Results 1  10
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402
Graphical models, exponential families, and variational inference
, 2008
"... The formalism of probabilistic graphical models provides a unifying framework for capturing complex dependencies among random variables, and building largescale multivariate statistical models. Graphical models have become a focus of research in many statistical, computational and mathematical fiel ..."
Abstract

Cited by 819 (28 self)
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fields, including bioinformatics, communication theory, statistical physics, combinatorial optimization, signal and image processing, information retrieval and statistical machine learning. Many problems that arise in specific instances — including the key problems of computing marginals and modes
Regularization and semisupervised learning on large graphs
 In COLT
, 2004
"... Abstract. We consider the problem of labeling a partially labeled graph. This setting may arise in a number of situations from survey sampling to information retrieval to pattern recognition in manifold settings. It is also of potential practical importance, when the data is abundant, but labeling i ..."
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Cited by 148 (1 self)
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is expensive or requires human assistance. Our approach develops a framework for regularization on such graphs. The algorithms are very simple and involve solving a single, usually sparse, system of linear equations. Using the notion of algorithmic stability, we derive bounds on the generalization error
Learning query intent from regularized click graphs
 In SIGIR 2008
, 2008
"... This work presents the use of click graphs in improving query intent classifiers, which are critical if vertical search and generalpurpose search services are to be offered in a unified user interface. Previous works on query classification have primarily focused on improving feature representation ..."
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Cited by 114 (12 self)
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, we infer class memberships of unlabeled queries from those of labeled ones according to their proximities in a click graph. Moreover, we regularize the learning with click graphs by contentbased classification to avoid propagating erroneous labels. We demonstrate the effectiveness of our algorithms
Tracking Looselimbed People
, 2004
"... We pose the problem of 3D human tracking as one of inference in a graphical model. Unlike traditional kinematic tree representations, our model of the body is a collection of looselyconnected limbs. Conditional probabilities relating the 3D pose of connected limbs are learned from motioncaptured tr ..."
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Cited by 191 (7 self)
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training data. Similarly, we learn probabilistic models for the temporal evolution of each limb (forward and backward in time). Human pose and motion estimation is then solved with nonparametric belief propagation using a variation of particle filtering that can be applied over a general loopy graph
A statistical model for general contextual object recog. ECCV
, 2004
"... Abstract. We consider object recognition as the process of attaching meaningful labels to specific regions of an image, and propose a model that learns spatial relationships between objects. Given a set of images and their associated text (e.g. keywords, captions, descriptions), the objective is to ..."
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Cited by 129 (7 self)
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propagation in the inference step and iterative scaling on the pseudolikelihood approximation in the parameter update step. The experiments indicate that our approximate inference and learning algorithm converges to good local solutions. Experiments on a diverse array of images show that spatial context
Learning to Rank with Graph Consistency
"... The ranking models of existing image search engines are generally based on associated text while the image visual content is actually neglected. Imperfect search results frequently appear due to the mismatch between the textual features and the actual image content. Visual reranking, in which visua ..."
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visual information is applied to refine text based search results, has been proven to be effective. However, the improvement brought by visual reranking is limited, and the main reason is that the errors in the textbased results will propagate to the refinement stage. In this paper, we propose a
Synthesis of interface specifications for Java classes
 In POPL
, 2005
"... While a typical software component has a clearly specified (static) interface in terms of the methods and the input/output types they support, information about the correct sequencing of method calls the client must invoke is usually undocumented. In this paper, we propose a novel solution for autom ..."
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Cited by 142 (9 self)
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synthesis method first constructs a symbolic representation of the finite statetransition system obtained from the class using predicate abstraction. Constructing the interface then corresponds to solving a partialinformation twoplayer game on this symbolic graph. We present a sound approach to solve
Correlation Clustering in General Weighted Graphs
 Theoretical Computer Science
, 2006
"... We consider the following general correlationclustering problem [1]: given a graph with real nonnegative edge weights and a 〈+〉/〈− 〉 edge labeling, partition the vertices into clusters to minimize the total weight of cut 〈+ 〉 edges and uncut 〈− 〉 edges. Thus, 〈+ 〉 edges with large weights (represen ..."
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Cited by 41 (0 self)
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hardness and gave constantfactor approximation algorithms for the special case in which the graph is complete (full information) and every edge has the same weight. We give an O(log n)approximation algorithm for the general case based on a linearprogramming rounding and the “regiongrowing ” technique. We also
Learning markov networks: maximum bounded treewidth graphs
 In Proceedings of the 12th ACMSIAM Symposium on Discrete Algorithms
, 2001
"... AbstractMarkov networks are a common class of graphical models used in machine learning. Such models use an undirected graph tocapture dependency information among random variables in a joint probability distribution. Once one has chosen to use a Markovnetwork model, one aims to choose the model tha ..."
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Cited by 73 (6 self)
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AbstractMarkov networks are a common class of graphical models used in machine learning. Such models use an undirected graph tocapture dependency information among random variables in a joint probability distribution. Once one has chosen to use a Markovnetwork model, one aims to choose the model
Graphbased Transfer Learning
"... Transfer learning is the task of leveraging the information from labeled examples in some domains to predict the labels for examples in another domain. It finds abundant practical applications, such as sentiment prediction, image classification and network intrusion detection. In this paper, we prop ..."
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Cited by 12 (3 self)
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propose a graphbased transfer learning framework. It propagates the label information from the source domain to the target domain via the examplefeatureexample tripartite graph, and puts more emphasis on the labeled examples from the target domain via the exampleexample bipartite graph. Our framework
Results 1  10
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402