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63
Variational Belief Networks for Approximate Inference
, 1998
"... Exact inference in large, densely connected probabilistic networks is computationally intractable, and approximate schemes are therefore of great importance. One approach is to use mean field theory, in which the exact loglikelihood is bounded from below using a simpler approximating distribution. ..."
Abstract

Cited by 8 (3 self)
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Exact inference in large, densely connected probabilistic networks is computationally intractable, and approximate schemes are therefore of great importance. One approach is to use mean field theory, in which the exact loglikelihood is bounded from below using a simpler approximating distribution
Markovian Inference in Belief Networks
 Presented at Machines That Learn
, 1998
"... Bayesian belief networks can represent the complicated probabilistic processes that form natural sensory inputs. Once the parameters of the network have been learned, nonlinear inferences about the input can be made by computing the posterior distribution over the hidden units (e.g., depth in stereo ..."
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Cited by 1 (1 self)
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in stereo vision) given the input. Computing the posterior distribution exactly is not practical in richlyconnected networks, but it turns out that by using a variational (a.k.a., mean field) method, it is easy to find a productform distribution that approximates the true posterior distribution
Efficient variational inference for gaussian process regression networks
 In AISTATS
, 2013
"... In multioutput regression applications the correlations between the response variables may vary with the input space and can be highly nonlinear. Gaussian process regression networks (GPRNs) are flexible and effective models to represent such complex adaptive output dependencies. However, infer ..."
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Cited by 2 (1 self)
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, inference in GPRNs is intractable. In this paper we propose two efficient variational inference methods for GPRNs. The first method, gprnmf, adopts a meanfield approach with full Gaussians over the GPRN’s parameters as its factorizing distributions. The second method, gprnnpv, uses a nonparametric
Dynamic Trees: A Structured Variational Method Giving Efficient Propagation Rules
 Uncertainty in Artificial Intelligence
, 2000
"... Dynamic trees are mixtures of tree structured belief networks. They solve some of the problems of fixed tree networks at the cost of making exact inference intractable. For this reason approximate methods such as sampling or mean field approaches have been used. However, mean field approximations as ..."
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Cited by 14 (4 self)
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Dynamic trees are mixtures of tree structured belief networks. They solve some of the problems of fixed tree networks at the cost of making exact inference intractable. For this reason approximate methods such as sampling or mean field approaches have been used. However, mean field approximations
Dynamic Trees: A Structured Variational Method Giving Efficient
"... Abstract Dynamic trees are mixtures of tree structured belief networks. They solve some of the problems of fixed tree networks at the cost of making exact inference intractable. For this reason approximate methods such as sampling or mean field approaches have been used. However, mean field approxim ..."
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Abstract Dynamic trees are mixtures of tree structured belief networks. They solve some of the problems of fixed tree networks at the cost of making exact inference intractable. For this reason approximate methods such as sampling or mean field approaches have been used. However, mean field
Expectation backpropagation: Parameterfree training of multilayer neural networks with continuous or discrete weights
 In Advances in Neural Information Processing Systems (NIPS
, 2014
"... Multilayer Neural Networks (MNNs) are commonly trained using gradient descentbased methods, such as BackPropagation (BP). Inference in probabilistic graphical models is often done using variational Bayes methods, such as Expectation Propagation (EP). We show how an EP based approach can also be us ..."
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Cited by 2 (1 self)
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Multilayer Neural Networks (MNNs) are commonly trained using gradient descentbased methods, such as BackPropagation (BP). Inference in probabilistic graphical models is often done using variational Bayes methods, such as Expectation Propagation (EP). We show how an EP based approach can also
SIMULATION, DEVELOPMENT AND DEPLOYMENT OF MOBILE WIRELESS SENSOR NETWORKS FOR MIGRATORY BIRD TRACKING
, 2012
"... This thesis presents CraneTracker, a multimodal sensing and communication system for monitoring migratory species at the continental level. By exploiting the robust and extensive cellular infrastructure across the continent, traditional mobile wireless sensor networks can be extended to enable reli ..."
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This thesis presents CraneTracker, a multimodal sensing and communication system for monitoring migratory species at the continental level. By exploiting the robust and extensive cellular infrastructure across the continent, traditional mobile wireless sensor networks can be extended to enable
3esis Supervisor Accepted by
, 2005
"... in partial ful2llment of the requirements for the degree of ..."
RESEARCH ARTICLE Balanced Sparse Model for Tight Frames in Compressed Sensing Magnetic Resonance Imaging
"... Compressed sensing has shown to be promising to accelerate magnetic resonance imaging. In this new technology, magnetic resonance images are usually reconstructed by enforcing its sparsity in sparse image reconstruction models, including both synthesis and analysis models. The synthesis model assu ..."
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algorithm constrained split augmented Lagrangian shrinkage algorithm for balanced model (CSALSAB) converges faster than previously proposed algorithms accelerated proximal algorithm (APG) and alternating directional method of multipliers for balanced model (ADMMB).
Abstract Multichannel Communication in Contiki's Lowpower IPv6 Stack
"... Vast majority of wireless appliances used in household, industry and medical field share the ISM frequency band. These devices need to coexist and thus are challenged to tolerate their mutual interference. One way of dealing with this is by using frequency hopping; where the device changes its radio ..."
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Vast majority of wireless appliances used in household, industry and medical field share the ISM frequency band. These devices need to coexist and thus are challenged to tolerate their mutual interference. One way of dealing with this is by using frequency hopping; where the device changes its
Results 1  10
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