Ravi Krishna
Impact in
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- Multimodal Machine Learning Applications
- Advanced Neural Network Applications
- Advanced Image and Video Retrieval Techniques
- Artificial Intelligence top 5%
- Domain Adaptation and Few-Shot Learning
- Machine Learning and ELM
- Sentiment Analysis and Opinion Mining
- Anomaly Detection Techniques and Applications
Papers in
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- Domain Adaptation and Few-Shot Learning 4
- Topic Modeling 2
- Natural Language Processing Techniques 1
- Authorship Attribution and Profiling 1
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- Video Surveillance and Tracking Methods 2
- Multimodal Machine Learning Applications 2
- Generative Adversarial Networks and Image Synthesis 2
- Co-authors
- Sicheng Zhao (5 shared papers)Kurt Keutzer (6 shared papers)Xiangyu Yue (4 shared papers)Alberto Sangiovanni‐Vincentelli (3 shared papers)BoRui Wu (2 shared papers)Shanghang Zhang (2 shared papers)Han Zhao (2 shared papers)Sanjit A. Seshia (2 shared papers)
- Journals
- IEEE Transactions on Cybernetics (1 paper)IEEE Transactions on Neural Networks and Learning Systems (1 paper)ACM Transactions on Asian and Low-Resource Language Information Processing (1 paper)arXiv (Cornell University) (1 paper)Proceedings of the AAAI Conference on Artificial Intelligence (1 paper)
- Partner nations
- United StatesChina
In The Last Decade
Ravi Krishna
6 papers receiving 323 citations
Peers
Comparison fields: 5 of 73
- Computer Vision and Pattern Recognition 147
- Artificial Intelligence 209
- Media Technology 21
- Health Informatics 3
- Signal Processing 16
Countries citing papers authored by Ravi Krishna
This map shows the geographic impact of Ravi Krishna'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 Ravi Krishna with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ravi Krishna more than expected).
Fields of papers citing papers by Ravi Krishna
This network shows the impact of papers produced by Ravi Krishna. 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 Ravi Krishna. The network helps show where Ravi Krishna may publish in the future.
Co-authors
The 20 scholars most cited alongside Ravi Krishna, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 242 | |
| 2 | 2019 | 41 | |
| 3 | 2021 | 15 | |
| 4 | 2021 | 15 | |
| 5 | 2020 | 15 | |
| 6 | 2020 | 1 |
About Ravi Krishna
Ravi Krishna is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Experimental and Cognitive Psychology, Radiology, Nuclear Medicine and Imaging and Infectious Diseases, having authored 6 papers that have together received 329 indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (4 papers), Video Surveillance and Tracking Methods (2 papers), Multimodal Machine Learning Applications (2 papers), Topic Modeling (2 papers), Generative Adversarial Networks and Image Synthesis (2 papers), Emotion and Mood Recognition (1 paper), Natural Language Processing Techniques (1 paper) and Authorship Attribution and Profiling (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (147 citations), Artificial Intelligence (209 citations), Media Technology (21 citations), Health Informatics (3 citations) and Signal Processing (16 citations). Ravi Krishna has collaborated with scholars based in United States and China. Frequent co-authors include Sicheng Zhao, Kurt Keutzer, Xiangyu Yue, Alberto Sangiovanni‐Vincentelli, BoRui Wu, Shanghang Zhang, Han Zhao, Sanjit A. Seshia, Joseph E. Gonzalez and Bo Li. Their work appears in journals such as IEEE Transactions on Cybernetics, IEEE Transactions on Neural Networks and Learning Systems, ACM Transactions on Asian and Low-Resource Language Information Processing, arXiv (Cornell University) and Proceedings of the AAAI Conference on Artificial Intelligence.
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.