Andrei A. Rusu
Impact in
- Artificial Intelligence top 0.02%
- Reinforcement Learning in Robotics
- Domain Adaptation and Few-Shot Learning
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- Multimodal Machine Learning Applications
- Robotic Path Planning Algorithms
Papers in
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- Model Reduction and Neural Networks 3
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- Domain Adaptation and Few-Shot Learning 4
- Neural Networks and Applications 3
- Neural Networks and Reservoir Computing 3
- Co-authors
- Demis HassabisJoel VenessDharshan KumaranVolodymyr MnihKoray KavukcuogluIoannis AntonoglouCharles BeattieMartin Riedmiller
- Journals
- Cerebral Cortex (1 paper)Trends in Cognitive Sciences (1 paper)Nature (1 paper)Proceedings of the National Academy of Sciences (1 paper)Research Explorer (The University of Manchester) (1 paper)
- Partner nations
- United KingdomUnited StatesNetherlands
In The Last Decade
Andrei A. Rusu
10 papers receiving 20.6k citations
Hit Papers
Peers
Comparison fields: 5 of 206
- Artificial Intelligence 10.4k
- Computer Vision and Pattern Recognition 4.4k
- Control and Systems Engineering 3.6k
- Computer Networks and Communications 3.5k
- Automotive Engineering 1.8k
Countries citing papers authored by Andrei A. Rusu
This map shows the geographic impact of Andrei A. Rusu'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 Andrei A. Rusu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Andrei A. Rusu more than expected).
Fields of papers citing papers by Andrei A. Rusu
This network shows the impact of papers produced by Andrei A. Rusu. 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 Andrei A. Rusu. The network helps show where Andrei A. Rusu may publish in the future.
Co-authors
The 25 scholars most cited alongside Andrei A. Rusu, 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 | Meta-Learning with Warped Gradient Descent | 2020 | 20 |
| 2 | 2020 | 250 | |
| 3 | 2018 | 126 | |
| 4 | 2018 | 11 | |
| 5 | 2018 | 9 | |
| 6 | Overcoming catastrophic forgetting in neural networks Hit paper breakdown → | 2017 | 3594 |
| 7 | 2016 | 42 | |
| 8 | Human-level control through deep reinforcement learning Hit paper breakdown → | 2015 | 17153 |
| 9 | 2015 | 24 | |
| 10 | 2013 | 165 |
About Andrei A. Rusu
Andrei A. Rusu is a scholar working on Statistical and Nonlinear Physics, Artificial Intelligence, Computer Vision and Pattern Recognition, Cognitive Neuroscience and Experimental and Cognitive Psychology, having authored 10 papers that have together received 21.4k indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (4 papers), Neural Networks and Applications (3 papers), Neural Networks and Reservoir Computing (3 papers), Model Reduction and Neural Networks (3 papers), Neural and Behavioral Psychology Studies (2 papers), Advanced Vision and Imaging (2 papers), Mental Health Research Topics (1 paper) and Multimodal Machine Learning Applications (1 paper). The work is most often cited by research in Artificial Intelligence (10.4k citations), Computer Vision and Pattern Recognition (4.4k citations), Control and Systems Engineering (3.6k citations), Computer Networks and Communications (3.5k citations) and Automotive Engineering (1.8k citations). Andrei A. Rusu has collaborated with scholars based in United Kingdom, United States and Netherlands. Frequent co-authors include Demis Hassabis, Joel Veness, Dharshan Kumaran, Volodymyr Mnih, Koray Kavukcuoglu, Ioannis Antonoglou, Charles Beattie, Martin Riedmiller, Andreas Fidjeland and Shane Legg. Their work appears in journals such as Cerebral Cortex, Trends in Cognitive Sciences, Nature, Proceedings of the National Academy of Sciences and Research Explorer (The University of Manchester).
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.