C
Chen Yanover
Researcher at IBM
Publications - 65
Citations - 2003
Chen Yanover is an academic researcher from IBM. The author has contributed to research in topics: Belief propagation & Peptide binding. The author has an hindex of 24, co-authored 61 publications receiving 1838 citations. Previous affiliations of Chen Yanover include Ben-Gurion University of the Negev & Fred Hutchinson Cancer Research Center.
Papers
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Journal Article
Linear Programming Relaxations and Belief Propagation -- An Empirical Study
TL;DR: This paper compares tree-reweighted belief propagation (TRBP) and powerful general-purpose LP solvers (CPLEX) on relaxations of real-world graphical models from the fields of computer vision and computational biology and finds that TRBP almost always finds the solution significantly faster than all the solvers in CPLEX and more importantly, TRBP can be applied to large scale problems for which the solver in CLEX cannot be applied.
Proceedings Article
MAP estimation, linear programming and belief propagation with convex free energies
TL;DR: Convex BP is defined as BP algorithms based on a convex free energy approximation and it is shown that this class includes ordinary BP with single-cycle, tree reweighted BP and many other BP variants, and fixed-points of convex max-product BP will provably give the MAP solution when there are no ties.
Proceedings Article
Approximate Inference and Protein-Folding
Chen Yanover,Yair Weiss +1 more
TL;DR: It is shown that finding a minimal energy side-chain configuration is equivalent to performing inference in an undirected graphical model, and this equivalence was used to assess the performance of approximate inference algorithms in a real-world setting.
Proceedings ArticleDOI
Globally optimal solutions for energy minimization in stereo vision using reweighted belief propagation
TL;DR: It is shown that for standard benchmark stereo pairs, the global optimum can be found in about 30 minutes using a variant of the belief propagation (BP) algorithm.
Proceedings Article
Finding the M Most Probable Configurations using Loopy Belief Propagation
Chen Yanover,Yair Weiss +1 more
TL;DR: This work develops a new exact inference algorithm for calculating the best configurations that uses only max-marginals, and shows empirically that the algorithm can accurately and rapidly approximate the M best configurations in graphs with hundreds of variables.