Yonatan Geifman

524 total citations
6 papers, 85 citations indexed

About

Yonatan Geifman is a scholar working on Artificial Intelligence, Control and Systems Engineering and Computer Vision and Pattern Recognition. According to data from OpenAlex, Yonatan Geifman has authored 6 papers receiving a total of 85 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Artificial Intelligence, 1 paper in Control and Systems Engineering and 1 paper in Computer Vision and Pattern Recognition. Recurrent topics in Yonatan Geifman's work include Adversarial Robustness in Machine Learning (4 papers), Anomaly Detection Techniques and Applications (3 papers) and Machine Learning and Algorithms (2 papers). Yonatan Geifman is often cited by papers focused on Adversarial Robustness in Machine Learning (4 papers), Anomaly Detection Techniques and Applications (3 papers) and Machine Learning and Algorithms (2 papers). Yonatan Geifman collaborates with scholars based in Israel and United States. Yonatan Geifman's co-authors include Ran El‐Yaniv and has published in prestigious journals such as arXiv (Cornell University) and Neural Information Processing Systems.

In The Last Decade

Yonatan Geifman

6 papers receiving 82 citations

Peers

Yonatan Geifman
Tsung-Wei Ke United States
Lewei Lu China
Davide Testuggine United States
Quinn Jones United States
Zachary Nado United States
Tsung-Wei Ke United States
Yonatan Geifman
Citations per year, relative to Yonatan Geifman Yonatan Geifman (= 1×) peers Tsung-Wei Ke

Countries citing papers authored by Yonatan Geifman

Since Specialization
Citations

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

Fields of papers citing papers by Yonatan Geifman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yonatan Geifman

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

All Works

6 of 6 papers shown
1.
Geifman, Yonatan & Ran El‐Yaniv. (2019). Deep Active Learning with a Neural Architecture Search. Neural Information Processing Systems. 32. 5974–5984. 7 indexed citations
2.
Geifman, Yonatan & Ran El‐Yaniv. (2019). SelectiveNet: A Deep Neural Network with an Integrated Reject Option. arXiv (Cornell University). 2151–2159. 26 indexed citations
3.
Geifman, Yonatan & Ran El‐Yaniv. (2019). Uncertainty Estimation and Its Applications in Deep Neural Networks. 2 indexed citations
4.
Geifman, Yonatan, et al.. (2018). Boosting Uncertainty Estimation for Deep Neural Classifiers.. 1 indexed citations
5.
Geifman, Yonatan, et al.. (2018). Bias-Reduced Uncertainty Estimation for Deep Neural Classifiers. arXiv (Cornell University). 16 indexed citations
6.
Geifman, Yonatan & Ran El‐Yaniv. (2017). Selective Classification for Deep Neural Networks. Neural Information Processing Systems. 30. 4878–4887. 33 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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