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3,175
Randomized Gossip Algorithms
 IEEE TRANSACTIONS ON INFORMATION THEORY
, 2006
"... Motivated by applications to sensor, peertopeer, and ad hoc networks, we study distributed algorithms, also known as gossip algorithms, for exchanging information and for computing in an arbitrarily connected network of nodes. The topology of such networks changes continuously as new nodes join a ..."
Abstract

Cited by 532 (5 self)
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distribute the computational burden and in which a node communicates with a randomly chosen neighbor. We analyze the averaging problem under the gossip constraint for an arbitrary network graph, and find that the averaging time of a gossip algorithm depends on the second largest eigenvalue of a doubly
Random Key Predistribution Schemes for Sensor Networks”,
 IEEE Symposium on Security and Privacy,
, 2003
"... Abstract Efficient key distribution is the basis for providing secure communication, a necessary requirement for many emerging sensor network applications. Many applications require authentic and secret communication among neighboring sensor nodes. However, establishing keys for secure communicatio ..."
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Cited by 832 (12 self)
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before deployment. In the field, neighboring nodes exchange information to find one common key within their random subset and use that key as their shared secret to secure subsequent communication. In this paper, we generalize the EschenauerGligor key distribution approach. First, we propose two new
Fast approximate nearest neighbors with automatic algorithm configuration
 In VISAPP International Conference on Computer Vision Theory and Applications
, 2009
"... nearestneighbors search, randomized kdtrees, hierarchical kmeans tree, clustering. For many computer vision problems, the most time consuming component consists of nearest neighbor matching in highdimensional spaces. There are no known exact algorithms for solving these highdimensional problems ..."
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Cited by 455 (2 self)
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nearestneighbors search, randomized kdtrees, hierarchical kmeans tree, clustering. For many computer vision problems, the most time consuming component consists of nearest neighbor matching in highdimensional spaces. There are no known exact algorithms for solving these high
Segmentation of brain MR images through a hidden Markov random field model and the expectationmaximization algorithm
 IEEE TRANSACTIONS ON MEDICAL. IMAGING
, 2001
"... The finite mixture (FM) model is the most commonly used model for statistical segmentation of brain magnetic resonance (MR) images because of its simple mathematical form and the piecewise constant nature of ideal brain MR images. However, being a histogrambased model, the FM has an intrinsic limi ..."
Abstract

Cited by 639 (15 self)
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based methods produce unreliable results. In this paper, we propose a novel hidden Markov random field (HMRF) model, which is a stochastic process generated by a MRF whose state sequence cannot be observed directly but which can be indirectly estimated through observations. Mathematically, it can be shown
The capacity of wireless networks
 IEEE TRANSACTIONS ON INFORMATION THEORY
, 2000
"... When n identical randomly located nodes, each capable of transmitting at bits per second and using a fixed range, form a wireless network, the throughput @ A obtainable by each node for a randomly chosen destination is 2 bits per second under a noninterference protocol. If the nodes are optimally p ..."
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Cited by 3243 (42 self)
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When n identical randomly located nodes, each capable of transmitting at bits per second and using a fixed range, form a wireless network, the throughput @ A obtainable by each node for a randomly chosen destination is 2 bits per second under a noninterference protocol. If the nodes are optimally
Experiments with a New Boosting Algorithm
, 1996
"... In an earlier paper, we introduced a new “boosting” algorithm called AdaBoost which, theoretically, can be used to significantly reduce the error of any learning algorithm that consistently generates classifiers whose performance is a little better than random guessing. We also introduced the relate ..."
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Cited by 2213 (20 self)
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In an earlier paper, we introduced a new “boosting” algorithm called AdaBoost which, theoretically, can be used to significantly reduce the error of any learning algorithm that consistently generates classifiers whose performance is a little better than random guessing. We also introduced
Loopy belief propagation for approximate inference: An empirical study. In:
 Proceedings of Uncertainty in AI,
, 1999
"... Abstract Recently, researchers have demonstrated that "loopy belief propagation" the use of Pearl's polytree algorithm in a Bayesian network with loops can perform well in the context of errorcorrecting codes. The most dramatic instance of this is the near Shannonlimit performanc ..."
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Cited by 676 (15 self)
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modification to the update rules in that we normalized both ..\ and 1r messages at each iteration. As Pearl Nodes were updated in parallel: at each iteration all nodes calculated their outgoing messages based on the incoming messages of their neighbors from the pre vious iteration. The messages were said
Routing indices for peertopeer systems
, 2002
"... Finding information in a peertopeer system currently requires either a costly and vulnerable central index, or ooding the network with queries. In this paper we introduce the concept of Routing Indices (RIs), which allow nodes to forward queries to neighbors that are more likely to have answers. I ..."
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Cited by 423 (15 self)
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. If a node cannot answer a query, it forwards the query to a subset of its neighbors, based on its local RI, rather than by selecting neighbors at random or by ooding the network by forwarding the query to all neighbors. We present three RI schemes: the compound, the hopcount, and the exponential
Robust Positioning Algorithms for Distributed AdHoc Wireless Sensor Networks
, 2002
"... A distributed algorithm for determining the positions of nodes in an adhoc, wireless sensor network is explained in detail. Details regarding the implementation of such an algorithm are also discussed. Experimentation is performed on networks containing 400 nodes randomly placed within a square are ..."
Abstract

Cited by 383 (9 self)
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A distributed algorithm for determining the positions of nodes in an adhoc, wireless sensor network is explained in detail. Details regarding the implementation of such an algorithm are also discussed. Experimentation is performed on networks containing 400 nodes randomly placed within a square
Eigentaste: A Constant Time Collaborative Filtering Algorithm
, 2000
"... Eigentaste is a collaborative filtering algorithm that uses universal queries to elicit realvalued user ratings on a common set of items and applies principal component analysis (PCA) to the resulting dense subset of the ratings matrix. PCA facilitates dimensionality reduction for offline clusterin ..."
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Cited by 378 (6 self)
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clustering of users and rapid computation of recommendations. For a database of n users, standard nearestneighbor techniques require O(n) processing time to compute recommendations, whereas Eigentaste requires O(1) (constant) time. We compare Eigentaste to alternative algorithms using data from Jester
Results 1  10
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