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ICML
2008
IEEE
14 years 10 months ago
Training restricted Boltzmann machines using approximations to the likelihood gradient
A new algorithm for training Restricted Boltzmann Machines is introduced. The algorithm, named Persistent Contrastive Divergence, is different from the standard Contrastive Diverg...
Tijmen Tieleman
BSN
2009
IEEE
140views Sensor Networks» more  BSN 2009»
14 years 4 months ago
A Distributed Hidden Markov Model for Fine-grained Annotation in Body Sensor Networks
—Human movement models often divide movements into parts. In walking the stride can be segmented into four different parts, and in golf and other sports, the swing is divided int...
Eric Guenterberg, Hassan Ghasemzadeh, Roozbeh Jafa...
SIAMAM
2008
91views more  SIAMAM 2008»
13 years 9 months ago
A Fluid Dynamic Model for Telecommunication Networks with Sources and Destinations
This paper proposes a macroscopic fluid dynamic model dealing with the flows of information on a telecommunication network with sources and destinations. The model consists of a co...
Ciro D'Apice, Rosanna Manzo, Benedetto Piccoli
JMLR
2010
137views more  JMLR 2010»
13 years 4 months ago
Covariance in Unsupervised Learning of Probabilistic Grammars
Probabilistic grammars offer great flexibility in modeling discrete sequential data like natural language text. Their symbolic component is amenable to inspection by humans, while...
Shay B. Cohen, Noah A. Smith
PDCAT
2009
Springer
14 years 3 months ago
Modular Consensus Algorithms for the Crash-Recovery Model
—In the crash-recovery failure model of asynchronous distributed systems, processes can temporarily stop to execute steps and later restart their computation from a predefined l...
Felix C. Freiling, Christian Lambertz, Mila E. Maj...