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NIPS
2003
13 years 9 months ago
Wormholes Improve Contrastive Divergence
In models that define probabilities via energies, maximum likelihood learning typically involves using Markov Chain Monte Carlo to sample from the model’s distribution. If the ...
Geoffrey E. Hinton, Max Welling, Andriy Mnih
JCNS
1998
134views more  JCNS 1998»
13 years 7 months ago
Analytical and Simulation Results for Stochastic Fitzhugh-Nagumo Neurons and Neural Networks
An analytical approach is presented for determining the response of a neuron or of the activity in a network of connected neurons, represented by systems of nonlinear ordinary stoc...
Henry C. Tuckwell, Roger Rodriguez
PROMISE
2010
13 years 2 months ago
On the value of learning from defect dense components for software defect prediction
BACKGROUND: Defect predictors learned from static code measures can isolate code modules with a higher than usual probability of defects. AIMS: To improve those learners by focusi...
Hongyu Zhang, Adam Nelson, Tim Menzies
BMCBI
2010
108views more  BMCBI 2010»
13 years 5 months ago
Prediction of RNA secondary structure by maximizing pseudo-expected accuracy
Background: Recent studies have revealed the importance of considering the entire distribution of possible secondary structures in RNA secondary structure predictions; therefore, ...
Michiaki Hamada, Kengo Sato, Kiyoshi Asai
IJCV
2008
188views more  IJCV 2008»
13 years 7 months ago
Partial Linear Gaussian Models for Tracking in Image Sequences Using Sequential Monte Carlo Methods
The recent development of Sequential Monte Carlo methods (also called particle filters) has enabled the definition of efficient algorithms for tracking applications in image sequen...
Elise Arnaud, Étienne Mémin