Ariel Gordon

597 total citations
3 papers, 175 citations indexed

About

Ariel Gordon is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Information Systems. According to data from OpenAlex, Ariel Gordon has authored 3 papers receiving a total of 175 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 1 paper in Information Systems. Recurrent topics in Ariel Gordon's work include Advanced Image and Video Retrieval Techniques (1 paper), Information Retrieval and Search Behavior (1 paper) and Multimodal Machine Learning Applications (1 paper). Ariel Gordon is often cited by papers focused on Advanced Image and Video Retrieval Techniques (1 paper), Information Retrieval and Search Behavior (1 paper) and Multimodal Machine Learning Applications (1 paper). Ariel Gordon collaborates with scholars based in United States and Israel. Ariel Gordon's co-authors include Elad Eban, Bo Chen, Edward Choi, Ofir Nachum, Tien-Ju Yang, Hao Wu, Rif A. Saurous, Mariano Schain, Gal Elidan and Apostol Natsev and has published in prestigious journals such as International Conference on Artificial Intelligence and Statistics.

In The Last Decade

Ariel Gordon

3 papers receiving 165 citations

Peers

Ariel Gordon
Chengzhi Mao United States
Jindong Gu United Kingdom
Danlu Chen United States
Tan M. Nguyen United States
Mary Phuong Austria
Vikas Verma Finland
Ariel Gordon
Citations per year, relative to Ariel Gordon Ariel Gordon (= 1×) peers Jinmian Ye

Countries citing papers authored by Ariel Gordon

Since Specialization
Citations

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

Fields of papers citing papers by Ariel Gordon

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ariel Gordon

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

All Works

3 of 3 papers shown
1.
Hwang, Seong Jae, Joonseok Lee, Balakrishnan Varadarajan, et al.. (2019). Large-Scale Training Framework for Video Annotation. 2394–2402. 1 indexed citations
2.
Gordon, Ariel, Elad Eban, Ofir Nachum, et al.. (2018). MorphNet: Fast & Simple Resource-Constrained Structure Learning of Deep Networks. 1586–1595. 159 indexed citations
3.
Eban, Elad, et al.. (2016). Scalable Learning of Non-Decomposable Objectives. International Conference on Artificial Intelligence and Statistics. 832–840. 15 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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