Russell Mendonca
Impact in
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- Multimodal Machine Learning Applications
- Human Pose and Action Recognition
- Robotic Path Planning Algorithms
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- Robot Manipulation and Learning
- Human Motion and Animation
Papers in
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- Multimodal Machine Learning Applications 3
- Human Pose and Action Recognition 2
- Image and Object Detection Techniques 1
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- Reinforcement Learning in Robotics 4
- Domain Adaptation and Few-Shot Learning 2
- Machine Learning and Data Classification 1
- Co-authors
- Deepak Pathak (4 shared papers)Shikhar Bahl (3 shared papers)Lili Chen (1 shared paper)Unnat Jain (1 shared paper)Abhishek Gupta (2 shared papers)YuXuan Liu (1 shared paper)Pieter Abbeel (2 shared papers)Sergey Levine (2 shared papers)
- Journals
- arXiv (Cornell University) (1 paper)Neural Information Processing Systems (2 papers)
- Partner nations
- United StatesBrazil
In The Last Decade
Russell Mendonca
6 papers receiving 98 citations
Peers
Comparison fields: 5 of 27
- Computer Vision and Pattern Recognition 46
- Control and Systems Engineering 45
- Artificial Intelligence 42
- Human-Computer Interaction 7
- Health Informatics 1
Countries citing papers authored by Russell Mendonca
This map shows the geographic impact of Russell Mendonca'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 Russell Mendonca with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Russell Mendonca more than expected).
Fields of papers citing papers by Russell Mendonca
This network shows the impact of papers produced by Russell Mendonca. 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 Russell Mendonca. The network helps show where Russell Mendonca may publish in the future.
Co-authors
The 12 scholars most cited alongside Russell Mendonca, 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 | 2023 | 54 | |
| 2 | 2023 | 22 | |
| 3 | Meta-Reinforcement Learning of Structured Exploration Strategies | 2018 | 14 |
| 4 | 2023 | 4 | |
| 5 | Guided Meta-Policy Search | 2019 | 3 |
| 6 | 2021 | 3 | |
| 7 | 2002 | 0 |
About Russell Mendonca
Russell Mendonca is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Control and Systems Engineering, Geology and Biomedical Engineering, having authored 7 papers that have together received 100 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (4 papers), Multimodal Machine Learning Applications (3 papers), Domain Adaptation and Few-Shot Learning (2 papers), Human Pose and Action Recognition (2 papers), Robot Manipulation and Learning (2 papers), Machine Learning and Data Classification (1 paper), 3D Surveying and Cultural Heritage (1 paper) and Image and Object Detection Techniques (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (46 citations), Control and Systems Engineering (45 citations), Artificial Intelligence (42 citations), Human-Computer Interaction (7 citations) and Health Informatics (1 citation). Russell Mendonca has collaborated with scholars based in United States and Brazil. Frequent co-authors include Deepak Pathak, Shikhar Bahl, Lili Chen, Unnat Jain, Abhishek Gupta, YuXuan Liu, Pieter Abbeel, Sergey Levine, Kostas Daniilidis and Danijar Hafner. Their work appears in journals such as arXiv (Cornell University) and Neural Information Processing Systems.
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.