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48
Extracting places and activities from gps traces using hierarchical conditional random fields
 International Journal of Robotics Research
, 2007
"... Learning patterns of human behavior from sensor data is extremely important for highlevel activity inference. We show how to extract a person’s activities and significant places from traces of GPS data. Our system uses hierarchically structured conditional random fields to generate a consistent mod ..."
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Cited by 119 (3 self)
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Learning patterns of human behavior from sensor data is extremely important for highlevel activity inference. We show how to extract a person’s activities and significant places from traces of GPS data. Our system uses hierarchically structured conditional random fields to generate a consistent model of a person’s activities and places. In contrast to existing techniques, our approach takes highlevel context into account in order to detect the significant places of a person. Our experiments show significant improvements over existing techniques. Furthermore, they indicate that our system is able to robustly estimate a person’s activities using a model that is trained from data collected by other persons. 1
Long Term Arm and Hand Tracking for Continuous Sign Language TV Broadcasts
"... The goal of this work is to detect hand and arm positions over continuous sign language video sequences of more than one hour in length. We cast the problem as inference in a generative model of the image. Under this model, limb detection is expensive due to the very large number of possible configu ..."
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Cited by 44 (14 self)
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The goal of this work is to detect hand and arm positions over continuous sign language video sequences of more than one hour in length. We cast the problem as inference in a generative model of the image. Under this model, limb detection is expensive due to the very large number of possible configurations each part can assume. We make the following contributions to reduce this cost: (i) using efficient sampling from a pictorial structure proposal distribution to obtain reasonable configurations; (ii) identifying a large set of frames where correct configurations can be inferred, and using temporal tracking elsewhere. Results are reported for signing footage with changing background, challenging image conditions, and different signers; and we show that the method is able to identify the true arm and hand locations. The results exceed the stateoftheart for the length and stability of continuous limb tracking. 1
An LP View of the Mbest MAP problem
"... We consider the problem of finding the M assignments with maximum probability in a probabilistic graphical model. We show how this problem can be formulated as a linear program (LP) on a particular polytope. We prove that, for tree graphs (and junction trees in general), this polytope has a particul ..."
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Cited by 24 (1 self)
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We consider the problem of finding the M assignments with maximum probability in a probabilistic graphical model. We show how this problem can be formulated as a linear program (LP) on a particular polytope. We prove that, for tree graphs (and junction trees in general), this polytope has a particularly simple form and differs from the marginal polytope in a single inequality constraint. We use this characterization to provide an approximation scheme for nontree graphs, by using the set of spanning trees over such graphs. The method we present puts the Mbest inference problem in the context of LP relaxations, which have recently received considerable attention and have proven useful in solving difficult inference problems. We show empirically that our method often finds the provably exact M best configurations for problems of high treewidth. A common task in probabilistic modeling is finding the assignment with maximum probability given a model. This is often referred to as the MAP (maximum aposteriori) problem.
A Parallel Framework For Loopy Belief Propagation ABSTRACT
"... There are many innovative proposals introduced in the literature under the evolutionary computation field, from which estimation of distribution algorithms (EDAs) is one of them. Their main characteristic is the use of probabilistic models to represent the (in)dependencies between the variables of a ..."
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Cited by 14 (0 self)
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There are many innovative proposals introduced in the literature under the evolutionary computation field, from which estimation of distribution algorithms (EDAs) is one of them. Their main characteristic is the use of probabilistic models to represent the (in)dependencies between the variables of a concrete problem. Such probabilistic models have also been applied to the theoretical analysis of EDAs, providing a platform for the implementation of other optimization methods that can be incorporated into the EDA framework. Some of these methods, typically used for probabilistic inference, are belief propagation algorithms. In this paper we present a parallel approach for one of these inferencebased algorithms, the loopy belief propagation algorithm for factor graphs. Our parallel implementation was designed to provide an algorithm that can be executed in clusters of computers or multiprocessors in order to reduce the total execution time. In addition, this framework was also designed as a flexible tool where many parameters, such as scheduling rules or stopping criteria, can be adjusted according to the requirements of each particular experiment and problem.
Accurate prediction for atomiclevel protein design and its application in diversifying the nearoptimal sequence space
 Proteins: Structure, Function, and Bioinformatics, page In
, 2008
"... The task of engineering a protein to assume a target threedimensional structure is known as protein design. Computational search algorithms are devised to predict a minimal energy amino acid sequence for a particular structure. In practice, however, an ensemble of low energy sequences is often soug ..."
