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13,950
Lifted firstorder probabilistic inference
 In Proceedings of IJCAI05, 19th International Joint Conference on Artificial Intelligence
, 2005
"... Most probabilistic inference algorithms are specified and processed on a propositional level. In the last decade, many proposals for algorithms accepting firstorder specifications have been presented, but in the inference stage they still operate on a mostly propositional representation level. [Poo ..."
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Cited by 126 (8 self)
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Most probabilistic inference algorithms are specified and processed on a propositional level. In the last decade, many proposals for algorithms accepting firstorder specifications have been presented, but in the inference stage they still operate on a mostly propositional representation level
Firstorder Bayesball
 In Proc. of ECML10
, 2010
"... Abstract. Efficient probabilistic inference is key to the success of statistical relational learning. One issue that increases the cost of inference is the presence of irrelevant random variables. The Bayesball algorithm can identify the requisite variables in a propositional Bayesian network and ..."
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Cited by 4 (2 self)
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and thus ignore irrelevant variables. This paper presents a lifted version of Bayesball, which works directly on the firstorder level, and shows how this algorithm applies to (lifted) inference in directed firstorder probabilistic models. 1
Firstorder decomposition trees
 In Advances in Neural Information Processing Systems
, 2013
"... Lifting attempts to speedup probabilistic inference by exploiting symmetries in the model. Exact lifted inference methods, like their propositional counterparts, work by recursively decomposing the model and the problem. In the propositional case, there exist formal structures, such as decomposition ..."
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Cited by 1 (0 self)
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to the firstorder level. We show how these trees can characterize a lifted inference solution for a probabilistic logical model (in terms of a sequence of lifted operations), and make a theoretical analysis of the complexity of lifted inference in terms of the novel notion of lifted width for the tree. 1
A review of image denoising algorithms, with a new one
 SIMUL
, 2005
"... The search for efficient image denoising methods is still a valid challenge at the crossing of functional analysis and statistics. In spite of the sophistication of the recently proposed methods, most algorithms have not yet attained a desirable level of applicability. All show an outstanding perf ..."
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Cited by 508 (6 self)
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The search for efficient image denoising methods is still a valid challenge at the crossing of functional analysis and statistics. In spite of the sophistication of the recently proposed methods, most algorithms have not yet attained a desirable level of applicability. All show an outstanding
Stable signal recovery from incomplete and inaccurate measurements,”
 Comm. Pure Appl. Math.,
, 2006
"... Abstract Suppose we wish to recover a vector x 0 ∈ R m (e.g., a digital signal or image) from incomplete and contaminated observations y = Ax 0 + e; A is an n × m matrix with far fewer rows than columns (n m) and e is an error term. Is it possible to recover x 0 accurately based on the data y? To r ..."
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Cited by 1397 (38 self)
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? To recover x 0 , we consider the solution x to the 1 regularization problem where is the size of the error term e. We show that if A obeys a uniform uncertainty principle (with unitnormed columns) and if the vector x 0 is sufficiently sparse, then the solution is within the noise level As a first example
Mining Sequential Patterns: Generalizations and Performance Improvements
 RESEARCH REPORT RJ 9994, IBM ALMADEN RESEARCH
, 1995
"... The problem of mining sequential patterns was recently introduced in [3]. We are given a database of sequences, where each sequence is a list of transactions ordered by transactiontime, and each transaction is a set of items. The problem is to discover all sequential patterns with a userspecified ..."
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Cited by 759 (5 self)
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The problem of mining sequential patterns was recently introduced in [3]. We are given a database of sequences, where each sequence is a list of transactions ordered by transactiontime, and each transaction is a set of items. The problem is to discover all sequential patterns with a user
Ecology of the family as a context for human development: Research perspectives.
 Developmental Psychology,
, 1986
"... This review collates and examines critically a theoretically convergent but widely dispersed body of research on the influence of external environments on the functioning of families as contexts of human development. Investigations falling within this expanding domain include studies of the interac ..."
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Cited by 518 (0 self)
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scientific paradigms for investigating the impact of environment on development. These paradigms provide a useful framework for ordering and analyzing studies bearing on the topic of this review. At the most general level, the research models vary simultaneously along two dimensions. As applied
The Foundation of a Generic Theorem Prover
 Journal of Automated Reasoning
, 1989
"... Isabelle [28, 30] is an interactive theorem prover that supports a variety of logics. It represents rules as propositions (not as functions) and builds proofs by combining rules. These operations constitute a metalogic (or `logical framework') in which the objectlogics are formalized. Isabell ..."
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Cited by 471 (48 self)
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. Isabelle is now based on higherorder logic  a precise and wellunderstood foundation. Examples illustrate use of this metalogic to formalize logics and proofs. Axioms for firstorder logic are shown sound and complete. Backwards proof is formalized by metareasoning about objectlevel entailment
Firstorder Bayesball for CPlogic
 In SRL09
, 2009
"... Efficient probabilistic inference is key to the success of statistical relational learning. One issue that affects inference cost is the presence of irrelevant random variables. The Bayesball algorithm can identify such irrelevant variables in a propositional Bayesian network. This paper presents ..."
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Cited by 3 (1 self)
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presents a lifted version of Bayesball, which works directly on the firstorder level, and shows how this algorithm applies to CPlogic inference. 1.
Lifted firstorder belief propagation
 In Association for the Advancement of Artificial Intelligence (AAAI
, 2008
"... Unifying firstorder logic and probability is a longstanding goal of AI, and in recent years many representations combining aspects of the two have been proposed. However, inference in them is generally still at the level of propositional logic, creating all ground atoms and formulas and applying s ..."
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Cited by 115 (15 self)
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Unifying firstorder logic and probability is a longstanding goal of AI, and in recent years many representations combining aspects of the two have been proposed. However, inference in them is generally still at the level of propositional logic, creating all ground atoms and formulas and applying
Results 1  10
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13,950