Kate Rakelly

755 total citations
4 papers, 203 citations indexed

About

Kate Rakelly is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Infectious Diseases. According to data from OpenAlex, Kate Rakelly has authored 4 papers receiving a total of 203 indexed citations (citations by other indexed papers that have themselves been cited), including 3 papers in Computer Vision and Pattern Recognition, 2 papers in Artificial Intelligence and 0 papers in Infectious Diseases. Recurrent topics in Kate Rakelly's work include Generative Adversarial Networks and Image Synthesis (2 papers), Advanced Image and Video Retrieval Techniques (1 paper) and Advanced Neural Network Applications (1 paper). Kate Rakelly is often cited by papers focused on Generative Adversarial Networks and Image Synthesis (2 papers), Advanced Image and Video Retrieval Techniques (1 paper) and Advanced Neural Network Applications (1 paper). Kate Rakelly collaborates with scholars based in United States. Kate Rakelly's co-authors include Sergey Levine, Trevor Darrell, Evan Shelhamer, Deirdre Quillen, Chelsea Finn, Aurick Zhou, Alexei A. Efros, Shiry Ginosar, Crystal Lee and Philipp Krähenbühl and has published in prestigious journals such as IEEE Transactions on Computational Imaging, arXiv (Cornell University) and International Conference on Learning Representations.

In The Last Decade

Kate Rakelly

4 papers receiving 195 citations

Peers

Kate Rakelly
Zhi Hou China
Guanyu Cai China
Qiushan Guo China
Quentin Duval France
Mehdi Cherti Germany
Xinyue Liu China
Bingxin Xu China
Yucheng Zhou China
Nazir Ahmed Malik Pakistan
Wenhao Chai United States
Zhi Hou China
Kate Rakelly
Citations per year, relative to Kate Rakelly Kate Rakelly (= 1×) peers Zhi Hou

Countries citing papers authored by Kate Rakelly

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of Kate Rakelly

This figure shows the co-authorship network connecting the top 25 collaborators of Kate Rakelly. A scholar is included among the top collaborators of Kate Rakelly 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 Kate Rakelly. Kate Rakelly is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

4 of 4 papers shown
1.
Rakelly, Kate, Aurick Zhou, Deirdre Quillen, Chelsea Finn, & Sergey Levine. (2019). Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables. arXiv (Cornell University). 5331–5340. 75 indexed citations
2.
Rakelly, Kate, et al.. (2018). Conditional Networks for Few-Shot Semantic Segmentation. International Conference on Learning Representations. 97 indexed citations
3.
Ginosar, Shiry, et al.. (2017). A Century of Portraits: A Visual Historical Record of American High School Yearbooks. IEEE Transactions on Computational Imaging. 3(3). 421–431. 4 indexed citations
4.
Ginosar, Shiry, et al.. (2015). A Century of Portraits: A Visual Historical Record of American High School Yearbooks. 652–658. 27 indexed citations

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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2026