Léonard Hussenot
- Artificial Intelligence
- Control and Systems Engineering
- Computer Vision and Pattern Recognition
- Electrical and Electronic Engineering
- Information Systems
- Co-authors
- Matthieu GeistOlivier PietquinOlivier BachemSertan GirginPiotr StańczykMarcin MichalskiSylvain GellyRobert Dadashi
- Topics
- Reinforcement Learning in Robotics (3 papers)Anomaly Detection Techniques and Applications (2 papers)Human Motion and Animation (1 paper)
- Journals
- arXiv (Cornell University)International Conference on Learning RepresentationsProceedings of the AAAI Conference on Artificial Intelligence
- Partner nations
- United StatesGermanySwitzerland
In The Last Decade
Léonard Hussenot
4 papers receiving 49 citations
Peers
Comparison fields: 5 of 31
- Artificial Intelligence 40
- Control and Systems Engineering 10
- Computer Vision and Pattern Recognition 5
- Electrical and Electronic Engineering 5
- Information Systems 4
Countries citing papers authored by Léonard Hussenot
This map shows the geographic impact of Léonard Hussenot'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 Léonard Hussenot with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Léonard Hussenot more than expected).
Fields of papers citing papers by Léonard Hussenot
This network shows the impact of papers produced by Léonard Hussenot. 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 Léonard Hussenot. The network helps show where Léonard Hussenot may publish in the future.
Co-authorship network of co-authors of Léonard Hussenot
This figure shows the co-authorship network connecting the top 25 collaborators of Léonard Hussenot. A scholar is included among the top collaborators of Léonard Hussenot 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 Léonard Hussenot. Léonard Hussenot is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 10 | |
| 2 | 9 | |
| 3 | What Matters for On-Policy Deep Actor-Critic Methods? A Large-Scale Study | 26 |
| 4 | 0 | |
| 5 | Targeted Attacks on Deep Reinforcement Learning Agents through Adversarial Observations. | 5 |
About Léonard Hussenot
Léonard Hussenot is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Control and Systems Engineering, having authored 5 papers that have together received 50 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (3 papers), Anomaly Detection Techniques and Applications (2 papers) and Human Motion and Animation (1 paper). The work is most often cited by research in Health Informatics (3 citations), Artificial Intelligence (40 citations) and Software (2 citations). Léonard Hussenot has collaborated with scholars based in United States, Germany and Switzerland. Frequent co-authors include Matthieu Geist, Olivier Pietquin, Olivier Bachem, Sertan Girgin, Piotr Stańczyk, Marcin Michalski, Sylvain Gelly, Robert Dadashi, Nino Vieillard and Marcin Andrychowicz. Their work appears in journals such as arXiv (Cornell University), International Conference on Learning Representations and Proceedings of the AAAI Conference on Artificial 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.