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Cited by 13 (3 self)
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The task of engineering a protein to assume a target threedimensional structure is known as protein design. Computational search algorithms are devised to predict a minimal energy amino acid sequence for a particular structure. In practice, however, an ensemble of low energy sequences is often sought. Primarily, this is performed since an individual predicted low energy sequence may not necessarily fold to the target structure due to both inaccuracies in modeling protein energetics and the nonoptimal nature of search algorithms employed. Additionally, some low energy sequences may be overly stable and thus lack the dynamic flexibility required for biological functionality. In this paper, we present a conceptually novel algorithm that rapidly predicts the set of lowest energy sequences. Based on the theory of probabilistic graphical models, it performs efficient inspection and partitioning of the nearoptimal sequence space, without making any assumptions of positional independence. We benchmark its performance on a diverse set of relevant protein design examples and show that it consistently yields sequences of lower energy than those derived from stateoftheart techniques. Furthermore, examination of the predicted ensembles indicates that, for each structure, the
Dynamic Quantization for Belief Propagation in Sparse Spaces Abstract
"... Graphical models provide an attractive framework for modeling a variety of problems in computer vision. The advent of powerful inference techniques such as belief propagation (BP) has recently made inference with many of these models tractable. Even so, the enormous size of the state spaces required ..."
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Cited by 11 (0 self)
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Graphical models provide an attractive framework for modeling a variety of problems in computer vision. The advent of powerful inference techniques such as belief propagation (BP) has recently made inference with many of these models tractable. Even so, the enormous size of the state spaces required for some applications can create a heavy computational burden. Pruning is a standard technique for reducing this burden, but since pruning is irreversible it carries the risk of greedily deleting important states, which can subsequently result in gross errors in BP. To address this problem, we propose a novel extension of pruning, which we call dynamic quantization (DQ), that allows BP to adaptively add as well as subtract states as needed. We examine DQ in the context of graphicalmodel based deformable template matching, in which the state space size is on the order of the number of pixels in an image. The combination of BP and DQ yields deformable templates that are both fast and robust to significant occlusions, without requiring any user initialization. Experimental results are shown on deformable templates of planar shapes. Finally, we argue that DQ is applicable to a variety of graphical models in which the state spaces are sparsely populated. Key words: belief propagation, graphical models, pruning, deformable templates 1
Bucket and minibucket Schemes for M Best Solutions over Graphical Models
"... The paper focuses on finding the m best solutions of a combinatorial optimization problem defined over a graphical model (e.g., the m most probable explanations for a Bayesian network). We describe elimmopt, a new bucket elimination algorithm for solving the mbest task, provide efficient implement ..."
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Cited by 10 (3 self)
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The paper focuses on finding the m best solutions of a combinatorial optimization problem defined over a graphical model (e.g., the m most probable explanations for a Bayesian network). We describe elimmopt, a new bucket elimination algorithm for solving the mbest task, provide efficient implementation of its defining combination and marginalization operators, analyze its worstcase performance, and compare it with that of recent related algorithms. An extension to the minibucket framework, yielding a collection of bounds for each of the mbest solutions is discussed and empirically evaluated. We also formulate the mbest task as a regular reasoning task over general graphical models defined axiomatically, which makes all other inference algorithms applicable. 1
Multiple choice learning: Learning to produce multiple structured outputs
 In NIPS
, 2012
"... We address the problem of generating multiple hypotheses for structured prediction tasks that involve interaction with users or successive components in a cascaded architecture. Given a set of multiple hypotheses, such components/users typically have the ability to retrieve the best (or approximatel ..."
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Cited by 9 (4 self)
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We address the problem of generating multiple hypotheses for structured prediction tasks that involve interaction with users or successive components in a cascaded architecture. Given a set of multiple hypotheses, such components/users typically have the ability to retrieve the best (or approximately the best) solution in this set. The standard approach for handling such a scenario is to first learn a singleoutput model and then produce MBest Maximum a Posteriori (MAP) hypotheses from this model. In contrast, we learn to produce multiple outputs by formulating this task as a multipleoutput structuredoutput prediction problem with a lossfunction that effectively captures the setup of the problem. We present a maxmargin formulation that minimizes an upperbound on this lossfunction. Experimental results on image segmentation and protein sidechain prediction show that our method outperforms conventional approaches used for this type of scenario and leads to substantial improvements in prediction accuracy.
An Efficient MessagePassing Algorithm for the MBest MAP Problem
"... Much effort has been directed at algorithms for obtaining the highest probability configuration in a probabilistic random field model – known as the maximum a posteriori (MAP) inference problem. In many situations, one could benefit from having not just a single solution, but the top M most probable ..."
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Cited by 9 (3 self)
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Much effort has been directed at algorithms for obtaining the highest probability configuration in a probabilistic random field model – known as the maximum a posteriori (MAP) inference problem. In many situations, one could benefit from having not just a single solution, but the top M most probable solutions – known as the MBest MAP problem. In this paper, we propose an efficient messagepassing based algorithm for solving the MBest MAP problem. Specifically, our algorithm solves the recently proposed Linear Programming (LP) formulation of MBest MAP [7], while being orders of magnitude faster than a generic LPsolver. Our approach relies on studying a particular partial Lagrangian relaxation of the MBest MAP LP which exposes a natural combinatorial structure of the problem that we exploit. 1