Yoni Kasten

1.2k total citations
11 papers, 95 citations indexed

About

Yoni Kasten is a scholar working on Computer Vision and Pattern Recognition, Aerospace Engineering and Artificial Intelligence. According to data from OpenAlex, Yoni Kasten has authored 11 papers receiving a total of 95 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Computer Vision and Pattern Recognition, 4 papers in Aerospace Engineering and 2 papers in Artificial Intelligence. Recurrent topics in Yoni Kasten's work include Advanced Vision and Imaging (6 papers), Robotics and Sensor-Based Localization (4 papers) and Advanced Image and Video Retrieval Techniques (3 papers). Yoni Kasten is often cited by papers focused on Advanced Vision and Imaging (6 papers), Robotics and Sensor-Based Localization (4 papers) and Advanced Image and Video Retrieval Techniques (3 papers). Yoni Kasten collaborates with scholars based in Israel, United Kingdom and Germany. Yoni Kasten's co-authors include Ronen Basri, FRANCIS H. WILLIAMS, Žan Gojčič, Sanja Fidler, David Jacobs, Konrad Schindler, Shengyu Huang, Or Litany, Zian Wang and Xin Wang and has published in prestigious journals such as SHILAP Revista de lepidopterología, ACM Transactions on Graphics and ISPRS Journal of Photogrammetry and Remote Sensing.

In The Last Decade

Yoni Kasten

10 papers receiving 92 citations

Peers

Yoni Kasten
Comparison fields: 5 of 30
  • Computer Vision and Pattern Recognition 54
  • Aerospace Engineering 24
  • Computational Mechanics 18
  • Geology 18
  • Computer Graphics and Computer-Aided Design 15
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Ujwala Patil India View profile →
Citations per field, relative to Yoni Kasten
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Citations per year, relative to Yoni Kasten
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Countries citing papers authored by Yoni Kasten

Since Specialization
Citations

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

Fields of papers citing papers by Yoni Kasten

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yoni Kasten

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

All Works

11 of 11 papers shown
# Work Indexed citations
1 0
2 15
3 6
4 30
5 8
6 8
7
On the Similarity between the Laplace and Neural Tangent Kernels
2
8
Multiview Neural Surface Reconstruction with Implicit Lighting and Material
6
9 2
10
The Convergence Rate of Neural Networks for Learned Functions of Different Frequencies
13
11 5

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