75 search results - page 3 / 15 » Sparse Feature Learning for Deep Belief Networks |
ICASSP
2011
IEEE
12 years 9 months ago
2011
IEEE
In this paper, we extend the work done on integrating multilayer perceptron (MLP) networks with HMM systems via the Tandem approach. In particular, we explore whether the use of D...
ICASSP
2011
IEEE
12 years 9 months ago
2011
IEEE
Deep Belief Networks (DBNs) are multi-layer generative models. They can be trained to model windows of coefficients extracted from speech and they discover multiple layers of fea...
NIPS
2007
13 years 7 months ago
2007
We show how to use unlabeled data and a deep belief net (DBN) to learn a good covariance kernel for a Gaussian process. We first learn a deep generative model of the unlabeled da...
ICML
2008
IEEE
14 years 6 months ago
2008
IEEE
Deep Belief Networks (DBN's) are generative models that contain many layers of hidden variables. Efficient greedy algorithms for learning and approximate inference have allow...
NECO
2008
13 years 5 months ago
2008
In this paper we show that exponentially deep belief networks [3, 7, 4] can approximate any distribution over binary vectors to arbitrary accuracy, even when the width of each lay...