Vikram Voleti
Impact in
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- Advanced Vision and Imaging
- Advanced Image Processing Techniques
- Human Pose and Action Recognition
- Multimodal Machine Learning Applications
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- Neural dynamics and brain function
- Functional Brain Connectivity Studies
Papers in
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- Advanced Vision and Imaging 1
- Generative Adversarial Networks and Image Synthesis 1
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- Neural Networks and Applications 2
- Adversarial Robustness in Machine Learning 1
- Co-authors
- Irina Rish (2 shared papers)Guillaume Dumas (2 shared papers)Rajiv R. Sahay (1 shared paper)Mohan Kankanhalli (1 shared paper)Boris N. Oreshkin (1 shared paper)Matt Deitke (1 shared paper)Michael C. Mozer (1 shared paper)Christopher Pal (2 shared papers)
- Journals
- Annals of Mathematics and Artificial Intelligence (1 paper)Frontiers in Artificial Intelligence (1 paper)arXiv (Cornell University) (2 papers)Pure (University of Bath) (1 paper)PolyPublie (École Polytechnique de Montréal) (1 paper)
In The Last Decade
Vikram Voleti
9 papers receiving 77 citations
Peers
Comparison fields: 5 of 41
- Computer Vision and Pattern Recognition 31
- Cognitive Neuroscience 20
- Equine 1
- Computer Graphics and Computer-Aided Design 2
- Artificial Intelligence 18
Countries citing papers authored by Vikram Voleti
This map shows the geographic impact of Vikram Voleti'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 Vikram Voleti with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Vikram Voleti more than expected).
Fields of papers citing papers by Vikram Voleti
This network shows the impact of papers produced by Vikram Voleti. 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 Vikram Voleti. The network helps show where Vikram Voleti may publish in the future.
Co-authors
The 25 scholars most cited alongside Vikram Voleti, 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 | 2022 | 31 | |
| 2 | 2015 | 16 | |
| 3 | 2023 | 10 | |
| 4 | 2022 | 8 | |
| 5 | 2020 | 7 | |
| 6 | 2019 | 5 | |
| 7 | 2021 | 2 | |
| 8 | 2022 | 1 | |
| 9 | 2017 | 1 | |
| 10 | 2024 | 0 |
About Vikram Voleti
Vikram Voleti is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Computational Mechanics, Media Technology and Control and Systems Engineering, having authored 10 papers that have together received 81 indexed citations. Recurring topics across this work include Neural Networks and Applications (2 papers), Image Processing Techniques and Applications (2 papers), Robotics and Sensor-Based Localization (1 paper), Adversarial Robustness in Machine Learning (1 paper), Advanced Vision and Imaging (1 paper), Neural dynamics and brain function (1 paper), Generative Adversarial Networks and Image Synthesis (1 paper) and Human Motion and Animation (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (31 citations), Cognitive Neuroscience (20 citations), Equine (1 citation), Computer Graphics and Computer-Aided Design (2 citations) and Artificial Intelligence (18 citations). Vikram Voleti has collaborated with scholars based in Canada, India and Argentina. Frequent co-authors include Irina Rish, Guillaume Dumas, Rajiv R. Sahay, Mohan Kankanhalli, Boris N. Oreshkin, Matt Deitke, Michael C. Mozer, Christopher Pal, Ruoshi Liu and Aniruddha Kembhavi. Their work appears in journals such as Annals of Mathematics and Artificial Intelligence, Frontiers in Artificial Intelligence, arXiv (Cornell University), Pure (University of Bath) and PolyPublie (École Polytechnique de Montréal).
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.