Thomas Degris
- Artificial Intelligence top 5%
- Cognitive Neuroscience top 10%
- Biomedical Engineering
- Control and Systems Engineering top 10%
- Cellular and Molecular Neuroscience
- Co-authors
- Richard S. SuttonPatrick M. PilarskiMichael R. DawsonJason P. CareyAdam WhiteDoina PrecupJoseph ModayilFarbod Fahimi
- Topics
- Reinforcement Learning in Robotics (6 papers)Muscle activation and electromyography studies (3 papers)EEG and Brain-Computer Interfaces (2 papers)
- Journals
- NeurocomputingJournal of Artificial Intelligence ResearchIEEE Robotics & Automation Magazine
- Partner nations
- CanadaFranceUnited Kingdom
In The Last Decade
Thomas Degris
12 papers receiving 505 citations
Peers
Comparison fields: 5 of 73
- Artificial Intelligence 284
- Cognitive Neuroscience 136
- Biomedical Engineering 128
- Control and Systems Engineering 109
- Cellular and Molecular Neuroscience 76
Countries citing papers authored by Thomas Degris
This map shows the geographic impact of Thomas Degris'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 Thomas Degris with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Thomas Degris more than expected).
Fields of papers citing papers by Thomas Degris
This network shows the impact of papers produced by Thomas Degris. 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 Thomas Degris. The network helps show where Thomas Degris may publish in the future.
Co-authorship network of co-authors of Thomas Degris
This figure shows the co-authorship network connecting the top 25 collaborators of Thomas Degris. A scholar is included among the top collaborators of Thomas Degris based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Thomas Degris. Thomas Degris is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 14 | |
| 2 | The predictron: end-to-end learning and planning | 25 |
| 3 | 42 | |
| 4 | Linear Off-Policy Actor-Critic. | 9 |
| 5 | 23 | |
| 6 | 135 | |
| 7 | 40 | |
| 8 | 27 | |
| 9 | 0 | |
| 10 | 123 | |
| 11 | 93 | |
| 12 | Software - Visualization Tools | 0 |
| 13 | Chisquare Tests Driven Method for Learning the Structure of Factored MDPs | 1 |
| 14 | 7 |
About Thomas Degris
Thomas Degris is a scholar working on Software, Artificial Intelligence and Cellular and Molecular Neuroscience, having authored 14 papers that have together received 539 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (6 papers), Muscle activation and electromyography studies (3 papers) and EEG and Brain-Computer Interfaces (2 papers). The work is most often cited by research in Artificial Intelligence (284 citations), Cognitive Neuroscience (136 citations) and Control and Systems Engineering (109 citations). Thomas Degris has collaborated with scholars based in Canada, France and United Kingdom. Frequent co-authors include Richard S. Sutton, Patrick M. Pilarski, Michael R. Dawson, Jason P. Carey, Adam White, Doina Precup, Joseph Modayil, Farbod Fahimi, Martha White and Jacqueline S. Hebert. Their work appears in journals such as Neurocomputing, Journal of Artificial Intelligence Research and IEEE Robotics & Automation Magazine.
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.