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
- Topic Modeling
- Neural Networks and Applications
- Machine Learning and Data Classification
Papers in
-
- Neural Networks and Applications 35
- Machine Learning and Algorithms 31
- Machine Learning and Data Classification 12
- Domain Adaptation and Few-Shot Learning 9
- Stochastic Gradient Optimization Techniques 8
-
- Face and Expression Recognition 11
- Advanced Data Compression Techniques 9
- Advanced Image and Video Retrieval Techniques 9
- Co-authors
- Yoshua Bengio (9 shared papers)Yann LeCun (22 shared papers)Patrick Haffner (11 shared papers)James Bergstra (1 shared paper)Martín Arjovsky (3 shared papers)Soumith Chintala (2 shared papers)Dale Schuurmans (1 shared paper)Daphne Koller (1 shared paper)
- Journals
- Journal of Machine Learning Research (3 papers)Neural Computation (2 papers)IEEE Transactions on Circuits and Systems for Video Technology (2 papers)Proceedings of the IEEE (1 paper)Applied Stochastic Models in Business and Industry (1 paper)
- Partner nations
- United StatesFranceGermany
In The Last Decade
Léon Bottou
104 papers receiving 80.9k citations
Léon Bottou's Hit Papers
Peers
Comparison fields: 5 of 232
- Computer Vision and Pattern Recognition 31.9k
- Artificial Intelligence 38.9k
- Media Technology 4.9k
- Signal Processing 6.0k
- Computational Mathematics 225
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 108 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 | 40941 |
| 2 | Random search for hyper-parameter optimization Hit paper breakdown → | 2012 | 6085 |
| 3 | Large-Scale Machine Learning with Stochastic Gradient Descent Hit paper breakdown → | 2010 | 3969 |
| 4 | Proceedings of the 21st International Conference on Neural Information Processing Systems Hit paper breakdown → | 2008 | 3969 |
| 5 | Natural Language Processing (almost) from Scratch Hit paper breakdown → | 2011 | 3252 |
| 6 | Wasserstein Generative Adversarial Networks Hit paper breakdown → | 2017 | 3200 |
| 7 | Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks Hit paper breakdown → | 2014 | 2375 |
| 8 | SIGNATURE VERIFICATION USING A “SIAMESE” TIME DELAY NEURAL NETWORK Hit paper breakdown → | 1993 | 1603 |
| 9 | SIGNATURE VERIFICATION USING A “SIAMESE” TIME DELAY NEURAL NETWORK Hit paper breakdown → | 1994 | 1467 |
| 10 | Stochastic Gradient Descent Tricks Hit paper breakdown → | 2012 | 1380 |
| 11 | Proceedings of the 26th International Conference on Neural Information Processing Systems Hit paper breakdown → | 2013 | 1186 |
| 12 | Efficient BackProp Hit paper breakdown → | 1998 | 1082 |
| 13 | Efficient BackProp Hit paper breakdown → | 2012 | 975 |
| 14 | Learning methods for generic object recognition with invariance to pose and lighting Hit paper breakdown → | 2004 | 954 |
| 15 | The Tradeoffs of Large-Scale Learning Hit paper breakdown → | 2011 | 721 |
| 16 | Efficient BackProp Hit paper breakdown → | 1998 | 716 |
| 17 | Object Recognition with Gradient-Based Learning Hit paper breakdown → | 1999 | 675 |
| 18 | Is object localization for free? - Weakly-supervised learning with convolutional neural networks Hit paper breakdown → | 2015 | 631 |
| 19 | Comparison of classifier methods: a case study in handwritten digit recognition Hit paper breakdown → | 2002 | 478 |
| 20 | Fast Kernel Classifiers With Online And Active Learning Hit paper breakdown → | 2005 | 461 |
About Léon Bottou
Léon Bottou is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing, Management Science and Operations Research and Computational Theory and Mathematics, having authored 108 papers that have together received 84.1k indexed citations. Recurring topics across this work include Neural Networks and Applications (35 papers), Machine Learning and Algorithms (31 papers), Machine Learning and Data Classification (12 papers), Face and Expression Recognition (11 papers), Advanced Data Compression Techniques (9 papers), Domain Adaptation and Few-Shot Learning (9 papers), Advanced Image and Video Retrieval Techniques (9 papers) and Stochastic Gradient Optimization Techniques (8 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (31.9k citations), Artificial Intelligence (38.9k citations), Media Technology (4.9k citations), Signal Processing (6.0k citations) and Computational Mathematics (225 citations). Léon Bottou has collaborated with scholars based in United States, France and Germany. Frequent co-authors include Yoshua Bengio, Yann LeCun, Patrick Haffner, James Bergstra, Martín Arjovsky, Soumith Chintala, Dale Schuurmans, Daphne Koller, Jason Weston and Josef Šivic. Their work appears in journals such as Journal of Machine Learning Research, Neural Computation, IEEE Transactions on Circuits and Systems for Video Technology, Proceedings of the IEEE and Applied Stochastic Models in Business and Industry.
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.