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» Kolmogorov-Loveland Randomness and Stochasticity
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NIPS
1996
13 years 10 months ago
Continuous Sigmoidal Belief Networks Trained using Slice Sampling
Real-valued random hidden variables can be useful for modelling latent structure that explains correlations among observed variables. I propose a simple unit that adds zero-mean G...
Brendan J. Frey
ICIP
2003
IEEE
14 years 10 months ago
Algorithms for stochastic approximations of curvature flows
Curvature flows have been extensively considered from a deterministic point of view. They have been shown to be useful for a number of applications including crystal growth, flame...
Gozde B. Unal, Delphine Nain, G. Ben-Arous, Nahum ...
GRID
2004
Springer
14 years 2 months ago
A Stochastic Control Model for Deployment of Dynamic Grid Services
We introduce a formal model for deployment and hosting of a dynamic grid service wherein the service provider must pay a resource provider for the use of computational resources. ...
Darin England, Jon B. Weissman
NIPS
2001
13 years 10 months ago
Stochastic Mixed-Signal VLSI Architecture for High-Dimensional Kernel Machines
A mixed-signal paradigm is presented for high-resolution parallel innerproduct computation in very high dimensions, suitable for efficient implementation of kernels in image proce...
Roman Genov, Gert Cauwenberghs
VISAPP
2007
13 years 10 months ago
Image deconvolution using a stochastic differential equation approach
We consider the problem of image deconvolution. We foccus on a Bayesian approach which consists of maximizing an energy obtained by a Markov Random Field modeling. MRFs are classi...
Xavier Descombes, M. Lebellego, Elena Zhizhina