Léon Bottou
Impact in
- Computer Vision and Pattern Recognition top 0.01%
- Advanced Neural Network Applications
- Advanced Image and Video Retrieval Techniques
- Face and Expression Recognition
- Artificial Intelligence top 0.01%
- Domain Adaptation and Few-Shot Learning
- Anomaly Detection Techniques and Applications
- Neural Networks and Applications
- Adversarial Robustness in Machine Learning
Papers in
-
- Neural Networks and Applications 24
- Machine Learning and Algorithms 21
- Stochastic Gradient Optimization Techniques 9
- Domain Adaptation and Few-Shot Learning 7
- Machine Learning and Data Classification 7
-
- Advanced Data Compression Techniques 9
- Face and Expression Recognition 7
- Handwritten Text Recognition Techniques 6
- Co-authors
- Yoshua Bengio (7 shared papers)Yann LeCun (18 shared papers)Patrick Haffner (9 shared papers)Martín Arjovsky (2 shared papers)Soumith Chintala (1 shared paper)Daphne Koller (1 shared paper)Dale Schuurmans (1 shared paper)Maxime Oquab (2 shared papers)
- Journals
- Journal of Machine Learning Research (4 papers)Neural Computation (2 papers)Machine Learning (2 papers)IEEE Transactions on Circuits and Systems for Video Technology (2 papers)Proceedings of the IEEE (1 paper)
- Partner nations
- United StatesGermanyFrance
In The Last Decade
Léon Bottou
82 papers receiving 52.7k citations
Léon Bottou's Hit Papers
Peers
Comparison fields: 5 of 231
- Computer Vision and Pattern Recognition 22.1k
- Artificial Intelligence 24.1k
- Media Technology 3.8k
- Signal Processing 4.0k
- Computational Mathematics 155
Countries citing papers authored by Léon Bottou
This map shows the geographic impact of Léon Bottou'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 Léon Bottou with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Léon Bottou more than expected).
Fields of papers citing papers by Léon Bottou
This network shows the impact of papers produced by Léon Bottou. 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 Léon Bottou. The network helps show where Léon Bottou may publish in the future.
Co-authors
The 25 scholars most cited alongside Léon Bottou, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 86 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Gradient-based learning applied to document recognition Hit paper breakdown → | 1998 | 35445 |
| 2 | Proceedings of the 21st International Conference on Neural Information Processing Systems Hit paper breakdown → | 2008 | 3740 |
| 3 | Wasserstein Generative Adversarial Networks Hit paper breakdown → | 2017 | 2813 |
| 4 | Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks Hit paper breakdown → | 2014 | 2020 |
| 5 | SIGNATURE VERIFICATION USING A “SIAMESE” TIME DELAY NEURAL NETWORK Hit paper breakdown → | 1993 | 1419 |
| 6 | Proceedings of the 26th International Conference on Neural Information Processing Systems Hit paper breakdown → | 2013 | 1124 |
| 7 | Efficient BackProp Hit paper breakdown → | 1998 | 952 |
| 8 | Learning methods for generic object recognition with invariance to pose and lighting Hit paper breakdown → | 2004 | 811 |
| 9 | Proceedings of the 26th Annual International Conference on Machine Learning Hit paper breakdown → | 2009 | 586 |
| 10 | Comparison of classifier methods: a case study in handwritten digit recognition Hit paper breakdown → | 2002 | 412 |
| 11 | Comparison of learning algorithms for handwritten digit recognition | 1995 | 364 |
| 12 | Large Scale Transductive SVMs | 2006 | 357 |
| 13 | 1992 | 349 | |
| 14 | Stochastic Gradient Learning in Neural Networks | 1991 | 309 |
| 15 | 1995 | 308 | |
| 16 | Parallel Support Vector Machines: The Cascade SVM | 2004 | 266 |
| 17 | 2007 | 262 | |
| 18 | Convergence Properties of the K-Means Algorithms | 1994 | 256 |
| 19 | 2006 | 254 | |
| 20 | Counterfactual reasoning and learning systems: the example of computational advertising | 2013 | 211 |
About Léon Bottou
Léon Bottou is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing, Computational Mechanics and Control and Systems Engineering, having authored 86 papers that have together received 54.9k indexed citations. Recurring topics across this work include Neural Networks and Applications (24 papers), Machine Learning and Algorithms (21 papers), Stochastic Gradient Optimization Techniques (9 papers), Advanced Data Compression Techniques (9 papers), Face and Expression Recognition (7 papers), Domain Adaptation and Few-Shot Learning (7 papers), Machine Learning and Data Classification (7 papers) and Handwritten Text Recognition Techniques (6 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (22.1k citations), Artificial Intelligence (24.1k citations), Media Technology (3.8k citations), Signal Processing (4.0k citations) and Computational Mathematics (155 citations). Léon Bottou has collaborated with scholars based in United States, Germany and France. Frequent co-authors include Yoshua Bengio, Yann LeCun, Patrick Haffner, Martín Arjovsky, Soumith Chintala, Daphne Koller, Dale Schuurmans, Maxime Oquab, Ivan Laptev and Josef Šivic. Their work appears in journals such as Journal of Machine Learning Research, Neural Computation, Machine Learning, IEEE Transactions on Circuits and Systems for Video Technology and Proceedings of the IEEE.
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.