Charles A. Sutton
Impact in
- Artificial Intelligence top 0.05%
- Topic Modeling
- Natural Language Processing Techniques
- Advanced Graph Neural Networks
- Anomaly Detection Techniques and Applications
- Domain Adaptation and Few-Shot Learning
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- Advanced Neural Network Applications
- Advanced Image and Video Retrieval Techniques
Papers in
-
- Topic Modeling 23
- Natural Language Processing Techniques 23
- Bayesian Modeling and Causal Inference 12
- Gaussian Processes and Bayesian Inference 8
- Bayesian Methods and Mixture Models 7
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- Software Engineering Research 20
- Co-authors
- Yichuan Zhang (3 shared papers)Amos Storkey (2 shared papers)Zoubin Ghahramani (1 shared paper)Andrew McCallum (18 shared papers)Akash Kumar Srivastava (5 shared papers)Nigel Goddard (4 shared papers)Mingjun Zhong (4 shared papers)Miltiadis Allamanis (10 shared papers)
- Journals
- Proceedings of the VLDB Endowment (2 papers)IEEE Transactions on Software Engineering (2 papers)Machine Learning (1 paper)Journal of Machine Learning Research (1 paper)ACM Transactions on Modeling and Computer Simulation (1 paper)
- Partner nations
- United StatesUnited KingdomItaly
In The Last Decade
Charles A. Sutton
103 papers receiving 20.1k citations
Charles A. Sutton's Hit Papers
Peers
Comparison fields: 5 of 215
- Artificial Intelligence 9.4k
- Computer Vision and Pattern Recognition 5.4k
- Computational Mathematics 115
- Software 726
- Signal Processing 1.7k
Countries citing papers authored by Charles A. Sutton
This map shows the geographic impact of Charles A. Sutton'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 Charles A. Sutton with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Charles A. Sutton more than expected).
Fields of papers citing papers by Charles A. Sutton
This network shows the impact of papers produced by Charles A. Sutton. 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 Charles A. Sutton. The network helps show where Charles A. Sutton may publish in the future.
Co-authors
The 25 scholars most cited alongside Charles A. Sutton, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 104 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Advances in Neural Information Processing Systems 25 Hit paper breakdown → | 2012 | 11659 |
| 2 | Advances in Neural Information Processing Systems 27 (NIPS 2014) Hit paper breakdown → | 2014 | 1771 |
| 3 | Proceedings for the 5th International Conference on Learning Representations Hit paper breakdown → | 2017 | 1560 |
| 4 | An Introduction to Conditional Random Fields for Relational Learning Hit paper breakdown → | 2007 | 636 |
| 5 | Sequence-to-Point Learning With Neural Networks for Non-Intrusive Load Monitoring Hit paper breakdown → | 2018 | 413 |
| 6 | An Introduction to Conditional Random Fields Hit paper breakdown → | 2012 | 402 |
| 7 | Suggesting accurate method and class names Hit paper breakdown → | 2015 | 272 |
| 8 | Learning natural coding conventions Hit paper breakdown → | 2014 | 270 |
| 9 | 2006 | 229 | |
| 10 | 2013 | 222 | |
| 11 | 2007 | 220 | |
| 12 | Exploiting machine learning to subvert your spam filter | 2008 | 204 |
| 13 | 2019 | 198 | |
| 14 | Statistical machine learning makes automatic control practical for internet datacenters | 2009 | 163 |
| 15 | VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning | 2017 | 135 |
| 16 | 2007 | 134 | |
| 17 | 2013 | 120 | |
| 18 | 4th Biennial Conference on Innovative Data Systems Research (CIDR) | 2009 | 118 |
| 19 | 2014 | 116 | |
| 20 | 2020 | 112 |
About Charles A. Sutton
Charles A. Sutton is a scholar working on Artificial Intelligence, Information Systems, Signal Processing, Software and Computer Networks and Communications, having authored 104 papers that have together received 20.6k indexed citations. Recurring topics across this work include Topic Modeling (23 papers), Natural Language Processing Techniques (23 papers), Software Engineering Research (20 papers), Bayesian Modeling and Causal Inference (12 papers), Software Testing and Debugging Techniques (9 papers), Gaussian Processes and Bayesian Inference (8 papers), Bayesian Methods and Mixture Models (7 papers) and Software System Performance and Reliability (7 papers). The work is most often cited by research in Artificial Intelligence (9.4k citations), Computer Vision and Pattern Recognition (5.4k citations), Computational Mathematics (115 citations), Software (726 citations) and Signal Processing (1.7k citations). Charles A. Sutton has collaborated with scholars based in United States, United Kingdom and Italy. Frequent co-authors include Yichuan Zhang, Amos Storkey, Zoubin Ghahramani, Andrew McCallum, Akash Kumar Srivastava, Nigel Goddard, Mingjun Zhong, Miltiadis Allamanis, Earl Theodore Barr and Christian Bird. Their work appears in journals such as Proceedings of the VLDB Endowment, IEEE Transactions on Software Engineering, Machine Learning, Journal of Machine Learning Research and ACM Transactions on Modeling and Computer Simulation.
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.