Timothée Lesort
Impact in
- Artificial Intelligence top 5%
- Domain Adaptation and Few-Shot Learning
- Reinforcement Learning in Robotics
- Machine Learning and ELM
- Anomaly Detection Techniques and Applications
- Data Stream Mining Techniques
-
- Multimodal Machine Learning Applications
- Human Pose and Action Recognition
Papers in
-
- Domain Adaptation and Few-Shot Learning 5
- Reinforcement Learning in Robotics 2
- Machine Learning and Algorithms 1
- Computational Physics and Python Applications 1
-
- Multimodal Machine Learning Applications 1
- Co-authors
- David Filliat (5 shared papers)Natalia Díaz-Rodríguez (4 shared papers)Andrei Stoian (2 shared papers)Vincenzo Lomonaco (2 shared papers)Davide Maltoni (2 shared papers)Mohammad Sajjad Ghaemi (1 shared paper)Jocelyn Faubert (1 shared paper)Irina Rish (1 shared paper)
- Journals
- Computers in Biology and Medicine (1 paper)Neural Networks (1 paper)arXiv (Cornell University) (3 papers)HAL (Le Centre pour la Communication Scientifique Directe) (1 paper)
In The Last Decade
Timothée Lesort
7 papers receiving 447 citations
Timothée Lesort's Hit Papers
Peers
Comparison fields: 5 of 80
- Artificial Intelligence 313
- Computer Vision and Pattern Recognition 136
- Health Informatics 9
- Control and Systems Engineering 76
- Cognitive Neuroscience 41
Countries citing papers authored by Timothée Lesort
This map shows the geographic impact of Timothée Lesort'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 Timothée Lesort with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Timothée Lesort more than expected).
Fields of papers citing papers by Timothée Lesort
This network shows the impact of papers produced by Timothée Lesort. 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 Timothée Lesort. The network helps show where Timothée Lesort may publish in the future.
Co-authors
The 11 scholars most cited alongside Timothée Lesort, 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 | Continual Learning for Robotics: Definition, Framework, Learning\n Strategies, Opportunities and Challenges Hit paper breakdown → | 2019 | 288 |
| 2 | 2018 | 145 | |
| 3 | Continual Learning for Robotics | 2019 | 10 |
| 4 | 2023 | 9 | |
| 5 | 2019 | 5 | |
| 6 | Evaluation of generative networks through their data augmentation capacity | 2018 | 1 |
| 7 | 2022 | 1 |
About Timothée Lesort
Timothée Lesort is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Infectious Diseases, Cardiology and Cardiovascular Medicine and Control and Systems Engineering, having authored 7 papers that have together received 459 indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (5 papers), Reinforcement Learning in Robotics (2 papers), COVID-19 diagnosis using AI (1 paper), Viral Infections and Outbreaks Research (1 paper), EEG and Brain-Computer Interfaces (1 paper), Multimodal Machine Learning Applications (1 paper), Machine Learning and Algorithms (1 paper) and Computational Physics and Python Applications (1 paper). The work is most often cited by research in Artificial Intelligence (313 citations), Computer Vision and Pattern Recognition (136 citations), Health Informatics (9 citations), Control and Systems Engineering (76 citations) and Cognitive Neuroscience (41 citations). Timothée Lesort has collaborated with scholars based in France, Australia and Canada. Frequent co-authors include David Filliat, Natalia Díaz-Rodríguez, Andrei Stoian, Vincenzo Lomonaco, Davide Maltoni, Mohammad Sajjad Ghaemi, Jocelyn Faubert, Irina Rish, Xinrui Li and Alexander Gepperth. Their work appears in journals such as Computers in Biology and Medicine, Neural Networks, arXiv (Cornell University) and HAL (Le Centre pour la Communication Scientifique Directe).
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.