Émile Mathieu
Impact in
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- Generative Adversarial Networks and Image Synthesis
- Advanced Neural Network Applications
- Multimodal Machine Learning Applications
Papers in
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- Machine Learning and Data Classification 1
- Computational Physics and Python Applications 1
- Adversarial Robustness in Machine Learning 1
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- Fish Ecology and Management Studies 1
- Co-authors
- Yee Whye Teh (2 shared papers)Stefan Gelcich (1 shared paper)Ryota Tomioka (1 shared paper)Rodrigo Oyanedel (1 shared paper)Charline Le Lan (1 shared paper)E.J. Milner‐Gulland (1 shared paper)Chris J. Maddison (1 shared paper)Tom Rainforth (1 shared paper)
- Journals
- Conservation Biology (1 paper)arXiv (Cornell University) (2 papers)
- Partner nations
- United KingdomUnited StatesChile
In The Last Decade
Émile Mathieu
3 papers receiving 25 citations
Peers
Comparison fields: 5 of 29
- Computer Vision and Pattern Recognition 11
- General Social Sciences 1
- Computer Graphics and Computer-Aided Design 1
- Artificial Intelligence 9
- Nature and Landscape Conservation 3
Countries citing papers authored by Émile Mathieu
This map shows the geographic impact of Émile Mathieu'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 Émile Mathieu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Émile Mathieu more than expected).
Fields of papers citing papers by Émile Mathieu
This network shows the impact of papers produced by Émile Mathieu. 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 Émile Mathieu. The network helps show where Émile Mathieu may publish in the future.
Co-authors
The 9 scholars most cited alongside Émile Mathieu, 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 | 2019 | 13 | |
| 2 | 2021 | 10 | |
| 3 | Disentangling Disentanglement | 2018 | 3 |
About Émile Mathieu
Émile Mathieu is a scholar working on Artificial Intelligence, Nature and Landscape Conservation, Computer Vision and Pattern Recognition, Ecology and Global and Planetary Change, having authored 3 papers that have together received 26 indexed citations. Recurring topics across this work include Machine Learning and Data Classification (1 paper), Fish Ecology and Management Studies (1 paper), Conservation, Biodiversity, and Resource Management (1 paper), Image Processing and 3D Reconstruction (1 paper), Computational Physics and Python Applications (1 paper), Adversarial Robustness in Machine Learning (1 paper), Generative Adversarial Networks and Image Synthesis (1 paper) and Wildlife Ecology and Conservation (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (11 citations), General Social Sciences (1 citation), Computer Graphics and Computer-Aided Design (1 citation), Artificial Intelligence (9 citations) and Nature and Landscape Conservation (3 citations). Émile Mathieu has collaborated with scholars based in United Kingdom, United States and Chile. Frequent co-authors include Yee Whye Teh, Stefan Gelcich, Ryota Tomioka, Rodrigo Oyanedel, Charline Le Lan, E.J. Milner‐Gulland, Chris J. Maddison, Tom Rainforth and N. Siddharth. Their work appears in journals such as Conservation Biology 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.