Armen Aghajanyan
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 5%
- Information Systems
- Signal Processing
- Biomedical Engineering
- Co-authors
- Luke ZettlemoyerGargi GhoshDmytro OkhonkoFlorian MetzePo-Yao HuangChristoph FeichtenhoferHu XuXilun Chen
- Topics
- Natural Language Processing Techniques (6 papers)Topic Modeling (6 papers)Multimodal Machine Learning Applications (4 papers)
- Journals
- Journal of Chemical Information and ModelingarXiv (Cornell University)Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- Partner nations
- United StatesCanadaIsrael
In The Last Decade
Armen Aghajanyan
7 papers receiving 408 citations
Hit Papers
Peers
Comparison fields: 5 of 59
- Artificial Intelligence 290
- Computer Vision and Pattern Recognition 253
- Information Systems 24
- Signal Processing 15
- Biomedical Engineering 11
Countries citing papers authored by Armen Aghajanyan
This map shows the geographic impact of Armen Aghajanyan'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 Armen Aghajanyan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Armen Aghajanyan more than expected).
Fields of papers citing papers by Armen Aghajanyan
This network shows the impact of papers produced by Armen Aghajanyan. 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 Armen Aghajanyan. The network helps show where Armen Aghajanyan may publish in the future.
Co-authorship network of co-authors of Armen Aghajanyan
This figure shows the co-authorship network connecting the top 25 collaborators of Armen Aghajanyan. A scholar is included among the top collaborators of Armen Aghajanyan 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 Armen Aghajanyan. Armen Aghajanyan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 7 | |
| 2 | 37 | |
| 3 | 8 | |
| 4 | 104 | |
| 5 | VideoCLIP: Contrastive Pre-training for Zero-shot Video-Text Understandingbreakdown → | 247 |
| 6 | Pre-training via Paraphrasing | 23 |
| 7 | 1 |
About Armen Aghajanyan
Armen Aghajanyan is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Computational Theory and Mathematics, having authored 7 papers that have together received 427 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (6 papers), Topic Modeling (6 papers) and Multimodal Machine Learning Applications (4 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (253 citations), Artificial Intelligence (290 citations) and Health Informatics (4 citations). Armen Aghajanyan has collaborated with scholars based in United States, Canada and Israel. Frequent co-authors include Luke Zettlemoyer, Gargi Ghosh, Dmytro Okhonko, Florian Metze, Po-Yao Huang, Christoph Feichtenhofer, Hu Xu, Xilun Chen, Sonal Gupta and Anchit Gupta. Their work appears in journals such as Journal of Chemical Information and Modeling, arXiv (Cornell University) and Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing.
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.