Daniel Toyama
Impact in
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- Reinforcement Learning in Robotics
- Evolutionary Algorithms and Applications
- Data Stream Mining Techniques
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- Adaptive Dynamic Programming Control
Papers in
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- Reinforcement Learning in Robotics 2
- Evolutionary Algorithms and Applications 1
- Machine Learning and Algorithms 1
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- Computability, Logic, AI Algorithms 1
- Co-authors
- Shaobo Hou (2 shared papers)David Silver (1 shared paper)Doina Precup (2 shared papers)André Barreto (2 shared papers)Diana Borsa (1 shared paper)Philippe Hamel (2 shared papers)Jonathan J. Hunt (1 shared paper)
- Journals
- arXiv (Cornell University) (1 paper)
- Partner nations
- CanadaUnited States
In The Last Decade
Daniel Toyama
2 papers receiving 16 citations
Peers
Comparison fields: 5 of 13
- Artificial Intelligence 15
- Computational Theory and Mathematics 6
- Control and Systems Engineering 5
- Signal Processing 2
- Computer Science Applications 1
Countries citing papers authored by Daniel Toyama
This map shows the geographic impact of Daniel Toyama'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 Daniel Toyama with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Toyama more than expected).
Fields of papers citing papers by Daniel Toyama
This network shows the impact of papers produced by Daniel Toyama. 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 Daniel Toyama. The network helps show where Daniel Toyama may publish in the future.
Co-authors
The 7 scholars most cited alongside Daniel Toyama, 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 | 15 | |
| 2 | Knowledge Representation for Reinforcement Learning using General Value Functions | 2018 | 1 |
About Daniel Toyama
Daniel Toyama is a scholar working on Artificial Intelligence, Computational Theory and Mathematics, Infectious Diseases, Organic Chemistry and Surgery, having authored 2 papers that have together received 16 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (2 papers), Computability, Logic, AI Algorithms (1 paper), Evolutionary Algorithms and Applications (1 paper) and Machine Learning and Algorithms (1 paper). The work is most often cited by research in Artificial Intelligence (15 citations), Computational Theory and Mathematics (6 citations), Control and Systems Engineering (5 citations), Signal Processing (2 citations) and Computer Science Applications (1 citation). Daniel Toyama has collaborated with scholars based in Canada and United States. Frequent co-authors include Shaobo Hou, David Silver, Doina Precup, André Barreto, Diana Borsa, Philippe Hamel and Jonathan J. Hunt. Their work appears in journals such as 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.