James Bergstra

According to our database, James Bergstra authored at least 35 papers between 2006 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2023

A Statistical Guarantee for Representation Transfer in Multitask Imitation Learning.

Bryan Chan, Karime Pereida, James Bergstra

CoRR, 2023

2020

Active Perception and Representation for Robotic Manipulation.

Youssef Zaky, Gaurav Paruthi, Bryan P. Tripp, James Bergstra

CoRR, 2020

2019

Autoregressive Policies for Continuous Control Deep Reinforcement Learning.

Dmytro Korenkevych, A. Rupam Mahmood, Gautham Vasan, James Bergstra

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

Hyperopt-Sklearn.

Brent Komer, James Bergstra, Chris Eliasmith

Proceedings of the Automated Machine Learning - Methods, Systems, Challenges, 2019

2018

Setting up a Reinforcement Learning Task with a Real-World Robot.

A. Rupam Mahmood, Dmytro Korenkevych, Brent J. Komer, James Bergstra

Proceedings of the 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2018

Benchmarking Reinforcement Learning Algorithms on Real-World Robots.

A. Rupam Mahmood, Dmytro Korenkevych, Gautham Vasan, William Ma, James Bergstra

Proceedings of the 2nd Annual Conference on Robot Learning, 2018

2016

Theano: A Python framework for fast computation of mathematical expressions.

Rami Al-Rfou, Guillaume Alain, Amjad Almahairi, Christof Angermüller, Dzmitry Bahdanau, Nicolas Ballas, Frédéric Bastien, Justin Bayer, Anatoly Belikov, Alexander Belopolsky, Yoshua Bengio, Arnaud Bergeron, James Bergstra, Valentin Bisson, Josh Bleecher Snyder, Nicolas Bouchard, Nicolas Boulanger-Lewandowski, Xavier Bouthillier, Alexandre de Brébisson, Olivier Breuleux, Pierre Luc Carrier, Kyunghyun Cho, Jan Chorowski, Paul F. Christiano, Tim Cooijmans, Marc-Alexandre Côté, Myriam Côté, Aaron C. Courville, Yann N. Dauphin, Olivier Delalleau, Julien Demouth, Guillaume Desjardins, Sander Dieleman, Laurent Dinh, Melanie Ducoffe, Vincent Dumoulin, Samira Ebrahimi Kahou, Dumitru Erhan, Ziye Fan, Orhan Firat, Mathieu Germain, Xavier Glorot, Ian J. Goodfellow, Matthew Graham, Çaglar Gülçehre, Philippe Hamel, Iban Harlouchet, Jean-Philippe Heng, Balázs Hidasi, Sina Honari, Arjun Jain, Sébastien Jean, Kai Jia, Mikhail Korobov, Vivek Kulkarni, Alex Lamb, Pascal Lamblin, Eric Larsen, César Laurent, Sean Lee, Simon Lefrançois, Simon Lemieux, Nicholas Léonard, Zhouhan Lin, Jesse A. Livezey, Cory Lorenz, Jeremiah Lowin, Qianli Ma, Pierre-Antoine Manzagol, Olivier Mastropietro, Robert McGibbon, Roland Memisevic, Bart van Merriënboer, Vincent Michalski, Mehdi Mirza, Alberto Orlandi, Christopher Joseph Pal, Razvan Pascanu, Mohammad Pezeshki, Colin Raffel, Daniel Renshaw, Matthew Rocklin, Adriana Romero, Markus Roth, Peter Sadowski, John Salvatier, François Savard, Jan Schlüter, John Schulman, Gabriel Schwartz, Iulian Vlad Serban, Dmitriy Serdyuk, Samira Shabanian, Étienne Simon, Sigurd Spieckermann, S. Ramana Subramanyam, Jakub Sygnowski, Jérémie Tanguay, Gijs van Tulder, Joseph P. Turian, Sebastian Urban, Pascal Vincent, Francesco Visin, Harm de Vries, David Warde-Farley, Dustin J. Webb, Matthew Willson, Kelvin Xu, Lijun Xue, Li Yao, Saizheng Zhang, Ying Zhang

CoRR, 2016

2015

Challenges in representation learning: A report on three machine learning contests.

Ian J. Goodfellow, Dumitru Erhan, Pierre Luc Carrier, Aaron C. Courville, Mehdi Mirza, Benjamin Hamner, William Cukierski, Yichuan Tang, David Thaler, Dong-Hyun Lee, Yingbo Zhou, Chetan Ramaiah, Fangxiang Feng, Ruifan Li, Xiaojie Wang, Dimitris Athanasakis, John Shawe-Taylor, Maxim Milakov, John Park, Radu Tudor Ionescu, Marius Popescu, Cristian Grozea, James Bergstra, Jingjing Xie, Lukasz Romaszko, Bing Xu, Zhang Chuang, Yoshua Bengio

Neural Networks, 2015

2014

The Spike-and-Slab RBM and Extensions to Discrete and Sparse Data Distributions.

Aaron C. Courville, Guillaume Desjardins, James Bergstra, Yoshua Bengio

IEEE Trans. Pattern Anal. Mach. Intell., 2014

Preface.

Andy Terrel, Jonathan Rocher, Stéfan van der Walt, James Bergstra

Proceedings of the 13th Python in Science Conference, 2014

Hyperopt-Sklearn: Automatic Hyperparameter Configuration for Scikit-Learn.

Brent Komer, James Bergstra, Chris Eliasmith

Proceedings of the 13th Python in Science Conference, 2014

2013

Nengo: a Python tool for building large-scale functional brain models.

