Connectionist learning of belief networks
Artificial Intelligence
Training products of experts by minimizing contrastive divergence
Neural Computation
A New Learning Algorithm for Mean Field Boltzmann Machines
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Combining Top-Down and Bottom-Up Segmentation
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 4 - Volume 04
The rate adapting poisson model for information retrieval and object recognition
ICML '06 Proceedings of the 23rd international conference on Machine learning
A fast learning algorithm for deep belief nets
Neural Computation
An empirical evaluation of deep architectures on problems with many factors of variation
Proceedings of the 24th international conference on Machine learning
Restricted Boltzmann machines for collaborative filtering
Proceedings of the 24th international conference on Machine learning
On the quantitative analysis of deep belief networks
Proceedings of the 25th international conference on Machine learning
Factored conditional restricted Boltzmann Machines for modeling motion style
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Using fast weights to improve persistent contrastive divergence
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Herding dynamical weights to learn
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Justifying and generalizing contrastive divergence
Neural Computation
Learning Deep Architectures for AI
Foundations and Trends® in Machine Learning
Products of Hidden Markov Models: it takes N1 to tango
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Herding dynamic weights for partially observed random field models
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Deep belief networks are compact universal approximators
Neural Computation
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
Exploiting local structure in Boltzmann machines
Neurocomputing
Quickly generating representative samples from an rbm-derived process
Neural Computation
Unsupervised learning of hierarchical representations with convolutional deep belief networks
Communications of the ACM
Two Distributed-State Models For Generating High-Dimensional Time Series
The Journal of Machine Learning Research
Improved learning of Gaussian-Bernoulli restricted Boltzmann machines
ICANN'11 Proceedings of the 21th international conference on Artificial neural networks - Volume Part I
Weakly supervised learning of foreground-background segmentation using masked RBMs
ICANN'11 Proceedings of the 21st international conference on Artificial neural networks - Volume Part II
On the expressive power of deep architectures
ALT'11 Proceedings of the 22nd international conference on Algorithmic learning theory
In All Likelihood, Deep Belief Is Not Enough
The Journal of Machine Learning Research
The Journal of Machine Learning Research
Learning algorithms for the classification restricted Boltzmann machine
The Journal of Machine Learning Research
Detonation Classification from Acoustic Signature with the Restricted Boltzmann Machine
Computational Intelligence
An efficient learning procedure for deep boltzmann machines
Neural Computation
Learning a generative model of images by factoring appearance and shape
Neural Computation
WCCI'12 Proceedings of the 2012 World Congress conference on Advances in Computational Intelligence
Training restricted boltzmann machines with multi-tempering: harnessing parallelization
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
Tikhonov-Type regularization for restricted boltzmann machines
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I
Training restricted Boltzmann machines: An introduction
Pattern Recognition
Learning ensemble classifiers via restricted Boltzmann machines
Pattern Recognition Letters
Training energy-based models for time-series imputation
The Journal of Machine Learning Research
The Shape Boltzmann Machine: A Strong Model of Object Shape
International Journal of Computer Vision
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A new algorithm for training Restricted Boltzmann Machines is introduced. The algorithm, named Persistent Contrastive Divergence, is different from the standard Contrastive Divergence algorithms in that it aims to draw samples from almost exactly the model distribution. It is compared to some standard Contrastive Divergence and Pseudo-Likelihood algorithms on the tasks of modeling and classifying various types of data. The Persistent Contrastive Divergence algorithm outperforms the other algorithms, and is equally fast and simple.