Shan Carter
Impact in
- Health Informatics top 5%
- Artificial Intelligence in Healthcare and Education
- Artificial Intelligence top 5%
- Explainable Artificial Intelligence (XAI)
- Adversarial Robustness in Machine Learning
- Anomaly Detection Techniques and Applications
- Topic Modeling
Papers in
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- Adversarial Robustness in Machine Learning 3
- Neural Networks and Applications 2
- Reinforcement Learning in Robotics 2
- Explainable Artificial Intelligence (XAI) 2
- Machine Learning in Healthcare 1
- Natural Language Processing Techniques 1
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- Generative Adversarial Networks and Image Synthesis 2
- Co-authors
- Chris Olah (8 shared papers)Ludwig Schubert (4 shared papers)Ian Johnson (3 shared papers)Alexander Mordvintsev (2 shared papers)Arvind Satyanarayan (1 shared paper)Nick Cammarata (3 shared papers)Gabriel Goh (3 shared papers)Michael Petrov (2 shared papers)
- Partner nations
- United States
In The Last Decade
Shan Carter
10 papers receiving 593 citations
Shan Carter's Hit Papers
Peers
Comparison fields: 5 of 113
- Health Informatics 43
- Artificial Intelligence 402
- Computer Vision and Pattern Recognition 189
- Biophysics 43
- Safety Research 47
Countries citing papers authored by Shan Carter
This map shows the geographic impact of Shan Carter'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 Shan Carter with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shan Carter more than expected).
Fields of papers citing papers by Shan Carter
This network shows the impact of papers produced by Shan Carter. 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 Shan Carter. The network helps show where Shan Carter may publish in the future.
Co-authors
The 18 scholars most cited alongside Shan Carter, 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 | The Building Blocks of Interpretability Hit paper breakdown → | 2018 | 295 |
| 2 | 2021 | 115 | |
| 3 | 2019 | 82 | |
| 4 | 2017 | 77 | |
| 5 | 2016 | 31 | |
| 6 | 2020 | 21 | |
| 7 | 2020 | 12 | |
| 8 | 2017 | 9 | |
| 9 | 2016 | 6 | |
| 10 | 2019 | 3 |
About Shan Carter
Shan Carter is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics, Signal Processing and Hardware and Architecture, having authored 10 papers that have together received 651 indexed citations. Recurring topics across this work include Adversarial Robustness in Machine Learning (3 papers), Neural Networks and Applications (2 papers), Generative Adversarial Networks and Image Synthesis (2 papers), Reinforcement Learning in Robotics (2 papers), Explainable Artificial Intelligence (XAI) (2 papers), Machine Learning in Healthcare (1 paper), Quantum Chromodynamics and Particle Interactions (1 paper) and Natural Language Processing Techniques (1 paper). The work is most often cited by research in Health Informatics (43 citations), Artificial Intelligence (402 citations), Computer Vision and Pattern Recognition (189 citations), Biophysics (43 citations) and Safety Research (47 citations). Shan Carter has collaborated with scholars based in United States. Frequent co-authors include Chris Olah, Ludwig Schubert, Ian Johnson, Alexander Mordvintsev, Arvind Satyanarayan, Nick Cammarata, Gabriel Goh, Michael Petrov, Zan Armstrong and Alec Radford.
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.