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All publications by Geoffrey E. Hinton
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A Better Way to Pretrain Deep Boltzmann Machines
Geoffrey E. Hinton and Ruslan Salakhutdinov
Advances in Neural Information Processing Systems 25, 2012


ImageNet Classification with Deep Convolutional Neural Networks
Alex Krizhevsky, Ilya Sutskever and Geoffrey E. Hinton
Advances in Neural Information Processing Systems 25, 2012


Deep Lambertian Networks
Yichuan Tang, Geoffrey E. Hinton and Ruslan Salakhutdinov
Proceedings of the 29th International Conference on Machine Learning (ICML-12), 2012


Deep Mixtures of Factor Analysers
Yichuan Tang, Geoffrey E. Hinton and Ruslan Salakhutdinov
Proceedings of the 29th International Conference on Machine Learning (ICML-12), 2012


Learning to Label Aerial Images from Noisy Data
Volodymyr Mnih and Geoffrey E. Hinton
Proceedings of the 29th International Conference on Machine Learning (ICML-12), 2012


Generating Text with Recurrent Neural Networks
Ilya Sutskever, James Martens and Geoffrey E. Hinton
Proceedings of the 28th International Conference on Machine Learning (ICML-11), 2011


Rectified Linear Units Improve Restricted Boltzmann Machines
Vinod Nair and Geoffrey E. Hinton
Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010


Generating more realistic images using gated MRF's
Marc'aurelio Ranzato, Volodymyr Mnih and Geoffrey E. Hinton
Advances in Neural Information Processing Systems 23, 2010


Learning to combine foveal glimpses with a third-order Boltzmann machine
Hugo Larochelle and Geoffrey E. Hinton
Advances in Neural Information Processing Systems 23, 2010


Gated Softmax Classification
Roland Memisevic, Christopher Zach, Marc Pollefeys and Geoffrey E. Hinton
Advances in Neural Information Processing Systems 23, 2010


Phone Recognition with the Mean-Covariance Restricted Boltzmann Machine
George Dahl, Marc'aurelio Ranzato, Abdel-rahman Mohamed and Geoffrey E. Hinton
Advances in Neural Information Processing Systems 23, 2010


Factored 3-Way Restricted Boltzmann Machines For Modeling Natural Images
Marc'aurelio Ranzato, Alex Krizhevsky and Geoffrey E. Hinton
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Workshop summary: Workshop on learning feature hierarchies
Kay Yu, Ruslan Salakhutdinov, Yann Lecun, Geoffrey E. Hinton and Yoshua Bengio
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Using fast weights to improve persistent contrastive divergence
Tijmen Tieleman and Geoffrey E. Hinton
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Factored conditional restricted Boltzmann Machines for modeling motion style
Graham W. Taylor and Geoffrey E. Hinton
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Replicated Softmax: an Undirected Topic Model
Geoffrey E. Hinton and Ruslan Salakhutdinov
Advances in Neural Information Processing Systems 22, 2009


3D Object Recognition with Deep Belief Nets
Vinod Nair and Geoffrey E. Hinton
Advances in Neural Information Processing Systems 22, 2009


Zero-shot Learning with Semantic Output Codes
Mark Palatucci, Dean Pomerleau, Geoffrey E. Hinton and Tom M. Mitchell
Advances in Neural Information Processing Systems 22, 2009


Deep Boltzmann Machines
Ruslan Salakhutdinov and Geoffrey E. Hinton
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Generative versus discriminative training of RBMs for classification of fMRI images
Tanya Schmah, Geoffrey E. Hinton, Steven L. Small, Stephen Strother and Richard S. Zemel
Advances in Neural Information Processing Systems 21, 2008


Implicit Mixtures of Restricted Boltzmann Machines
Vinod Nair and Geoffrey E. Hinton
Advances in Neural Information Processing Systems 21, 2008


A Scalable Hierarchical Distributed Language Model
Andriy Mnih and Geoffrey E. Hinton
Advances in Neural Information Processing Systems 21, 2008


