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/Subject (Neural Information Processing Systems http\072\057\057nips\056cc\057)
/Publisher (Curran Associates\054 Inc\056)
/Language (en\055US)
/Created (2016)
/EventType (Poster)
/Description-Abstract (Inference in Markov random fields subject to consistency structure is a fundamental problem that arises in many real\055life applications\056 In order to enforce consistency\054 classical approaches utilize consistency potentials or encode constraints over feasible instances\056 Unfortunately this comes at the price of a serious computational bottleneck\056 In this paper we suggest to tackle consistency by incorporating constraints on beliefs\056 This permits derivation of a closed\055form message\055passing algorithm which we refer to as the Constraints Based Convex Belief Propagation \050CBCBP\051\056 Experiments show that CBCBP outperforms the standard approach while being at least an order of magnitude faster\056)
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/Title (Constraints Based Convex Belief Propagation)
/Date (2016)
/ModDate (D\07220161119112617\05508\04700\047)
/Published (2016)
/Type (Conference Proceedings)
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/Book (Advances In Neural Information Processing Systems 29)
/Description (Paper accepted and presented at the Neural Information Processing Systems Conference \050http\072\057\057nips\056cc\057\051)
/Editors (D\056D\056 Lee and U\056V\056 Luxburg and I\056 Guyon and R\056 Garnett)
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