Sara Sabour

24 total papers · 1.6k total citations
5 papers, 461 citations indexed

About

Sara Sabour is a scholar working on Computer Vision and Pattern Recognition, Computational Mechanics and Artificial Intelligence. According to data from OpenAlex, Sara Sabour has authored 5 papers receiving a total of 461 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Computer Vision and Pattern Recognition, 2 papers in Computational Mechanics and 2 papers in Artificial Intelligence. Recurrent topics in Sara Sabour's work include Adversarial Robustness in Machine Learning (2 papers), Computer Graphics and Visualization Techniques (2 papers) and 3D Shape Modeling and Analysis (2 papers). Sara Sabour is often cited by papers focused on Adversarial Robustness in Machine Learning (2 papers), Computer Graphics and Visualization Techniques (2 papers) and 3D Shape Modeling and Analysis (2 papers). Sara Sabour collaborates with scholars based in United States, Canada and Germany. Sara Sabour's co-authors include Geoffrey E. Hinton, Nicholas Frosst, Andrea Tagliasacchi, David J. Fleet, Ivan Krasin, Daniel Duckworth, Animesh Garg, Yao Qin, Garrison W. Cottrell and Colin Raffel and has published in prestigious journals such as ACM Transactions on Graphics, arXiv (Cornell University) and International Conference on Learning Representations.

In The Last Decade

Sara Sabour

5 papers receiving 438 citations

Hit Papers

Matrix capsules with EM r... 2018 2026 2020 2023 2018 100 200 300 400

Author Peers

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

Author Last Decade Papers Cites
Sara Sabour 269 220 40 30 29 5 461
Tiancheng Shen 256 1.0× 127 0.6× 24 0.6× 26 0.9× 12 0.4× 13 429
Gautier Izacard 236 0.9× 214 1.0× 63 1.6× 21 0.7× 32 1.1× 6 482
E-Liang Chen 284 1.1× 179 0.8× 96 2.4× 18 0.6× 48 1.7× 10 426
Nicholas Frosst 244 0.9× 234 1.1× 39 1.0× 29 1.0× 28 1.0× 4 434
Kan Wu 361 1.3× 114 0.5× 24 0.6× 16 0.5× 12 0.4× 10 519
Feng Wang 242 0.9× 245 1.1× 25 0.6× 32 1.1× 5 0.2× 15 504
Tailin Liang 242 0.9× 226 1.0× 12 0.3× 40 1.3× 16 0.6× 4 497
Shi Shao-bo 243 0.9× 232 1.1× 12 0.3× 40 1.3× 16 0.6× 8 545
Baoyuan Liu 313 1.2× 193 0.9× 15 0.4× 20 0.7× 11 0.4× 11 451
Rongyao Fang 335 1.2× 229 1.0× 39 1.0× 23 0.8× 5 0.2× 6 505

Countries citing papers authored by Sara Sabour

Since Specialization
Citations

This map shows the geographic impact of Sara Sabour'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 Sara Sabour with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sara Sabour more than expected).

Fields of papers citing papers by Sara Sabour

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sara Sabour. 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 Sara Sabour. The network helps show where Sara Sabour may publish in the future.

Co-authorship network of co-authors of Sara Sabour

This figure shows the co-authorship network connecting the top 25 collaborators of Sara Sabour. A scholar is included among the top collaborators of Sara Sabour 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 Sara Sabour. Sara Sabour 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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