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A MajorizeMinimize Memory Gradient Method for ComplexValued Inverse Problems
, 2013
"... Complexvalued data are encountered in many application areas of signalandimageprocessing. Inthecontextofoptimizationoffunctions of real variables, subspace algorithms have recently attracted much interest, owing to their efficiency for solving largesize problems while simultaneously offering theor ..."
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theoretical convergence guarantees. The goal of this paper is to show how some of these methods can be successfully extended to the complex case. More precisely, we investigate the properties of the proposed complexvalued MajorizeMinimize Memory Gradient (3MG) algorithm. Important practical applications
The nas parallel benchmarks
 The International Journal of Supercomputer Applications
, 1991
"... A new set of benchmarks has been developed for the performance evaluation of highly parallel supercomputers. These benchmarks consist of ve \parallel kernel " benchmarks and three \simulated application" benchmarks. Together they mimic the computation and data movement characterist ..."
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Cited by 686 (10 self)
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A new set of benchmarks has been developed for the performance evaluation of highly parallel supercomputers. These benchmarks consist of ve \parallel kernel " benchmarks and three \simulated application" benchmarks. Together they mimic the computation and data movement
A theory of memory retrieval
 PSYCHOL. REV
, 1978
"... A theory of memory retrieval is developed and is shown to apply over a range of experimental paradigms. Access to memory traces is viewed in terms of a resonance metaphor. The probe item evokes the search set on the basis of probememory item relatedness, just as a ringing tuning fork evokes sympath ..."
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Cited by 728 (81 self)
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sympathetic vibrations in other tuning forks. Evidence is accumulated in parallel from each probememory item comparison, and each comparison is modeled by a continuous random walk process. In item recognition, the decision process is selfterminating on matching comparisons and exhaustive on nonmatching
Greedy Function Approximation: A Gradient Boosting Machine
 Annals of Statistics
, 2000
"... Function approximation is viewed from the perspective of numerical optimization in function space, rather than parameter space. A connection is made between stagewise additive expansions and steepest{descent minimization. A general gradient{descent \boosting" paradigm is developed for additi ..."
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Cited by 951 (12 self)
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Function approximation is viewed from the perspective of numerical optimization in function space, rather than parameter space. A connection is made between stagewise additive expansions and steepest{descent minimization. A general gradient{descent \boosting" paradigm is developed
Learning to rank using gradient descent
 In ICML
, 2005
"... We investigate using gradient descent methods for learning ranking functions; we propose a simple probabilistic cost function, and we introduce RankNet, an implementation of these ideas using a neural network to model the underlying ranking function. We present test results on toy data and on data f ..."
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Cited by 510 (17 self)
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We investigate using gradient descent methods for learning ranking functions; we propose a simple probabilistic cost function, and we introduce RankNet, an implementation of these ideas using a neural network to model the underlying ranking function. We present test results on toy data and on data
Histograms of Oriented Gradients for Human Detection
 In CVPR
, 2005
"... We study the question of feature sets for robust visual object recognition, adopting linear SVM based human detection as a test case. After reviewing existing edge and gradient based descriptors, we show experimentally that grids of Histograms of Oriented Gradient (HOG) descriptors significantly out ..."
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Cited by 3678 (9 self)
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We study the question of feature sets for robust visual object recognition, adopting linear SVM based human detection as a test case. After reviewing existing edge and gradient based descriptors, we show experimentally that grids of Histograms of Oriented Gradient (HOG) descriptors significantly
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 622 (6 self)
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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
Treadmarks: Shared memory computing on networks of workstations
 Computer
, 1996
"... TreadMarks supports parallel computing on networks of workstations by providing the application with a shared memory abstraction. Shared memory facilitates the transition from sequential to parallel programs. After identifying possible sources of parallelism in the code, most of the data structures ..."
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Cited by 484 (37 self)
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TreadMarks supports parallel computing on networks of workstations by providing the application with a shared memory abstraction. Shared memory facilitates the transition from sequential to parallel programs. After identifying possible sources of parallelism in the code, most of the data structures
UNet: A UserLevel Network Interface for Parallel and Distributed Computing
 In Fifteenth ACM Symposium on Operating System Principles
, 1995
"... The UNet communication architecture provides processes with a virtual view of a network interface to enable userlevel access to highspeed communication devices. The architecture, implemented on standard workstations using offtheshelf ATM communication hardware, removes the kernel from the communi ..."
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Cited by 596 (17 self)
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The UNet communication architecture provides processes with a virtual view of a network interface to enable userlevel access to highspeed communication devices. The architecture, implemented on standard workstations using offtheshelf ATM communication hardware, removes the kernel from
Dryad: Distributed DataParallel Programs from Sequential Building Blocks
 In EuroSys
, 2007
"... Dryad is a generalpurpose distributed execution engine for coarsegrain dataparallel applications. A Dryad application combines computational “vertices ” with communication “channels ” to form a dataflow graph. Dryad runs the application by executing the vertices of this graph on a set of availa ..."
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Cited by 730 (27 self)
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Dryad is a generalpurpose distributed execution engine for coarsegrain dataparallel applications. A Dryad application combines computational “vertices ” with communication “channels ” to form a dataflow graph. Dryad runs the application by executing the vertices of this graph on a set
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