Information processing in dynamical systems: foundations of harmony theory
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Connectionist learning of belief networks
Artificial Intelligence
A comparison of approaches to on-line handwritten character recognition
A comparison of approaches to on-line handwritten character recognition
On the use of Bernoulli Mixture Models for Text Classification
PRIS '01 Proceedings of the 1st International Workshop on Pattern Recognition in Information Systems: In conjunction with ICEIS 2001
Bernoulli Mixture Models for Binary Images
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Naive Bayes models for probability estimation
ICML '05 Proceedings of the 22nd international conference on Machine learning
A fast learning algorithm for deep belief nets
Neural Computation
On the quantitative analysis of deep belief networks
Proceedings of the 25th international conference on Machine learning
Training restricted Boltzmann machines using approximations to the likelihood gradient
Proceedings of the 25th 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
Hi-index | 0.00 |
We investigate the problem of estimating the density function of multivariate binary data. In particular, we focus on models for which computing the estimated probability of any data point is tractable. In such a setting, previous work has mostly concentrated on mixture modeling approaches. We argue that for the problem of tractable density estimation, the restricted Boltzmann machine (RBM) provides a competitive framework for multivariate binary density modeling. With this in mind, we also generalize the RBM framework and present the restricted Boltzmann forest (RBForest), which replaces the binary variables in the hidden layer of RBMs with groups of tree-structured binary variables. This extension allows us to obtain models that have more modeling capacity but remain tractable. In experiments on several data sets, we demonstrate the competitiveness of this approach and study some of its properties.