Network Verification with Exactness Characterization | 2019 | 9 |
| 9 | Toward Evaluating Robustness of Deep Reinforcement Learning with Continuous Control | 2020 | 8 |
| 10 | Towards Verified Robustness under Text Deletion Interventions | 2020 | 3 |
| 11 | 2019 | 3 | |
| 12 | 2021 | 2 |
About Robert Stanforth
Robert Stanforth is a scholar working on Artificial Intelligence, Electrical and Electronic Engineering, Hardware and Architecture, Computational Theory and Mathematics and Molecular Biology, having authored 12 papers that have together received 341 indexed citations. Recurring topics across this work include Adversarial Robustness in Machine Learning (8 papers), Topic Modeling (3 papers), Anomaly Detection Techniques and Applications (3 papers), Integrated Circuits and Semiconductor Failure Analysis (3 papers), Physical Unclonable Functions (PUFs) and Hardware Security (2 papers), Computational Drug Discovery Methods (2 papers), Explainable Artificial Intelligence (XAI) (2 papers) and Sentiment Analysis and Opinion Mining (1 paper). The work is most often cited by research in Artificial Intelligence (261 citations), Health Informatics (5 citations), General Social Sciences (10 citations), Signal Processing (29 citations) and Computer Vision and Pattern Recognition (53 citations). Robert Stanforth has collaborated with scholars based in United States and United Kingdom. Frequent co-authors include Pushmeet Kohli, Sven Gowal, Krishnamurthy Dvijotham, Po-Sen Huang, Johannes Welbl, Dani Yogatama, Jonathan Uesato, Chongli Qin, Huan Zhang and Jack W. Rae. Their work appears in journals such as SAR and QSAR in environmental research, Uncertainty in Artificial Intelligence, QSAR & Combinatorial Science, Neural Information Processing Systems and International Conference on Learning Representations.
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.