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Text-Descrambling

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we network. a algorithm First, in for will belief present the MPE finding an
First, we will present an algorithm for finding the MPE in a belief network.
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are pignistic through the two subsystems transformation. connected The
The two subsystems are connected through the pignistic transformation.
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called by sign property of synergy. this is dependence a qualitative The product captured
The sign of this dependence is captured by a qualitative property called product synergy.
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truth diagnosis. Finally, several maintenance, have argument and reasoning, Nonmonotonic networks I show applications: that
Finally, I show that argument networks have several applications: Nonmonotonic reasoning, truth maintenance, and diagnosis.
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belief Dynamic network for models reasoning. (DNMs) are networks temporal
Dynamic network models (DNMs) are belief networks for temporal reasoning.
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belief-network forecasting, use simulation DNMs. discrete inference perform on control, event algorithms We to and
We use belief-network inference algorithms to perform forecasting, control, and discrete event simulation on DNMs.
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methodology an We in DNM important demonstrate medicine. the problem on forecasting
We demonstrate the DNM methodology on an important forecasting problem in medicine.
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information. This of allows for a wide and logical logic of range the probabilistic representation
This logic allows for the representation of a wide range of logical and probabilistic information.
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a as These composed network. a pieces are probability Bayesian to joint specified generate distribution
These pieces are composed to generate a joint probability distribution specified as a Bayesian network.
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statistical representations Bayesian models. knowledge ideal for diagrams Influence are
Influence diagrams are ideal knowledge representations for Bayesian statistical models.
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manipulate. diagrams However, interpret to users and end are difficult these for to
However, these diagrams are difficult for end users to interpret and to manipulate.
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statistical restricted a of of subgraphs class influence as models are encoded diagram. of Elements
Elements of statistical models are encoded as subgraphs of a restricted class of influence diagram.
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actions construction the algorithm The called runs system that modular uses steps.
The algorithm that runs the system uses modular actions called construction steps.
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model for Noisy-Or describes Pearl variables. the Boolean
Pearl describes the Noisy-Or model for Boolean variables.
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construction This Bayesian useful networks. for modeling of a aid is generalization
This generalization is a useful modeling aid for construction of Bayesian networks.
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reliability examples illustrate analysis. We some circuit and diagnosis network with digital including
We illustrate with some examples including digital circuit diagnosis and network reliability analysis.
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must Most algorithms assumption. this at least inference make
Most inference algorithms must make at least this assumption.
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situation We as several belief information experts envision independently which networks. in a encode
We envision a situation in which several experts independently encode information as belief networks.
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procedures, one of types for This and probabilities, for approach one graphs. requires two combination
This approach requires two types of combination procedures, one for probabilities, and one for graphs.
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on generalize functions. We models of it based the framework within the belief
We generalize it within the framework of the models based on belief functions.
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This optimal finding an paper of studies the tree. problem approximating
This paper studies the problem of finding an optimal approximating tree.
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time-critical making sequential a method for involving stochastic decision tasks and We describe processes.
We describe a method for time-critical decision making involving sequential tasks and stochastic processes.
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through probabilities. of achieved counterfactual This use is the
This is achieved through the use of counterfactual probabilities.
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We method to also imaging. Lewis's the demonstrate connection of
We also demonstrate the connection to Lewis's method of imaging.
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loudness temporal addition we to this exploit set of In a and features.
In addition to this we exploit a set of temporal and loudness features.
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for are different Thereafter classification. used classifiers
Thereafter different classifiers are used for classification.
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incorporate is propositional assumptions. extended probabilities classical the for model assumption-based to The
The classical propositional assumption-based model is extended to incorporate probabilities for the assumptions.
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theory. is evidence placed it the Then of into framework
Then it is placed into the framework of evidence theory.
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recently techniques proposed for computing presented. degrees are support Finally, of
Finally, recently proposed techniques for computing degrees of support are presented.
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limitation or the often distinctions, approach these the of ignored Bayesian Without is underestimated.
Without these distinctions, the limitation of the Bayesian approach is often ignored or underestimated.
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knowledge guide can to be values used elicitation. These
These values can be used to guide knowledge elicitation.
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differentiation and local In adaptation maintained. case, this are
In this case, differentiation and local adaptation are maintained.
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migration amount linkage minimum We depends admitting invasion study pattern. how on the of this
We study how this minimum amount of linkage admitting invasion depends on the migration pattern.
