Pinar Yanardag
- Artificial Intelligence top 2%
- Computer Vision and Pattern Recognition top 5%
- Statistical and Nonlinear Physics top 5%
- Molecular Biology
- Information Systems top 10%
- Co-authors
- S. V. N. VishwanathanIyad RahwanManuel CebriánShihao JiHyokun YunFederico TombariJames M. RehgThomas Hofmann
- Topics
- Generative Adversarial Networks and Image Synthesis (8 papers)Topic Modeling (3 papers)Music and Audio Processing (2 papers)
- Cited by
- Artificial IntelligenceStatistical and Nonlinear PhysicsComputer Vision and Pattern Recognition
- Journals
- 2021 IEEE/CVF International Conference on Computer Vision (ICCV)2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)Neural Information Processing Systems
- Partner nations
- United StatesTürkiyeGermany
In The Last Decade
Pinar Yanardag
14 papers receiving 678 citations
Hit Papers
Peers
Comparison fields: 5 of 70
- Artificial Intelligence 540
- Computer Vision and Pattern Recognition 265
- Statistical and Nonlinear Physics 184
- Molecular Biology 106
- Information Systems 74
Countries citing papers authored by Pinar Yanardag
This map shows the geographic impact of Pinar Yanardag'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 Pinar Yanardag with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pinar Yanardag more than expected).
Fields of papers citing papers by Pinar Yanardag
This network shows the impact of papers produced by Pinar Yanardag. 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 Pinar Yanardag. The network helps show where Pinar Yanardag may publish in the future.
Co-authorship network of co-authors of Pinar Yanardag
This figure shows the co-authorship network connecting the top 25 collaborators of Pinar Yanardag. A scholar is included among the top collaborators of Pinar Yanardag 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 Pinar Yanardag. Pinar Yanardag is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 1 | |
| 3 | 5 | |
| 4 | 5 | |
| 5 | 4 | |
| 6 | 20 | |
| 7 | 1 | |
| 8 | 37 | |
| 9 | 2 | |
| 10 | 1 | |
| 11 | 35 | |
| 12 | 11 | |
| 13 | 17 | |
| 14 | A structural smoothing framework for Robust graph-comparison | 28 |
| 15 | Deep Graph Kernelsbreakdown → | 543 |
About Pinar Yanardag
Pinar Yanardag is a scholar working on Computer Vision and Pattern Recognition, Signal Processing and Biophysics, having authored 15 papers that have together received 710 indexed citations. Recurring topics across this work include Generative Adversarial Networks and Image Synthesis (8 papers), Topic Modeling (3 papers) and Music and Audio Processing (2 papers). The work is most often cited by research in Artificial Intelligence (540 citations), Statistical and Nonlinear Physics (184 citations) and Computer Vision and Pattern Recognition (265 citations). Pinar Yanardag has collaborated with scholars based in United States, Türkiye and Germany. Frequent co-authors include S. V. N. Vishwanathan, Iyad Rahwan, Manuel Cebrián, Shihao Ji, Hyokun Yun, Federico Tombari, James M. Rehg and Thomas Hofmann. Their work appears in journals such as 2021 IEEE/CVF International Conference on Computer Vision (ICCV), 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) and Neural Information Processing Systems.
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.