Collin Burns
Impact in
- Health Informatics top 10%
- Artificial Intelligence in Healthcare and Education
- Artificial Intelligence top 10%
- Topic Modeling
- Natural Language Processing Techniques
- Text Readability and Simplification
- Explainable Artificial Intelligence (XAI)
- Machine Learning in Healthcare
- Speech and dialogue systems
Papers in
-
- Topic Modeling 2
- Explainable Artificial Intelligence (XAI) 2
- Natural Language Processing Techniques 1
- Machine Learning and Data Classification 1
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- Artificial Intelligence in Law 1
- Co-authors
- Dan Hendrycks (2 shared papers)Dawn Song (1 shared paper)Andy Zou (1 shared paper)Steven Basart (1 shared paper)Jacob Steinhardt (1 shared paper)Mantas Mazeika (1 shared paper)Jesse Thomason (1 shared paper)Wesley Tansey (1 shared paper)
- Journals
- Neural Information Processing Systems (1 paper)International Conference on Learning Representations (1 paper)arXiv (Cornell University) (1 paper)
- Partner nations
- United States
In The Last Decade
Collin Burns
3 papers receiving 181 citations
Collin Burns's Hit Papers
Peers
Comparison fields: 5 of 51
- Health Informatics 17
- Artificial Intelligence 164
- Computer Vision and Pattern Recognition 38
- General Social Sciences 3
- Structural Biology 1
Countries citing papers authored by Collin Burns
This map shows the geographic impact of Collin Burns'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 Collin Burns with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Collin Burns more than expected).
Fields of papers citing papers by Collin Burns
This network shows the impact of papers produced by Collin Burns. 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 Collin Burns. The network helps show where Collin Burns may publish in the future.
Co-authors
The 8 scholars most cited alongside Collin Burns, 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 | Measuring Massive Multitask Language Understanding Hit paper breakdown → | 2021 | 192 |
| 2 | Interpreting Black Box Models with Statistical Guarantees. | 2019 | 2 |
| 3 | CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review | 2021 | 1 |
About Collin Burns
Collin Burns is a scholar working on Artificial Intelligence, Political Science and International Relations, Law, Economics and Econometrics and Infectious Diseases, having authored 3 papers that have together received 195 indexed citations. Recurring topics across this work include Topic Modeling (2 papers), Explainable Artificial Intelligence (XAI) (2 papers), Law, Economics, and Judicial Systems (1 paper), Legal Education and Practice Innovations (1 paper), Natural Language Processing Techniques (1 paper), Artificial Intelligence in Law (1 paper) and Machine Learning and Data Classification (1 paper). The work is most often cited by research in Health Informatics (17 citations), Artificial Intelligence (164 citations), Computer Vision and Pattern Recognition (38 citations), General Social Sciences (3 citations) and Structural Biology (1 citation). Collin Burns has collaborated with scholars based in United States. Frequent co-authors include Dan Hendrycks, Dawn Song, Andy Zou, Steven Basart, Jacob Steinhardt, Mantas Mazeika, Jesse Thomason and Wesley Tansey. Their work appears in journals such as Neural Information Processing Systems, 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.