James Bergstra
Impact in
- Artificial Intelligence top 0.1%
- Machine Learning and Data Classification
- Neural Networks and Applications
- Metaheuristic Optimization Algorithms Research
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- Advanced Neural Network Applications
- Face recognition and analysis
Papers in
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- Machine Learning and Data Classification 11
- Neural Networks and Applications 8
- Domain Adaptation and Few-Shot Learning 3
- Gaussian Processes and Bayesian Inference 2
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- Parallel Computing and Optimization Techniques 3
- Co-authors
- Yoshua Bengio (12 shared papers)Léon Bottou (1 shared paper)David Cox (6 shared papers)Balázs Kégl (2 shared papers)Rémi Bardenet (1 shared paper)Daniel L. K. Yamins (1 shared paper)Dan Yamins (2 shared papers)Chris Eliasmith (5 shared papers)
- Journals
- Neural Networks (1 paper)IEEE Transactions on Pattern Analysis and Machine Intelligence (1 paper)Frontiers in Neuroinformatics (1 paper)Cognitive Science (1 paper)Neural Computation (1 paper)
- Partner nations
- CanadaUnited StatesFrance
In The Last Decade
James Bergstra
30 papers receiving 15.1k citations
James Bergstra's Hit Papers
Peers
Comparison fields: 5 of 205
- Artificial Intelligence 4.9k
- Computer Vision and Pattern Recognition 2.1k
- Experimental and Cognitive Psychology 1.5k
- Signal Processing 514
- Hardware and Architecture 379
Countries citing papers authored by James Bergstra
This map shows the geographic impact of James Bergstra'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 James Bergstra with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites James Bergstra more than expected).
Fields of papers citing papers by James Bergstra
This network shows the impact of papers produced by James Bergstra. 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 James Bergstra. The network helps show where James Bergstra may publish in the future.
Co-authors
The 25 scholars most cited alongside James Bergstra, 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 30 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Random search for hyper-parameter optimization Hit paper breakdown → | 2012 | 6133 |
| 2 | Algorithms for Hyper-Parameter Optimization Hit paper breakdown → | 2011 | 2197 |
| 3 | Challenges in Representation Learning: A Report on Three Machine Learning Contests Hit paper breakdown → | 2013 | 1149 |
| 4 | Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures Hit paper breakdown → | 2013 | 1098 |
| 5 | Theano: A CPU and GPU Math Compiler in Python Hit paper breakdown → | 2010 | 868 |
| 6 | An empirical evaluation of deep architectures on problems with many factors of variation Hit paper breakdown → | 2007 | 770 |
| 7 | Hyperopt: a Python library for model selection and hyperparameter optimization Hit paper breakdown → | 2015 | 763 |
| 8 | Hyperopt: A Python Library for Optimizing the Hyperparameters of Machine Learning Algorithms Hit paper breakdown → | 2013 | 601 |
| 9 | Challenges in representation learning: A report on three machine learning contests Hit paper breakdown → | 2014 | 502 |
| 10 | Nengo: a Python tool for building large-scale functional brain models Hit paper breakdown → | 2014 | 364 |
| 11 | 2014 | 224 | |
| 12 | 2006 | 220 | |
| 13 | Theano: Deep Learning on GPUs with Python | 2012 | 142 |
| 14 | Unsupervised and Transfer Learning Challenge: a Deep Learning Approach | 2011 | 115 |
| 15 | 2013 | 82 | |
| 16 | 2012 | 50 | |
| 17 | 2018 | 48 | |
| 18 | 2019 | 42 | |
| 19 | Slow, Decorrelated Features for Pretraining Complex Cell-like Networks | 2009 | 41 |
| 20 | A Spike and Slab Restricted Boltzmann Machine | 2011 | 41 |
About James Bergstra
James Bergstra is a scholar working on Artificial Intelligence, Hardware and Architecture, Signal Processing, Cognitive Neuroscience and Computer Vision and Pattern Recognition, having authored 30 papers that have together received 15.6k indexed citations. Recurring topics across this work include Machine Learning and Data Classification (11 papers), Neural Networks and Applications (8 papers), Domain Adaptation and Few-Shot Learning (3 papers), Parallel Computing and Optimization Techniques (3 papers), Music and Audio Processing (3 papers), Neural dynamics and brain function (3 papers), Advanced Neural Network Applications (3 papers) and Gaussian Processes and Bayesian Inference (2 papers). The work is most often cited by research in Artificial Intelligence (4.9k citations), Computer Vision and Pattern Recognition (2.1k citations), Experimental and Cognitive Psychology (1.5k citations), Signal Processing (514 citations) and Hardware and Architecture (379 citations). James Bergstra has collaborated with scholars based in Canada, United States and France. Frequent co-authors include Yoshua Bengio, Léon Bottou, David Cox, Balázs Kégl, Rémi Bardenet, Daniel L. K. Yamins, Dan Yamins, Chris Eliasmith, Brent Komer and Dumitru Erhan. Their work appears in journals such as Neural Networks, IEEE Transactions on Pattern Analysis and Machine Intelligence, Frontiers in Neuroinformatics, Cognitive Science and Neural Computation.
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.