Michaël Mathieu
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- Generative Adversarial Networks and Image Synthesis 4
- Advanced Image Processing Techniques 2
- Human Pose and Action Recognition 2
- Digital Media Forensic Detection 2
- Image and Signal Denoising Methods 1
- Artificial Intelligence top 2%
- Neural Networks and Applications 2
- Media Technology top 5%
- Signal Processing top 10%
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- Sparse and Compressive Sensing Techniques 1
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- Interconnection Networks and Systems 1
- Co-authors
- Yann LeCunMikael HenaffAnna ChoromanskaGérard Ben ArousPierre SermanetY. Le CunKarol GregorY-Lan Boureau
- Journals
- arXiv (Cornell University) (5 papers)International Conference on Learning Representations (4 papers)Neural Information Processing Systems (1 paper)
- Partner nations
- United StatesSwitzerlandChina
In The Last Decade
Michaël Mathieu
11 papers receiving 1.0k citations
Hit Papers
Peers
Comparison fields: 5 of 104
- Computer Vision and Pattern Recognition 629
- Artificial Intelligence 487
- Computational Mathematics 8
- Media Technology 78
- Signal Processing 91
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-authorship network
The 21 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 | Energy-based Generative Adversarial Networks | 2017 | 55 |
| 2 | 2016 | 95 | |
| 3 | 2015 | 17 | |
| 4 | Fast training of convolutional networks through FFTS: International Conference on Learning Representations (ICLR2014), CBLS, April 2014 | 2014 | 5 |
| 5 | The Loss Surface of Multilayer Networks. | 2014 | 30 |
| 6 | Fast Training of Convolutional Networks through FFTs | 2014 | 228 |
| 7 | Overfeat: Integrated recognition, localization and detection using convolutional networks. 2nd International Conference on Learning Representations, ICLR 2014 | 2014 | 16 |
| 8 | Learning Longer Memory in Recurrent Neural Networks | 2014 | 11 |
| 9 | 2014 | 284 | |
| 10 | Learning Convolutional Feature Hierarchies for Visual Recognitionbreakdown → | 2010 | 328 |
| 11 | 1990 | 2 |
About Michaël Mathieu
Michaël Mathieu is a scholar working on Computer Vision and Pattern Recognition, Statistics and Probability and Media Technology, having authored 11 papers that have together received 1.1k indexed citations. Recurring topics across this work include Generative Adversarial Networks and Image Synthesis (4 papers), Neural Networks and Applications (2 papers), Advanced Image Processing Techniques (2 papers), Human Pose and Action Recognition (2 papers), Digital Media Forensic Detection (2 papers), Sparse and Compressive Sensing Techniques (1 paper), Image and Signal Denoising Methods (1 paper) and Interconnection Networks and Systems (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (629 citations), Artificial Intelligence (487 citations) and Computational Mathematics (8 citations). Michaël Mathieu has collaborated with scholars based in United States, Switzerland and China. Frequent co-authors include Yann LeCun, Mikael Henaff, Anna Choromanska, Gérard Ben Arous, Pierre Sermanet, Y. Le Cun, Karol Gregor, Y-Lan Boureau, Koray Kavukcuoglu and Junbo Zhao. Their work appears in journals such as arXiv (Cornell University), International Conference on Learning Representations, Neural Information Processing Systems and Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE.
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.