Gungor Polatkan

595 total citations
11 papers, 294 citations indexed

About

Gungor Polatkan is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Cognitive Neuroscience. According to data from OpenAlex, Gungor Polatkan has authored 11 papers receiving a total of 294 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Computer Vision and Pattern Recognition, 5 papers in Artificial Intelligence and 3 papers in Cognitive Neuroscience. Recurrent topics in Gungor Polatkan's work include Aesthetic Perception and Analysis (3 papers), Image Retrieval and Classification Techniques (2 papers) and Advanced Graph Neural Networks (2 papers). Gungor Polatkan is often cited by papers focused on Aesthetic Perception and Analysis (3 papers), Image Retrieval and Classification Techniques (2 papers) and Advanced Graph Neural Networks (2 papers). Gungor Polatkan collaborates with scholars based in United States. Gungor Polatkan's co-authors include Lawrence Carin, David B. Dunson, Guillermo Sapiro, Bo Chen, Mingyuan Zhou, Ingrid Daubechies, Shannon M. Hughes, Ingrid Daubechies, Sina Jafarpour and David M. Blei and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Transportation Research Record Journal of the Transportation Research Board and arXiv (Cornell University).

In The Last Decade

Gungor Polatkan

11 papers receiving 284 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Gungor Polatkan United States	8	152	76	62	50	34	11	294
Felipe Petroski Such United States	6	123 0.8×	123 1.6×	43 0.7×	31 0.6×	11 0.3×	7	279
Jinlong Shi China	12	255 1.7×	65 0.9×	42 0.7×	15 0.3×	10 0.3×	48	388
Limei Zhang China	7	226 1.5×	118 1.6×	75 1.2×	28 0.6×	22 0.6×	17	366
Keyang Cheng China	9	135 0.9×	57 0.8×	29 0.5×	19 0.4×	18 0.5×	45	255
Yancong Lin China	8	140 0.9×	37 0.5×	48 0.8×	20 0.4×	39 1.1×	14	329
Kaiqi Huang China	11	342 2.3×	90 1.2×	70 1.1×	22 0.4×	11 0.3×	23	410
Shagan Sah United States	10	150 1.0×	111 1.5×	17 0.3×	28 0.6×	13 0.4×	23	336
Zhang Zhi China	10	229 1.5×	80 1.1×	62 1.0×	8 0.2×	26 0.8×	37	388
Jean-Christophe Burie France	13	300 2.0×	102 1.3×	51 0.8×	9 0.2×	34 1.0×	44	515
Josh Harguess United States	10	198 1.3×	90 1.2×	37 0.6×	7 0.1×	7 0.2×	44	308

Countries citing papers authored by Gungor Polatkan

Since Specialization

Citations

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

Fields of papers citing papers by Gungor Polatkan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Gungor Polatkan

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

All Works

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11 of 11 papers shown

1.

Polatkan, Gungor, et al.. (2019). Learning to be Relevant. 2625–2633. 2 indexed citations

2.

Ramanath, Rohan, Hakan Inan, Gungor Polatkan, et al.. (2018). Towards Deep and Representation Learning for Talent Search at LinkedIn. 2253–2261. 44 indexed citations

3.

Szabó, Gábor, et al.. (2018). Social Media Data Mining and Analytics. 9 indexed citations

4.

Polatkan, Gungor, Mingyuan Zhou, Lawrence Carin, David M. Blei, & Ingrid Daubechies. (2014). A Bayesian Nonparametric Approach to Image Super-Resolution. IEEE Transactions on Pattern Analysis and Machine Intelligence. 37(2). 346–358. 62 indexed citations

5.

Chen, Bo, et al.. (2013). Deep Learning with Hierarchical Convolutional Factor Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence. 35(8). 1887–1901. 78 indexed citations

6.

Polatkan, Gungor & Oncel Tuzel. (2012). Compressed Inference for Probabilistic Sequential Models. arXiv (Cornell University). 609–618. 1 indexed citations

7.

Chen, Bo, Gungor Polatkan, Guillermo Sapiro, Lawrence Carin, & David B. Dunson. (2011). The Hierarchical Beta Process for Convolutional Factor Analysis and Deep Learning. International Conference on Machine Learning. 361–368. 20 indexed citations

8.

Jafarpour, Sina, et al.. (2009). Stylistic analysis of paintings usingwavelets and machine learning. European Signal Processing Conference. 1220–1224. 21 indexed citations

9.

Brevdo, Eugene, et al.. (2009). Stylistic Analysis Of Paintings Using Complex Wavelets And Random Forest Learning Algorithm. Zenodo (CERN European Organization for Nuclear Research). 1 indexed citations

10.

Polatkan, Gungor, et al.. (2009). Detection of forgery in paintings using supervised learning. 2921–2924. 41 indexed citations

11.

Shladover, Steven E, et al.. (2007). Dependence of Cooperative Vehicle System Performance on Market Penetration. Transportation Research Record Journal of the Transportation Research Board. 2000(1). 121–127. 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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