Collin Burns

1.2k citations
3 papers · 185 indexed · 1 hit paper · h-index 2
Topics
Explainable Artificial Intelligence (XAI) (2 papers)Topic Modeling (2 papers)Law, Economics, and Judicial Systems (1 paper)
Journals
arXiv (Cornell University)Neural Information Processing SystemsInternational Conference on Learning Representations
Partner nations
United States

In The Last Decade

Collin Burns

3 papers receiving 172 citations

Hit Papers

Measuring Massive Multitask Language Understanding2021202620222024202150100150

Peers

Collin Burns
Comparison fields: 5 of 50
  • Artificial Intelligence 158
  • Computer Vision and Pattern Recognition 38
  • Health Informatics 16
  • Information Systems 12
  • Molecular Biology 10
Replace Kalpesh Krishna with:
Kalpesh Krishna United States
Raul Puri United States
Ehsan Shareghi Australia
Trieu H. Trinh United States
Jasmijn Bastings United States
Jinhao Jiang China
Thomas Scialom France
Nikita Nangia United States
Or Honovich Israel
Collin Burns relative to Kalpesh Krishna United States Kalpesh Krishna's profile →
Citations per field
00.5×1.6×
Kalpesh Krishna · 1×
Citations per year

Countries citing papers authored by Collin Burns

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of Collin Burns

This figure shows the co-authorship network connecting the top 25 collaborators of Collin Burns. A scholar is included among the top collaborators of Collin Burns based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Collin Burns. Collin Burns is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

3 of 3 papers shown
#WorkIndexed citations
1
Measuring Massive Multitask Language Understandingbreakdown →
182
2
CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review
1
3
Interpreting Black Box Models with Statistical Guarantees.
2

About Collin Burns

Collin Burns is a scholar working on Law, Artificial Intelligence and Political Science and International Relations, having authored 3 papers that have together received 185 indexed citations. Recurring topics across this work include Explainable Artificial Intelligence (XAI) (2 papers), Topic Modeling (2 papers) and Law, Economics, and Judicial Systems (1 paper). The work is most often cited by research in Health Informatics (16 citations), Artificial Intelligence (158 citations) and Computer Vision and Pattern Recognition (38 citations). Collin Burns has collaborated with scholars based in United States. Frequent co-authors include Jacob Steinhardt, Steven Basart, Dan Hendrycks, Dawn Song, Mantas Mazeika, Andy Zou, Jesse Thomason and Wesley Tansey. Their work appears in journals such as arXiv (Cornell University), 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.

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