Cristina Gârbacea
- Artificial Intelligence
- Signal Processing top 10%
- Computer Vision and Pattern Recognition
- Control and Systems Engineering
- Computational Mechanics
- Co-authors
- Yazhe LiThomas C. WaltersFelicia S. C. LimAäron van den OordOriol VinyalsSamuel CartonQiaozhu MeiShiyan Yan
- Topics
- Topic Modeling (5 papers)Natural Language Processing Techniques (4 papers)Advanced Text Analysis Techniques (2 papers)
- Journals
- UvA-DARE (University of Amsterdam)arXiv (Cornell University)CLEF (Working Notes)
- Partner nations
- United StatesNetherlandsUnited Kingdom
In The Last Decade
Cristina Gârbacea
7 papers receiving 103 citations
Peers
Comparison fields: 5 of 28
- Artificial Intelligence 82
- Signal Processing 62
- Computer Vision and Pattern Recognition 36
- Control and Systems Engineering 5
- Computational Mechanics 4
Countries citing papers authored by Cristina Gârbacea
This map shows the geographic impact of Cristina Gârbacea'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 Cristina Gârbacea with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Cristina Gârbacea more than expected).
Fields of papers citing papers by Cristina Gârbacea
This network shows the impact of papers produced by Cristina Gârbacea. 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 Cristina Gârbacea. The network helps show where Cristina Gârbacea may publish in the future.
Co-authorship network of co-authors of Cristina Gârbacea
This figure shows the co-authorship network connecting the top 25 collaborators of Cristina Gârbacea. A scholar is included among the top collaborators of Cristina Gârbacea 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 Cristina Gârbacea. Cristina Gârbacea is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 18 | |
| 2 | 1 | |
| 3 | 76 | |
| 4 | 12 | |
| 5 | Combining Multiple Signals for Semanticizing Tweets: University of Amsterdam at #Microposts2015 | 2 |
| 6 | Feature Selection and Data Sampling Methods for Learning Reputation Dimensions. | 1 |
| 7 | Feature selection and data sampling methods for learning reputation dimensions: The University of Amsterdam at RepLab 2014 | 1 |
About Cristina Gârbacea
Cristina Gârbacea is a scholar working on General Social Sciences, Artificial Intelligence and Signal Processing, having authored 7 papers that have together received 111 indexed citations. Recurring topics across this work include Topic Modeling (5 papers), Natural Language Processing Techniques (4 papers) and Advanced Text Analysis Techniques (2 papers). The work is most often cited by research in Signal Processing (62 citations), Artificial Intelligence (82 citations) and Computer Vision and Pattern Recognition (36 citations). Cristina Gârbacea has collaborated with scholars based in United States, Netherlands and United Kingdom. Frequent co-authors include Yazhe Li, Thomas C. Walters, Felicia S. C. Lim, Aäron van den Oord, Oriol Vinyals, Samuel Carton, Qiaozhu Mei, Shiyan Yan, Manos Tsagkias and Maarten de Rijke. Their work appears in journals such as UvA-DARE (University of Amsterdam), arXiv (Cornell University) and CLEF (Working Notes).
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.