James Bergstra
Impact in
- Artificial Intelligence top 0.1%
- Machine Learning and Data Classification
- Neural Networks and Applications
- Anomaly Detection Techniques and Applications
- Topic Modeling
-
- Advanced Neural Network Applications
Papers in
-
- Machine Learning and Data Classification 8
- Neural Networks and Applications 4
- Computational Physics and Python Applications 4
- Machine Learning and Algorithms 4
-
- Generative Adversarial Networks and Image Synthesis 5
- Music Technology and Sound Studies 3
- Co-authors
- Yoshua Bengio (11 shared papers)David Cox (5 shared papers)Daniel Yamins (1 shared paper)Dan Yamins (2 shared papers)Chris Eliasmith (4 shared papers)Brent Komer (2 shared papers)Dumitru Erhan (2 shared papers)Aaron Courville (5 shared papers)
- Journals
- Machine Learning (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
25 papers receiving 9.9k citations
James Bergstra's Hit Papers
Peers
Comparison fields: 5 of 214
- Artificial Intelligence 4.1k
- Computer Vision and Pattern Recognition 2.1k
- Signal Processing 901
- Environmental Engineering 542
- Software 141
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 25 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Random search for hyper-parameter optimization Hit paper breakdown → | 2012 | 5591 |
| 2 | Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures Hit paper breakdown → | 2013 | 949 |
| 3 | Theano: A CPU and GPU Math Compiler in Python Hit paper breakdown → | 2010 | 692 |
| 4 | An empirical evaluation of deep architectures on problems with many factors of variation Hit paper breakdown → | 2007 | 672 |
| 5 | Hyperopt: a Python library for model selection and hyperparameter optimization Hit paper breakdown → | 2015 | 653 |
| 6 | Hyperopt: A Python Library for Optimizing the Hyperparameters of Machine Learning Algorithms Hit paper breakdown → | 2013 | 493 |
| 7 | 2014 | 299 | |
| 8 | 2014 | 188 | |
| 9 | 2006 | 186 | |
| 10 | Theano: Deep Learning on GPUs with Python | 2012 | 126 |
| 11 | Unsupervised and Transfer Learning Challenge: a Deep Learning Approach | 2011 | 91 |
| 12 | 2013 | 74 | |
| 13 | 2012 | 42 | |
| 14 | A Spike and Slab Restricted Boltzmann Machine | 2011 | 41 |
| 15 | Unsupervised Models of Images by Spike-and-Slab RBMs | 2011 | 38 |
| 16 | 2009 | 35 | |
| 17 | Slow, Decorrelated Features for Pretraining Complex Cell-like Networks | 2009 | 31 |
| 18 | 2018 | 20 | |
| 19 | 2014 | 17 | |
| 20 | 2010 | 13 |
About James Bergstra
James Bergstra is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing, Cognitive Neuroscience and Computational Theory and Mathematics, having authored 25 papers that have together received 10.3k indexed citations. Recurring topics across this work include Machine Learning and Data Classification (8 papers), Music and Audio Processing (6 papers), Generative Adversarial Networks and Image Synthesis (5 papers), Neural dynamics and brain function (4 papers), Neural Networks and Applications (4 papers), Computational Physics and Python Applications (4 papers), Machine Learning and Algorithms (4 papers) and Music Technology and Sound Studies (3 papers). The work is most often cited by research in Artificial Intelligence (4.1k citations), Computer Vision and Pattern Recognition (2.1k citations), Signal Processing (901 citations), Environmental Engineering (542 citations) and Software (141 citations). James Bergstra has collaborated with scholars based in Canada, United States and France. Frequent co-authors include Yoshua Bengio, David Cox, Daniel Yamins, Dan Yamins, Chris Eliasmith, Brent Komer, Dumitru Erhan, Aaron Courville, Hugo Larochelle and Guillaume Desjardins. Their work appears in journals such as Machine Learning, 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.