Michaël Mathieu
Impact in
- Artificial Intelligence top 0.5%
- Reinforcement Learning in Robotics
- Artificial Intelligence in Games
- Neural Networks and Applications
- Domain Adaptation and Few-Shot Learning
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- Advanced Neural Network Applications
- Advanced Image and Video Retrieval Techniques
Papers in
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- Advanced Neural Network Applications 4
- Generative Adversarial Networks and Image Synthesis 1
- Advanced Image and Video Retrieval Techniques 1
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- Neural Networks and Applications 1
- Artificial Intelligence in Games 1
- Stochastic Gradient Optimization Techniques 1
- Co-authors
- Yann LeCun (5 shared papers)Mikael Henaff (2 shared papers)Pierre Sermanet (2 shared papers)Koray Kavukcuoglu (1 shared paper)Karol Gregor (1 shared paper)Y-Lan Boureau (1 shared paper)Junbo Zhao (1 shared paper)Rob Fergus (1 shared paper)
- Journals
- Nature (1 paper)International Conference on Learning Representations (4 papers)neural information processing systems (1 paper)
- Partner nations
- United StatesFranceUnited Kingdom
In The Last Decade
Michaël Mathieu
6 papers receiving 2.9k citations
Michaël Mathieu's Hit Papers
Peers
Comparison fields: 5 of 123
- Artificial Intelligence 1.7k
- Computer Vision and Pattern Recognition 511
- Hardware and Architecture 111
- Health Informatics 24
- Industrial and Manufacturing Engineering 68
Countries citing papers authored by Michaël Mathieu
This map shows the geographic impact of Michaël 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 Michaël Mathieu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michaël Mathieu more than expected).
Fields of papers citing papers by Michaël Mathieu
This network shows the impact of papers produced by Michaël 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 Michaël Mathieu. The network helps show where Michaël Mathieu may publish in the future.
Co-authors
The 10 scholars most cited alongside Michaël 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 | Grandmaster level in StarCraft II using multi-agent reinforcement learning Hit paper breakdown → | 2019 | 2262 |
| 2 | Learning Convolutional Feature Hierarchies for Visual Recognition Hit paper breakdown → | 2010 | 370 |
| 3 | Fast Training of Convolutional Networks through FFTs | 2014 | 245 |
| 4 | Energy-based Generative Adversarial Networks | 2017 | 60 |
| 5 | Overfeat: Integrated recognition, localization and detection using convolutional networks. 2nd International Conference on Learning Representations, ICLR 2014 | 2014 | 22 |
| 6 | Fast training of convolutional networks through FFTS: International Conference on Learning Representations (ICLR2014), CBLS, April 2014 | 2014 | 6 |
About Michaël Mathieu
Michaël Mathieu is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Nuclear Energy and Engineering, Waste Management and Disposal and Infectious Diseases, having authored 6 papers that have together received 3k indexed citations. Recurring topics across this work include Advanced Neural Network Applications (4 papers), Neural Networks and Applications (1 paper), Artificial Intelligence in Games (1 paper), Generative Adversarial Networks and Image Synthesis (1 paper), Advanced Image and Video Retrieval Techniques (1 paper) and Stochastic Gradient Optimization Techniques (1 paper). The work is most often cited by research in Artificial Intelligence (1.7k citations), Computer Vision and Pattern Recognition (511 citations), Hardware and Architecture (111 citations), Health Informatics (24 citations) and Industrial and Manufacturing Engineering (68 citations). Michaël Mathieu has collaborated with scholars based in United States, France and United Kingdom. Frequent co-authors include Yann LeCun, Mikael Henaff, Pierre Sermanet, Koray Kavukcuoglu, Karol Gregor, Y-Lan Boureau, Junbo Zhao, Rob Fergus, David Eigen and Xiang Zhang. Their work appears in journals such as Nature, International Conference on Learning Representations and neural information processing systems.
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.