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the This used describes PAGODA. representation mechanism paper in probabilistic inference and
This paper describes the probabilistic representation and inference mechanism used in PAGODA.
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probability conditional represented as distributions. are theories These
These theories are represented as conditional probability distributions.
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These are restricted theories. theories called predictive uniquely
These restricted theories are called uniquely predictive theories.
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nodes of tens with networks results thousands having of Empirical are presented.
Empirical results with networks having tens of thousands of nodes are presented.
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NP-hard. Optimization generally however, energies, of such is
Optimization of such energies, however, is generally NP-hard.
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and speed. it gives near minimum global deconvolution); curvature stereo, better regularization,
curvature regularization, stereo, deconvolution); it gives near global minimum and better speed.
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main of is curvature regularization. application Our interest
Our main application of interest is curvature regularization.
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common knowledge, provides uncertainty. representing conceptual It knowledge, framework hierarchical for a and
It provides a common framework for representing conceptual knowledge, hierarchical knowledge, and uncertainty.
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of levels dynamic at abstraction. decision facilitates construction varying It of categorization models
It facilitates dynamic construction of categorization decision models at varying levels of abstraction.
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for rule examines combination the concept belief of paper This a functions.
This paper examines the concept of a combination rule for belief functions.
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that problem general is such exists the show a We of deciding dag whether NP-complete.
We show that the general problem of deciding whether such a dag exists is NP-complete.
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to accept. Therefore, conclusions about uncertainty inconsistency in a logical causes database which
Therefore, inconsistency in a logical database causes uncertainty about which conclusions to accept.
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uncertainty uncertainty. This kind logical called of is
This kind of uncertainty is called logical uncertainty.
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"acceptability", define means arguments. differentiating of induces concept which for a a We
We define a concept of "acceptability", which induces a means for differentiating arguments.
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more the we an argument, more it. acceptable The are in confident
The more acceptable an argument, the more confident we are in it.
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and by be aggregated variety symbolic functions. numeric flattening of Arguments can a
Arguments can be aggregated by a variety of numeric and symbolic flattening functions.
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with Dean Kanazawa's probabilistic approach and projection. compare our We
We compare our approach with Dean and Kanazawa's probabilistic projection.
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formal problem. give modelling decreasing of the a We persistence
We give a formal modelling of the decreasing persistence problem.
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show persistence. Lastly, of nonmonotonic principle the how decreasing using conclusions infer to we
Lastly, we show how to infer nonmonotonic conclusions using the principle of decreasing persistence.
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of The of the a maximum-likelihood point. location each estimate output is
The output is a maximum-likelihood estimate of the location of each point.
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Gaussian all assumes that constraints It have noise.
It assumes that all constraints have Gaussian noise.
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expected conceptual, of structural the and refinement decision quantitative, analyze of we models. value Specifically,
Specifically, we analyze the expected value of quantitative, conceptual, and structural refinement of decision models.
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examples. We with key illustrate refinement of the dimensions
We illustrate the key dimensions of refinement with examples.
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relevance. networks distinct notion We types based of each of similarity two a examine on
We examine two types of similarity networks each based on a distinct notion of relevance.
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new Screening, design. Predictive predictor approach Correlation variable called for We introduce a to selection,
We introduce a new approach to variable selection, called Predictive Correlation Screening, for predictor design.
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problem. design the We Predictive predictor apply following Correlation to Screening two-stage
We apply Predictive Correlation Screening to the following two-stage predictor design problem.
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of is The probability scoring from concept proper for elicitation considered refinement rules.
The concept of refinement from probability elicitation is considered for proper scoring rules.
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The new with terms. convection system linear is hyperbolic
The new system is hyperbolic with linear convection terms.
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the original new Under the recover can model. certain system assumptions,
Under certain assumptions, the new system can recover the original model.
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a model using consider detection natural object shapes. We generic for
We consider object detection using a generic model for natural shapes.
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object approach to directly models images. object matching involves recognition common A for
A common approach for object recognition involves matching object models directly to images.
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via grouping building intermediate processes. a generic involves Another representations approach
Another approach involves building intermediate representations via a generic grouping processes.
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computational two use We mechanisms. (model-based argue similar that grouping) recognition these processes may and
We argue that these two processes (model-based recognition and grouping) may use similar computational mechanisms.
