14 search results - page 2 / 3 » A multi-FPGA architecture for stochastic Restricted Boltzman... |
ICANN
2010
Springer
13 years 6 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...
FPL
2009
Springer
13 years 9 months ago
2009
Springer
Restricted Boltzmann Machines (RBMs) — the building block for newly popular Deep Belief Networks (DBNs) — are a promising new tool for machine learning practitioners. However,...
JMLR
2010
12 years 11 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...
JMLR
2010
12 years 11 months ago
2010
Deep belief nets have been successful in modeling handwritten characters, but it has proved more difficult to apply them to real images. The problem lies in the restricted Boltzma...
ECCV
2010
Springer
13 years 10 months ago
2010
Springer
Abstract. We address the problem of learning good features for understanding video data. We introduce a model that learns latent representations of image sequences from pairs of su...