J. Bergstra

发表

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

Yoshua Bengio, Dumitru Erhan, Hugo Larochelle, 2007, ICML '07.

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

John Salvatier, Razvan Pascanu, Sina Honari, 2016, ArXiv.

Pylearn2: a machine learning research library pdf

Razvan Pascanu, Yoshua Bengio, Vincent Dumoulin, 2013, ArXiv.

Incorporating complex cells into neural networks for pattern classification

Yoshua Bengio, J. Bergstra, 2011 .

Theano: new features and speed improvements pdf

Razvan Pascanu, Yoshua Bengio, Ian J. Goodfellow, 2012, ArXiv.

Algorithms for Classifying Recorded Music by Genre

J. Bergstra, 2006 .

Theano: A CPU and GPU Math Compiler in Python

Razvan Pascanu, Yoshua Bengio, Guillaume Desjardins, 2010, SciPy.

Algorithms for Hyper-Parameter Optimization

Yoshua Bengio, Balázs Kégl, Rémi Bardenet, 2011, NIPS.

Random Search for Hyper-Parameter Optimization

Yoshua Bengio, James Bergstra, Yoshua Bengio, 2012, J. Mach. Learn. Res..

Hyperopt-Sklearn: Automatic Hyperparameter Configuration for Scikit-Learn

Chris Eliasmith, James Bergstra, Brent Komer, 2014, SciPy.

Unsupervised and Transfer Learning Challenge: a Deep Learning Approach

Yoshua Bengio, Yann Dauphin, Aaron C. Courville, 2011, ICML Unsupervised and Transfer Learning.

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

David D. Cox, James Bergstra, Dan Yamins, 2013, ICML.

Theano: Deep Learning on GPUs with Python

Razvan Pascanu, Ian Goodfellow, Yoshua Bengio, 2012 .

Aggregate features and ADABOOST for music classification

Douglas Eck, Dumitru Erhan, Balázs Kégl, 2006, Machine Learning.

A Spike and Slab Restricted Boltzmann Machine

Yoshua Bengio, Aaron C. Courville, James Bergstra, 2011, AISTATS.

Unsupervised Models of Images by Spikeand-Slab RBMs

Yoshua Bengio, Aaron C. Courville, James Bergstra, 2011, ICML.

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

Yoshua Bengio, John Shawe-Taylor, Dumitru Erhan, 2013, Neural Networks.

Meta-Features and AdaBoost for Music Classification

D. Erhan, J. Bergstra, 2006 .

Hyperopt: a Python library for model selection and hyperparameter optimization

Chris Eliasmith, David D. Cox, David Cox, 2015 .

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

David D. Cox, David Cox, Daniel Yamins, 2013, SciPy.

Making a Science of Model Search pdf

David D. Cox, James Bergstra, Dan Yamins, 2012, ArXiv.

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

James Bergstra, Dmytro Korenkevych, A. Rupam Mahmood, 2018, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

Active Perception and Representation for Robotic Manipulation pdf

Bryan Tripp, James Bergstra, Gaurav Paruthi, 2020, ArXiv.

Modeling Natural Image Covariance with a Spike and Slab Restricted Boltzmann Machine

Aaron C. Courville, Yoshua Bengio, J. Bergstra, 2010 .

Pylearn2: a machine learning research library

Ian J. Goodfellow, Yoshua Bengio, J. Bergstra, 2013, ArXiv.

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

Yoshua Bengio, Aaron C. Courville, James Bergstra, 2011, ArXiv.

Machine learning for predictive auto-tuning with boosted regression trees

David D. Cox, J. Bergstra, N. Pinto, 2012, 2012 Innovative Parallel Computing (InPar).

A Neural Model of Human Image Categorization

Peter Blouw, Chris Eliasmith, James Bergstra, 2013, CogSci.

Suitability of V1 Energy Models for Object Classification

Yoshua Bengio, Jérôme Louradour, James Bergstra, 2011, Neural Computation.

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

Trevor Bekolay, Terrence C. Stewart, Chris Eliasmith, 2014, Front. Neuroinform..

Benchmarking Reinforcement Learning Algorithms on Real-World Robots

James Bergstra, Gautham Vasan, Dmytro Korenkevych, 2018, CoRL.

Yoshua Bengio, James Bergstra, Yoshua Bengio, 2009, NIPS.

Autoregressive Policies for Continuous Control Deep Reinforcement Learning

James Bergstra, Gautham Vasan, Dmytro Korenkevych, 2019, IJCAI.

Implementations of Algorithms for Hyper-Parameter Optimization

James Bergstra, J. Bergstra, 2011 .

Multi-view learning and deep learning for heterogeneous biological data to maintain oral health

J. Bergstra, Pascal Lamblin, Razvan Pascanu, 2016 .

Quadratic Features and Deep Architectures for Chunking

Yoshua Bengio, James Bergstra, Joseph P. Turian, 2009, NAACL.

Unsupervised and Transfer Learning Challenge: a Deep Learning Approach

Xavier Glorot, J. Bergstra, Guillaume Desjardins, 2011, ICML Unsupervised and Transfer Learning.