Jamie Kiros
- Computer Vision and Pattern Recognition top 5%
- Artificial Intelligence top 5%
- Molecular Biology
- Signal Processing
- Experimental and Cognitive Psychology
- Co-authors
- Sanja FidlerDavid J. FleetFartash FaghriWilliam ChanJakob UszkoreitMitchell SternGeoffrey E. HintonXuanli He
- Topics
- Topic Modeling (9 papers)Multimodal Machine Learning Applications (6 papers)Natural Language Processing Techniques (6 papers)
- Journals
- Journal of the American Medical Informatics AssociationTransactions of the Association for Computational LinguisticsarXiv (Cornell University)
- Partner nations
- CanadaUnited StatesAustralia
In The Last Decade
Jamie Kiros
10 papers receiving 301 citations
Peers
Comparison fields: 5 of 39
- Computer Vision and Pattern Recognition 245
- Artificial Intelligence 215
- Molecular Biology 9
- Signal Processing 8
- Experimental and Cognitive Psychology 7
Countries citing papers authored by Jamie Kiros
This map shows the geographic impact of Jamie Kiros'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 Jamie Kiros with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jamie Kiros more than expected).
Fields of papers citing papers by Jamie Kiros
This network shows the impact of papers produced by Jamie Kiros. 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 Jamie Kiros. The network helps show where Jamie Kiros may publish in the future.
Co-authorship network of co-authors of Jamie Kiros
This figure shows the co-authorship network connecting the top 25 collaborators of Jamie Kiros. A scholar is included among the top collaborators of Jamie Kiros 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 Jamie Kiros. Jamie Kiros 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 | 5 | |
| 3 | Modular Length Control for Sentence Generation. | 0 |
| 4 | 2 | |
| 5 | Graph Normalizing Flows | 9 |
| 6 | DOM-Q-NET: Grounded RL on Structured Language | 0 |
| 7 | 60 | |
| 8 | VSE++: Improving Visual-Semantic Embeddings with Hard Negatives. | 110 |
| 9 | 26 | |
| 10 | 4 | |
| 11 | VSE++: Improved Visual-Semantic Embeddings. | 80 |
| 12 | 5 |
About Jamie Kiros
Jamie Kiros is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems, having authored 12 papers that have together received 319 indexed citations. Recurring topics across this work include Topic Modeling (9 papers), Multimodal Machine Learning Applications (6 papers) and Natural Language Processing Techniques (6 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (245 citations), Artificial Intelligence (215 citations) and Health Informatics (2 citations). Jamie Kiros has collaborated with scholars based in Canada, United States and Australia. Frequent co-authors include Sanja Fidler, David J. Fleet, Fartash Faghri, William Chan, Jakob Uszkoreit, Mitchell Stern, Geoffrey E. Hinton, Xuanli He, Mohammad Norouzi and Gholamreza Haffari. Their work appears in journals such as Journal of the American Medical Informatics Association, Transactions of the Association for Computational Linguistics and arXiv (Cornell University).
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.