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easily Such by terms and expression. representations affected illumination, are of variations alignment, in pose
Such representations are easily affected by variations in terms of alignment, illumination, pose and expression.
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the word. of emphasizes of script the for energies of directional It significance identification
It emphasizes the significance of directional energies for identification of script of the word.
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sizes styles It and writing. of varied image different is robust to
It is robust to varied image sizes and different styles of writing.
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of GMM. deviation is distributions of built-in and horizontal to vertical energies also Furthermore,
Furthermore, deviation of horizontal and vertical distributions of energies is also built-in to GMM.
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biological of often is context-dependent. genomic The significance features
The biological significance of genomic features is often context-dependent.
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Second, associated cognitive transcripts function. relevant map lincRNA with functions to variants of
Second, variants associated with cognitive functions map to lincRNA transcripts of relevant function.
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genes. domains highly lamina-associated are olfaction-related in Third, enriched
Third, lamina-associated domains are highly enriched in olfaction-related genes.
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stratification having a optimal. other refined is hand, (too) not On the
On the other hand, having a (too) refined stratification is not optimal.
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points more of variations sample the or are function larger. the where noise
sample more points where the noise or variations of the function are larger.
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partition data is fundamental algorithms The points. clustering aim to of
The fundamental aim of clustering algorithms is to partition data points.
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processes. arbitrary space on Gaussian an covariate partition-valued define a We process using
We define a partition-valued process on an arbitrary covariate space using Gaussian processes.
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of Fiser to al. et comment A a response
A response to a comment of Fiser et al.
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diagnostic with Several label assess samplers tools convergence. to are switching along implemented and moves
Several samplers and label switching moves are implemented along with diagnostic tools to assess convergence.
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post-processing provided. are number of A for the functions also of output R
A number of R functions for post-processing of the output are also provided.
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as This selection. variable implemented in the is package
This is implemented in the package as variable selection.
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Concept experimented Concept theoretically largely grounded and are and Hierarchies well Formal Analysis methods.
Concept Hierarchies and Formal Concept Analysis are theoretically well grounded and largely experimented methods.
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on called analysing for Galois and They sets. rely lattices diagrams visualizing object-attribute line
They rely on line diagrams called Galois lattices for visualizing and analysing object-attribute sets.
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are experts. rich for Galois and visually lattices conceptually seducing
Galois lattices are visually seducing and conceptually rich for experts.
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reduced Semantic visual Galois which objects centred and sub-hierarchies. organize extract probes user are
Semantic probes are visual user centred objects which extract and organize reduced Galois sub-hierarchies.
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text statistical this handwritten printed paper, use for texture In features classification. and we
In this paper, we use statistical texture features for handwritten and printed text classification.
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for south Indian level classification word We primarily aim in scripts.
We primarily aim for word level classification in south Indian scripts.
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document. Words are from scanned extracted the first
Words are first extracted from the scanned document.
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vectors classify to then classifier. used feature via k-NN These words are
These feature vectors are then used to classify words via k-NN classifier.
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the several validated have different over datasets. We approach
We have validated the approach over several different datasets.
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in manipulation elegans. we optogenetic and techniques methods describe for Here C.
Here we describe techniques and methods for optogenetic manipulation in C. elegans.
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Image on basis. block-by-block analysed are a sequences
Image sequences are analysed on a block-by-block basis.
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the network. cascade an We MAPK using give example also
We also give an example using the MAPK cascade network.
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memory size by or the disk The storage. available limited is buffer
The buffer size is limited by the available memory or disk storage.
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for geometric standard image fitting based approaches model are on features. Many pre-matched
Many standard approaches for geometric model fitting are based on pre-matched image features.
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such appearances uses Typically, only feature (e.g. pre-matching
Typically, such pre-matching uses only feature appearances (e.g.
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In multi-model framework. a we and contrast, problems feature joint fitting optimization matching in solve
In contrast, we solve feature matching and multi-model fitting problems in a joint optimization framework.
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energy generalization based a paper of proposes problem. on the several This fit-&-match assignment formulations
This paper proposes several fit-&-match energy formulations based on a generalization of the assignment problem.
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developed optimal We on an solver that min-cost-max-flow efficient solutions. finds near based algorithm
We developed an efficient solver based on min-cost-max-flow algorithm that finds near optimal solutions.
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matches. the detected Our number significantly increases approach of
Our approach significantly increases the number of detected matches.
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