Results 1  10
of
1,626,182
SelfStabilization
 ACM Computing Surveys
, 1993
"... In 1973Dijkstra introduced tocomputer science thenotion ofselfstabilization in the context ofdistributed systems. Hedefineda system as selfstabilizing when’’regardless ofitsinitial state, itis guaranteed to arrive at alegitimate state in afinite number of steps. ” A system which is not selfstabil ..."
Abstract

Cited by 42 (4 self)
 Add to MetaCart
In 1973Dijkstra introduced tocomputer science thenotion ofselfstabilization in the context ofdistributed systems. Hedefineda system as selfstabilizing when’’regardless ofitsinitial state, itis guaranteed to arrive at alegitimate state in afinite number of steps. ” A system which is not selfstabilizing
Planning Algorithms
, 2004
"... This book presents a unified treatment of many different kinds of planning algorithms. The subject lies at the crossroads between robotics, control theory, artificial intelligence, algorithms, and computer graphics. The particular subjects covered include motion planning, discrete planning, planning ..."
Abstract

Cited by 1108 (51 self)
 Add to MetaCart
This book presents a unified treatment of many different kinds of planning algorithms. The subject lies at the crossroads between robotics, control theory, artificial intelligence, algorithms, and computer graphics. The particular subjects covered include motion planning, discrete planning
A data locality optimizing algorithm
, 1991
"... 1 Introduction As processor speed continues to increase faster than memory speed, optimizations to use the memory hierarchy efficiently become ever more important. Blocking [9] ortiling [18] is a wellknown technique that improves the data locality of numerical algorithms [1, 6, 7, 12, 13].Tiling c ..."
Abstract

Cited by 805 (16 self)
 Add to MetaCart
1 Introduction As processor speed continues to increase faster than memory speed, optimizations to use the memory hierarchy efficiently become ever more important. Blocking [9] ortiling [18] is a wellknown technique that improves the data locality of numerical algorithms [1, 6, 7, 12, 13].Tiling
Efficient Variants of the ICP Algorithm
 INTERNATIONAL CONFERENCE ON 3D DIGITAL IMAGING AND MODELING
, 2001
"... The ICP (Iterative Closest Point) algorithm is widely used for geometric alignment of threedimensional models when an initial estimate of the relative pose is known. Many variants of ICP have been proposed, affecting all phases of the algorithm from the selection and matching of points to the minim ..."
Abstract

Cited by 702 (5 self)
 Add to MetaCart
to the minimization strategy. We enumerate and classify many of these variants, and evaluate their effect on the speed with which the correct alignment is reached. In order to improve convergence for nearlyflat meshes with small features, such as inscribed surfaces, we introduce a new variant based on uniform
Factor Graphs and the SumProduct Algorithm
 IEEE TRANSACTIONS ON INFORMATION THEORY
, 1998
"... A factor graph is a bipartite graph that expresses how a "global" function of many variables factors into a product of "local" functions. Factor graphs subsume many other graphical models including Bayesian networks, Markov random fields, and Tanner graphs. Following one simple c ..."
Abstract

Cited by 1787 (72 self)
 Add to MetaCart
A factor graph is a bipartite graph that expresses how a "global" function of many variables factors into a product of "local" functions. Factor graphs subsume many other graphical models including Bayesian networks, Markov random fields, and Tanner graphs. Following one simple
Randomized Algorithms
, 1995
"... Randomized algorithms, once viewed as a tool in computational number theory, have by now found widespread application. Growth has been fueled by the two major benefits of randomization: simplicity and speed. For many applications a randomized algorithm is the fastest algorithm available, or the simp ..."
Abstract

Cited by 2210 (37 self)
 Add to MetaCart
Randomized algorithms, once viewed as a tool in computational number theory, have by now found widespread application. Growth has been fueled by the two major benefits of randomization: simplicity and speed. For many applications a randomized algorithm is the fastest algorithm available
Algorithms for Scalable Synchronization on SharedMemory Multiprocessors
 ACM Transactions on Computer Systems
, 1991
"... Busywait techniques are heavily used for mutual exclusion and barrier synchronization in sharedmemory parallel programs. Unfortunately, typical implementations of busywaiting tend to produce large amounts of memory and interconnect contention, introducing performance bottlenecks that become marke ..."
Abstract

Cited by 567 (32 self)
 Add to MetaCart
markedly more pronounced as applications scale. We argue that this problem is not fundamental, and that one can in fact construct busywait synchronization algorithms that induce no memory or interconnect contention. The key to these algorithms is for every processor to spin on separate locally
The StructureMapping Engine: Algorithm and Examples
 Artificial Intelligence
, 1989
"... This paper describes the StructureMapping Engine (SME), a program for studying analogical processing. SME has been built to explore Gentner's Structuremapping theory of analogy, and provides a "tool kit" for constructing matching algorithms consistent with this theory. Its flexibili ..."
Abstract

Cited by 512 (115 self)
 Add to MetaCart
This paper describes the StructureMapping Engine (SME), a program for studying analogical processing. SME has been built to explore Gentner's Structuremapping theory of analogy, and provides a "tool kit" for constructing matching algorithms consistent with this theory. Its
Learning with local and global consistency
 Advances in Neural Information Processing Systems 16
, 2004
"... We consider the general problem of learning from labeled and unlabeled data, which is often called semisupervised learning or transductive inference. A principled approach to semisupervised learning is to design a classifying function which is sufficiently smooth with respect to the intrinsic stru ..."
Abstract

Cited by 666 (21 self)
 Add to MetaCart
structure collectively revealed by known labeled and unlabeled points. We present a simple algorithm to obtain such a smooth solution. Our method yields encouraging experimental results on a number of classification problems and demonstrates effective use of unlabeled data. 1
Results 1  10
of
1,626,182