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Making centralized (graph) computation faster, distributed and (at times) better
Published on 2012-10-162591 Views
I will introduce a generic method for approximate inference in graphical models using graph partitioning. The resulting algorithm is linear time and provides an excellent approximation for the maximum
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Presentation
Making Centralized (Graph) Computation Faster, Distributed And (At Times) Better00:00
Graphical models01:05
Example - 102:11
Example - 204:03
Probabilistic model05:29
Inference: Clustering - 106:35
Inference: Clustering - 209:43
Inference: Clustering - 313:40
Inference: pair-wise graphical model - 114:47
Inference: pair-wise graphical model - 215:12
Inference: pair-wise graphical model - 315:48
Inference: pair-wise graphical model - 417:40
Optimization: "prominent" methods18:38
Graphical model: "prominent" methods18:55
This talk19:34
Algorithm - 122:45
Algorithm - 223:43
Algorithm - 330:46
Graph with poly-growth31:41
Algorithm: general graph36:56
Randomized decomposition38:52
Algorithm for modularity opt41:16
Is it an excellent result?41:46
Algorithm: a useful variation - 142:53
Algorithm: a useful variation - 243:43
Example - 343:59
Example: Blondel et al44:20
Example: Blondel et al + partition44:48
Algorithm: variations - 145:24
Algorithm: variations - 247:33
Algorithm: variations - 348:10
Summary48:35