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Generative Latent Space Models for Text and Image
Published on Feb 25, 20079288 Views
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Chapter list
Generative Latent Space Models for Text and Image - a probabilistic graphical overview00:00
Outline01:23
Apoptosis + Medicine02:19
Apoptosis + Medicine 0103:31
Apoptosis + Medicine 0203:52
Probabilistic Graphical Model Primer04:18
What is a graphical model?05:03
Recap of Basic Prob. Concepts06:37
Dependencies among variables08:13
Graphical Models09:13
Graphical Models, con'd12:16
Two types of GMs15:35
Directed Graphical Models16:10
Bayesian Network17:00
Conditional probability tables<br>(CPTs)19:59
Conditional probability density<br>func. (CPDs)20:31
Markov Random Fields21:11
An (incomplete) genealogy of graphical models22:22
GM application: Speech<br>recognition23:20
GM application: Evolution24:30
GM application: Solid State <br>Physics25:26
Probabilistic Inference26:10
Learning Graphical Models30:14
A GM course33:47
The Problem34:17
Modeling document collections36:32
Probabilistic Modeling of Text<br> Documents37:34
Questions of Interest38:10
Connecting Probability Models to <br>Data38:33
Conditionally Independent <br>Observations39:36
“Plate” Notation40:14
Example: Gaussian Model40:28
Example: Bayesian Gaussian<br> Model41:43
Latent Semantic Structure41:59
GENERATIVE PROCESS43:20
A generative model for<br> documents46:14
Probabilistic LSI46:40
Probabilistic LSI0148:43
Latent Dirichlet Allocation49:58
LDA50:46
Correlated Topic Model50:53
Inference Tasks54:08
Bayesian inference54:20
Approximate Inference55:16
Bayesian model selection57:00
Bayesian model selection 0157:10
Bayesian model selection 0257:32
The desired LL curve57:33
Integrating Topics and Syntax57:46
Topic Hierarchies58:45
Modeling Topic Evolution59:51
Latent Space Models for Images01:00:30
Image representation01:01:25
Exchangeability01:02:46
Corr-LDA01:03:43
Automatic annotation01:05:05
Text-based image retrieval01:06:08
Appendix: approximate inference01:06:28