Kate Rakelly

14 total papers · 754 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), Domain Adaptation and Few-Shot Learning (1 paper) and Machine Learning and Data Classification (1 paper). Kate Rakelly is often cited by papers focused on Generative Adversarial Networks and Image Synthesis (2 papers), Domain Adaptation and Few-Shot Learning (1 paper) and Machine Learning and Data Classification (1 paper). Kate Rakelly collaborates with scholars based in United States. Kate Rakelly's co-authors include Sergey Levine, Evan Shelhamer, Trevor Darrell, Aurick Zhou, Chelsea Finn, Deirdre Quillen, Shiry Ginosar, Alexei A. Efros, Philipp Krähenbühl and Crystal Lee 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

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
Kate Rakelly	144	119	15	13	13	4	203
Ilija Ilievski	142 1.0×	93 0.8×	7 0.5×	19 1.5×	9 0.7×	8	233
Linjun Zhou	166 1.2×	101 0.8×	15 1.0×	13 1.0×	6 0.5×	7	234
Guanyu Cai	127 0.9×	176 1.5×	14 0.9×	6 0.5×	10 0.8×	10	245
Amir Najafi	154 1.1×	123 1.0×	8 0.5×	32 2.5×	5 0.4×	7	225
Linus Ericsson	97 0.7×	56 0.5×	17 1.1×	20 1.5×	18 1.4×	3	213
Bernd Malle	164 1.1×	39 0.3×	10 0.7×	19 1.5×	11 0.8×	4	264
Shukang Yin	93 0.6×	56 0.5×	8 0.5×	12 0.9×	4 0.3×	6	204
Xiaoyu Bie	100 0.7×	59 0.5×	27 1.8×	5 0.4×	15 1.2×	6	212
Alfredo Nazábal	114 0.8×	58 0.5×	8 0.5×	6 0.5×	17 1.3×	6	193
Jonathan Milgram	81 0.6×	118 1.0×	18 1.2×	4 0.3×	10 0.8×	6	217

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

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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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