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Lecture 8 and 9: Message passing on Factor Graphsmlg.eng.cam.ac.uk/teaching/4f13/1213/lect0809.pdf · Factor trees: separation (1) p(w) = X v X x X y X z f 1(v,w)f 2(w,x)f 3(x,y)f
Composition of Correspondences978-1-4419-8534-7/1.pdf · Let U, V, W be three varieties, X a cycle on U X V, and Y a cycle on V X W. If the intersection (X X W) . (U X Y) is defined
Locality-Aware Software Throttling for Sparse Matrix ...1 x 2 x 3 w 0 w 1 w 2 w 3 w 4 w 5 w 0 w 1 w 2 w 3 w 4 w 4 y = Ax where y i = reduce_op{A ik! x k, 1
Sampling W&W, Chapter 6. Rules for Expectation Examples Mean: E(X) = xp(x) Variance: E(X- ) 2 = (x- ) 2 p(x) Covariance: E(X- x )(Y- y ) =
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Warm-up 1/29/08 1. Multiple these exponents ( x 6 ) ( w 2 ) ( x 5 ) ( y 9 ) ( w 4 ) ( x 8 ) 2. Simplify. x -4 · y 3 · z 5 · x 5 · y -1 ÷ z 2.
1 Depth Based Image Registration via Geometric Segmentation · W(x) I 2 x P P W(x) IxIy W(x) IxIy P W(x) I 2 y ‚ (2) where W(x) is a rectangular window centered at x and Ix and
Arc-consistency for alldiff(x,y,...,z) Example: P=(X,D,C) Variables: {w,x,y,z} X Domains: w {b,c,d,e} x {b,c} y {a,b,c,d} z {b,c} Constraints:
6.869 Advances in Computer Vision - …...1 W x y u v W x y W x y u v W x y W x y m m m m m I I I x y I I x y I I u v I I u v − − = = = ∑ ∑ ∈ ∈ 9 Images as Vectors “Unwrap”
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