Efficient learning in Boltzmann machines using linear response theory
Neural Computation
Training products of experts by minimizing contrastive divergence
Neural Computation
Some extensions of score matching
Computational Statistics & Data Analysis
Piecewise pseudolikelihood for efficient training of conditional random fields
Proceedings of the 24th international conference on Machine learning
A unified approach to building hybrid recommender systems
Proceedings of the third ACM conference on Recommender systems
Piecewise training for structured prediction
Machine Learning
Gamma Markov random fields for audio source modeling
IEEE Transactions on Audio, Speech, and Language Processing
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
Learning algorithms for the classification restricted Boltzmann machine
The Journal of Machine Learning Research
| Hi-index | 0.00 |
A Boltzmann machine is a classic model of neural computation, and a number of methods have been proposed for its estimation. Most methods are plagued by either very slow convergence or asymptotic bias in the resulting estimates. Here we consider estimation in the basic case of fully visible Boltzmann machines. We show that the old principle of pseudolikelihood estimation provides an estimator that is computationally very simple yet statistically consistent.