en
0.25
0.5
0.75
1.25
1.5
1.75
2
Making centralized (graph) computation faster, distributed and (at times) better
Published on Feb 4, 20252591 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
Related categories
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