Matthieu Devin
Impact in
- Artificial Intelligence top 0.5%
- Stochastic Gradient Optimization Techniques
- Speech Recognition and Synthesis
- Privacy-Preserving Technologies in Data
- Domain Adaptation and Few-Shot Learning
-
- Advanced Neural Network Applications
- Advanced Image and Video Retrieval Techniques
Papers in
-
- Domain Adaptation and Few-Shot Learning 2
- Speech Recognition and Synthesis 2
-
- Advanced Neural Network Applications 2
- Advanced Image and Video Retrieval Techniques 1
- Face recognition and analysis 1
- Video Surveillance and Tracking Methods 1
- Co-authors
- Rajat Monga (3 shared papers)Andrew Y. Ng (3 shared papers)Greg S. Corrado (3 shared papers)Quoc V. Le (3 shared papers)Marc’Aurelio Ranzato (2 shared papers)Jay B. Dean (2 shared papers)Andrew Senior (2 shared papers)Kai Chen (1 shared paper)
- Journals
- Neural Information Processing Systems (1 paper)International Conference on Machine Learning (1 paper)
- Partner nations
- United States
In The Last Decade
Matthieu Devin
6 papers receiving 2.2k citations
Matthieu Devin's Hit Papers
Peers
Comparison fields: 5 of 125
- Artificial Intelligence 1.6k
- Computer Vision and Pattern Recognition 962
- Signal Processing 381
- Hardware and Architecture 140
- Computational Mathematics 10
Countries citing papers authored by Matthieu Devin
This map shows the geographic impact of Matthieu Devin'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 Matthieu Devin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matthieu Devin more than expected).
Fields of papers citing papers by Matthieu Devin
This network shows the impact of papers produced by Matthieu Devin. 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 Matthieu Devin. The network helps show where Matthieu Devin may publish in the future.
Co-authors
The 19 scholars most cited alongside Matthieu Devin, 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 | Large Scale Distributed Deep Networks Hit paper breakdown → | 2012 | 1718 |
| 2 | Building high-level features using large scale unsupervised learning Hit paper breakdown → | 2012 | 406 |
| 3 | 2013 | 201 | |
| 4 | 2013 | 34 | |
| 5 | Appendix: Building high-level features using large scale unsupervised learning | 2012 | 22 |
| 6 | 2001 | 3 |
About Matthieu Devin
Matthieu Devin is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing, Infectious Diseases and Organic Chemistry, having authored 6 papers that have together received 2.4k indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (2 papers), Music and Audio Processing (2 papers), Speech and Audio Processing (2 papers), Advanced Neural Network Applications (2 papers), Speech Recognition and Synthesis (2 papers), Advanced Image and Video Retrieval Techniques (1 paper), Face recognition and analysis (1 paper) and Video Surveillance and Tracking Methods (1 paper). The work is most often cited by research in Artificial Intelligence (1.6k citations), Computer Vision and Pattern Recognition (962 citations), Signal Processing (381 citations), Hardware and Architecture (140 citations) and Computational Mathematics (10 citations). Matthieu Devin has collaborated with scholars based in United States. Frequent co-authors include Rajat Monga, Andrew Y. Ng, Greg S. Corrado, Quoc V. Le, Marc’Aurelio Ranzato, Jay B. Dean, Andrew Senior, Kai Chen, Paul A. Tucker and M. Mao. Their work appears in journals such as Neural Information Processing Systems and International Conference on Machine Learning.
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.