Thomas D. Barrett
Impact in
- Artificial Intelligence top 10%
- Neural Networks and Reservoir Computing
- Quantum Information and Cryptography
- Reinforcement Learning in Robotics
Papers in
-
- Neural Networks and Reservoir Computing 3
- Quantum Information and Cryptography 2
- Metaheuristic Optimization Algorithms Research 2
- Artificial Intelligence in Games 1
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- Quantum optics and atomic interactions 2
- Mechanical and Optical Resonators 2
- Co-authors
- A. I. Lvovsky (3 shared papers)William R. Clements (2 shared papers)Jakob Foerster (1 shared paper)Xianxin Guo (2 shared papers)Zhiming Wang (1 shared paper)Axel Kuhn (2 shared papers)Scott Cameron (1 shared paper)Zhiming M. Wang (1 shared paper)
- Journals
- Nature Communications (1 paper)Journal of Physics B Atomic Molecular and Optical Physics (1 paper)Photonics Research (1 paper)Physical Review Letters (1 paper)Nature Machine Intelligence (1 paper)
- Partner nations
- United KingdomRussiaCanada
In The Last Decade
Thomas D. Barrett
10 papers receiving 245 citations
Peers
Comparison fields: 5 of 58
- Artificial Intelligence 130
- Acoustics and Ultrasonics 3
- Industrial and Manufacturing Engineering 31
- Computational Mathematics 1
- Atomic and Molecular Physics, and Optics 52
Countries citing papers authored by Thomas D. Barrett
This map shows the geographic impact of Thomas D. Barrett'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 D. Barrett with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Thomas D. Barrett more than expected).
Fields of papers citing papers by Thomas D. Barrett
This network shows the impact of papers produced by Thomas D. Barrett. 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 D. Barrett. The network helps show where Thomas D. Barrett may publish in the future.
Co-authors
The 11 scholars most cited alongside Thomas D. Barrett, 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 | 2020 | 100 | |
| 2 | 2021 | 67 | |
| 3 | 2022 | 59 | |
| 4 | 2023 | 11 | |
| 5 | 2019 | 9 | |
| 6 | Learning Disentangled Representations and Group Structure of Dynamical Environments | 2020 | 4 |
| 7 | 2025 | 2 | |
| 8 | 2023 | 2 | |
| 9 | End-to-end optical backpropagation for training neural networks. | 2019 | 1 |
| 10 | 2022 | 1 |
About Thomas D. Barrett
Thomas D. Barrett is a scholar working on Artificial Intelligence, Atomic and Molecular Physics, and Optics, Molecular Biology, Electrical and Electronic Engineering and Computer Vision and Pattern Recognition, having authored 10 papers that have together received 256 indexed citations. Recurring topics across this work include Neural Networks and Reservoir Computing (3 papers), Optical Network Technologies (2 papers), Quantum optics and atomic interactions (2 papers), Quantum Information and Cryptography (2 papers), Metaheuristic Optimization Algorithms Research (2 papers), Protein Structure and Dynamics (2 papers), Mechanical and Optical Resonators (2 papers) and Artificial Intelligence in Games (1 paper). The work is most often cited by research in Artificial Intelligence (130 citations), Acoustics and Ultrasonics (3 citations), Industrial and Manufacturing Engineering (31 citations), Computational Mathematics (1 citation) and Atomic and Molecular Physics, and Optics (52 citations). Thomas D. Barrett has collaborated with scholars based in United Kingdom, Russia and Canada. Frequent co-authors include A. I. Lvovsky, William R. Clements, Jakob Foerster, Xianxin Guo, Zhiming Wang, Axel Kuhn, Scott Cameron, Zhiming M. Wang, Matthew Greenig and Timothy Atkinson. Their work appears in journals such as Nature Communications, Journal of Physics B Atomic Molecular and Optical Physics, Photonics Research, Physical Review Letters and Nature Machine Intelligence.
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.