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NECO
2008
170views more  NECO 2008»
13 years 8 months ago
Representational Power of Restricted Boltzmann Machines and Deep Belief Networks
Deep Belief Networks (DBN) are generative neural network models with many layers of hidden explanatory factors, recently introduced by Hinton et al., along with a greedy layer-wis...
Nicolas Le Roux, Yoshua Bengio
JSS
2007
109views more  JSS 2007»
13 years 7 months ago
Using Bayesian belief networks for change impact analysis in architecture design
Research into design rationale in the past has focused on argumentation-based design deliberations. These approaches cannot be used to support change impact analysis effectively ...
Antony Tang, Ann E. Nicholson, Yan Jin, Jun Han
INFFUS
2010
121views more  INFFUS 2010»
13 years 6 months ago
The statistical mechanics of belief sharing in multi-agent systems
- Many exciting, emerging applications require that a group of agents share a coherent view of the world given spatial distribution, incomplete and uncertain sensors, and communica...
Robin Glinton, Katia P. Sycara, David Scerri, Paul...
ASP
2005
Springer
14 years 1 months ago
Nelson's Strong Negation, Safe Beliefs and the Answer Set Semantics
In this paper we consider an extension of the answer set semantics allowing arbitrary use of strong negation. We prove that the strong negation extension of any intermediate logic ...
Magdalena Ortiz, Mauricio Osorio
NIPS
2007
13 years 9 months ago
Sparse Feature Learning for Deep Belief Networks
Unsupervised learning algorithms aim to discover the structure hidden in the data, and to learn representations that are more suitable as input to a supervised machine than the ra...
Marc'Aurelio Ranzato, Y-Lan Boureau, Yann LeCun