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842,561
Extending and Implementing the Stable Model Semantics
, 2002
"... A novel logic program like language, weight constraint rules, is developed for answer set programming purposes. It generalizes normal logic programs by allowing weight constraints in place of literals to represent, e.g., cardinality and resource constraints and by providing optimization capabilities ..."
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Cited by 396 (9 self)
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A novel logic program like language, weight constraint rules, is developed for answer set programming purposes. It generalizes normal logic programs by allowing weight constraints in place of literals to represent, e.g., cardinality and resource constraints and by providing optimization
Greedy layerwise training of deep networks
, 2006
"... Complexity theory of circuits strongly suggests that deep architectures can be much more efficient (sometimes exponentially) than shallow architectures, in terms of computational elements required to represent some functions. Deep multilayer neural networks have many levels of nonlinearities allow ..."
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Cited by 394 (48 self)
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introduced a greedy layerwise unsupervised learning algorithm for Deep Belief Networks (DBN), a generative model with many layers of hidden causal variables. In the context of the above optimization problem, we study this algorithm empirically and explore variants to better understand its success
Sparse signal reconstruction from limited data using FOCUSS: A reweighted minimum norm algorithm
 IEEE TRANS. SIGNAL PROCESSING
, 1997
"... We present a nonparametric algorithm for finding localized energy solutions from limited data. The problem we address is underdetermined, and no prior knowledge of the shape of the region on which the solution is nonzero is assumed. Termed the FOcal Underdetermined System Solver (FOCUSS), the algor ..."
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Cited by 368 (22 self)
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), the algorithm has two integral parts: a lowresolution initial estimate of the real signal and the iteration process that refines the initial estimate to the final localized energy solution. The iterations are based on weighted norm minimization of the dependent variable with the weights being a function
Determinants of longterm growth: a Bayesian Averaging of Classical Estimates (BACE) approach
, 2003
"... This paper examines the robustness and joint interaction of explanatory variables in crosscountry economic growth regressions. It employs a novel approach, Bayesian Averaging of Classical Estimates (BACE), which constructs estimates as a weighted average of OLS estimates for every possible combina ..."
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Cited by 374 (3 self)
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This paper examines the robustness and joint interaction of explanatory variables in crosscountry economic growth regressions. It employs a novel approach, Bayesian Averaging of Classical Estimates (BACE), which constructs estimates as a weighted average of OLS estimates for every possible
Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure
, 2004
"... This paper presents a new approach to estimation and inference in panel data models with a multifactor error structure where the unobserved common factors are (possibly) correlated with exogenously given individualspecific regressors, and the factor loadings differ over the cross section units. The ..."
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Cited by 383 (44 self)
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. The estimation procedure has the advantage that it can be computed by OLS applied to an auxiliary regression where the observed regressors are augmented by (weighted) cross sectional averages of the dependent variable and the individual specific regressors. Two different but related problems are addressed: one
Political conservatism as motivated social cognition
 Psychological Bulletin
, 2003
"... Analyzing political conservatism as motivated social cognition integrates theories of personality (authoritarianism, dogmatism–intolerance of ambiguity), epistemic and existential needs (for closure, regulatory focus, terror management), and ideological rationalization (social dominance, system just ..."
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Cited by 333 (39 self)
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justification). A metaanalysis (88 samples, 12 countries, 22,818 cases) confirms that several psychological variables predict political conservatism: death anxiety (weighted mean r �.50); system instability (.47); dogmatism–intolerance of ambiguity (.34); openness to experience (–.32); uncertainty tolerance
Schemas
"... The following full text is a publisher's version. For additional information about this publication click this link. ..."
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The following full text is a publisher's version. For additional information about this publication click this link.
Timevarying NAIRU and its implications for Economic Policy
 NBER WORKING PAPER
, 1996
"... This paper estimates the NAIRU (standing for the nonaccelerating Inflation Rate of unemployment) as a parameter that varies over time. The NAIRU is the unemployment rate that is consistent with a constant rate of inflation. Its value is determined in an econometric model in which the inflation rate ..."
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Cited by 320 (4 self)
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rate depends on its own past values (“inertia”), demand shocks proxied by the difference between the actual unemployment rate and the estimated NAIRU, and a set of supply shock variables. The estimated NAIRU for the U.S. economy differs somewhat for alternative measures of the inflation rate. The NAIRU
Comparing inertia weights and constriction factors in particle swarm optimization
 IN PROC. IEEE CONG. EVOL. COMPUT., LA JOLLA, CA
, 2000
"... The performance of particle swarm optimization using an inertia weight is compared with performance using a constriction factor. Five benchmark functions are used for the comparison. It is concluded that the best approach is to use the constriction factor while limiting the maximum velocity Vmax to ..."
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Cited by 260 (4 self)
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The performance of particle swarm optimization using an inertia weight is compared with performance using a constriction factor. Five benchmark functions are used for the comparison. It is concluded that the best approach is to use the constriction factor while limiting the maximum velocity Vmax
The robust beauty of improper linear models in decision making
 American Psychologist
, 1979
"... ABSTRACT: Proper linear models are those in which predictor variables are given weights in such a way that the resulting linear composite optimally predicts some criterion of interest; examples of proper linear models are standard regression analysis, discriminant function analysis, and ridge regres ..."
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Cited by 267 (1 self)
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ABSTRACT: Proper linear models are those in which predictor variables are given weights in such a way that the resulting linear composite optimally predicts some criterion of interest; examples of proper linear models are standard regression analysis, discriminant function analysis, and ridge
Results 11  20
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842,561