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NIPS
2007
15 years 19 days 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»
14 years 11 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
13 years 1 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»
14 years 11 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
15 years 11 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....