Cade Gordon
Impact in
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- Multimodal Machine Learning Applications
- Generative Adversarial Networks and Image Synthesis
- Advanced Neural Network Applications
- Advanced Image and Video Retrieval Techniques
- Artificial Intelligence top 10%
- Domain Adaptation and Few-Shot Learning
Papers in
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- Multimodal Machine Learning Applications 2
- Co-authors
- Romain Beaumont (2 shared papers)Mehdi Cherti (2 shared papers)Jenia Jitsev (2 shared papers)Ross Wightman (2 shared papers)Ludwig Schmidt (2 shared papers)Christoph G. Schuhmann (2 shared papers)Mitchell Wortsman (2 shared papers)Gabriel Ilharco (1 shared paper)
- Journals
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (1 paper)neural information processing systems (1 paper)
- Partner nations
- GermanyUnited States
In The Last Decade
Cade Gordon
2 papers receiving 440 citations
Cade Gordon's Hit Papers
Peers
Comparison fields: 5 of 55
- Computer Vision and Pattern Recognition 293
- Artificial Intelligence 134
- Media Technology 22
- Health Informatics 4
- Computer Graphics and Computer-Aided Design 7
Countries citing papers authored by Cade Gordon
This map shows the geographic impact of Cade Gordon'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 Cade Gordon with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Cade Gordon more than expected).
Fields of papers citing papers by Cade Gordon
This network shows the impact of papers produced by Cade Gordon. 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 Cade Gordon. The network helps show where Cade Gordon may publish in the future.
Co-authors
The 10 scholars most cited alongside Cade Gordon, 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 | Reproducible Scaling Laws for Contrastive Language-Image Learning Hit paper breakdown → | 2023 | 402 |
| 2 | 2022 | 53 |
About Cade Gordon
Cade Gordon is a scholar working on Computer Vision and Pattern Recognition, Nuclear Energy and Engineering, Waste Management and Disposal, Infectious Diseases and Organic Chemistry, having authored 2 papers that have together received 455 indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (293 citations), Artificial Intelligence (134 citations), Media Technology (22 citations), Health Informatics (4 citations) and Computer Graphics and Computer-Aided Design (7 citations). Cade Gordon has collaborated with scholars based in Germany and United States. Frequent co-authors include Romain Beaumont, Mehdi Cherti, Jenia Jitsev, Ross Wightman, Ludwig Schmidt, Christoph G. Schuhmann, Mitchell Wortsman, Gabriel Ilharco, Robert Kaczmarczyk and Patrick Schramowski. Their work appears in journals such as IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings and neural information processing systems.
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.