Mitchell Wortsman
Impact in
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- Multimodal Machine Learning Applications
- Advanced Image and Video Retrieval Techniques
- Advanced Neural Network Applications
- Artificial Intelligence top 5%
- Domain Adaptation and Few-Shot Learning
- Topic Modeling
- Natural Language Processing Techniques
- Anomaly Detection Techniques and Applications
- Adversarial Robustness in Machine Learning
Papers in
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- Domain Adaptation and Few-Shot Learning 5
- Adversarial Robustness in Machine Learning 2
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- Advanced Neural Network Applications 4
- Multimodal Machine Learning Applications 3
- Co-authors
- Gabriel Ilharco (4 shared papers)Ludwig Schmidt (4 shared papers)Christoph Schuhmann (2 shared papers)Romain Beaumont (2 shared papers)Jenia Jitsev (2 shared papers)Mehdi Cherti (2 shared papers)Ross Wightman (1 shared paper)Ali Farhadi (4 shared papers)
- Journals
- npj Digital Medicine (1 paper)International Conference on Machine Learning (2 papers)arXiv (Cornell University) (1 paper)2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (1 paper)
- Partner nations
- United StatesUgandaGermany
In The Last Decade
Mitchell Wortsman
10 papers receiving 649 citations
Mitchell Wortsman's Hit Papers
Peers
Comparison fields: 5 of 81
- Computer Vision and Pattern Recognition 424
- Artificial Intelligence 369
- Health Informatics 8
- Media Technology 20
- Computer Graphics and Computer-Aided Design 8
Countries citing papers authored by Mitchell Wortsman
This map shows the geographic impact of Mitchell Wortsman'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 Mitchell Wortsman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mitchell Wortsman more than expected).
Fields of papers citing papers by Mitchell Wortsman
This network shows the impact of papers produced by Mitchell Wortsman. 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 Mitchell Wortsman. The network helps show where Mitchell Wortsman may publish in the future.
Co-authors
The 25 scholars most cited alongside Mitchell Wortsman, 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 | 292 |
| 2 | Robust fine-tuning of zero-shot models Hit paper breakdown → | 2022 | 262 |
| 3 | 2023 | 79 | |
| 4 | 2022 | 16 | |
| 5 | Discovering Neural Wirings | 2019 | 7 |
| 6 | Learning Neural Network Subspaces | 2021 | 6 |
| 7 | 2022 | 6 | |
| 8 | 2024 | 5 | |
| 9 | Soft Threshold Weight Reparameterization for Learnable Sparsity | 2020 | 3 |
| 10 | 2023 | 1 | |
| 11 | 2022 | 0 | |
| 12 | 2022 | 0 |
About Mitchell Wortsman
Mitchell Wortsman is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Surgery, Information Systems and Radiology, Nuclear Medicine and Imaging, having authored 12 papers that have together received 677 indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (5 papers), Advanced Neural Network Applications (4 papers), Multimodal Machine Learning Applications (3 papers), Adversarial Robustness in Machine Learning (2 papers), Patient Safety and Medication Errors (1 paper), Healthcare Technology and Patient Monitoring (1 paper), Quality and Safety in Healthcare (1 paper) and Expert finding and Q&A systems (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (424 citations), Artificial Intelligence (369 citations), Health Informatics (8 citations), Media Technology (20 citations) and Computer Graphics and Computer-Aided Design (8 citations). Mitchell Wortsman has collaborated with scholars based in United States, Uganda and Germany. Frequent co-authors include Gabriel Ilharco, Ludwig Schmidt, Christoph Schuhmann, Romain Beaumont, Jenia Jitsev, Mehdi Cherti, Ross Wightman, Ali Farhadi, Hannaneh Hajishirzi and Hongseok Namkoong. Their work appears in journals such as npj Digital Medicine, International Conference on Machine Learning, arXiv (Cornell University) and 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
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.