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 3
- Anomaly Detection Techniques and Applications 1
- Evolutionary Algorithms and Applications 1
- Natural Language Processing 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)Olivier Bachem (3 shared papers)Matthieu Geist (3 shared papers)Nino Vieillard (2 shared papers)Saurabh Kumar (1 shared paper)
- Journals
- Neurophysiologie Clinique (1 paper)Neural Information Processing Systems (1 paper)Proceedings of the AAAI Conference on Artificial Intelligence (1 paper)arXiv (Cornell University) (3 papers)
- Partner nations
- United StatesUnited KingdomGermany
In The Last Decade
Robert Dadashi
7 papers receiving 51 citations
Peers
Comparison fields: 5 of 34
- Health Informatics 3
- Artificial Intelligence 36
- Statistical and Nonlinear Physics 4
- Computer Vision and Pattern Recognition 6
- Cognitive Neuroscience 5
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 | 2019 | 13 | |
| 2 | 2023 | 11 | |
| 3 | 2022 | 11 | |
| 4 | A Geometric Perspective on Optimal Representations for Reinforcement Learning | 2019 | 8 |
| 5 | 2021 | 6 | |
| 6 | 2015 | 5 | |
| 7 | 2019 | 1 | |
| 8 | 2021 | 0 |
About Robert Dadashi
Robert Dadashi is a scholar working on Artificial Intelligence, Molecular Biology, Control and Systems Engineering, Computer Vision and Pattern Recognition and Insect Science, having authored 8 papers that have together received 55 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (3 papers), Anomaly Detection Techniques and Applications (1 paper), Viral Infectious Diseases and Gene Expression in Insects (1 paper), Evolutionary Algorithms and Applications (1 paper), Behavioral and Psychological Studies (1 paper), Natural Language Processing Techniques (1 paper), Machine Learning and Data Classification (1 paper) and Human Motion and Animation (1 paper). The work is most often cited by research in Health Informatics (3 citations), Artificial Intelligence (36 citations), Statistical and Nonlinear Physics (4 citations), Computer Vision and Pattern Recognition (6 citations) and Cognitive Neuroscience (5 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, Olivier Bachem, Matthieu Geist, Nino Vieillard, Saurabh Kumar, Rémi Munos and Olivier Pietquin. Their work appears in journals such as Neurophysiologie Clinique, Neural Information Processing Systems, Proceedings of the AAAI Conference on Artificial Intelligence and arXiv (Cornell University).
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.