Martín Arjovsky
Impact in
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- Generative Adversarial Networks and Image Synthesis
- Advanced Image Processing Techniques
- Digital Media Forensic Detection
- Image and Signal Denoising Methods
- Advanced Vision and Imaging
- Artificial Intelligence top 1%
- Anomaly Detection Techniques and Applications
- Domain Adaptation and Few-Shot Learning
- Adversarial Robustness in Machine Learning
Papers in
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- Numerical methods for differential equations 1
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- Neural Networks and Applications 1
- Adversarial Robustness in Machine Learning 1
- Computational Physics and Python Applications 1
- Reinforcement Learning in Robotics 1
- Co-authors
- Léon BottouSoumith ChintalaZhengdao ChenJianyu ZhangAdrià Puigdomènech BadiaPablo SprechmannSteven KapturowskiAnil Kokaram
- Journals
- International Conference on Machine Learning (1 paper)arXiv (Cornell University) (1 paper)International Conference on Learning Representations (1 paper)Electronic Imaging (1 paper)
- Partner nations
- United StatesIsrael
In The Last Decade
Martín Arjovsky
4 papers receiving 2.7k citations
Hit Papers
Peers
Comparison fields: 5 of 144
- Computer Vision and Pattern Recognition 1.5k
- Artificial Intelligence 1.0k
- Computer Graphics and Computer-Aided Design 89
- Media Technology 223
- Signal Processing 267
Countries citing papers authored by Martín Arjovsky
This map shows the geographic impact of Martín Arjovsky'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 Martín Arjovsky with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Martín Arjovsky more than expected).
Fields of papers citing papers by Martín Arjovsky
This network shows the impact of papers produced by Martín Arjovsky. 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 Martín Arjovsky. The network helps show where Martín Arjovsky may publish in the future.
Co-authors
The 13 scholars most cited alongside Martín Arjovsky, 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 | Symplectic Recurrent Neural Networks | 2020 | 14 |
| 2 | 2020 | 27 | |
| 3 | Wasserstein Generative Adversarial Networks Hit paper breakdown → | 2017 | 2782 |
| 4 | 2016 | 8 |
About Martín Arjovsky
Martín Arjovsky is a scholar working on Numerical Analysis, Artificial Intelligence, Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics and Infectious Diseases, having authored 4 papers that have together received 2.8k indexed citations. Recurring topics across this work include Neural Networks and Applications (1 paper), Generative Adversarial Networks and Image Synthesis (1 paper), Adversarial Robustness in Machine Learning (1 paper), Computational Physics and Python Applications (1 paper), Human Pose and Action Recognition (1 paper), Numerical methods for differential equations (1 paper), Model Reduction and Neural Networks (1 paper) and Reinforcement Learning in Robotics (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (1.5k citations), Artificial Intelligence (1.0k citations), Computer Graphics and Computer-Aided Design (89 citations), Media Technology (223 citations) and Signal Processing (267 citations). Martín Arjovsky has collaborated with scholars based in United States and Israel. Frequent co-authors include Léon Bottou, Soumith Chintala, Zhengdao Chen, Jianyu Zhang, Adrià Puigdomènech Badia, Pablo Sprechmann, Steven Kapturowski, Anil Kokaram, Daniel Guo and Bilal Piot. Their work appears in journals such as International Conference on Machine Learning, arXiv (Cornell University), International Conference on Learning Representations and Electronic Imaging.
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.