The Recurrent Temporal Restricted Boltzmann Machine
Ilya Sutskever, Geoffrey E. Hinton and Graham W. Taylor
Advances in Neural Information Processing Systems 21, 2008


Using matrices to model symbolic relationship
Ilya Sutskever and Geoffrey E. Hinton
Advances in Neural Information Processing Systems 21, 2008


Restricted Boltzmann machines for collaborative filtering
Ruslan Salakhutdinov, Andriy Mnih and Geoffrey E. Hinton
Proceedings of the 24th International Conference on Machine Learning (ICML-07), 2007


Three new graphical models for statistical language modelling
Andriy Mnih and Geoffrey E. Hinton
Proceedings of the 24th International Conference on Machine Learning (ICML-07), 2007


Modeling image patches with a directed hierarchy of Markov random fields
Simon Osindero and Geoffrey E. Hinton
Advances in Neural Information Processing Systems 20, 2007


Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes
Geoffrey E. Hinton and Ruslan Salakhutdinov
Advances in Neural Information Processing Systems 20, 2007


Learning Multilevel Distributed Representations for High-Dimensional Sequences
Ilya Sutskever and Geoffrey E. Hinton
Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics (AISTATS-07), 2007


Learning a Nonlinear Embedding by Preserving Class Neighbourhood Structure
Ruslan Salakhutdinov and Geoffrey E. Hinton
Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics (AISTATS-07), 2007


Visualizing Similarity Data with a Mixture of Maps
James Cook, Ilya Sutskever, Andriy Mnih and Geoffrey E. Hinton
Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics (AISTATS-07), 2007


Modeling Human Motion Using Binary Latent Variables
Graham W. Taylor, Geoffrey E. Hinton and Sam T. Roweis
Advances in Neural Information Processing Systems 19, 2006


Inferring Motor Programs from Images of Handwritten Digits
Vinod Nair and Geoffrey E. Hinton
Advances in Neural Information Processing Systems 18, 2005


Reinforcement Learning with Factored States and Actions
Brian Sallans and Geoffrey E. Hinton
Journal of Machine Learning Research, 2004


Multiple Relational Embedding
Roland Memisevic and Geoffrey E. Hinton
Advances in Neural Information Processing Systems 17, 2004


Exponential Family Harmoniums with an Application to Information Retrieval
Max Welling, Michal Rosen-zvi and Geoffrey E. Hinton
Advances in Neural Information Processing Systems 17, 2004


Neighbourhood Components Analysis
Jacob Goldberger, Geoffrey E. Hinton, Sam T. Roweis and Ruslan Salakhutdinov
Advances in Neural Information Processing Systems 17, 2004


Energy-Based Models for Sparse Overcomplete Representations
Yee W. Teh, Max Welling, Simon Osindero and Geoffrey E. Hinton
Journal of Machine Learning Research, 2003


Wormholes Improve Contrastive Divergence
Max Welling, Andriy Mnih and Geoffrey E. Hinton
Advances in Neural Information Processing Systems 16, 2003


Learning Sparse Topographic Representations with Products of Student-t Distributions
Max Welling, Simon Osindero and Geoffrey E. Hinton
Advances in Neural Information Processing Systems 15, 2002


Stochastic Neighbor Embedding
Geoffrey E. Hinton and Sam T. Roweis
Advances in Neural Information Processing Systems 15, 2002


Self Supervised Boosting
Max Welling, Richard S. Zemel and Geoffrey E. Hinton
Advances in Neural Information Processing Systems 15, 2002


Relative Density Nets: A New Way to Combine Backpropagation with HMM's
Andrew D. Brown and Geoffrey E. Hinton
Advances in Neural Information Processing Systems 14, 2001


Learning Hierarchical Structures with Linear Relational Embedding
Alberto Paccanaro and Geoffrey E. Hinton
Advances in Neural Information Processing Systems 14, 2001


Global Coordination of Local Linear Models
Sam T. Roweis, Lawrence K. Saul and Geoffrey E. Hinton
Advances in Neural Information Processing Systems 14, 2001