Results 1 - 10 of 119,285

A Fast Algorithm for Particle Simulations

by L. Greengard, V. Rokhlin , 1987
"... this paper to the case where the potential (or force) at a point is a sum of pairwise An algorithm is presented for the rapid evaluation of the potential and force fields in systems involving large numbers of particles interactions. More specifically, we consider potentials of whose interactions a ..."
Abstract - Cited by 1152 (19 self) - Add to MetaCart
are Coulombic or gravitational in nature. For a the form system of N particles, an amount of work of the order O(N 2 ) has traditionally been required to evaluate all pairwise interactions, un- F5F far 1 (F near 1F external ), less some approximation or truncation method is used. The algorithm of the present

A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking

by M. Sanjeev Arulampalam, Simon Maskell, Neil Gordon - IEEE TRANSACTIONS ON SIGNAL PROCESSING , 2002
"... Increasingly, for many application areas, it is becoming important to include elements of nonlinearity and non-Gaussianity in order to model accurately the underlying dynamics of a physical system. Moreover, it is typically crucial to process data on-line as it arrives, both from the point of view o ..."
Abstract - Cited by 2006 (2 self) - Add to MetaCart
of storage costs as well as for rapid adaptation to changing signal characteristics. In this paper, we review both optimal and suboptimal Bayesian algorithms for nonlinear/non-Gaussian tracking problems, with a focus on particle filters. Particle filters are sequential Monte Carlo methods based on point mass

Large margin methods for structured and interdependent output variables

by Ioannis Tsochantaridis, Thorsten Joachims, Thomas Hofmann, Yasemin Altun - JOURNAL OF MACHINE LEARNING RESEARCH , 2005
"... Learning general functional dependencies between arbitrary input and output spaces is one of the key challenges in computational intelligence. While recent progress in machine learning has mainly focused on designing flexible and powerful input representations, this paper addresses the complementary ..."
Abstract - Cited by 624 (12 self) - Add to MetaCart
that solves the optimization problem in polynomial time for a large class of problems. The proposed method has important applications in areas such as computational biology, natural language processing, information retrieval/extraction, and optical character recognition. Experiments from various domains

Convergence Properties of the Nelder-Mead Simplex Method in Low Dimensions

by Jeffrey C. Lagarias, James A. Reeds, Margaret H. Wright, Paul E. Wright - SIAM Journal of Optimization , 1998
"... Abstract. The Nelder–Mead simplex algorithm, first published in 1965, is an enormously popular direct search method for multidimensional unconstrained minimization. Despite its widespread use, essentially no theoretical results have been proved explicitly for the Nelder–Mead algorithm. This paper pr ..."
Abstract - Cited by 598 (3 self) - Add to MetaCart
Abstract. The Nelder–Mead simplex algorithm, first published in 1965, is an enormously popular direct search method for multidimensional unconstrained minimization. Despite its widespread use, essentially no theoretical results have been proved explicitly for the Nelder–Mead algorithm. This paper

Probabilistic Inference Using Markov Chain Monte Carlo Methods

by Radford M. Neal , 1993
"... Probabilistic inference is an attractive approach to uncertain reasoning and empirical learning in artificial intelligence. Computational difficulties arise, however, because probabilistic models with the necessary realism and flexibility lead to complex distributions over high-dimensional spaces. R ..."
Abstract - Cited by 736 (24 self) - Add to MetaCart
. Related problems in other fields have been tackled using Monte Carlo methods based on sampling using Markov chains, providing a rich array of techniques that can be applied to problems in artificial intelligence. The "Metropolis algorithm" has been used to solve difficult problems in statistical

Rank Aggregation Methods for the Web

by Cynthia Dwork, Ravi Kumar, Moni Naor, D. Sivakumar , 2010
"... We consider the problem of combining ranking results from various sources. In the context of the Web, the main applications include building meta-search engines, combining ranking functions, selecting documents based on multiple criteria, and improving search precision through word associations. Wed ..."
Abstract - Cited by 478 (6 self) - Add to MetaCart
We consider the problem of combining ranking results from various sources. In the context of the Web, the main applications include building meta-search engines, combining ranking functions, selecting documents based on multiple criteria, and improving search precision through word associations

The particel swarm: Explosion, stability, and convergence in a multi-dimensional complex space

by Maurice Clerc, James Kennedy - IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTION
"... The particle swarm is an algorithm for finding optimal regions of complex search spaces through interaction of individuals in a population of particles. Though the algorithm, which is based on a metaphor of social interaction, has been shown to perform well, researchers have not adequately explained ..."
Abstract - Cited by 852 (10 self) - Add to MetaCart
’s convergence tendencies. Some results of the particle swarm optimizer, implementing modifications derived from the analysis, suggest methods for altering the original algorithm in ways that eliminate problems and increase the optimization power of the particle swarm

Nonparametric estimation of average treatment effects under exogeneity: a review

by Guido W. Imbens - REVIEW OF ECONOMICS AND STATISTICS , 2004
"... Recently there has been a surge in econometric work focusing on estimating average treatment effects under various sets of assumptions. One strand of this literature has developed methods for estimating average treatment effects for a binary treatment under assumptions variously described as exogen ..."
Abstract - Cited by 630 (25 self) - Add to MetaCart
Recently there has been a surge in econometric work focusing on estimating average treatment effects under various sets of assumptions. One strand of this literature has developed methods for estimating average treatment effects for a binary treatment under assumptions variously described

Symmetry and Related Properties via the Maximum Principle

by B. Gidas, Wei-ming Ni, L. Nirenberg , 1979
"... We prove symmetry, and some related properties, of positive solutions of second order elliptic equations. Our methods employ various forms of the maximum principle, and a device of moving parallel planes to a critical position, and then showing that the solution is symmetric about the limiting plan ..."
Abstract - Cited by 538 (4 self) - Add to MetaCart
We prove symmetry, and some related properties, of positive solutions of second order elliptic equations. Our methods employ various forms of the maximum principle, and a device of moving parallel planes to a critical position, and then showing that the solution is symmetric about the limiting

Large steps in cloth simulation

by David Baraff, Andrew Witkin - SIGGRAPH 98 Conference Proceedings , 1998
"... The bottle-neck in most cloth simulation systems is that time steps must be small to avoid numerical instability. This paper describes a cloth simulation system that can stably take large time steps. The simulation system couples a new technique for enforcing constraints on individual cloth particle ..."
Abstract - Cited by 576 (5 self) - Add to MetaCart
particles with an implicit integration method. The simulator models cloth as a triangular mesh, with internal cloth forces derived using a simple continuum formulation that supports modeling operations such as local anisotropic stretch or compression; a unified treatment of damping forces is included
Results 1 - 10 of 119,285