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This paper introduces hinge-loss Markov random fields (HL-MRFs), a new class of probabilistic graphical models particularly well-suited to large-scale structured prediction and learning. We derive HL-MRFs by unifying and then generalizing three different approaches to scalable inference in structured models: (1) randomized algorithms for MAX SAT, (2) local(More)
Graphical models for structured domains are powerful tools, but the computational complexities of combinatorial prediction spaces can force restrictions on models, or require approximate inference in order to be tractable. Instead of working in a combina-torial space, we use hinge-loss Markov random fields (HL-MRFs), an expressive class of graphical models(More)
We prove the equivalence of first-order local consistency relaxations and the MAX SAT relaxation of Goemans and Williamson (1994) for a class of MRFs we refer to as logical MRFs. This allows us to combine the advantages of each into a single MAP inference technique: solving the local consistency relaxation with any of a number of highly scal-able(More)
Probabilistic graphical models are powerful tools for analyzing constrained, continuous domains. However, finding most-probable explanations (MPEs) in these models can be computationally expensive. In this paper, we improve the scala-bility of MPE inference in a class of graphical models with piecewise-linear and piecewise-quadratic dependencies and linear(More)
We propose hinge-loss Markov random fields (HL-MRFs), a powerful class of continuous-valued graphical models, for high-level computer vision tasks. HL-MRFs are characterized by log-concave density functions, and are able to perform efficient, exact inference. Their templated hinge-loss potential functions naturally encode soft-valued logical rules. Using(More)
Annotation graphs, made available through the Linked Data initiative and Semantic Web, have significant scientific value. However, their increasing complexity makes it difficult to fully exploit this value. Graph summaries, which group similar entities and relations for a more abstract view on the data, can help alleviate this problem, but new methods for(More)