Yasemin Altün
- Artificial Intelligence top 0.5%
- Computer Vision and Pattern Recognition top 0.5%
- Cognitive Neuroscience top 5%
- Signal Processing top 2%
- Molecular Biology
- Co-authors
- Thomas HofmannIoannis TsochantaridisThorsten JoachimsJan PetersKatharina MüllingMoritz Grosse‐WentrupMorteza AlamgirBernhard Schölkopf
- Topics
- Natural Language Processing Techniques (17 papers)Topic Modeling (14 papers)Machine Learning and Algorithms (6 papers)
- Partner nations
- United StatesGermanyUnited Kingdom
In The Last Decade
Yasemin Altün
37 papers receiving 3.5k citations
Hit Papers
Peers
Comparison fields: 5 of 139
- Artificial Intelligence 2.4k
- Computer Vision and Pattern Recognition 1.3k
- Cognitive Neuroscience 390
- Signal Processing 354
- Molecular Biology 314
Countries citing papers authored by Yasemin Altün
This map shows the geographic impact of Yasemin Altün'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 Yasemin Altün with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yasemin Altün more than expected).
Fields of papers citing papers by Yasemin Altün
This network shows the impact of papers produced by Yasemin Altün. 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 Yasemin Altün. The network helps show where Yasemin Altün may publish in the future.
Co-authorship network of co-authors of Yasemin Altün
This figure shows the co-authorship network connecting the top 25 collaborators of Yasemin Altün. A scholar is included among the top collaborators of Yasemin Altün 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 Yasemin Altün. Yasemin Altün 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 | 20 | |
| 4 | 23 | |
| 5 | 3 | |
| 6 | 10 | |
| 7 | Translate & Fill: Improving Zero-Shot Multilingual Semantic Parsing with Synthetic Data | 7 |
| 8 | Seminar in Artificial Intelligence | 0 |
| 9 | 75 | |
| 10 | Multitask Learning for Brain-Computer Interfaces | 62 |
| 11 | 9 | |
| 12 | 27 | |
| 13 | 8 | |
| 14 | Maximum Margin Semi-Supervised Learning for Structured Variables | 70 |
| 15 | Large Margin Methods for Structured and Interdependent Output Variablesbreakdown → | 1221 |
| 16 | Margin Semi-Supervised Learning for Structured Variables. | 6 |
| 17 | 21 | |
| 18 | Loss Functions and Optimization Methods for Discriminative Learning of Label Sequences | 8 |
| 19 | Hidden Markov support vector machines | 304 |
| 20 | Discriminative Learning for Label Sequences via Boosting | 32 |
About Yasemin Altün
Yasemin Altün is a scholar working on Artificial Intelligence, Leadership and Management and Computer Vision and Pattern Recognition, having authored 41 papers that have together received 3.8k indexed citations. Recurring topics across this work include Natural Language Processing Techniques (17 papers), Topic Modeling (14 papers) and Machine Learning and Algorithms (6 papers). The work is most often cited by research in Artificial Intelligence (2.4k citations), Computer Vision and Pattern Recognition (1.3k citations) and Signal Processing (354 citations). Yasemin Altün has collaborated with scholars based in United States, Germany and United Kingdom. Frequent co-authors include Thomas Hofmann, Ioannis Tsochantaridis, Thorsten Joachims, Jan Peters, Katharina Mülling, Moritz Grosse‐Wentrup, Morteza Alamgir, Bernhard Schölkopf, Massimiliano Ciaramita and Vinay Jayaram. Their work appears in journals such as BMC Bioinformatics, Journal of Machine Learning Research and Lecture notes in computer science.
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.