2014
- T. Bekolay, J. Bergstra, E. Hunsberger, T. DeWolf, T. C. Stewart, D. Rasmussen, X. Choo, A. R. Voelker and C. Eliasmith (2014).
Nengo: a python tool for building large-scale functional brain models.
Frontiers in Neuroinformatics 7(48):10.3389/fninf.2013.00048.
[pdf] [link] [doi]
2013
- K. Eggensperger, M. Feurer, F. Hutter, J. Bergstra, J. Snoek, H. Hoos and K. Leyton-Brown (2013).
Towards an Empirical Foundation for Assessing Bayesian Optimization of Hyperparameters.
NIPS workshop on Bayesian Optimization in Theory and Practice, Lake Tahoe, NV.
[pdf]
- I. J. Goodfellow, D. Warde-Farley, P. Lamblin, V. Dumoulin, M. Mirza, R. Pascanu, J. Bergstra, F. Bastien and Y. Bengio (2013).
Pylearn2: a machine learning research library.
Technical report 1308.4214, arXiv.
[pdf]
- I. J. Goodfellow, D. Erhan, P. L. Carrier, A. Courville, M. Mirza, B. Hamner, W. Cukierski, Y. Tang, D. Thaler, D. Lee, Y. Zhou, C. Ramaiah, F. Feng, R. Li, X. Wang, D. Athanasakis, J. Shawe-Taylor, M. Milakov, J. Park, R. Ionescu, M. Popescu, C. Grozea, J. Bergstra, J. Xie, L. Romaszko, B. Xu, Z. Chuang and Y. Bengio (2013).
Challenges in Representation Learning: A report on three machine learning contests.
Technical report 1307.0414, arXiv.
[pdf]
- E. Hunsberger, P. Blouw, J. Bergstra and C. Eliasmith (2013).
A Neural Model of Human Image Categorization.
Proc. Annual Meeting of the Cognitive Science Society (CogSci), 633–638.
[pdf] [slides]
- J. Bergstra, D. Yamins and D. D. Cox (2013).
Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures.
Proc. 30th International Conference on Machine Learning (ICML-13).
[pdf]
- J. Bergstra, D. Yamins and D. D. Cox (2013).
Hyperopt: A Python Library for Optimizing the Hyperparameters of Machine Learning Algorithms.
Proc. Python for Scientific Computing Conference (SciPy).
[pdf] [video]
- J. Bergstra, N. Pinto and D. D. Cox (2013).
Skdata: Data Sets and Algorithm Evaluation Protocols in Python.
Proc. Python for Scientific Computing Conference (SciPy).
[pdf] [video]
- J. Bergstra and D. D. Cox (2013).
Hyperparameter Optimization and Boosting for Classifying Facial Expressions: How good can a “Null” model be?.
Challenges in Representation Learning, Atlanta, Georgia, USA.
[pdf]
2012
- F. Bastien, P. Lamblin, R. Pascanu, J. Bergstra, I. Goodfellow, A. Bergeron, N. Bouchard and Y. Bengio (2012).
Theano: New Features and Speed Improvements.
Workshop on Deep Learning and Unsupervised Feature Learning, Lake Tahoe, NV.
[pdf]
- J. Bergstra and Y. Bengio (2012).
Random Search for Hyper-Parameter Optimization.
Journal of Machine Learning Research 13:281–305.
[pdf]
- J. Bergstra, N. Pinto and D. D. Cox (2012).
Machine Learning for Predictive Auto-Tuning with Boosted Regression Trees.
Proc. Innovative Parallel Computing (INPAR12).
[pdf]
2011
- J. Bergstra, R. Bardenet, Y. Bengio and B. Kégl (2011).
Algorithms for Hyper-parameter Optimization.
Proc. Neural Information Processing Systems 24 (NIPS2011), 2546–2554.
[pdf]
- J. Bergstra, A. Courville and Y. Bengio (2011).
The Statistical Inefficiency of Sparse Coding for Images (or, One Gabor to Rule them All).
Technical report 1109.6638v2, arXiv.
[pdf]
- J. Bergstra (2011).
Incorporating Complex Cells into Neural Networks for Pattern Classification.
Ph.D. Thesis, Université de Montréal.
[pdf]
- A. Courville, J. Bergstra and Y. Bengio (2011).
A Spike and Slab RBM Approach to Modeling Natural Images.
The Learning Workshop, Fort Lauderdale, FL, USA.
[pdf]
- J. Bergstra and Y. Bengio (2011).
Random Search for Hyper-parameter Optimization.
The Learning Workshop, Fort Lauderdale, FL, USA.
[pdf]
- J. Bergstra, Y. Bengio and J. Louradour (2011).
