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The Generic Approximation Lemma
 Information Processing Letters
, 2001
"... The approximation lemma is a simplification of the wellknown take lemma, and is used to prove properties of programs that produce lists of values. We show how the approximation lemma, unlike the take lemma, can naturally be generalised from lists to a large class of datatypes, and present a gen ..."
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Cited by 17 (3 self)
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generic approximation lemma that is parametric in the datatype to which it applies. As a useful byproduct, we find that generalising the approximation lemma in this way also simplifies its proof. Keywords: Programming calculi; Functional Programming 1 Introduction The standard proof method
Tapestry: A Resilient Globalscale Overlay for Service Deployment
 IEEE Journal on Selected Areas in Communications
, 2004
"... We present Tapestry, a peertopeer overlay routing infrastructure offering efficient, scalable, locationindependent routing of messages directly to nearby copies of an object or service using only localized resources. Tapestry supports a generic Decentralized Object Location and Routing (DOLR) API ..."
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Cited by 598 (14 self)
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We present Tapestry, a peertopeer overlay routing infrastructure offering efficient, scalable, locationindependent routing of messages directly to nearby copies of an object or service using only localized resources. Tapestry supports a generic Decentralized Object Location and Routing (DOLR) API
Robust convex optimization
 Mathematics of Operations Research
, 1998
"... We study convex optimization problems for which the data is not specified exactly and it is only known to belong to a given uncertainty set U, yet the constraints must hold for all possible values of the data from U. The ensuing optimization problem is called robust optimization. In this paper we la ..."
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Cited by 416 (21 self)
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lay the foundation of robust convex optimization. In the main part of the paper we show that if U is an ellipsoidal uncertainty set, then for some of the most important generic convex optimization problems (linear programming, quadratically constrained programming, semidefinite programming and others
Adaptive Duplicate Detection Using Learnable String Similarity Measures
 In Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD2003
, 2003
"... The problem of identifying approximately duplicate records in databases is an essential step for data cleaning and data integration processes. Most existing approaches have relied on generic or manually tuned distance metrics for estimating the similarity of potential duplicates. In this paper, we p ..."
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Cited by 344 (14 self)
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The problem of identifying approximately duplicate records in databases is an essential step for data cleaning and data integration processes. Most existing approaches have relied on generic or manually tuned distance metrics for estimating the similarity of potential duplicates. In this paper, we
Szemerédi's Regularity Lemma and Its Applications in Graph Theory
, 1996
"... Szemerédi's Regularity Lemma is an important tool in discrete mathematics. It says that, in some sense, all graphs can be approximated by randomlooking graphs. Therefore the lemma helps in proving theorems for arbitrary graphs whenever the corresponding result is easy for random graphs. Recent ..."
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Cited by 257 (3 self)
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Szemerédi's Regularity Lemma is an important tool in discrete mathematics. It says that, in some sense, all graphs can be approximated by randomlooking graphs. Therefore the lemma helps in proving theorems for arbitrary graphs whenever the corresponding result is easy for random graphs
Sharing Features: Efficient Boosting Procedures for Multiclass Object Detection
 IN CVPR
, 2004
"... We consider the problem of detecting a large number of different object classes in cluttered scenes. Traditional approaches require applying a battery of different classifiers to the image, which can be slow and require much training data. We present a multiclass boosting procedure (joint boosting) ..."
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Cited by 309 (16 self)
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) that reduces both the computational and sample complexity, by finding common features that can be shared across the classes. The detectors for each class are trained jointly, rather than independently. For a given performance level, the total number of features required is observed to scale approximately
Fields of experts: A framework for learning image priors
 In CVPR
, 2005
"... We develop a framework for learning generic, expressive image priors that capture the statistics of natural scenes and can be used for a variety of machine vision tasks. The approach extends traditional Markov Random Field (MRF) models by learning potential functions over extended pixel neighborhood ..."
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Cited by 292 (4 self)
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of this Field of Experts model with two example applications, image denoising and image inpainting, which are implemented using a simple, approximate inference scheme. While the model is trained on a generic image database and is not tuned toward a specific application, we obtain results that compete
An elementary proof of the JohnsonLindenstrauss Lemma
, 1999
"... The JohnsonLindenstrauss lemma shows that a set of n points in high dimensional Euclidean space can be mapped down into an O(log n=ffl 2 ) dimensional Euclidean space such that the distance between any two points changes by only a factor of (1 \Sigma ffl). In this note, we prove this lemma using ..."
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Cited by 152 (1 self)
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The JohnsonLindenstrauss lemma shows that a set of n points in high dimensional Euclidean space can be mapped down into an O(log n=ffl 2 ) dimensional Euclidean space such that the distance between any two points changes by only a factor of (1 \Sigma ffl). In this note, we prove this lemma using
Quick Approximation to Matrices and Applications
, 1998
"... We give algorithms to find the following simply described approximation to a given matrix. Given an m \Theta n matrix A with entries between say1 and 1, and an error parameter ffl between 0 and 1, we find a matrix D (implicitly) which is the sum of O(1=ffl 2 ) simple rank 1 matrices so that the ..."
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Cited by 145 (7 self)
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Regularity Lemma of Szemerédi in Graph Theory and the constructive version of Alon, Duke, Leffman, Rödl and Yuster. The second one is from the papers of Arora, Karger and Karpinski, Fernandez de la Vega and most directly Goldwasser, Goldreich and Ron who develop approximation algorithms for a set of graph
ON BLOCH’S APPROXIMATION LEMMA
, 711
"... Abstract. In this note we generalize Bloch’s approximation lemma. We prove that for a smooth arithmetic scheme with a given finite set of closed points we can find a curve on the arithmetic scheme which contains these points as regular points. ..."
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Abstract. In this note we generalize Bloch’s approximation lemma. We prove that for a smooth arithmetic scheme with a given finite set of closed points we can find a curve on the arithmetic scheme which contains these points as regular points.
Results 1  10
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