Kate Rakelly
Impact in
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- Advanced Neural Network Applications
- Multimodal Machine Learning Applications
- Advanced Image and Video Retrieval Techniques
- Generative Adversarial Networks and Image Synthesis
- Artificial Intelligence top 10%
- Domain Adaptation and Few-Shot Learning
- Reinforcement Learning in Robotics
- Adversarial Robustness in Machine Learning
Papers in
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- Generative Adversarial Networks and Image Synthesis 2
- Advanced Image and Video Retrieval Techniques 1
- Image Retrieval and Classification Techniques 1
- Advanced Neural Network Applications 1
- Multimodal Machine Learning Applications 1
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- Domain Adaptation and Few-Shot Learning 2
- Machine Learning and Data Classification 2
- Reinforcement Learning in Robotics 1
- Co-authors
- Sergey Levine (3 shared papers)Evan Shelhamer (2 shared papers)Trevor Darrell (2 shared papers)Chelsea Finn (1 shared paper)Deirdre Quillen (1 shared paper)Aurick Zhou (1 shared paper)Alexei A. Efros (3 shared papers)Shiry Ginosar (2 shared papers)
- Journals
- IEEE Transactions on Computational Imaging (1 paper)International Conference on Learning Representations (1 paper)arXiv (Cornell University) (1 paper)
- Partner nations
- United States
In The Last Decade
Kate Rakelly
5 papers receiving 224 citations
Peers
Comparison fields: 5 of 55
- Computer Vision and Pattern Recognition 139
- Artificial Intelligence 164
- Health Informatics 2
- Media Technology 8
- Control and Systems Engineering 18
Countries citing papers authored by Kate Rakelly
This map shows the geographic impact of Kate Rakelly'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 Kate Rakelly with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kate Rakelly more than expected).
Fields of papers citing papers by Kate Rakelly
This network shows the impact of papers produced by Kate Rakelly. 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 Kate Rakelly. The network helps show where Kate Rakelly may publish in the future.
Co-authors
The 10 scholars most cited alongside Kate Rakelly, 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 | Conditional Networks for Few-Shot Semantic Segmentation | 2018 | 108 |
| 2 | 2019 | 83 | |
| 3 | 2015 | 34 | |
| 4 | 2017 | 5 | |
| 5 | Meta-Learning to Guide Segmentation | 2018 | 2 |
About Kate Rakelly
Kate Rakelly is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Infectious Diseases, Organic Chemistry and Surgery, having authored 5 papers that have together received 232 indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (2 papers), Generative Adversarial Networks and Image Synthesis (2 papers), Machine Learning and Data Classification (2 papers), Advanced Image and Video Retrieval Techniques (1 paper), Image Retrieval and Classification Techniques (1 paper), Reinforcement Learning in Robotics (1 paper), Advanced Neural Network Applications (1 paper) and Multimodal Machine Learning Applications (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (139 citations), Artificial Intelligence (164 citations), Health Informatics (2 citations), Media Technology (8 citations) and Control and Systems Engineering (18 citations). Kate Rakelly has collaborated with scholars based in United States. Frequent co-authors include Sergey Levine, Evan Shelhamer, Trevor Darrell, Chelsea Finn, Deirdre Quillen, Aurick Zhou, Alexei A. Efros, Shiry Ginosar, Philipp Krähenbühl and Crystal Lee. Their work appears in journals such as IEEE Transactions on Computational Imaging, 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.