68 search results - page 1 / 14 » Inductive Principles for Restricted Boltzmann Machine Learni... |
JMLR
2010
13 years 2 months ago
2010
Recent research has seen the proposal of several new inductive principles designed specifically to avoid the problems associated with maximum likelihood learning in models with in...
JMLR
2010
13 years 2 months ago
2010
Alternating Gibbs sampling is the most common scheme used for sampling from Restricted Boltzmann Machines (RBM), a crucial component in deep architectures such as Deep Belief Netw...
ICML
2008
IEEE
14 years 8 months ago
2008
IEEE
Recently, many applications for Restricted Boltzmann Machines (RBMs) have been developed for a large variety of learning problems. However, RBMs are usually used as feature extrac...
ICANN
2010
Springer
13 years 8 months ago
2010
Springer
Abstract. Learning algorithms relying on Gibbs sampling based stochastic approximations of the log-likelihood gradient have become a common way to train Restricted Boltzmann Machin...
JMLR
2010
13 years 2 months ago
2010
In this paper, we consider the asymptotic form of the generalization error for the restricted Boltzmann machine in Bayesian estimation. It has been shown that obtaining the maximu...