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Mean field methods in performance analysis
 Dip Informatica  Università Piemonte Orientale; www.di.unipmn.it/TecnicalR/index.htm
, 2008
"... Abstract Modeling and analysing very large stochastic systems composed of interacting entities is a very challenging and complex task. The usual approach, relying on the generation of the whole state space, is bounded by the state space explosion, even if symmetry properties, often included in the ..."
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Cited by 2 (1 self)
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in the model, allow to apply lumping techniques and building the overall model by means of tensor algebra operations. In this paper we resort to the mean field theory. The main idea of the mean field theory is to focus on one particular tagged entity and to replace all interactions with the other entities
Mean field methods for classification with Gaussian processes
 PROCESSES, IN ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 11
, 1999
"... We discuss the application of TAP mean field methods known from the Statistical Mechanics of disordered systems to Bayesian classification models with Gaussian processes. In contrast to previous approaches, no knowledge about the distribution of inputs is needed. Simulation results for the Sonar dat ..."
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Cited by 12 (3 self)
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We discuss the application of TAP mean field methods known from the Statistical Mechanics of disordered systems to Bayesian classification models with Gaussian processes. In contrast to previous approaches, no knowledge about the distribution of inputs is needed. Simulation results for the Sonar
DETECTING HUMANS UNDER OCCLUSION USING VARIATIONAL MEAN FIELD METHOD
"... Detecting humans under occlusion using variational mean field method ..."
On the reliability of meanfield methods in polymer statistical mechanics
, 2000
"... The reliability of the meanfield approach to polymer statistical mechanics is investigated by comparing results from a recently developed lattice meanfield theory (LMFT) method to statistically exact results from two independent numerical Monte Carlo simulations for the problems of a polymer chain ..."
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The reliability of the meanfield approach to polymer statistical mechanics is investigated by comparing results from a recently developed lattice meanfield theory (LMFT) method to statistically exact results from two independent numerical Monte Carlo simulations for the problems of a polymer
Mean Field Methods for Cortical Network Dynamics
"... Abstract. We review the use of mean field theory for describing the dynamics of dense, randomly connected cortical circuits. For a simple network of excitatory and inhibitory leaky integrateandfire neurons, we can show how the firing irregularity, as measured by the Fano factor, increases with the ..."
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Cited by 4 (0 self)
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Abstract. We review the use of mean field theory for describing the dynamics of dense, randomly connected cortical circuits. For a simple network of excitatory and inhibitory leaky integrateandfire neurons, we can show how the firing irregularity, as measured by the Fano factor, increases
Comparing the mean field method and belief propagation for approximate inference in MRFs
, 2001
"... re the \beliefs") while in the physics setting mean eld approximations may be used to predict the macroscopic behavior of the system (e.g. critical temperature, correlation lengths etc.). I will therefore start this chapter reformulating both methods in a common language. I will then show simu ..."
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Cited by 36 (0 self)
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re the \beliefs") while in the physics setting mean eld approximations may be used to predict the macroscopic behavior of the system (e.g. critical temperature, correlation lengths etc.). I will therefore start this chapter reformulating both methods in a common language. I will then show
Graphical models, exponential families, and variational inference
, 2008
"... The formalism of probabilistic graphical models provides a unifying framework for capturing complex dependencies among random variables, and building largescale multivariate statistical models. Graphical models have become a focus of research in many statistical, computational and mathematical fiel ..."
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Cited by 819 (28 self)
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likelihoods, marginal probabilities and most probable configurations. We describe how a wide varietyof algorithms — among them sumproduct, cluster variational methods, expectationpropagation, mean field methods, maxproduct and linear programming relaxation, as well as conic programming relaxations — can
Inducing Features of Random Fields
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 1997
"... We present a technique for constructing random fields from a set of training samples. The learning paradigm builds increasingly complex fields by allowing potential functions, or features, that are supported by increasingly large subgraphs. Each feature has a weight that is trained by minimizing the ..."
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Cited by 670 (10 self)
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We present a technique for constructing random fields from a set of training samples. The learning paradigm builds increasingly complex fields by allowing potential functions, or features, that are supported by increasingly large subgraphs. Each feature has a weight that is trained by minimizing
Light Field Rendering
, 1996
"... A number of techniques have been proposed for flying through scenes by redisplaying previously rendered or digitized views. Techniques have also been proposed for interpolating between views by warping input images, using depth information or correspondences between multiple images. In this paper, w ..."
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Cited by 1337 (22 self)
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, we describe a simple and robust method for generating new views from arbitrary camera positions without depth information or feature matching, simply by combining and resampling the available images. The key to this technique lies in interpreting the input images as 2D slices of a 4D function
Shallow Parsing with Conditional Random Fields
, 2003
"... Conditional random fields for sequence labeling offer advantages over both generative models like HMMs and classifiers applied at each sequence position. Among sequence labeling tasks in language processing, shallow parsing has received much attention, with the development of standard evaluati ..."
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Cited by 581 (8 self)
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evaluation datasets and extensive comparison among methods. We show here how to train a conditional random field to achieve performance as good as any reported base nounphrase chunking method on the CoNLL task, and better than any reported single model. Improved training methods based on modern
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