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Determinantal Point Processes
Published on Jan 23, 20136805 Views
Determinantal point processes (DPPs) arise in random matrix theory and quantum physics as models of random variables with negative correlations. Among many remarkable properties, they offer tractable
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Determinantal Point Processes00:00
Image search: "jaguar" - 100:36
Image search: "jaguar" - 200:53
Summarization - 101:10
Summarization - 201:18
Summarization - 301:22
Summarization - 401:40
Point processes02:04
Discrete point processes02:27
Independent point process - 102:43
Independent point process - 202:51
Point process samples - 103:05
Point process samples - 203:18
Feature function g on items in γ - 103:40
Feature function g on items in γ - 204:07
Feature function g on items in γ - 304:14
Feature function g on items in γ - 404:27
Feature function g on items in γ - 504:39
Take all of the items04:41
The kernel matrix - 104:49
The kernel matrix - 204:51
Determinantal point process - 104:58
Determinantal point process - 205:12
Determinantal point process - 305:17
Determinantal point process - 405:26
Determinantal point process - 505:31
Determinantal point process - 605:36
Determinantal point process - 705:41
Inference: normalization - 105:50
Inference: normalization - 205:59
Inference: marginals - 106:15
Inference: marginals - 206:32
Inference: marginals - 306:46
Inference: marginals - 406:47
Inference: marginals - 506:56
Diversity07:30
Sampling: requires eigendecomposition - 107:44
Sampling: requires eigendecomposition - 208:20
Quality vs. diversity - 108:22
Quality vs. diversity - 208:38
Quality vs. diversity - 308:46
Quality vs. diversity - 408:50
Quality vs. diversity - 509:16
Quality vs. diversity - 609:23
Quality vs. diversity - 709:32
Quality vs. diversity - 809:53
News summarization11:18
Methods - 112:09
Methods - 212:12
Methods - 312:31
Methods - 412:36
Methods - 512:45
Methods - 613:13
Large N?13:53
Dual representation - 114:07
Dual representation - 214:33
Dual representation - 314:35
Dual representation - 414:40
Dual representation - 515:09
Some applications15:12
Projection - 115:30
Projection - 215:45
Projection - 315:49
Projection - 415:54
Random projection - 115:56
Random projection - 215:58
Random projections for preserving distances - 116:02
Random projections for preserving distances - 216:09
Random projections for preserving distances - 316:16
Random projections for preserving distances - 416:23
Random projections for preserving distances - 516:24
Random projections for preserving distances - 616:26
Random projections for preserving distances - 716:53
Random projection for DPPs - 116:55
Random projection for DPPs - 217:34
Random projection for DPPs - 317:37
DPPs at scale17:41
Exponential N? - 118:11
Exponential N? - 218:47
Exponential N? - 318:53
Structured DPPs - 119:02
Structured DPPs - 219:15
Structure19:28
Factorization - 119:53
Factorization - 220:05
Factorization - 320:07
Factorization - 420:23
Multiple-pose estimation20:24
Quality - 120:49
Quality - 220:56
Quality - 320:59
Quality - 421:01
Quality - 521:14
Diversity - 121:15
Diversity - 321:27
Diversity - 521:34
Diversity - 221:38
Diversity - 421:42
Dual representation - 121:59
Dual representation - 222:30
Dual representation - 322:53
Second-order message passing23:26
Sample - 124:12
Sample - 224:33
Sample - 324:47
Sample - 425:10
Sample - 525:21
Pose accuracy - 125:43
Pose accuracy - 226:20
News threading - 126:33
News threading - 226:45
News threading - 326:54
News threading - 426:59
News threading - 527:22
News threading - 627:24
Dynamic topic model - 128:16
Dynamic topic model - 228:36
DPP threads - 128:47
DPP threads - 228:55
Scale29:17
Evaluation29:46
Results: Human summaries & ratings - 130:23
Results: Human summaries & ratings - 230:32
Results: Human summaries & ratings - 330:43
Results: Human summaries & ratings - 430:47
Results: Human summaries & ratings - 531:13
Summary31:37
Supporting Materials32:08