Suitability of V1 Energy Models for Object Classification.
Neural Computation 23(3):774–790.
[pdf]
- G. Mesnil, Y. Dauphin, X. Glorot, S. Rifai, Y. Bengio, I. Goodfellow, E. Lavoie, X. Muller, G. Desjardins, D. Warde-Farley, P. Vincent, A. Courville and J. Bergstra (2011).
Unsupervised and Transfer Learning Challenge: a Deep Learning Approach.
Workshop on Unsupervised and Transfer Learning, Bellevue, Washington, USA.
[pdf]
- A. Courville, J. Bergstra and Y. Bengio (2011).
Unsupervised Models of Images by Spike and Slab RBMs.
Proc. 28th International Conference on Machine Learning (ICML-11), 1145–1152.
[pdf] [bibtex]
- A. Courville, J. Bergstra and Y. Bengio (2011).
The Spike and Slab Restricted Boltzmann Machine.
Proc. Artificial Intelligence and Statistics (AISTATS), 233–241.
[pdf]
2010
- A. Courville, J. Bergstra and Y. Bengio (2010).
The Spike and Slab Restricted Boltzmann Machine.
NIPS Deep Learning and Unsupervised Feature Learning Workshop, NIPS23.
[pdf]
- J. Bergstra, M. Mandel and D. Eck (2010).
Scalable Genre and Tag Prediction with Spectral Covariance.
Proc. International Conference on Music Information Retrieval (ISMIR), 507–512.
[pdf]
- J. Bergstra, O. Breuleux, F. Bastien, P. Lamblin, R. Pascanu, G. Desjardins, J. Turian and Y. Bengio (2010).
Theano: a CPU and GPU Math Expression Compiler.
Proc. Python for Scientific Computing Conference (SciPy), 3–11.
[slides] [video]
- J. Bergstra, O. Breuleux, F. Bastien, P. Lamblin, J. Turian, G. Desjardins, R. Pascanu, D. Erhan, O. Delalleau and Y. Bengio (2010).
Deep Learning on GPUs with Theano.
The Learning Workshop, Snowbird, Utah.
[pdf]
- J. Bergstra, Y. Bengio, P. Lamblin, G. Desjardins and J. Louradour (2010).
Image Classification with Complex Cell Neural Networks.
Frontiers in Neuroscience Conference Abstract: Computational and Systems Neuroscience 2010.
[link] [doi]
2009
- J. Bergstra and Y. Bengio (2009).
Slow, Decorrelated Features for Pretraining Complex Cell-like Networks.
Proc. Neural Information Processing Systems 22 (NIPS), 99–107.
[pdf] [bibtex]
- J. Turian, J. Bergstra and Y. Bengio (2009).
Quadratic Features and Deep Architectures for Chunking.
Proc. North American Chapter of the Association for Computational Linguistics - Human Language Technologies (NAACL-HLT), 245–248.
[pdf] [bibtex]
- J. Bergstra, G. Desjardins, P. Lamblin and Y. Bengio (2009).
Quadratic Polynomials Learn Better Image Features.
Technical report 1337, Département d’Informatique et de Recherche Opérationnelle, Université de Montréal.
[pdf]
2008
- J. Bergstra, Y. Bengio and J. Louradour (2008).
Image Classification using Higher-Order Neural Models.
The Learning Workshop, Snowbird, Utah.
[pdf]
2007
- H. Larochelle, D. Erhan, A. Courville, J. Bergstra and Y. Bengio (2007).
An Empirical Evaluation of Deep Architectures on Problems with Many Factors of Variation.
Proc. 24th International Conference on Machine Learning (ICML-07), 473–480.
[pdf] [doi]
2006
- J. Bergstra, N. Casagrande, D. Eck and B. Kégl (2006).
Aggregate Features and AdaBoost for Music Classification.
Machine Learning 65:473–484.
[pdf]
- J. Bergstra, A. Lacoste and D. Eck (2006).
Predicting Genre Labels for Artists using FreeDB.
Proc. International Conference on Music Information Retrieval (ISMIR), 85–88.
[pdf]
- J. Bergstra (2006).
Algorithms for Classifying Recorded Music by Genre.
Masters Thesis, Université de Montréal.
[pdf]
2005
- J. Bergstra, N. Casagrande and D. Eck (2005).
Genre Classification: A Timbre- and Rhythm-Based Multiresolution Approach.
MIREX Genre Classification Contest, London, England.
[link]
- J. Bergstra, N. Casagrande and D. Eck (2005).
Artist Recognition: A Timbre- and Rhythm-Based Multiresolution Approach.
MIREX Artist Recognition Contest, London, England.
[link]