Via an oracle experiment, we show that the upper bound on accuracy of a CCG parser is significantly lowered when its search space is pruned using a supertagger, though the supert...
—This paper considers the reconstruction of structured-sparse signals from noisy linear observations. In particular, the support of the signal coefficients is parameterized by h...
—Gaussian belief propagation is an iterative algorithm for computing the mean of a multivariate Gaussian distribution. Equivalently, the min-sum algorithm can be used to compute ...
We consider the problem of semi-supervised segmentation of textured images. Recently, reweighted belief propagation has been introduced as a solution for Bayesian inference with r...
Belief propagation is shown to be an instance of a hybrid between two projection algorithms in the convex programming literature: Dykstra's algorithm with cyclic Bregman proje...
Loopy belief propagation has been employed in a wide variety of applications with great empirical success, but it comes with few theoretical guarantees. In this paper we analyze t...
Sujay Sanghavi, Dmitry M. Malioutov, Alan S. Wills...
A major bottleneck in high-throughput protein crystallography is producing protein-structure models from an electrondensity map. In previous work, we developed Acmi, a probabilist...
Recent work on early vision such as image segmentation, image restoration, stereo matching, and optical flow models these problems using Markov Random Fields. Although this formula...
Belief propagation is widely used in inference of graphical models. It yields exact solutions when the underlying graph is singly connected. When the graph contains loops, double-c...
The paper investigates parameterized approximate message-passing schemes that are based on bounded inference and are inspired by Pearl’s belief propagation algorithm (BP). We st...
Robert Mateescu, Kalev Kask, Vibhav Gogate, Rina D...