Gökhan Bakır
- Computer Vision and Pattern Recognition top 2%
- Artificial Intelligence top 5%
- Signal Processing top 10%
- Control and Systems Engineering top 10%
- Biomedical Engineering
- Co-authors
- Bernhard SchölkopfSebastian NowozinKoji TsudaJason WestonMingrui WuGerhard WeikumGeorgiana IfrimWolf Kienzle
- Topics
- Sparse and Compressive Sensing Techniques (4 papers)Face and Expression Recognition (3 papers)Neural Networks and Applications (2 papers)
- Journals
- Neural NetworksJournal of Machine Learning ResearchInternational Journal of Humanoid Robotics
- Partner nations
- GermanySwitzerlandUnited States
In The Last Decade
Gökhan Bakır
12 papers receiving 547 citations
Peers
Comparison fields: 5 of 76
- Computer Vision and Pattern Recognition 372
- Artificial Intelligence 287
- Signal Processing 67
- Control and Systems Engineering 65
- Biomedical Engineering 61
Countries citing papers authored by Gökhan Bakır
This map shows the geographic impact of Gökhan Bakır'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 Gökhan Bakır with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gökhan Bakır more than expected).
Fields of papers citing papers by Gökhan Bakır
This network shows the impact of papers produced by Gökhan Bakır. 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 Gökhan Bakır. The network helps show where Gökhan Bakır may publish in the future.
Co-authorship network of co-authors of Gökhan Bakır
This figure shows the co-authorship network connecting the top 25 collaborators of Gökhan Bakır. A scholar is included among the top collaborators of Gökhan Bakır 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 Gökhan Bakır. Gökhan Bakır is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 52 | |
| 2 | 8 | |
| 3 | 0 | |
| 4 | 51 | |
| 5 | 121 | |
| 6 | A Direct Method for Building Sparse Kernel Learning Algorithms | 64 |
| 7 | Influence of Noise on the Inference of Dynamic Bayesian Networks from Short Time Series | 0 |
| 8 | 30 | |
| 9 | 9 | |
| 10 | Breaking SVM Complexity with Cross-Training | 45 |
| 11 | Face Detection --- Efficient and Rank Deficient | 65 |
| 12 | 32 | |
| 13 | 11 | |
| 14 | Learning to Find Pre-Images | 95 |
About Gökhan Bakır
Gökhan Bakır is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Computational Mechanics, having authored 14 papers that have together received 583 indexed citations. Recurring topics across this work include Sparse and Compressive Sensing Techniques (4 papers), Face and Expression Recognition (3 papers) and Neural Networks and Applications (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (372 citations), Artificial Intelligence (287 citations) and Human-Computer Interaction (38 citations). Gökhan Bakır has collaborated with scholars based in Germany, Switzerland and United States. Frequent co-authors include Bernhard Schölkopf, Sebastian Nowozin, Koji Tsuda, Jason Weston, Mingrui Wu, Gerhard Weikum, Georgiana Ifrim, Wolf Kienzle, Matthias Franz and Léon Bottou. Their work appears in journals such as Neural Networks, Journal of Machine Learning Research and International Journal of Humanoid Robotics.
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.