Results 1  10
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13
Learning HigherOrder Graph Structure with Features by Structure Penalty
"... In discrete undirected graphical models, the conditional independence of node labels Y is specified by the graph structure. We study the case where there is another input random vector X (e.g. observed features) such that the distribution P(Y  X) is determined by functions of X that characterize th ..."
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Cited by 4 (2 self)
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In discrete undirected graphical models, the conditional independence of node labels Y is specified by the graph structure. We study the case where there is another input random vector X (e.g. observed features) such that the distribution P(Y  X) is determined by functions of X that characterize the (higherorder) interactions among the Y ’s. The main contribution of this paper is to learn the graph structure and the functions conditioned on X at the same time. We prove that discrete undirected graphical models with feature X are equivalent to multivariate discrete models. The reparameterization of the potential functions in graphical models by conditional log odds ratios of the latter offers advantages in representation of the conditional independence structure. The functional spaces can be flexibly determined by kernels. Additionally, we impose a Structure Lasso (SLasso) penalty on groups of functions to learn the graph structure. These groups with overlaps are designed to enforce hierarchical function selection. In this way, we are able to shrink higher order interactions to obtain a sparse graph structure. 1
Infinite latent conditional random fields for modeling environments through humans
 in RSS
, 2013
"... Abstract—Humans cast a substantial influence on their environments by interacting with it. Therefore, even though an environment may physically contain only objects, it cannot be modeled well without considering humans. In this paper, we model environments not only through objects, but also through ..."
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Cited by 4 (3 self)
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Abstract—Humans cast a substantial influence on their environments by interacting with it. Therefore, even though an environment may physically contain only objects, it cannot be modeled well without considering humans. In this paper, we model environments not only through objects, but also through latent human poses and humanobject interactions. However, the number of potential human poses is large and unknown, and the humanobject interactions vary not only in type but also in which human pose relates to each object. In order to handle such properties, we present Infinite Latent Conditional Random Fields (ILCRFs) that model a scene as a mixture of CRFs generated from Dirichlet processes. Each CRF represents one possible explanation of the scene. In addition to visible object nodes and edges, it generatively models the distribution of different CRF structures over the latent human nodes and corresponding edges. We apply the model to the challenging application of robotic scene arrangement. In extensive experiments, we show that our model significantly outperforms the stateoftheart results. We further use our algorithm on a robot for placing objects in a new scene. I.
EvidenceSpecific Structures for Rich Tractable CRFs
"... We present a simple and effective approach to learning tractable conditional random fields with structure that depends on the evidence. Our approach retains the advantages of tractable discriminative models, namely efficient exact inference and arbitrarily accurate parameter learning in polynomial t ..."
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Cited by 3 (1 self)
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We present a simple and effective approach to learning tractable conditional random fields with structure that depends on the evidence. Our approach retains the advantages of tractable discriminative models, namely efficient exact inference and arbitrarily accurate parameter learning in polynomial time. At the same time, our algorithm does not suffer a large expressive power penalty inherent to fixed tractable structures. On reallife relational datasets, our approach matches or exceeds state of the art accuracy of the dense models, and at the same time provides an order of magnitude speedup. 1
Probabilistic Label Trees for Efficient Large Scale Image Classification
"... Largescale recognition problems with thousands of classes pose a particular challenge because applying the classifier requires more computation as the number of classes grows. The label tree model integrates classification with the traversal of the tree so that complexity grows logarithmically. In ..."
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Cited by 1 (0 self)
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Largescale recognition problems with thousands of classes pose a particular challenge because applying the classifier requires more computation as the number of classes grows. The label tree model integrates classification with the traversal of the tree so that complexity grows logarithmically. In this paper, we show how the parameters of the label tree can be found using maximum likelihood estimation. This new probabilistic learning technique produces a label tree with significantly improved recognition accuracy. 1.
Modeling and
 Diagnosis of Multistage Manufacturing Processes: Part IStage Space Model, Japan/USA Symposium on Flexible Automation
, 2000
"... i ii ..."
On regularity conditions for random fields
 Proc. Amer. Math. Soc
, 1994
"... Conditional Random Fields (CRFs) [Lafferty et al., 2001] can offer computational and statistical advantages over generative models, yet traditional CRF parameter and structure learning methods are often too expensive to scale up to large problems. This thesis develops methods capable of learning CRF ..."
