James Bergstra

Publication Activity (10 Years)

Years Active: 2006-2024
Publications (10 Years): 10

Top Topics

Perceptual Aliasing

Reinforcement Learning

Top Venues

IEEE Trans. Pattern Anal. Mach. Intell.

Publications

Gautham Vasan, Yan Wang, Fahim Shahriar, James Bergstra, Martin Jägersand, A. Rupam Mahmood
Revisiting Sparse Rewards for Goal-Reaching Reinforcement Learning. CoRR (2024)
Bryan Chan, Karime Pereida, James Bergstra
A Statistical Guarantee for Representation Transfer in Multitask Imitation Learning. CoRR (2023)
Youssef Zaky, Gaurav Paruthi, Bryan Tripp, James Bergstra
Active Perception and Representation for Robotic Manipulation. CoRR (2020)
Dmytro Korenkevych, A. Rupam Mahmood, Gautham Vasan, James Bergstra
Autoregressive Policies for Continuous Control Deep Reinforcement Learning. CoRR (2019)
Dmytro Korenkevych, A. Rupam Mahmood, Gautham Vasan, James Bergstra
Autoregressive Policies for Continuous Control Deep Reinforcement Learning. IJCAI (2019)
A. Rupam Mahmood, Dmytro Korenkevych, Brent J. Komer, James Bergstra
Setting up a Reinforcement Learning Task with a Real-World Robot. IROS (2018)
A. Rupam Mahmood, Dmytro Korenkevych, Brent J. Komer, James Bergstra
Setting up a Reinforcement Learning Task with a Real-World Robot. CoRR (2018)
A. Rupam Mahmood, Dmytro Korenkevych, Gautham Vasan, William Ma, James Bergstra
Benchmarking Reinforcement Learning Algorithms on Real-World Robots. CoRL (2018)
A. Rupam Mahmood, Dmytro Korenkevych, Gautham Vasan, William Ma, James Bergstra
Benchmarking Reinforcement Learning Algorithms on Real-World Robots. CoRR (2018)
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
Theano: A Python framework for fast computation of mathematical expressions. CoRR (2016)
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
Challenges in representation learning: A report on three machine learning contests. Neural Networks 64 (2015)
Andy Terrel, Jonathan Rocher, Stéfan van der Walt, James Bergstra
Preface. SciPy (2014)
Brent Komer, James Bergstra, Chris Eliasmith
Hyperopt-Sklearn: Automatic Hyperparameter Configuration for Scikit-Learn. SciPy (2014)
Aaron C. Courville, Guillaume Desjardins, James Bergstra, Yoshua Bengio
The Spike-and-Slab RBM and Extensions to Discrete and Sparse Data Distributions. IEEE Trans. Pattern Anal. Mach. Intell. 36 (9) (2014)
Trevor Bekolay, James Bergstra, Eric Hunsberger, Travis DeWolf, Terrence C. Stewart, Daniel Rasmussen, Feng-Xuan Choo, Aaron Voelker, Chris Eliasmith
Nengo: a Python tool for building large-scale functional brain models. Frontiers Neuroinformatics 7 (2013)
Eric Hunsberger, Peter Blouw, James Bergstra, Chris Eliasmith
A Neural Model of Human Image Categorization. CogSci (2013)
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
Challenges in Representation Learning: A report on three machine learning contests. CoRR (2013)
James Bergstra, Daniel Yamins, David D. Cox
Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures. ICML (1) (2013)
James Bergstra, Dan Yamins, David D. Cox
Hyperopt: A Python Library for Optimizing the Hyperparameters of Machine Learning Algorithms. SciPy (2013)
James Bergstra, David D. Cox
Hyperparameter Optimization and Boosting for Classifying Facial Expressions: How good can a "Null" Model be? CoRR (2013)
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
Challenges in Representation Learning: A Report on Three Machine Learning Contests. ICONIP (3) (2013)
James Bergstra, Nicolas Pinto, David D. Cox
SkData: Data Sets and Algorithm Evaluation Protocols in Python. SciPy (2013)
Ian J. Goodfellow, David Warde-Farley, Pascal Lamblin, Vincent Dumoulin, Mehdi Mirza, Razvan Pascanu, James Bergstra, Frédéric Bastien, Yoshua Bengio
Pylearn2: a machine learning research library. CoRR (2013)
Frédéric Bastien, Pascal Lamblin, Razvan Pascanu, James Bergstra, Ian J. Goodfellow, Arnaud Bergeron, Nicolas Bouchard, David Warde-Farley, Yoshua Bengio
Theano: new features and speed improvements CoRR (2012)
James Bergstra, Yoshua Bengio
Random Search for Hyper-Parameter Optimization. J. Mach. Learn. Res. 13 (2012)
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
Unsupervised and Transfer Learning Challenge: a Deep Learning Approach. ICML Unsupervised and Transfer Learning (2012)
James Bergstra, Dan Yamins, David D. Cox
Making a Science of Model Search CoRR (2012)
Aaron C. Courville, James Bergstra, Yoshua Bengio
A Spike and Slab Restricted Boltzmann Machine. AISTATS (2011)
James Bergstra, Aaron C. Courville, Yoshua Bengio
The Statistical Inefficiency of Sparse Coding for Images (or, One Gabor to Rule them All) CoRR (2011)
James Bergstra, Rémi Bardenet, Yoshua Bengio, Balázs Kégl
Algorithms for Hyper-Parameter Optimization. NIPS (2011)
Aaron C. Courville, James Bergstra, Yoshua Bengio
Unsupervised Models of Images by Spikeand-Slab RBMs. ICML (2011)
James Bergstra, Yoshua Bengio, Jérôme Louradour
Suitability of V1 Energy Models for Object Classification. Neural Comput. 23 (3) (2011)
James Bergstra, Olivier Breuleux, Frédéric Bastien, Pascal Lamblin, Razvan Pascanu, Guillaume Desjardins, Joseph P. Turian, David Warde-Farley, Yoshua Bengio
Theano: A CPU and GPU Math Compiler in Python. SciPy (2010)
James Bergstra, Michael I. Mandel, Douglas Eck
Scalable Genre and Tag Prediction with Spectral Covariance. ISMIR (2010)
Joseph P. Turian, James Bergstra, Yoshua Bengio
Quadratic Features and Deep Architectures for Chunking. HLT-NAACL (Short Papers) (2009)
James Bergstra, Yoshua Bengio
Slow, Decorrelated Features for Pretraining Complex Cell-like Networks. NIPS (2009)
Hugo Larochelle, Dumitru Erhan, Aaron C. Courville, James Bergstra, Yoshua Bengio
An empirical evaluation of deep architectures on problems with many factors of variation. ICML (2007)
James Bergstra, Alexandre Lacoste, Douglas Eck
Predicting genre labels for artist using FreeDB. ISMIR (2006)
James Bergstra, Norman Casagrande, Dumitru Erhan, Douglas Eck, Balázs Kégl
Aggregate features and ADABOOSTfor music classification. Mach. Learn. 65 (2-3) (2006)