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433,479
Assumeguarantee verification for probabilistic systems
, 2009
"... Abstract. We present a compositional verification technique for systems that exhibit both probabilistic and nondeterministic behaviour. We adopt an assumeguarantee approach to verification, where both the assumptions made about system components and the guarantees that they provide are regular sa ..."
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Cited by 41 (15 self)
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safety properties, represented by finite automata. Unlike previous proposals for assumeguarantee reasoning about probabilistic systems, our approach does not require that components interact in a fully synchronous fashion. In addition, the compositional verification method is efficient and fully
Assumeguarantee reasoning for deadlock
 IN: PROC. OF FMCAD.
, 2006
"... We extend the learningbased automated assume guarantee paradigm to perform compositional deadlock detection. We define Failure Automata, a generalization of finite automata that accept regular failure sets. We develop a learning algorithm L F that constructs the minimal deterministic failure autom ..."
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Cited by 8 (3 self)
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We extend the learningbased automated assume guarantee paradigm to perform compositional deadlock detection. We define Failure Automata, a generalization of finite automata that accept regular failure sets. We develop a learning algorithm L F that constructs the minimal deterministic failure
Automatic Assume/Guarantee Reasoning for
 In 1st AIOOL Workshop
, 2005
"... Assume/Guarantee (A/G) reasoning for heapmanipulating programs is challenging because the heap can be mutated in an arbitrary way by procedure calls. Moreover, specifying the potential sideeffects of a procedure is nontrivial. We report on an ongoing effort to reduce the burden of A/G reasoning ..."
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Assume/Guarantee (A/G) reasoning for heapmanipulating programs is challenging because the heap can be mutated in an arbitrary way by procedure calls. Moreover, specifying the potential sideeffects of a procedure is nontrivial. We report on an ongoing effort to reduce the burden of A/G reasoning
Learning probabilistic relational models
 In IJCAI
, 1999
"... A large portion of realworld data is stored in commercial relational database systems. In contrast, most statistical learning methods work only with "flat " data representations. Thus, to apply these methods, we are forced to convert our data into a flat form, thereby losing much ..."
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Cited by 619 (31 self)
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A large portion of realworld data is stored in commercial relational database systems. In contrast, most statistical learning methods work only with "flat " data representations. Thus, to apply these methods, we are forced to convert our data into a flat form, thereby losing much
algorithm to automate assumeguarantee reasoning
, 2008
"... conquer: applying the L algorithm to automate ..."
Instancebased learning algorithms
 Machine Learning
, 1991
"... Abstract. Storing and using specific instances improves the performance of several supervised learning algorithms. These include algorithms that learn decision trees, classification rules, and distributed networks. However, no investigation has analyzed algorithms that use only specific instances to ..."
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Cited by 1359 (18 self)
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to solve incremental learning tasks. In this paper, we describe a framework and methodology, called instancebased learning, that generates classification predictions using only specific instances. Instancebased learning algorithms do not maintain a set of abstractions derived from specific instances
Mixtures of Probabilistic Principal Component Analysers
, 1998
"... Principal component analysis (PCA) is one of the most popular techniques for processing, compressing and visualising data, although its effectiveness is limited by its global linearity. While nonlinear variants of PCA have been proposed, an alternative paradigm is to capture data complexity by a com ..."
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Cited by 537 (6 self)
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maximumlikelihood framework, based on a specific form of Gaussian latent variable model. This leads to a welldefined mixture model for probabilistic principal component analysers, whose parameters can be determined using an EM algorithm. We discuss the advantages of this model in the context
Probabilistic Visual Learning for Object Representation
, 1996
"... We present an unsupervised technique for visual learning which is based on density estimation in highdimensional spaces using an eigenspace decomposition. Two types of density estimates are derived for modeling the training data: a multivariate Gaussian (for unimodal distributions) and a Mixtureof ..."
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Cited by 705 (15 self)
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ofGaussians model (for multimodal distributions). These probability densities are then used to formulate a maximumlikelihood estimation framework for visual search and target detection for automatic object recognition and coding. Our learning technique is applied to the probabilistic visual modeling, detection
Assumeguarantee abstraction refinement for probabilistic systems
 In: Proc. of CAV. Vol. 7358 of LNCS
, 2012
"... ar ..."
Probabilistic Latent Semantic Indexing
, 1999
"... Probabilistic Latent Semantic Indexing is a novel approach to automated document indexing which is based on a statistical latent class model for factor analysis of count data. Fitted from a training corpus of text documents by a generalization of the Expectation Maximization algorithm, the utilized ..."
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Cited by 1207 (11 self)
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Probabilistic Latent Semantic Indexing is a novel approach to automated document indexing which is based on a statistical latent class model for factor analysis of count data. Fitted from a training corpus of text documents by a generalization of the Expectation Maximization algorithm, the utilized
Results 1  10
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433,479