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14,298
Image denoising by sparse 3D transformdomain collaborative filtering
 IEEE TRANS. IMAGE PROCESS
, 2007
"... We propose a novel image denoising strategy based on an enhanced sparse representation in transform domain. The enhancement of the sparsity is achieved by grouping similar 2D image fragments (e.g., blocks) into 3D data arrays which we call “groups.” Collaborative filtering is a special procedure d ..."
Abstract

Cited by 424 (32 self)
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different estimates which need to be combined. Aggregation is a particular averaging procedure which is exploited to take advantage of this redundancy. A significant improvement is obtained by a specially developed collaborative Wiener filtering. An algorithm based on this novel denoising strategy and its
Matching output queueing with a combined input output queued switch
 IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
, 1999
"... The Internet is facing two problems simultaneously: there is a need for a faster switching/routing infrastructure, and a need to introduce guaranteed qualities of service (QoS). Each problem can be solved independently: switches and routers can be made faster by using inputqueued crossbars, instead ..."
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Cited by 191 (21 self)
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. This paper demonstrates that a Combined Input Output Queueing (CIOQ) switch running twice as fast as an inputqueued switch can provide precise emulation of a broad class of packet scheduling algorithms, including WFQ and strict priorities. More precisely, we show that for an switch, a "
A Graduated Assignment Algorithm for Graph Matching
, 1996
"... A graduated assignment algorithm for graph matching is presented which is fast and accurate even in the presence of high noise. By combining graduated nonconvexity, twoway (assignment) constraints, and sparsity, large improvements in accuracy and speed are achieved. Its low order computational comp ..."
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Cited by 373 (15 self)
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A graduated assignment algorithm for graph matching is presented which is fast and accurate even in the presence of high noise. By combining graduated nonconvexity, twoway (assignment) constraints, and sparsity, large improvements in accuracy and speed are achieved. Its low order computational
How to Use Expert Advice
 JOURNAL OF THE ASSOCIATION FOR COMPUTING MACHINERY
, 1997
"... We analyze algorithms that predict a binary value by combining the predictions of several prediction strategies, called experts. Our analysis is for worstcase situations, i.e., we make no assumptions about the way the sequence of bits to be predicted is generated. We measure the performance of the ..."
Abstract

Cited by 377 (79 self)
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We analyze algorithms that predict a binary value by combining the predictions of several prediction strategies, called experts. Our analysis is for worstcase situations, i.e., we make no assumptions about the way the sequence of bits to be predicted is generated. We measure the performance
PopulationBased Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
, 1994
"... Genetic algorithms (GAs) are biologically motivated adaptive systems which have been used, with varying degrees of success, for function optimization. In this study, an abstraction of the basic genetic algorithm, the Equilibrium Genetic Algorithm (EGA), and the GA in turn, are reconsidered within th ..."
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Cited by 356 (12 self)
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the framework of competitive learning. This new perspective reveals a number of different possibilities for performance improvements. This paper explores populationbased incremental learning (PBIL), a method of combining the mechanisms of a generational genetic algorithm with simple competitive learning
Morphological grayscale reconstruction in image analysis: Applications and efficient algorithms
 IEEE Transactions on Image Processing
, 1993
"... Morphological reconstruction is part of a set of image operators often referred to as geodesic. In the binary case, reconstruction simply extracts the connected components of a binary image I (the mask) which are \marked " by a (binary) image J contained in I. This transformation can be ext ..."
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Cited by 336 (3 self)
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this situation, a new algorithm is introduced, which is based on the notion of regional maxima and makes use of breadthrst image scannings implemented via a queue of pixels. Its combination with the sequential technique results in a hybrid grayscale reconstruction algorithm which is an order of magnitude faster
Transactional Locking II
 In Proc. of the 20th Intl. Symp. on Distributed Computing
, 2006
"... Abstract. The transactional memory programming paradigm is gaining momentum as the approach of choice for replacing locks in concurrent programming. This paper introduces the transactional locking II (TL2) algorithm, a software transactional memory (STM) algorithm based on a combination of committi ..."
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Cited by 359 (17 self)
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Abstract. The transactional memory programming paradigm is gaining momentum as the approach of choice for replacing locks in concurrent programming. This paper introduces the transactional locking II (TL2) algorithm, a software transactional memory (STM) algorithm based on a combination of commit
Improving Elevator Performance Using Reinforcement Learning
 Advances in Neural Information Processing Systems 8
, 1996
"... This paper describes the application of reinforcement learning (RL) to the difficult real world problem of elevator dispatching. The elevator domain poses a combination of challenges not seen in most RL research to date. Elevator systems operate in continuous state spaces and in continuous time as d ..."
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Cited by 324 (5 self)
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This paper describes the application of reinforcement learning (RL) to the difficult real world problem of elevator dispatching. The elevator domain poses a combination of challenges not seen in most RL research to date. Elevator systems operate in continuous state spaces and in continuous time
Large scale multiple kernel learning
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2006
"... While classical kernelbased learning algorithms are based on a single kernel, in practice it is often desirable to use multiple kernels. Lanckriet et al. (2004) considered conic combinations of kernel matrices for classification, leading to a convex quadratically constrained quadratic program. We s ..."
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Cited by 340 (20 self)
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show that the proposed algorithm works for hundred thousands of examples or hundreds of kernels to be combined, and helps for automatic model selection, improving the interpretability of the learning result. In a second part we discuss general speed up mechanism for SVMs, especially when used
A Practical Scheduling Algorithm to Achieve 100% Throughput in InputQueued Switches.
"... Input queueing is becoming increasingly used for highbandwidth switches and routers. In previous work, it was proved that it is possible to achieve 100 % throughput for inputqueued switches using a combination of virtual output queueing and a scheduling algorithm called LQF. However, this is only a ..."
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Cited by 126 (7 self)
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Input queueing is becoming increasingly used for highbandwidth switches and routers. In previous work, it was proved that it is possible to achieve 100 % throughput for inputqueued switches using a combination of virtual output queueing and a scheduling algorithm called LQF. However, this is only
Results 21  30
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