- Page 1 and 2: Geometry of Diversity and Determina
- Page 3 and 4: Eichner & Ferrari BMVC09
- Page 5 and 6: Human pose estimation: what’s so
- Page 7 and 8: Human pose estimation: what’s so
- Page 9 and 10: Local signal is weak = true right e
- Page 11 and 12: Graphical Models vs. Tyranny of Sma
- Page 13 and 14: Graphical Models vs. Tyranny of Sma
- Page 15 and 16: Graphical Models vs. Tyranny of Sma
- Page 17 and 18: Accuracy of Wrist Localization 90 8
- Page 19 and 20: The Catch • Problem: inference ex
- Page 21 and 22: Graphical Models vs. Tyranny of Sma
- Page 23: A distribution over sets of poses?
- Page 27 and 28: Summarization
- Page 29 and 30: Graphical models?
- Page 31 and 32: Graphical models? 0/1 sentence i
- Page 33 and 34: Graphical models?
- Page 35 and 36: Determinantal point processes (DPPs
- Page 37 and 38: Determinantal point processes Quali
- Page 39 and 40: Discrete point process • N items
- Page 41 and 42: Independent point process • Each
- Page 43 and 44: Feature function g on items in Y
- Page 45 and 46: Feature function g on items in Y
- Page 49 and 50: L = L ij = g(i) ⊤ g(j)
- Page 51 and 52: Determinantal point process L = P(Y
- Page 53 and 54: Determinantal point process • Giv
- Page 55 and 56: Determinantal point process • Giv
- Page 57 and 58: DPP inference • Normalization: P(
- Page 59 and 60: DPP inference • Marginals: P(A Y
- Page 61 and 62: P(A Y ) = det(K A )
- Page 63 and 64: P(A Y ) = det(K A ) P(i Y ) = det(K
- Page 65 and 66: Point process samples Independent D
- Page 67 and 68: L =
- Page 69 and 70: L = L ij = q(i) (i) (j)q(j)
- Page 71 and 72: L = L ij = q(i) (i) (j)q(j) q(i)
- Page 73 and 74: q(i) (i) Vol 2 P({i, j}) q(j) (j)
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q(i) (i) Vol 2 P({i, j}) q(j) (j) I
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Quality vs. diversity • Intuitive
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Quality vs. diversity • Intuitive
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Eigendecomposition N∑ L = λ n v
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1 2 3 4 5 6 v 4 v 6 v 1 v 2 v 3 v 5
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Elementary DPP v 1 v 2 v 3 v 5 P {2
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Key insight Every DPP is a “facto
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∑ P ∝ 1 2 3 4 5 6 J⊆{1,...,N}
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Choose elementary DPP P J PHASE ONE
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Sampling algorithm Choose elementar
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Consequences • Phase one determin
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What if we need exactly k diverse i
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k-DPPs • Simple idea: condition D
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k-DPP ∑ P ∝ λ n J⊆{1,...,N}
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∑ P ∝ λ n J⊆{1,...,N} P J
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k-DPP sampling • Solution: recurs
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Quality features • Dubbed the “
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Quality features 2. Pizza Hut has r
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Quality features Mean similarity 2.
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Diversity features • φ fixed to
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Diversity features • φ fixed to
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System ROUGE-1F ROUGE-1R R-SU4F Beg
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System ROUGE-1F ROUGE-1R R-SU4F Beg
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? ? ? ?
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Y Y Y I""""""""really""""""""love""
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Structured DPPs • Exponentially m
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1. Factorization • Quality scores
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1. Factorization • Quality scores
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Quality
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Quality x x
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Diversity
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Diversity Low diversity
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Diversity Low diversity High divers
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2. Dual representation Φ Q Q Φ
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2. Dual representation L = C = 2 N
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2. Dual representation L = C = 2 N
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3. Second-order message passing
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3. Second-order message passing •
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Multiple-pose estimation
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Pose accuracy Overall F Arms F 1 1
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News happens Apr 3: Instagram reach
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News happens Apr 9: Facebook buys I
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News happens
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iraq iraqi killed baghdad arab mari
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hotel kitchen casa inches post shad
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Scale • ~35,000 articles per six
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Scale • ~35,000 articles per six
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Results: Human summaries & Turk rat
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Results: Human summaries & Turk rat
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k-SDPPs vs. Tyranny of Small Decisi
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• DPPs capture global, negative c