Thomas Mesnard
Impact in
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- Neural dynamics and brain function
- EEG and Brain-Computer Interfaces
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- Neural Networks and Applications
- Neural Networks and Reservoir Computing
- Domain Adaptation and Few-Shot Learning
Papers in
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- Neural Networks and Reservoir Computing 2
- Reinforcement Learning in Robotics 1
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- Advanced Memory and Neural Computing 3
- Ferroelectric and Negative Capacitance Devices 1
- Co-authors
- Yoshua Bengio (3 shared papers)Asja Fischer (1 shared paper)Yuhuai Wu (1 shared paper)Saizheng Zhang (1 shared paper)Walter Senn (1 shared paper)Gaëtan Vignoud (1 shared paper)Benjamin Scellier (1 shared paper)Mohammad Gheshlaghi Azar (1 shared paper)
- Journals
- Neural Computation (1 paper)International Conference on Learning Representations (1 paper)arXiv (Cornell University) (1 paper)
- Partner nations
- CanadaFranceSwitzerland
In The Last Decade
Thomas Mesnard
4 papers receiving 54 citations
Peers
Comparison fields: 5 of 22
- Cognitive Neuroscience 34
- Artificial Intelligence 25
- Cellular and Molecular Neuroscience 14
- Health Informatics 1
- Medical Laboratory Technology 1
Countries citing papers authored by Thomas Mesnard
This map shows the geographic impact of Thomas Mesnard'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 Mesnard with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Thomas Mesnard more than expected).
Fields of papers citing papers by Thomas Mesnard
This network shows the impact of papers produced by Thomas Mesnard. 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 Mesnard. The network helps show where Thomas Mesnard may publish in the future.
Co-authors
The 18 scholars most cited alongside Thomas Mesnard, 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 | 2017 | 49 | |
| 2 | 2019 | 3 | |
| 3 | 2018 | 2 | |
| 4 | Extending the Framework of Equilibrium Propagation to General Dynamics | 2018 | 1 |
About Thomas Mesnard
Thomas Mesnard is a scholar working on Artificial Intelligence, Electrical and Electronic Engineering, Cognitive Neuroscience, Accounting and Infectious Diseases, having authored 4 papers that have together received 55 indexed citations. Recurring topics across this work include Advanced Memory and Neural Computing (3 papers), Neural Networks and Reservoir Computing (2 papers), Neural dynamics and brain function (2 papers), Functional Brain Connectivity Studies (1 paper), Financial Distress and Bankruptcy Prediction (1 paper), Ferroelectric and Negative Capacitance Devices (1 paper) and Reinforcement Learning in Robotics (1 paper). The work is most often cited by research in Cognitive Neuroscience (34 citations), Artificial Intelligence (25 citations), Cellular and Molecular Neuroscience (14 citations), Health Informatics (1 citation) and Medical Laboratory Technology (1 citation). Thomas Mesnard has collaborated with scholars based in Canada, France and Switzerland. Frequent co-authors include Yoshua Bengio, Asja Fischer, Yuhuai Wu, Saizheng Zhang, Walter Senn, Gaëtan Vignoud, Benjamin Scellier, Mohammad Gheshlaghi Azar, Jonathan Binas and Anna Harutyunyan. Their work appears in journals such as Neural Computation, International Conference on Learning Representations 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.