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2,261
Limits on superresolution and how to break them
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2002
"... AbstractÐNearly all superresolution algorithms are based on the fundamental constraints that the superresolution image should generate the low resolution input images when appropriately warped and downsampled to model the image formation process. �These reconstruction constraints are normally com ..."
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Cited by 421 (7 self)
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AbstractÐNearly all superresolution algorithms are based on the fundamental constraints that the superresolution image should generate the low resolution input images when appropriately warped and downsampled to model the image formation process. �These reconstruction constraints are normally
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 ..."
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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
Convergence of Interior Point Algorithms for the Monotone Linear Complementarity Problem
, 1994
"... The literature on interior point algorithms shows impressive results related to the speed of convergence of the objective values, but very little is known about the convergence of the iterate sequences. This paper studies the horizontal linear complementarity problem, and derives general convergence ..."
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Cited by 24 (4 self)
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convergence properties for algorithms based on Newton iterations. This problem provides a simple and general framework for most existing primaldual interior point methods. The conclusion is that most of the published algorithms of this kind generate convergent sequences. In many cases (whenever
Computation of channel capacity and ratedistortion functions
 IEEE Trans. Inform. Theory
, 1972
"... A&r&By defining mutual information as a maximum over an appropriate space, channel capacities can be defined as double maxima and ratedistortion functions as double minima. This approach yields valuable new insights regarding the computation of channel capacities and ratedistortion functi ..."
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Cited by 280 (1 self)
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distortion functions. In particular, it suggests a simple algorithm for computing channel capacity that consists of a mapping from the set of channel input probability vectors into itself such that the sequence of probability vectors generated by successive applications of the mapping converges to the vector
A Rigorous Framework for Optimization of Expensive Functions by Surrogates
, 1998
"... The goal of the research reported here is to develop rigorous optimization algorithms to apply to some engineering design problems for which direct application of traditional optimization approaches is not practical. This paper presents and analyzes a framework for generating a sequence of approxima ..."
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Cited by 204 (15 self)
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The goal of the research reported here is to develop rigorous optimization algorithms to apply to some engineering design problems for which direct application of traditional optimization approaches is not practical. This paper presents and analyzes a framework for generating a sequence
Mining sequential patterns by patterngrowth: The PrefixSpan approach
 IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
, 2004
"... Sequential pattern mining is an important data mining problem with broad applications. However, it is also a difficult problem since the mining may have to generate or examine a combinatorially explosive number of intermediate subsequences. Most of the previously developed sequential pattern mining ..."
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Cited by 206 (10 self)
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mining methods, such as GSP, explore a candidate generationandtest approach [1] to reduce the number of candidates to be examined. However, this approach may not be efficient in mining large sequence databases having numerous patterns and/or long patterns. In this paper, we propose a projection
Coil sensitivity encoding for fast MRI. In:
 Proceedings of the ISMRM 6th Annual Meeting,
, 1998
"... New theoretical and practical concepts are presented for considerably enhancing the performance of magnetic resonance imaging (MRI) by means of arrays of multiple receiver coils. Sensitivity encoding (SENSE) is based on the fact that receiver sensitivity generally has an encoding effect complementa ..."
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Cited by 193 (3 self)
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position. That is, knowledge of spatial receiver sensitivity implies information about the origin of detected MR signals, which may be utilized for image generation. Unlike position in kspace, sensitivity is a receiver property and does not refer to the state of the object under examination. Therefore
NESTA: A Fast and Accurate FirstOrder Method for Sparse Recovery
, 2009
"... Accurate signal recovery or image reconstruction from indirect and possibly undersampled data is a topic of considerable interest; for example, the literature in the recent field of compressed sensing is already quite immense. Inspired by recent breakthroughs in the development of novel firstorder ..."
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Cited by 171 (2 self)
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order methods in convex optimization, most notably Nesterov’s smoothing technique, this paper introduces a fast and accurate algorithm for solving common recovery problems in signal processing. In the spirit of Nesterov’s work, one of the key ideas of this algorithm is a subtle averaging of sequences
Monte Carlo smoothing for nonlinear time series
 JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
, 2004
"... We develop methods for performing smoothing computations in general statespace models. The methods rely on a particle representation of the filtering distributions, and their evolution through time using sequential importance sampling and resampling ideas. In particular, novel techniques are pr ..."
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Cited by 153 (16 self)
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are presented for generation of sample realizations of historical state sequences. This is carried out in a forwardfiltering backwardsmoothing procedure which can be viewed as the nonlinear, nonGaussian counterpart of standard Kalman filterbased simulation smoothers in the linear Gaussian case
Modeling human motion using binary latent variables
 Advances in Neural Information Processing Systems
, 2006
"... We propose a nonlinear generative model for human motion data that uses an undirected model with binary latent variables and realvalued “visible ” variables that represent joint angles. The latent and visible variables at each time step receive directed connections from the visible variables at th ..."
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Cited by 151 (20 self)
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We propose a nonlinear generative model for human motion data that uses an undirected model with binary latent variables and realvalued “visible ” variables that represent joint angles. The latent and visible variables at each time step receive directed connections from the visible variables
Results 1  10
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2,261