Nick Cammarata
Impact in
- Health Informatics top 10%
- Artificial Intelligence top 5%
- Explainable Artificial Intelligence (XAI)
- Adversarial Robustness in Machine Learning
- Topic Modeling
- Domain Adaptation and Few-Shot Learning
- Anomaly Detection Techniques and Applications
- Natural Language Processing Techniques
Papers in
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- Adversarial Robustness in Machine Learning 2
- Neural Networks and Applications 2
- Anomaly Detection Techniques and Applications 2
- Reinforcement Learning in Robotics 1
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- Advanced Vision and Imaging 1
- Generative Adversarial Networks and Image Synthesis 1
- Co-authors
- Chris Olah (10 shared papers)Gabriel Goh (10 shared papers)Ludwig Schubert (9 shared papers)Michael Petrov (8 shared papers)Shan Carter (6 shared papers)Chelsea Voss (5 shared papers)Alec Radford (1 shared paper)Jacob Hilton (1 shared paper)
- Partner nations
- United StatesCanada
In The Last Decade
Nick Cammarata
10 papers receiving 304 citations
Peers
Comparison fields: 5 of 73
- Health Informatics 18
- Artificial Intelligence 211
- Computer Vision and Pattern Recognition 95
- Biophysics 19
- Cognitive Neuroscience 43
Countries citing papers authored by Nick Cammarata
This map shows the geographic impact of Nick Cammarata'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 Nick Cammarata with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nick Cammarata more than expected).
Fields of papers citing papers by Nick Cammarata
This network shows the impact of papers produced by Nick Cammarata. 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 Nick Cammarata. The network helps show where Nick Cammarata may publish in the future.
Co-authors
The 9 scholars most cited alongside Nick Cammarata, 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 | 2021 | 121 | |
| 2 | 2020 | 109 | |
| 3 | 2020 | 23 | |
| 4 | 2020 | 21 | |
| 5 | 2020 | 12 | |
| 6 | 2020 | 12 | |
| 7 | 2020 | 12 | |
| 8 | 2021 | 9 | |
| 9 | 2021 | 8 | |
| 10 | 2021 | 5 |
About Nick Cammarata
Nick Cammarata is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, General Health Professions, Signal Processing and Public Health, Environmental and Occupational Health, having authored 10 papers that have together received 332 indexed citations. Recurring topics across this work include Adversarial Robustness in Machine Learning (2 papers), Neural Networks and Applications (2 papers), Anomaly Detection Techniques and Applications (2 papers), Advanced Vision and Imaging (1 paper), Reinforcement Learning in Robotics (1 paper), Generative Adversarial Networks and Image Synthesis (1 paper), Mobile Health and mHealth Applications (1 paper) and Advancements in Semiconductor Devices and Circuit Design (1 paper). The work is most often cited by research in Health Informatics (18 citations), Artificial Intelligence (211 citations), Computer Vision and Pattern Recognition (95 citations), Biophysics (19 citations) and Cognitive Neuroscience (43 citations). Nick Cammarata has collaborated with scholars based in United States and Canada. Frequent co-authors include Chris Olah, Gabriel Goh, Ludwig Schubert, Michael Petrov, Shan Carter, Chelsea Voss, Alec Radford, Jacob Hilton and Swee Han Lim.
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.