Trevor Bekolay, James Bergstra, Eric Hunsberger, Travis DeWolf, Terrence C. Stewart, Daniel Rasmussen, Feng-Xuan Choo, Aaron Voelker, Chris Eliasmith

Frontiers Neuroinformatics, 2013

Pylearn2: a machine learning research library.

Ian J. Goodfellow, David Warde-Farley, Pascal Lamblin, Vincent Dumoulin, Mehdi Mirza, Razvan Pascanu, James Bergstra, Frédéric Bastien, Yoshua Bengio

CoRR, 2013

Hyperparameter Optimization and Boosting for Classifying Facial Expressions: How good can a "Null" Model be?

James Bergstra, David D. Cox

CoRR, 2013

Hyperopt: A Python Library for Optimizing the Hyperparameters of Machine Learning Algorithms.

James Bergstra, Dan Yamins, David D. Cox

Proceedings of the 12th Python in Science Conference, 2013

SkData: Data Sets and Algorithm Evaluation Protocols in Python.

James Bergstra, Nicolas Pinto, David D. Cox

Proceedings of the 12th Python in Science Conference, 2013

Challenges in Representation Learning: A Report on Three Machine Learning Contests.

Ian J. Goodfellow, Dumitru Erhan, Pierre Luc Carrier, Aaron C. Courville, Mehdi Mirza, Benjamin Hamner, William Cukierski, Yichuan Tang, David Thaler, Dong-Hyun Lee, Yingbo Zhou, Chetan Ramaiah, Fangxiang Feng, Ruifan Li, Xiaojie Wang, Dimitris Athanasakis, John Shawe-Taylor, Maxim Milakov, John Park, Radu Tudor Ionescu, Marius Popescu, Cristian Grozea, James Bergstra, Jingjing Xie, Lukasz Romaszko, Bing Xu, Chuang Zhang, Yoshua Bengio

Proceedings of the Neural Information Processing - 20th International Conference, 2013

Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures.

James Bergstra, Daniel Yamins, David D. Cox

Proceedings of the 30th International Conference on Machine Learning, 2013

A Neural Model of Human Image Categorization.

Eric Hunsberger, Peter Blouw, James Bergstra, Chris Eliasmith

Proceedings of the 35th Annual Meeting of the Cognitive Science Society, 2013

2012

Unsupervised and Transfer Learning Challenge: a Deep Learning Approach.

Grégoire Mesnil, Yann N. Dauphin, Xavier Glorot, Salah Rifai, Yoshua Bengio, Ian J. Goodfellow, Erick Lavoie, Xavier Muller, Guillaume Desjardins, David Warde-Farley, Pascal Vincent, Aaron C. Courville, James Bergstra

Proceedings of the Unsupervised and Transfer Learning, 2012

Random Search for Hyper-Parameter Optimization.

James Bergstra, Yoshua Bengio

J. Mach. Learn. Res., 2012

Theano: new features and speed improvements

Frédéric Bastien, Pascal Lamblin, Razvan Pascanu, James Bergstra, Ian J. Goodfellow, Arnaud Bergeron, Nicolas Bouchard, David Warde-Farley, Yoshua Bengio

CoRR, 2012

Making a Science of Model Search

James Bergstra, Dan Yamins, David D. Cox

CoRR, 2012

2011

Suitability of V1 Energy Models for Object Classification.

James Bergstra, Yoshua Bengio, Jérôme Louradour

Neural Comput., 2011

A Spike and Slab Restricted Boltzmann Machine.

Aaron C. Courville, James Bergstra, Yoshua Bengio

Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011

The Statistical Inefficiency of Sparse Coding for Images (or, One Gabor to Rule them All)

James Bergstra, Aaron C. Courville, Yoshua Bengio

CoRR, 2011

Algorithms for Hyper-Parameter Optimization.

James Bergstra, Rémi Bardenet, Yoshua Bengio, Balázs Kégl

Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

Unsupervised Models of Images by Spikeand-Slab RBMs.

Aaron C. Courville, James Bergstra, Yoshua Bengio

Proceedings of the 28th International Conference on Machine Learning, 2011

2010

Theano: A CPU and GPU Math Compiler in Python.

James Bergstra, Olivier Breuleux, Frédéric Bastien, Pascal Lamblin, Razvan Pascanu, Guillaume Desjardins, Joseph P. Turian, David Warde-Farley, Yoshua Bengio

Proceedings of the 9th Python in Science Conference 2010 (SciPy 2010), Austin, Texas, June 28, 2010

Scalable Genre and Tag Prediction with Spectral Covariance.

James Bergstra, Michael I. Mandel, Douglas Eck

Proceedings of the 11th International Society for Music Information Retrieval Conference, 2010

2009

Slow, Decorrelated Features for Pretraining Complex Cell-like Networks.

James Bergstra, Yoshua Bengio

Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

Quadratic Features and Deep Architectures for Chunking.

Joseph P. Turian, James Bergstra, Yoshua Bengio

Proceedings of the Human Language Technologies: Conference of the North American Chapter of the Association of Computational Linguistics, Proceedings, May 31, 2009

2007

An empirical evaluation of deep architectures on problems with many factors of variation.

Hugo Larochelle, Dumitru Erhan, Aaron C. Courville, James Bergstra, Yoshua Bengio

Proceedings of the Machine Learning, 2007

2006

Aggregate features and ADABOOSTfor music classification.

James Bergstra, Norman Casagrande, Dumitru Erhan, Douglas Eck, Balázs Kégl

Mach. Learn., 2006

Predicting genre labels for artist using FreeDB.

James Bergstra, Alexandre Lacoste, Douglas Eck

Proceedings of the ISMIR 2006, 2006