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864
Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
, 2010
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GraphChi: Largescale Graph Computation On just a PC
 In Proceedings of the 10th USENIX conference on Operating Systems Design and Implementation, OSDI’12
, 2012
"... Current systems for graph computation require a distributed computing cluster to handle very large realworld problems, such as analysis on social networks or the web graph. While distributed computational resources have become more accessible, developing distributed graph algorithms still remains c ..."
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Cited by 109 (6 self)
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Current systems for graph computation require a distributed computing cluster to handle very large realworld problems, such as analysis on social networks or the web graph. While distributed computational resources have become more accessible, developing distributed graph algorithms still remains challenging, especially to nonexperts. In this work, we present GraphChi, a diskbased system for computing efficiently on graphs with billions of edges. By using a wellknown method to break large graphs into small parts, and a novel parallel sliding windows method, GraphChi is able to execute several advanced data mining, graph mining, and machine learning algorithms on very large graphs, using just a single consumerlevel computer. We further extend GraphChi to support graphs that evolve over time, and demonstrate that, on a single computer, GraphChi can process over one hundred thousand graph updates per second, while simultaneously performing computation. We show, through experiments and theoretical analysis, that GraphChi performs well on both SSDs and rotational hard drives. By repeating experiments reported for existing distributed systems, we show that, with only fraction of the resources, GraphChi can solve the same problems in very reasonable time. Our work makes largescale graph computation available to anyone with a modern PC. 1
Stochastic Variational Inference
 JOURNAL OF MACHINE LEARNING RESEARCH (2013, IN PRESS)
, 2013
"... We develop stochastic variational inference, a scalable algorithm for approximating posterior distributions. We develop this technique for a large class of probabilistic models and we demonstrate it with two probabilistic topic models, latent Dirichlet allocation and the hierarchical Dirichlet proce ..."
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Cited by 99 (23 self)
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We develop stochastic variational inference, a scalable algorithm for approximating posterior distributions. We develop this technique for a large class of probabilistic models and we demonstrate it with two probabilistic topic models, latent Dirichlet allocation and the hierarchical Dirichlet process topic model. Using stochastic variational inference, we analyze several large collections of documents: 300K articles from Nature, 1.8M articles from The New York Times, and 3.8M articles from Wikipedia. Stochastic inference can easily handle data sets of this size and outperforms traditional variational inference, which can only handle a smaller subset. (We also show that the Bayesian nonparametric topic model outperforms its parametric counterpart.) Stochastic variational inference lets us apply complex Bayesian models to massive data sets.
libDAI: A free/open source C++ library for discrete approximate inference methods
, 2008
"... This paper describes the software package libDAI, a free & open source C++ library that provides implementations of various exact and approximate inference methods for graphical models with discretevalued variables. libDAI supports directed graphical models (Bayesian networks) as well as undire ..."
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Cited by 72 (1 self)
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This paper describes the software package libDAI, a free & open source C++ library that provides implementations of various exact and approximate inference methods for graphical models with discretevalued variables. libDAI supports directed graphical models (Bayesian networks) as well as undirected ones (Markov random fields and factor graphs). It offers various approximations of the partition sum, marginal probability distributions and maximum probability states. Parameter learning is also supported. A feature comparison with other open source software packages for approximate inference is given. libDAI is licensed under the GPL v2+ license and is available at
iSAM2: Incremental Smoothing and Mapping Using the Bayes Tree
"... We present a novel data structure, the Bayes tree, that provides an algorithmic foundation enabling a better understanding of existing graphical model inference algorithms and their connection to sparse matrix factorization methods. Similar to a clique tree, a Bayes tree encodes a factored probabili ..."
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Cited by 69 (26 self)
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We present a novel data structure, the Bayes tree, that provides an algorithmic foundation enabling a better understanding of existing graphical model inference algorithms and their connection to sparse matrix factorization methods. Similar to a clique tree, a Bayes tree encodes a factored probability density, but unlike the clique tree it is directed and maps more naturally to the square root information matrix of the simultaneous localization and mapping (SLAM) problem. In this paper, we highlight three insights provided by our new data structure. First, the Bayes tree provides a better understanding of the matrix factorization in terms of probability densities. Second, we show how the fairly abstract updates to a matrix factorization translate to a simple editing of the Bayes tree and its conditional densities. Third, we apply the Bayes tree to obtain a completely novel algorithm for sparse nonlinear incremental optimization, named iSAM2, which achieves improvements in efficiency through incremental variable reordering and fluid relinearization, eliminating the need for periodic batch steps. We analyze various properties of iSAM2 in detail, and show on a range of real and simulated datasets that our algorithm compares favorably with other recent mapping algorithms in both quality and efficiency.
An Introduction to Conditional Random Fields
 Foundations and Trends in Machine Learning
, 2012
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Learning human activities and object affordances from rgbd videos. IJRR
, 2013
"... such as making cereal and arranging objects in a room (see Fig. 9). For example, the making cereal activity consists of around 12 subactivities on average, which includes reaching the pitcher, moving the pitcher to the bowl, and then pouring the milk into the bowl. This proves to be a very challeng ..."
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Cited by 59 (16 self)
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such as making cereal and arranging objects in a room (see Fig. 9). For example, the making cereal activity consists of around 12 subactivities on average, which includes reaching the pitcher, moving the pitcher to the bowl, and then pouring the milk into the bowl. This proves to be a very challenging task given the variability across individuals in performing each subactivity, and other environment induced conditions such as cluttered background and viewpoint changes. (See Fig. 2 for some examples.) In most previous works, object detection and activity recognition have been addressed as separate tasks. Only recently, some works have shown that modeling mutual context is beneficial (Gupta et al., 2009; Yao and FeiFei, 2010). The key idea in our work is to note that, in activity detection, it is sometimes more informative to know how an object is being used (associated affordances, Gibson, 1979) rather than knowing what the object is (i.e. the object category). For example, both chair and sofa might be categorized as ‘sittable, ’ and a cup might be categorized as both ‘drinkable ’ and ‘pourable. ’ Note that the affordances of an object change over time depending on its use, e.g., a pitcher may first be reachable, then movable and finally pourable. In addition to helping activity recognition, recognizing object affordances is important by itself because of their use in robotic applications (e.g., Kormushev et al., 2010; Jiang et al., 2012a; Jiang and Saxena, 2012). We propose a method to learn human activities by modarXiv:1210.1207v2
Probabilistic reasoning for assemblybased 3d modeling
 In Proc. SIGGRAPH, ACM
, 2011
"... Assemblybased modeling is a promising approach to broadening the accessibility of 3D modeling. In assemblybased modeling, new models are assembled from shape components extracted from a database. A key challenge in assemblybased modeling is the identification of relevant components to be presente ..."
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Cited by 57 (9 self)
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Assemblybased modeling is a promising approach to broadening the accessibility of 3D modeling. In assemblybased modeling, new models are assembled from shape components extracted from a database. A key challenge in assemblybased modeling is the identification of relevant components to be presented to the user. In this paper, we introduce a probabilistic reasoning approach to this problem. Given a repository of shapes, our approach learns a probabilistic graphical model that encodes semantic and geometric relationships among shape components. The probabilistic model is used to present components that are semantically and stylistically compatible with the 3D model that is being assembled. Our experiments indicate that the probabilistic model increases the relevance of presented components.