Fibonacci heaps and their uses in improved network optimization algorithms
Journal of the ACM (JACM)
An all pairs shortest path algorithm with expected time O(n2logn)
SIAM Journal on Computing
Finding the hidden path: time bounds for all-pairs shortest paths
SIAM Journal on Computing
On the exponent of the all pairs shortest path problem
Journal of Computer and System Sciences - Special issue: papers from the 32nd and 34th annual symposia on foundations of computer science, Oct. 2–4, 1991 and Nov. 3–5, 1993
Communications of the ACM
Data Structures and Algorithms
Data Structures and Algorithms
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
Finding Deformable Shapes Using Loopy Belief Propagation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Stereo Matching Using Belief Propagation
IEEE Transactions on Pattern Analysis and Machine Intelligence
The effect of algebraic structure on the computational complexity of matrix multiplication
The effect of algebraic structure on the computational complexity of matrix multiplication
Representation and Detection of Deformable Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Correctness of Local Probability Propagation in Graphical Models with Loops
Neural Computation
Efficient Belief Propagation for Early Vision
International Journal of Computer Vision
More algorithms for all-pairs shortest paths in weighted graphs
Proceedings of the thirty-ninth annual ACM symposium on Theory of computing
Matrix-vector multiplication in sub-quadratic time: (some preprocessing required)
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
Fast Generalized Belief Propagation for MAP Estimation on 2D and 3D Grid-Like Markov Random Fields
Proceedings of the 30th DAGM symposium on Pattern Recognition
Graph Rigidity, Cyclic Belief Propagation, and Point Pattern Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
A skip-chain conditional random field for ranking meeting utterances by importance
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
Thin junction tree filters for simultaneous localization and mapping
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Learning Context-Sensitive Shape Similarity by Graph Transduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Fast memory-efficient generalized belief propagation
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Predicting 3d people from 2d pictures
AMDO'06 Proceedings of the 4th international conference on Articulated Motion and Deformable Objects
Efficient belief propagation with learned higher-order markov random fields
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
The generalized distributive law
IEEE Transactions on Information Theory
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
Improved max-sum algorithm for DCOP with n-ary constraints
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Computer Vision and Image Understanding
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Maximum A Posteriori inference in graphical models is often solved via message-passing algorithms, such as the junction-tree algorithm or loopy belief-propagation. The exact solution to this problem is well-known to be exponential in the size of the maximal cliques of the triangulated model, while approximate inference is typically exponential in the size of the model's factors. In this paper, we take advantage of the fact that many models have maximal cliques that are larger than their constituent factors, and also of the fact that many factors consist only of latent variables (i.e., they do not depend on an observation). This is a common case in a wide variety of applications that deal with grid-, tree-, and ring-structured models. In such cases, we are able to decrease the exponent of complexity for message-passing by 0.5 for both exact and approximate inference. We demonstrate that message-passing operations in such models are equivalent to some variant of matrix multiplication in the tropical semiring, for which we offer an O(N2.5) expected-case solution.