'Cluster graph' diaporamas de présentation

Locality Sensitive Distributed Computing

Locality Sensitive Distributed Computing. David Peleg Weizmann Institute. Structure of mini-course. Basics of distributed network algorithms Locality-preserving network representations Constructions and applications. Part 2: Representations. Clustered representations

2.4k views • 230 slides

On the Complexity of Distributed Network Decomposition

Eliran Natan . On the Complexity of Distributed Network Decomposition. Alessandro Panconesi , Aravind Srinivasan.

769 views • 65 slides

Global Approximate Inference

695 views • 54 slides

Using Combinatorial Optimization within Max-Product Belief Propagation

Using Combinatorial Optimization within Max-Product Belief Propagation. Danny Tarlow Toronto Machine Learning Group Oct 2, 2006. Based on work to be presented at NIPS 06 with John Duchi, Gal Elidan, and Daphne Koller. Motivation.

521 views • 38 slides

Narrative Essay

484 views • 10 slides

Properties of BP Algorithm

Inference. Probabilistic Graphical Models. Message Passing. Properties of BP Algorithm. Calibration. Cluster beliefs: A cluster graph is calibrated if every pair of adjacent clusters C i , C j agree on their sepset S i,j. 1: A,B. 4: A,D. A. B. D. 2: B,C. 3: C,D. C.

168 views • 7 slides