- 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 and 24: A distribution over sets of poses?
- Page 25 and 26: Image search: “jaguar” Relevanc
- 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 50 and 51: Determinantal point process L = P(Y
- Page 52 and 53: Determinantal point process • Giv
- Page 54 and 55: Determinantal point process • Giv
- Page 56 and 57: Determinantal point process • Giv
- Page 58 and 59: DPP inference • Normalization: P(
- Page 60 and 61: DPP inference • Marginals: P(A Y
- Page 62 and 63: P(A Y ) = det(K A ) P(i Y ) = det(K
- Page 64 and 65: P(A Y ) = det(K A ) P(i Y ) = det(K
- Page 66 and 67: Determinantal point processes Quali
- Page 68 and 69: L = L ij = g(i) ⊤ g(j)
- Page 70 and 71: L = L ij = q(i) (i) (j)q(j) q(i)
- Page 72 and 73: q(i) (i) q(j) (j)
- Page 74 and 75: q(i) (i) Vol 2 P({i, j}) q(j) (j) I
- Page 76 and 77: Quality vs. diversity
- Page 78 and 79: Quality vs. diversity • Intuitive
- Page 80 and 81: Determinantal point processes Quali
- Page 82 and 83: Eigendecomposition N∑ L = λ n v
- Page 84 and 85: v 4 v 6 v 1 v 2 v 3 v 5
- Page 86 and 87: Elementary DPP P {2,3,6} v 1 v 2 v
- Page 88 and 89: ∑ P ∝ λ n J⊆{1,...,N} P J
- Page 90 and 91: Sampling algorithm Choose elementar
- Page 92 and 93: Draw sample from P J PHASE TWO Step
- Page 94 and 95: Consequences • Phase one determin
- Page 96 and 97: What if we need exactly k diverse i
- Page 98 and 99:
Determinantal point processes Quali
- Page 100 and 101:
k-DPP ∑ P ∝ λ n J⊆{1,...,N}
- Page 102 and 103:
∑ P ∝ λ n J⊆{1,...,N} P J
- Page 104 and 105:
k-DPP sampling • Need new PHASE O
- Page 106 and 107:
Hot dog in pizza is the stuff of dr
- Page 108 and 109:
Quality features • Dubbed the “
- Page 110 and 111:
Quality features 2. Pizza Hut has r
- Page 112 and 113:
Quality features 2. Pizza Hut has r
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Diversity features • φ fixed to
- Page 116 and 117:
News summarization • Input: 10 ne
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System ROUGE-1F ROUGE-1R R-SU4F Beg
- Page 120 and 121:
Determinantal point processes Quali
- Page 122 and 123:
? ? ? ?
- Page 124 and 125:
Structured DPPs • Exponentially m
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1. Factorization • Quality scores
- Page 128 and 129:
1. Factorization • Quality scores
- Page 130 and 131:
Quality
- Page 132 and 133:
Quality x
- Page 134 and 135:
Quality x x =
- Page 136 and 137:
Diversity
- Page 138 and 139:
Diversity Low diversity
- Page 140 and 141:
2. Dual representation Φ L = Q Φ
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2. Dual representation C = Φ Q 2 Q
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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 •
- Page 150 and 151:
3. Second-order message passing •
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Arms F Pose accuracy Overall F 1 0.
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Determinantal point processes Quali
- Page 161 and 162:
News happens Apr 9: Facebook buys I
- Page 163 and 164:
News happens
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iraq iraqi killed baghdad arab mari
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Dynamic topic model hotel kitchen c
- Page 169 and 170:
News threading • Input: large new
- Page 171 and 172:
Scale • ~35,000 articles per six
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Results: Human summaries & Turk rat
- Page 175 and 176:
Results: Human summaries & Turk rat
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Conclusion
- Page 179 and 180:
k-SDPPs vs. Tyranny of Small Decisi
- Page 181:
Papers, Tutorial, Code • Relevant