Michaël Mathieu

9.5k total citations · 1 hit paper
11 papers, 1.1k citations indexed

About

Michaël Mathieu is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Computer Networks and Communications. According to data from OpenAlex, Michaël Mathieu has authored 11 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Computer Vision and Pattern Recognition, 2 papers in Artificial Intelligence and 1 paper in Computer Networks and Communications. Recurrent topics in Michaël Mathieu's work include Generative Adversarial Networks and Image Synthesis (4 papers), Neural Networks and Applications (2 papers) and Advanced Image Processing Techniques (2 papers). Michaël Mathieu is often cited by papers focused on Generative Adversarial Networks and Image Synthesis (4 papers), Neural Networks and Applications (2 papers) and Advanced Image Processing Techniques (2 papers). Michaël Mathieu collaborates with scholars based in United States, Switzerland and France. Michaël Mathieu's co-authors include Yann LeCun, Mikael Henaff, Gérard Ben Arous, Anna Choromanska, Pierre Sermanet, Y. Le Cun, Y-Lan Boureau, Karol Gregor, Koray Kavukcuoglu and Junbo Zhao and has published in prestigious journals such as arXiv (Cornell University), Neural Information Processing Systems and International Conference on Learning Representations.

In The Last Decade

Michaël Mathieu

11 papers receiving 1.0k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Learning Convolutional Feature Hierarchies for Visual Recognition

2010 328 citations Koray Kavukcuoglu, Pierre Sermanet et al. Neural Information Processing Systems profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Michaël Mathieu United States	9	629	487	112	106	91	11	1.1k
Ludwig Schmidt United States	15	403 0.6×	552 1.1×	130 1.2×	77 0.7×	125 1.4×	38	974
Tamir Hazan Israel	17	534 0.8×	526 1.1×	136 1.2×	81 0.8×	134 1.5×	50	1.2k
Adrian G. Borş United Kingdom	25	1.2k 1.9×	522 1.1×	112 1.0×	76 0.7×	239 2.6×	130	1.8k
Hicham Karmouni Morocco	25	1.2k 1.9×	274 0.6×	106 0.9×	90 0.8×	157 1.7×	90	1.6k
Jianhua Wu China	21	834 1.3×	238 0.5×	63 0.6×	165 1.6×	67 0.7×	72	1.3k
Zhewei Yao United States	15	613 1.0×	777 1.6×	145 1.3×	198 1.9×	55 0.6×	37	1.4k
Levent Sagun United States	9	382 0.6×	433 0.9×	42 0.4×	62 0.6×	38 0.4×	18	867
Salah Rifai Canada	5	462 0.7×	541 1.1×	39 0.3×	45 0.4×	130 1.4×	7	931
Gagandeep Singh India	17	580 0.9×	721 1.5×	36 0.3×	136 1.3×	136 1.5×	55	1.3k

Countries citing papers authored by Michaël Mathieu

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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 of co-authors of Michaël Mathieu

This figure shows the co-authorship network connecting the top 25 collaborators of Michaël Mathieu. A scholar is included among the top collaborators of Michaël Mathieu 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 Michaël Mathieu. Michaël Mathieu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

11 of 11 papers shown

Zhao, Junbo, Michaël Mathieu, & Yann LeCun. (2017). Energy-based Generative Adversarial Networks. International Conference on Learning Representations. 55 indexed citations

Mathieu, Michaël, Junbo Zhao, Pablo Sprechmann, Aditya Ramesh, & Yann LeCun. (2016). Disentangling factors of variation in deep representations using adversarial training. arXiv (Cornell University). 95 indexed citations

Goroshin, Ross, Michaël Mathieu, & Yann LeCun. (2015). Learning to Linearize Under Uncertainty. arXiv (Cornell University). 28. 1234–1242. 17 indexed citations

Mikolov, Tomáš, Armand Joulin, Sumit Chopra, Michaël Mathieu, & Marc’Aurelio Ranzato. (2014). Learning Longer Memory in Recurrent Neural Networks. arXiv (Cornell University). 11 indexed citations

Mathieu, Michaël, Mikael Henaff, & Yann LeCun. (2014). Fast training of convolutional networks through FFTS: International Conference on Learning Representations (ICLR2014), CBLS, April 2014. International Conference on Learning Representations. 5 indexed citations

Mathieu, Michaël, Mikael Henaff, & Yann LeCun. (2014). Fast Training of Convolutional Networks through FFTs. International Conference on Learning Representations. 228 indexed citations

Sermanet, Pierre, David Eigen, Xiang Zhang, et al.. (2014). Overfeat: Integrated recognition, localization and detection using convolutional networks. 2nd International Conference on Learning Representations, ICLR 2014. International Conference on Learning Representations. 16 indexed citations

Choromanska, Anna, Mikael Henaff, Michaël Mathieu, Gérard Ben Arous, & Yann LeCun. (2014). The Loss Surface of Multilayer Networks.. arXiv (Cornell University). 30 indexed citations

Choromanska, Anna, Mikael Henaff, Michaël Mathieu, Gérard Ben Arous, & Yann LeCun. (2014). The Loss Surfaces of Multilayer Networks. arXiv (Cornell University). 38. 192–204. 284 indexed citations

10.

Kavukcuoglu, Koray, Pierre Sermanet, Y-Lan Boureau, et al.. (2010). Learning Convolutional Feature Hierarchies for Visual Recognition. Neural Information Processing Systems. 23. 1090–1098. 328 indexed citations breakdown →

11.

Barry, Michael J., et al.. (1990). <title>Effect of noise and MTF on the compressibility of high-resolution color images</title>. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 1244. 255–262. 2 indexed citations

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.

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