Vivek Veeriah
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- Human Pose and Action Recognition 1
- Human-Computer Interaction top 5%
- Artificial Intelligence top 10%
- Reinforcement Learning in Robotics 4
- Adversarial Robustness in Machine Learning 1
- Machine Learning and Data Classification 1
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- Gait Recognition and Analysis 1
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- Functional Brain Connectivity Studies 1
- Neural dynamics and brain function 1
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- Adaptive Dynamic Programming Control 1
- Co-authors
- Guo-Jun QiNaifan ZhuangHarm van SeijenRichard S. SuttonTom ZahavyMatteo HesselZhongwen XuDavid Silver
- Journals
- Adaptive Agents and Multi-Agents Systems (1 paper)arXiv (Cornell University) (2 papers)Deep Blue (University of Michigan) (1 paper)
- Partner nations
- United StatesCanada
In The Last Decade
Vivek Veeriah
7 papers receiving 335 citations
Hit Papers
Peers
Comparison fields: 5 of 45
- Computer Vision and Pattern Recognition 292
- Human-Computer Interaction 75
- Artificial Intelligence 173
- Biomedical Engineering 139
- Signal Processing 20
Countries citing papers authored by Vivek Veeriah
This map shows the geographic impact of Vivek Veeriah'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 Vivek Veeriah with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Vivek Veeriah more than expected).
Fields of papers citing papers by Vivek Veeriah
This network shows the impact of papers produced by Vivek Veeriah. 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 Vivek Veeriah. The network helps show where Vivek Veeriah may publish in the future.
Co-authorship network
The 14 scholars most cited alongside Vivek Veeriah, 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 | 1 | |
| 2 | A Self-Tuning Actor-Critic Algorithm | 2020 | 2 |
| 3 | Self-Tuning Deep Reinforcement Learning | 2020 | 3 |
| 4 | 2019 | 7 | |
| 5 | 2017 | 7 | |
| 6 | Differential Recurrent Neural Networks for Action Recognitionbreakdown → | 2015 | 312 |
| 7 | 2015 | 8 |
About Vivek Veeriah
Vivek Veeriah is a scholar working on Artificial Intelligence, Management Science and Operations Research, Computational Theory and Mathematics, Cognitive Neuroscience and Computer Vision and Pattern Recognition, having authored 7 papers that have together received 340 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (4 papers), Functional Brain Connectivity Studies (1 paper), Neural dynamics and brain function (1 paper), Adaptive Dynamic Programming Control (1 paper), Human Pose and Action Recognition (1 paper), Adversarial Robustness in Machine Learning (1 paper), Gait Recognition and Analysis (1 paper) and Machine Learning and Data Classification (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (292 citations), Human-Computer Interaction (75 citations), Artificial Intelligence (173 citations), Biomedical Engineering (139 citations) and Signal Processing (20 citations). Vivek Veeriah has collaborated with scholars based in United States and Canada. Frequent co-authors include Guo-Jun Qi, Naifan Zhuang, Harm van Seijen, Richard S. Sutton, Tom Zahavy, Matteo Hessel, Zhongwen Xu, David Silver, Junhyuk Oh and Richard L. Lewis. Their work appears in journals such as Adaptive Agents and Multi-Agents Systems, arXiv (Cornell University), Deep Blue (University of Michigan), Neural Information Processing Systems and Journal of International Crisis and Risk Communication Research.
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.