Robert Dadashi
Impact in
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- Reinforcement Learning in Robotics
- Topic Modeling
- Adversarial Robustness in Machine Learning
- Evolutionary Algorithms and Applications
- Anomaly Detection Techniques and Applications
- Natural Language Processing Techniques
Papers in
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- Reinforcement Learning in Robotics 4
- Topic Modeling 1
- Data Stream Mining Techniques 1
- Machine Learning and Data Classification 1
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- Viral Infectious Diseases and Gene Expression in Insects 1
- Co-authors
- Will Dabney (3 shared papers)Marc G. Bellemare (3 shared papers)Mark Rowland (2 shared papers)Léonard Hussenot (3 shared papers)Matthieu Geist (4 shared papers)Olivier Bachem (3 shared papers)Olivier Pietquin (3 shared papers)Saurabh Kumar (1 shared paper)
- Journals
- Neurophysiologie Clinique (1 paper)Lecture notes in computer science (1 paper)arXiv (Cornell University) (3 papers)Proceedings of the AAAI Conference on Artificial Intelligence (1 paper)Neural Information Processing Systems (1 paper)
- Partner nations
- United StatesUnited KingdomGermany
In The Last Decade
Robert Dadashi
9 papers receiving 57 citations
Peers
Comparison fields: 5 of 35
- Health Informatics 3
- Artificial Intelligence 42
- Software 2
- Computer Vision and Pattern Recognition 7
- Statistical and Nonlinear Physics 4
Countries citing papers authored by Robert Dadashi
This map shows the geographic impact of Robert Dadashi'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 Robert Dadashi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Robert Dadashi more than expected).
Fields of papers citing papers by Robert Dadashi
This network shows the impact of papers produced by Robert Dadashi. 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 Robert Dadashi. The network helps show where Robert Dadashi may publish in the future.
Co-authors
The 25 scholars most cited alongside Robert Dadashi, 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 | 13 | |
| 2 | 2019 | 13 | |
| 3 | 2023 | 11 | |
| 4 | A Geometric Perspective on Optimal Representations for Reinforcement Learning | 2019 | 9 |
| 5 | 2021 | 7 | |
| 6 | 2015 | 5 | |
| 7 | 2023 | 1 | |
| 8 | 2019 | 1 | |
| 9 | 2021 | 1 |
About Robert Dadashi
Robert Dadashi is a scholar working on Artificial Intelligence, Molecular Biology, Control and Systems Engineering, Computer Vision and Pattern Recognition and Management Science and Operations Research, having authored 9 papers that have together received 61 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (4 papers), Viral Infectious Diseases and Gene Expression in Insects (1 paper), Behavioral and Psychological Studies (1 paper), Human Motion and Animation (1 paper), Topic Modeling (1 paper), Human Pose and Action Recognition (1 paper), Data Stream Mining Techniques (1 paper) and Machine Learning and Data Classification (1 paper). The work is most often cited by research in Health Informatics (3 citations), Artificial Intelligence (42 citations), Software (2 citations), Computer Vision and Pattern Recognition (7 citations) and Statistical and Nonlinear Physics (4 citations). Robert Dadashi has collaborated with scholars based in United States, United Kingdom and Germany. Frequent co-authors include Will Dabney, Marc G. Bellemare, Mark Rowland, Léonard Hussenot, Matthieu Geist, Olivier Bachem, Olivier Pietquin, Saurabh Kumar, Nino Vieillard and Rémi Munos. Their work appears in journals such as Neurophysiologie Clinique, Lecture notes in computer science, arXiv (Cornell University), Proceedings of the AAAI Conference on Artificial Intelligence 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.