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NIPS
2007
13 years 8 months ago
Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes
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...
Ruslan Salakhutdinov, Geoffrey E. Hinton
CORR
2007
Springer
141views Education» more  CORR 2007»
13 years 6 months ago
Bootstrapping Deep Lexical Resources: Resources for Courses
We propose a range of deep lexical acquisition methods which make use of morphological, syntactic and ontological language resources to model word similarity and bootstrap from a ...
Timothy Baldwin
JMLR
2012
11 years 9 months ago
Deep Boltzmann Machines as Feed-Forward Hierarchies
The deep Boltzmann machine is a powerful model that extracts the hierarchical structure of observed data. While inference is typically slow due to its undirected nature, we argue ...
Grégoire Montavon, Mikio L. Braun, Klaus-Ro...
JWSR
2007
172views more  JWSR 2007»
13 years 6 months ago
Service Class Driven Dynamic Data Source Discovery with DynaBot
: Dynamic Web data sources – sometimes known collectively as the Deep Web – increase the utility of the Web by providing intuitive access to data repositories anywhere that Web...
Daniel Rocco, James Caverlee, Ling Liu, Terence Cr...
HPCA
2002
IEEE
14 years 7 months ago
Exploiting Choice in Resizable Cache Design to Optimize Deep-Submicron Processor Energy-Delay
Cache memories account for a significant fraction of a chip's overall energy dissipation. Recent research advocates using "resizable" caches to exploit cache requir...
Se-Hyun Yang, Michael D. Powell, Babak Falsafi, T....