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Cited by 1 (1 self)
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Conditional Random Fields (CRFs) [Lafferty et al., 2001] can offer computational and statistical advantages over generative models, yet traditional CRF parameter and structure learning methods are often too expensive to scale up to large problems. This thesis develops methods capable of learning CRFs for much larger problems. We do so by decomposing learning problems into smaller, simpler subproblems. These decompositions allow us to trade off sample complexity, computational complexity, and potential for parallelization, and we can often optimize these tradeoffs in model or dataspecific ways. The resulting methods are theoretically motivated, are often accompanied by strong guarantees, and are effective and highly scalable in practice. In the first part of our work, we develop core methods for CRF parameter and structure learning. For parameter learning, we analyze several methods and produce PAC learnability results for certain classes of CRFs. Structured composite likelihood estimation proves particularly successful in both theory and practice, and our results offer guidance for optimizing estimator structure. For structure learning, we develop a maximumweight spanning treebased method which outperforms other methods for recovering tree CRFs. In the second
Thesis Learning LargeScale Conditional Random Fields
, 2013
"... Conditional Random Fields (CRFs) [Lafferty et al., 2001] can offer computational and statistical advantages over generative models, yet traditional CRF parameter and structure learning methods are often too expensive to scale up to large problems. This thesis develops methods capable of learning CRF ..."
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Conditional Random Fields (CRFs) [Lafferty et al., 2001] can offer computational and statistical advantages over generative models, yet traditional CRF parameter and structure learning methods are often too expensive to scale up to large problems. This thesis develops methods capable of learning CRFs for much larger problems. We do so by decomposing learning problems into smaller, simpler subproblems. These decompositions allow us to trade off sample complexity, computational complexity, and potential for parallelization, and we can often optimize these tradeoffs in model or dataspecific ways. The resulting methods are theoretically motivated, are often accompanied by strong guarantees, and are effective and highly scalable in practice. In the first part of our work, we develop core methods for CRF parameter and structure learning. For parameter learning, we analyze several methods and produce PAC learnability results for certain classes of CRFs. Structured composite likelihood estimation proves particularly successful in both theory and practice, and our results offer guidance for optimizing estimator structure. For structure learning, we develop a maximumweight spanning treebased method which outperforms other methods for recovering tree CRFs. In the second
Author manuscript, published in "IEEE Conference on Computer Vision & Pattern Recognition (CVPR '11) (2011)" Learning Structured Prediction Models for Interactive Image Labeling
, 2011
"... We propose structured models for image labeling that take into account the dependencies among the image labels explicitly. These models are more expressive than independent label predictors, and lead to more accurate predictions. While the improvement is modest for fullyautomatic image labeling, th ..."
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We propose structured models for image labeling that take into account the dependencies among the image labels explicitly. These models are more expressive than independent label predictors, and lead to more accurate predictions. While the improvement is modest for fullyautomatic image labeling, the gain is significant in an interactive scenario where a user provides the value of some of the image labels. Such an interactive scenario offers an interesting tradeoff between accuracy and manual labeling effort. The structured models are used to decide which labels should be set by the user, and transfer the user input to more accurate predictions on other image labels. We also apply our models to attributebased image classification, where attribute predictions of a test image are mapped to class probabilities by means of a given attributeclass mapping. In this case the structured models are built at the attribute level. We also consider an interactive system where the system asks a user to set some of the attribute values in order to maximally improve class prediction performance. Experimental results on three publicly available benchmark data sets show that in all scenarios our structured models lead to more accurate predictions, and leverage user input much more effectively than stateoftheart independent models. 1.
QuerySpecific Learning and Inference for Probabilistic Graphical Models
, 2011
"... for the degree of Doctor of Philosophy. In numerous real world applications, from sensor networks to computer vision to natural text processing, one needs to reason about the system in question in the face of uncertainty. A key problem in all those settings is to compute the probability distribution ..."
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for the degree of Doctor of Philosophy. In numerous real world applications, from sensor networks to computer vision to natural text processing, one needs to reason about the system in question in the face of uncertainty. A key problem in all those settings is to compute the probability distribution over the variables of interest (the query) given the observed values of other random variables (the evidence). Probabilistic graphical models (PGMs) have become the approach of choice for representing and reasoning with highdimensional probability distributions. However, for most models capable of accurately representing reallife distributions, inference is fundamentally intractable. As a result, optimally balancing the expressive power and inference complexity of the models, as well as designing better approximate inference algorithms, remain important open problems with potential to significantly improve the quality of answers to probabilistic queries. This thesis contributes algorithms for learning and approximate inference in probabilistic graphical models that improve on the state of the art by emphasizing the computational aspects of inference over the representational properties of the models. Our contributions fall
Learning to Model Multilingual Unrestricted Coreference in OntoNotes
"... Coreference resolution, which aims at correctly linking meaningful expressions in text, is a much challenging problem in Natural Language Processing community. This paper describes the multilingual coreference modeling system of Web Information Processing Group, Henan University of Technology, China ..."
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Coreference resolution, which aims at correctly linking meaningful expressions in text, is a much challenging problem in Natural Language Processing community. This paper describes the multilingual coreference modeling system of Web Information Processing Group, Henan University of Technology, China, for the CoNLL2012 shared task (closed track). The system takes a supervised learning strategy, and consists of two cascaded components: one for detecting mentions, and the other for clustering mentions. To make the system applicable for multiple languages, generic syntactic and semantic features are used to model coreference in text. The system obtained combined official score 41.88 over three languages (Arabic, Chinese, and English) and ranked 7 th among the 15 systems in the closed track. 1