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Fast Parallel Algorithms for ShortRange Molecular Dynamics
 JOURNAL OF COMPUTATIONAL PHYSICS
, 1995
"... Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of interatomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dyn ..."
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Cited by 653 (7 self)
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. The algorithms are tested on a standard LennardJones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers  the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray YMP and C90 algorithm shows
Benchmarking Least Squares Support Vector Machine Classifiers
 NEURAL PROCESSING LETTERS
, 2001
"... In Support Vector Machines (SVMs), the solution of the classification problem is characterized by a (convex) quadratic programming (QP) problem. In a modified version of SVMs, called Least Squares SVM classifiers (LSSVMs), a least squares cost function is proposed so as to obtain a linear set of eq ..."
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Cited by 476 (46 self)
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In Support Vector Machines (SVMs), the solution of the classification problem is characterized by a (convex) quadratic programming (QP) problem. In a modified version of SVMs, called Least Squares SVM classifiers (LSSVMs), a least squares cost function is proposed so as to obtain a linear set
Efficient belief propagation for early vision
 In CVPR
, 2004
"... Markov random field models provide a robust and unified framework for early vision problems such as stereo, optical flow and image restoration. Inference algorithms based on graph cuts and belief propagation yield accurate results, but despite recent advances are often still too slow for practical u ..."
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Cited by 515 (8 self)
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the standard algorithm by several orders of magnitude. In practice we obtain stereo, optical flow and image restoration algorithms that are as accurate as other global methods (e.g., using the Middlebury stereo benchmark) while being as fast as local techniques. 1
A scaled conjugate gradient algorithm for fast supervised learning
 NEURAL NETWORKS
, 1993
"... A supervised learning algorithm (Scaled Conjugate Gradient, SCG) with superlinear convergence rate is introduced. The algorithm is based upon a class of optimization techniques well known in numerical analysis as the Conjugate Gradient Methods. SCG uses second order information from the neural netwo ..."
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Cited by 451 (0 self)
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network but requires only O(N) memory usage, where N is the number of weights in the network. The performance of SCG is benchmarked against the performance of the standard backpropagation algorithm (BP) [13], the conjugate gradient backpropagation (CGB) [6] and the onestep Broyden
An infeasible point method for minimizing the LennardJones potential
 Computational Optimization and Applications
, 1996
"... Minimizing the LennardJones potential, the moststudied model problem for molecular conformation, is an unconstrained global optimization problem with a large number of local minima. In this paper, the problem is reformulated as an equality constrained nonlinear programming problem with only linear ..."
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Cited by 4 (1 self)
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Minimizing the LennardJones potential, the moststudied model problem for molecular conformation, is an unconstrained global optimization problem with a large number of local minima. In this paper, the problem is reformulated as an equality constrained nonlinear programming problem with only
The Determinants of Credit Spread Changes.
 Journal of Finance
, 2001
"... ABSTRACT Using dealer's quotes and transactions prices on straight industrial bonds, we investigate the determinants of credit spread changes. Variables that should in theory determine credit spread changes have rather limited explanatory power. Further, the residuals from this regression are ..."
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Cited by 422 (2 self)
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changes are principally driven by local supply/demand shocks that are independent of both creditrisk factors and standard proxies for liquidity. * CollinDufresne is at Carnegie Mellon University. Goldstein is at Washington University in St. Louis. Martin is at Arizona State University. A significant
Prices and unit labor costs: A new test of price stickiness
, 1999
"... This paper investigates the predictions of a simple optimizing model of nominal price rigidity for the aggregate price level and the dynamics of inflation. I compare the model’s predictions with those of a perfectly competitive, flexible price ‘benchmark’ model (corresponding to the model of pricing ..."
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Cited by 356 (11 self)
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of pricing assumed in standard real business cycle models), and evaluate how much the introduction of nominal rigidities improves the model’s fit with the data. The model’s predictions are derived using only the firms optimal pricing problem; taking as given the paths of nominal labor compensation, labor
Basic Block Distribution Analysis to Find Periodic Behavior and Simulation Points in Applications
, 2001
"... Modern architecture research relies heavily on detailed pipeline simulation. Simulating the full execution of an industry standard benchmark can take weeks to months to complete. To overcome this problem researchers choose a very small portion of a program's execution to evaluate their results, ..."
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Cited by 315 (31 self)
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Modern architecture research relies heavily on detailed pipeline simulation. Simulating the full execution of an industry standard benchmark can take weeks to months to complete. To overcome this problem researchers choose a very small portion of a program's execution to evaluate their results
An empirical study of learning speed in backpropagation networks
, 1988
"... Most connectionist or "neural network" learning systems use some form of the backpropagation algorithm. However, backpropagation learning is too slow for many applications, and it scales up poorly as tasks become larger and more complex. The factors governing learning speed are poorly un ..."
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Cited by 278 (0 self)
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of this kind. This paper is a progress report describing the results obtained during the first six months of this study. To date I have looked only at a limited set of benchmark problems, but the results on these are encouraging: I have developed a new learning algorithm that is faster than standard backprop
Forecasting the term structure of government bond yields
 Journal of Econometrics
, 2006
"... Despite powerful advances in yield curve modeling in the last twenty years, comparatively little attention has been paid to the key practical problem of forecasting the yield curve. In this paper we do so. We use neither the noarbitrage approach, which focuses on accurately fitting the cross sectio ..."
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Cited by 287 (16 self)
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short and long horizons, with encouraging results. In particular, our forecasts appear much more accurate at long horizons than various standard benchmark forecasts. Finally, we discuss a number of extensions, including generalized duration measures, applications to active bond portfolio management
Results 1  10
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