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13,521
Easy Problems are
 Sometimes Hard, Artificial Intelligence
, 1994
"... An investigation of introductory physics students ’ approaches to ..."
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Cited by 3 (0 self)
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An investigation of introductory physics students ’ approaches to
Valued constraint satisfaction problems: Hard and easy problems
 IJCAI’95: PROCEEDINGS INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE
, 1995
"... In order to deal with overconstrained Constraint Satisfaction Problems, various extensions of the CSP framework have been considered by taking into account costs, uncertainties, preferences, priorities...Each extension uses a specific mathematical operator (+, max...) to aggregate constraint violat ..."
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Cited by 331 (42 self)
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In order to deal with overconstrained Constraint Satisfaction Problems, various extensions of the CSP framework have been considered by taking into account costs, uncertainties, preferences, priorities...Each extension uses a specific mathematical operator (+, max...) to aggregate constraint
Easy Problems are Sometimes Hard
 Artificial Intelligence
, 1994
"... We present a detailed experimental investigation of the easyhardeasy phase transition for randomly generated instances of satisfiability problems. Problems in the hard part of the phase transition have been extensively used for benchmarking satisfiability algorithms. This study demonstrates that p ..."
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Cited by 87 (20 self)
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We present a detailed experimental investigation of the easyhardeasy phase transition for randomly generated instances of satisfiability problems. Problems in the hard part of the phase transition have been extensively used for benchmarking satisfiability algorithms. This study demonstrates
The anatomy of easy problems: a constraintsatisfaction formulation
 Proceedings ofIJCAl85
, 1985
"... This work aims towards the automatic generation of advice to guide the solution of difficult constraintsatisfaction problems (CSPs). The advice is generated by consulting relaxed, easy models which are backtrackfree. We identify a subset of CSPs whose syntactic and semantic properties make them easy ..."
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Cited by 5 (2 self)
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This work aims towards the automatic generation of advice to guide the solution of difficult constraintsatisfaction problems (CSPs). The advice is generated by consulting relaxed, easy models which are backtrackfree. We identify a subset of CSPs whose syntactic and semantic properties make them
Where the REALLY Hard Problems Are
 IN J. MYLOPOULOS AND R. REITER (EDS.), PROCEEDINGS OF 12TH INTERNATIONAL JOINT CONFERENCE ON AI (IJCAI91),VOLUME 1
, 1991
"... It is well known that for many NPcomplete problems, such as KSat, etc., typical cases are easy to solve; so that computationally hard cases must be rare (assuming P != NP). This paper shows that NPcomplete problems can be summarized by at least one "order parameter", and that the hard p ..."
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Cited by 683 (1 self)
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It is well known that for many NPcomplete problems, such as KSat, etc., typical cases are easy to solve; so that computationally hard cases must be rare (assuming P != NP). This paper shows that NPcomplete problems can be summarized by at least one "order parameter", and that the hard
Evolving Robot Consciousness: The Easy Problems and the Rest
"... Car manufacturers need robots that reliably and mindlessly repeat sequences of actions in a wellorganised environment. For many other purposes autonomous robots are needed that will behave appropriately in a disorganised environment, that will react adaptively when faced with circumstances that ..."
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Cited by 5 (0 self)
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Car manufacturers need robots that reliably and mindlessly repeat sequences of actions in a wellorganised environment. For many other purposes autonomous robots are needed that will behave appropriately in a disorganised environment, that will react adaptively when faced with circumstances that
A Singular Value Thresholding Algorithm for Matrix Completion
, 2008
"... This paper introduces a novel algorithm to approximate the matrix with minimum nuclear norm among all matrices obeying a set of convex constraints. This problem may be understood as the convex relaxation of a rank minimization problem, and arises in many important applications as in the task of reco ..."
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Cited by 555 (22 self)
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of recovering a large matrix from a small subset of its entries (the famous Netflix problem). Offtheshelf algorithms such as interior point methods are not directly amenable to large problems of this kind with over a million unknown entries. This paper develops a simple firstorder and easy
A View Of The Em Algorithm That Justifies Incremental, Sparse, And Other Variants
 Learning in Graphical Models
, 1998
"... . The EM algorithm performs maximum likelihood estimation for data in which some variables are unobserved. We present a function that resembles negative free energy and show that the M step maximizes this function with respect to the model parameters and the E step maximizes it with respect to the d ..."
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Cited by 993 (18 self)
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to the distribution over the unobserved variables. From this perspective, it is easy to justify an incremental variant of the EM algorithm in which the distribution for only one of the unobserved variables is recalculated in each E step. This variant is shown empirically to give faster convergence in a mixture
Localitysensitive hashing scheme based on pstable distributions
 In SCG ’04: Proceedings of the twentieth annual symposium on Computational geometry
, 2004
"... inÇÐÓ�Ò We present a novel LocalitySensitive Hashing scheme for the Approximate Nearest Neighbor Problem underÐÔnorm, based onÔstable distributions. Our scheme improves the running time of the earlier algorithm for the case of theÐnorm. It also yields the first known provably efficient approximate ..."
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Cited by 521 (8 self)
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inÇÐÓ�Ò We present a novel LocalitySensitive Hashing scheme for the Approximate Nearest Neighbor Problem underÐÔnorm, based onÔstable distributions. Our scheme improves the running time of the earlier algorithm for the case of theÐnorm. It also yields the first known provably efficient approximate
Results 1  10
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13,521