Results 1 - 10 of 20,601

Approximate list-decoding of direct product . . .

by Russell Impagliazzo, Ragesh Jaiswal, Valentine Kabanets
"... Given a message msg ∈ {0, 1} N, its k-wise direct product encoding is the sequence of k-tuples (msg(i1),..., msg(ik)) over all possible k-tuples of indices (i1,..., ik) ∈ {1,..., N} k. We give an efficient randomized algorithm for approximate local list-decoding of direct product codes. That is, gi ..."
Abstract - Cited by 33 (8 self) - Add to MetaCart
Given a message msg ∈ {0, 1} N, its k-wise direct product encoding is the sequence of k-tuples (msg(i1),..., msg(ik)) over all possible k-tuples of indices (i1,..., ik) ∈ {1,..., N} k. We give an efficient randomized algorithm for approximate local list-decoding of direct product codes. That is

Fast approximate energy minimization via graph cuts

by Yuri Boykov, Olga Veksler, Ramin Zabih - IEEE Transactions on Pattern Analysis and Machine Intelligence , 2001
"... In this paper we address the problem of minimizing a large class of energy functions that occur in early vision. The major restriction is that the energy function’s smoothness term must only involve pairs of pixels. We propose two algorithms that use graph cuts to compute a local minimum even when v ..."
Abstract - Cited by 2120 (61 self) - Add to MetaCart
In this paper we address the problem of minimizing a large class of energy functions that occur in early vision. The major restriction is that the energy function’s smoothness term must only involve pairs of pixels. We propose two algorithms that use graph cuts to compute a local minimum even when

Loopy belief propagation for approximate inference: An empirical study. In:

by Kevin P Murphy , Yair Weiss , Michael I Jordan - 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 error-correcting codes. The most dramatic instance of this is the near Shannon-limit performanc ..."
Abstract - Cited by 676 (15 self) - Add to MetaCart
-limit performance of "Turbo Codes" -codes whose decoding algorithm is equivalent to loopy belief propagation in a chain-structured Bayesian network. In this paper we ask: is there something spe cial about the error-correcting code context, or does loopy propagation work as an ap proximate inference scheme

Robust Monte Carlo Localization for Mobile Robots

by Sebastian Thrun, Dieter Fox, Wolfram Burgard, Frank Dellaert , 2001
"... Mobile robot localization is the problem of determining a robot's pose from sensor data. This article presents a family of probabilistic localization algorithms known as Monte Carlo Localization (MCL). MCL algorithms represent a robot's belief by a set of weighted hypotheses (samples), whi ..."
Abstract - Cited by 839 (85 self) - Add to MetaCart
), which approximate the posterior under a common Bayesian formulation of the localization problem. Building on the basic MCL algorithm, this article develops a more robust algorithm called MixtureMCL, which integrates two complimentary ways of generating samples in the estimation. To apply this algorithm

Locality-sensitive hashing scheme based on p-stable distributions

by Mayur Datar, Piotr Indyk - In SCG ’04: Proceedings of the twentieth annual symposium on Computational geometry , 2004
"... inÇÐÓ�Ò We present a novel Locality-Sensitive Hashing scheme for the Approximate Nearest Neighbor Problem underÐÔnorm, based onÔstable distributions. Our scheme improves the running time of the earlier algorithm for the case of theÐnorm. It also yields the first known provably efficient approximate ..."
Abstract - Cited by 521 (8 self) - Add to MetaCart
inÇÐÓ�Ò We present a novel Locality-Sensitive Hashing scheme for the Approximate Nearest Neighbor Problem underÐÔnorm, based onÔstable distributions. Our scheme improves the running time of the earlier algorithm for the case of theÐnorm. It also yields the first known provably efficient approximate

Incorporating non-local information into information extraction systems by Gibbs sampling

by Jenny Rose Finkel, Trond Grenager, Christopher Manning - IN ACL , 2005
"... Most current statistical natural language processing models use only local features so as to permit dynamic programming in inference, but this makes them unable to fully account for the long distance structure that is prevalent in language use. We show how to solve this dilemma with Gibbs sampling, ..."
Abstract - Cited by 730 (25 self) - Add to MetaCart
, a simple Monte Carlo method used to perform approximate inference in factored probabilistic models. By using simulated annealing in place of Viterbi decoding in sequence models such as HMMs, CMMs, and CRFs, it is possible to incorporate non-local structure while preserving tractable inference. We

Active Learning with Statistical Models

by David A. Cohn, Zoubin Ghahramani, Michael I. Jordan , 1995
"... For manytypes of learners one can compute the statistically "optimal" way to select data. We review how these techniques have been used with feedforward neural networks [MacKay, 1992# Cohn, 1994]. We then showhow the same principles may be used to select data for two alternative, statist ..."
Abstract - Cited by 679 (10 self) - Add to MetaCart
, statistically-based learning architectures: mixtures of Gaussians and locally weighted regression. While the techniques for neural networks are expensive and approximate, the techniques for mixtures of Gaussians and locally weighted regression are both efficient and accurate.

A note on amplifying the error-tolerance of locally decodable codes

by Avraham Ben-Aroya, Klim Efremenko, Amnon Ta-Shma - COLLOQ. COMPUT. COMPLEX , 2010
"... Trevisan [Tre03] suggested a transformation that allows amplifying the error rate a code can handle. We observe that this transformation, that was suggested in the non-local setting, works also in the local setting and thus gives a generic, simple way to amplify the error-tolerance of locally decoda ..."
Abstract - Cited by 2 (0 self) - Add to MetaCart
decodable codes. Specifically, this shows how to transform a locally decodable code that can tolerate a constant fraction of errors to a locally decodable code that can recover from a much higher error-rate, and how to transform such locally decodable codes to locally list-decodable codes

Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories

by Cordelia Schmid - In CVPR
"... This paper presents a method for recognizing scene categories based on approximate global geometric correspondence. This technique works by partitioning the image into increasingly fine sub-regions and computing histograms of local features found inside each sub-region. The resulting “spatial pyrami ..."
Abstract - Cited by 1923 (47 self) - Add to MetaCart
This paper presents a method for recognizing scene categories based on approximate global geometric correspondence. This technique works by partitioning the image into increasingly fine sub-regions and computing histograms of local features found inside each sub-region. The resulting “spatial

Tapestry: A Resilient Global-scale Overlay for Service Deployment

by Ben Y. Zhao, Ling Huang, Jeremy Stribling, Sean C. Rhea, Anthony D. Joseph, John D. Kubiatowicz - IEEE Journal on Selected Areas in Communications , 2004
"... We present Tapestry, a peer-to-peer 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 ..."
Abstract - Cited by 598 (14 self) - Add to MetaCart
We present Tapestry, a peer-to-peer 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
Results 1 - 10 of 20,601