Beyza Ermiş
- Artificial Intelligence
- Computational Mathematics top 1%
- Statistical and Nonlinear Physics top 10%
- Information Systems
- Computer Vision and Pattern Recognition
- Co-authors
- Ali Taylan CemgilEvrim AcarUmut ŞimşekliPatrick A. LewisGiovanni ZappellaSara HookerGuillaume BouchardCédric Archambeau
- Topics
- Tensor decomposition and applications (5 papers)Topic Modeling (3 papers)Multimodal Machine Learning Applications (3 papers)
In The Last Decade
Beyza Ermiş
14 papers receiving 164 citations
Peers
Comparison fields: 5 of 38
- Artificial Intelligence 88
- Computational Mathematics 79
- Statistical and Nonlinear Physics 40
- Information Systems 27
- Computer Vision and Pattern Recognition 24
Countries citing papers authored by Beyza Ermiş
This map shows the geographic impact of Beyza Ermiş'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 Beyza Ermiş with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Beyza Ermiş more than expected).
Fields of papers citing papers by Beyza Ermiş
This network shows the impact of papers produced by Beyza Ermiş. 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 Beyza Ermiş. The network helps show where Beyza Ermiş may publish in the future.
Co-authorship network of co-authors of Beyza Ermiş
This figure shows the co-authorship network connecting the top 25 collaborators of Beyza Ermiş. A scholar is included among the top collaborators of Beyza Ermiş 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 Beyza Ermiş. Beyza Ermiş 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 | 2 | |
| 4 | 0 | |
| 5 | 10 | |
| 6 | 0 | |
| 7 | 6 | |
| 8 | Towards Robust Episodic Meta-Learning | 1 |
| 9 | Linear bandits with Stochastic Delayed Feedback | 8 |
| 10 | 10 | |
| 11 | 5 | |
| 12 | Contextual Bandits under Delayed Feedback. | 0 |
| 13 | 8 | |
| 14 | Liver CT Annotation via Generalized Coupled Tensor Factorization. | 5 |
| 15 | Iterative splits of quadratic bounds for scalable binary tensor factorization | 3 |
| 16 | 12 | |
| 17 | 4 | |
| 18 | 97 |
About Beyza Ermiş
Beyza Ermiş is a scholar working on Computational Mathematics, Artificial Intelligence and Transportation, having authored 18 papers that have together received 172 indexed citations. Recurring topics across this work include Tensor decomposition and applications (5 papers), Topic Modeling (3 papers) and Multimodal Machine Learning Applications (3 papers). The work is most often cited by research in Computational Mathematics (79 citations), Statistical and Nonlinear Physics (40 citations) and Artificial Intelligence (88 citations). Beyza Ermiş has collaborated with scholars based in Türkiye, Germany and Denmark. Frequent co-authors include Ali Taylan Cemgil, Evrim Acar, Umut Şimşekli, Patrick A. Lewis, Giovanni Zappella, Sara Hooker, Guillaume Bouchard, Cédric Archambeau, Claire Vernade and Aditya Rawal. Their work appears in journals such as Data Mining and Knowledge Discovery, Statistics and Computing and ACM Transactions on Knowledge Discovery from Data.
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.