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Deep Learning 2
Published on Sep 13, 201512120 Views
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Chapter list
Talk Roadmap - 100:00
Talk Roadmap - 200:43
Can you solve Zero-Shot Problem? - 101:00
Can you solve Zero-Shot Problem? - 202:06
Can you solve Zero-Shot Problem? - 302:42
The Model - 103:01
The Model - 203:02
Model Architecture04:18
Alternative View04:43
Problem Setup05:58
Calthech UCSD Bird and Oxford Flower Dataset06:58
Results: Area Under ROC - 107:57
Results: Area Under ROC - 208:39
Attribute Discovery - 109:56
Attribute Discovery - 211:16
Talk Roadmap - 312:18
Generating Sentences12:42
Encode-Decode Framework13:21
Representation of Words14:37
An image-text Encoder - 115:11
An image-text Encoder - 215:42
An image-text Encoder - 316:37
Retrieving Sentences for Images18:02
Tagging and Retrieval19:02
Retrieval with Adjectives20:51
Multimodal Linguistic Regularities - 121:38
Multimodal Linguistic Regularities - 222:06
How About Generating Sentences!23:02
Log-bilinear Neural Language Model - 123:21
Log-bilinear Neural Language Model - 224:49
Multiplicative Model25:09
Multiplicative Log-bilinear Model - 126:10
Multiplicative Log-bilinear Model - 227:34
Decoding: Neural Language Model27:50
Decoding: Structured NLM - 130:48
Decoding: Structured NLM - 231:35
Decoding: Structured NLM - 331:40
Decoding: Structured NLM - 431:42
Decoding: Structured NLM - 531:43
Caption Generation - 132:16
Caption Generation - 232:29
Caption Generation - 332:53
Filling in the Blanks - 133:23
Filling in the Blanks - 233:33
Caption Generation - 133:51
Caption Generation - 234:25
Caption Generation - 334:49
Caption Generation - 435:19
Results35:50
Caption Generation with Visual Attention - 136:14
Caption Generation with Visual Attention - 236:14
Caption Generation with Visual Attention - 336:52
Improving Action Recognition - 138:18
Improving Action Recognition - 238:59
Talk Roadmap - 440:40
Recurrent Attention Model41:04
Model Setup43:37
Model Definition44:35
Variational Learning - 145:24
Variational Learning - 246:37
Variational Learning - 346:49
Sampling from the Prior48:50
Key Observation49:32
Maximizing Marginal Likelihood51:18
Comparing the Two Estimators51:22
Another Key Observation - 153:11
Another Key Observation - 254:20
Relationship To Helmholtz Machines - 155:09
Relationship To Helmholtz Machines - 255:39
The Wake-Sleep Recurrent Attention Model56:33
Training Inference Network - 156:59
Training Inference Network - 257:36
MNIST Example58:17
Camption Generation: Flickr 8K58:37
MNIST Attention Demo59:01
Recurrent Attention Model01:01:08
Hard vs. Soft Attention01:01:37
Talk Roadmap - 501:02:56
Sequence to Sequence LEarning01:03:38
Skip-Though Model - 101:04:09
Skip-Though Model - 201:04:49
Learning Objective01:05:03
Book 11K corpus - 101:05:36
Book 11K corpus - 201:06:26
Semantic Relatedness - 101:06:42
Semantic Relatedness - 201:07:48
Semantic Relatedness - 301:08:49
Paraphrase Detection01:10:13
Classification Benchmarks01:11:02
Summary01:12:17
Thank you!01:14:20