• Documents
  • Authors
  • Tables
  • Log in
  • Sign up
  • MetaCart
  • DMCA
  • Donate

CiteSeerX logo

Advanced Search Include Citations

Tools

Sorted by:
Try your query at:
Semantic Scholar Scholar Academic
Google Bing DBLP
Results 1 - 10 of 15,879
Next 10 →

Fast Stochastic Alternating Direction Method of Multipliers

by Leon Wenliang Zhong, James T. Kwok
"... We propose a new stochastic alternating direc-tion method of multipliers (ADMM) algorith-m, which incrementally approximates the ful-l gradient in the linearized ADMM formulation. Besides having a low per-iteration complexity as existing stochastic ADMM algorithms, it im-proves the convergence rate ..."
Abstract - Cited by 4 (0 self) - Add to MetaCart
We propose a new stochastic alternating direc-tion method of multipliers (ADMM) algorith-m, which incrementally approximates the ful-l gradient in the linearized ADMM formulation. Besides having a low per-iteration complexity as existing stochastic ADMM algorithms, it im-proves the convergence rate

Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers

by Stephen Boyd, Neal Parikh, Eric Chu, Borja Peleato, Jonathan Eckstein , 2010
"... ..."
Abstract - Cited by 1001 (20 self) - Add to MetaCart
Abstract not found

Stochastic volatility: likelihood inference and comparison with ARCH models

by Sangjoon Kim, Salomon Brothers, Asia Limited, Neil Shephard - Review of Economic Studies , 1998
"... In this paper, Markov chain Monte Carlo sampling methods are exploited to provide a unified, practical likelihood-based framework for the analysis of stochastic volatility models. A highly effective method is developed that samples all the unobserved volatilities at once using an approximating offse ..."
Abstract - Cited by 592 (40 self) - Add to MetaCart
In this paper, Markov chain Monte Carlo sampling methods are exploited to provide a unified, practical likelihood-based framework for the analysis of stochastic volatility models. A highly effective method is developed that samples all the unobserved volatilities at once using an approximating

A Fast Marching Level Set Method for Monotonically Advancing Fronts

by J. A. Sethian - PROC. NAT. ACAD. SCI , 1995
"... We present a fast marching level set method for monotonically advancing fronts, which leads to an extremely fast scheme for solving the Eikonal equation. Level set methods are numerical techniques for computing the position of propagating fronts. They rely on an initial value partial differential eq ..."
Abstract - Cited by 630 (24 self) - Add to MetaCart
We present a fast marching level set method for monotonically advancing fronts, which leads to an extremely fast scheme for solving the Eikonal equation. Level set methods are numerical techniques for computing the position of propagating fronts. They rely on an initial value partial differential

Contour Tracking By Stochastic Propagation of Conditional Density

by Michael Isard, Andrew Blake , 1996
"... . In Proc. European Conf. Computer Vision, 1996, pp. 343--356, Cambridge, UK The problem of tracking curves in dense visual clutter is a challenging one. Trackers based on Kalman filters are of limited use; because they are based on Gaussian densities which are unimodal, they cannot represent s ..."
Abstract - Cited by 661 (23 self) - Add to MetaCart
simultaneous alternative hypotheses. Extensions to the Kalman filter to handle multiple data associations work satisfactorily in the simple case of point targets, but do not extend naturally to continuous curves. A new, stochastic algorithm is proposed here, the Condensation algorithm --- Conditional

Fast and robust fixed-point algorithms for independent component analysis

by Aapo Hyvärinen - IEEE TRANS. NEURAL NETW , 1999
"... Independent component analysis (ICA) is a statistical method for transforming an observed multidimensional random vector into components that are statistically as independent from each other as possible. In this paper, we use a combination of two different approaches for linear ICA: Comon’s informat ..."
Abstract - Cited by 884 (34 self) - Add to MetaCart
Independent component analysis (ICA) is a statistical method for transforming an observed multidimensional random vector into components that are statistically as independent from each other as possible. In this paper, we use a combination of two different approaches for linear ICA: Comon’s

The Vector Field Histogram -- Fast Obstacle Avoidance For Mobile Robots

by J. Borenstein, Y. Koren - IEEE JOURNAL OF ROBOTICS AND AUTOMATION , 1991
"... A new real-time obstacle avoidance method for mobile robots has been developed and implemented. This method, named the vector field histogram(VFH), permits the detection of unknown obstacles and avoids collisions while simultaneously steering the mobile robot toward the target. The VFH method uses a ..."
Abstract - Cited by 484 (24 self) - Add to MetaCart
A new real-time obstacle avoidance method for mobile robots has been developed and implemented. This method, named the vector field histogram(VFH), permits the detection of unknown obstacles and avoids collisions while simultaneously steering the mobile robot toward the target. The VFH method uses

CONDENSATION -- conditional density propagation for visual tracking

by Michael Isard, Andrew Blake , 1998
"... The problem of tracking curves in dense visual clutter is challenging. Kalman filtering is inadequate because it is based on Gaussian densities which, being unimodal, cannot represent simultaneous alternative hypotheses. The Condensation algorithm uses “factored sampling”, previously applied to th ..."
Abstract - Cited by 1503 (12 self) - Add to MetaCart
. Notwithstanding the use of stochastic methods, the algorithm runs in near real-time.

Pegasos: Primal Estimated sub-gradient solver for SVM

by Shai Shalev-Shwartz, Yoram Singer, Nathan Srebro, Andrew Cotter
"... We describe and analyze a simple and effective stochastic sub-gradient descent algorithm for solving the optimization problem cast by Support Vector Machines (SVM). We prove that the number of iterations required to obtain a solution of accuracy ɛ is Õ(1/ɛ), where each iteration operates on a singl ..."
Abstract - Cited by 542 (20 self) - Add to MetaCart
single training example. In contrast, previous analyses of stochastic gradient descent methods for SVMs require Ω(1/ɛ2) iterations. As in previously devised SVM solvers, the number of iterations also scales linearly with 1/λ, where λ is the regularization parameter of SVM. For a linear kernel, the total

Image Quilting for Texture Synthesis and Transfer

by Alexei A. Efros , William T. Freeman , 2001
"... We present a simple image-based method of generating novel visual appearance in which a new image is synthesized by stitching together small patches of existing images. We call this process image quilting. First, we use quilting as a fast and very simple texture synthesis algorithm which produces s ..."
Abstract - Cited by 699 (18 self) - Add to MetaCart
We present a simple image-based method of generating novel visual appearance in which a new image is synthesized by stitching together small patches of existing images. We call this process image quilting. First, we use quilting as a fast and very simple texture synthesis algorithm which produces
Next 10 →
Results 1 - 10 of 15,879
Powered by: Apache Solr
  • About CiteSeerX
  • Submit and Index Documents
  • Privacy Policy
  • Help
  • Data
  • Source
  • Contact Us

Developed at and hosted by The College of Information Sciences and Technology

© 2007-2019 The Pennsylvania State University