Generative Latent Space Models for Text and Image

Posted in Science on July 26, 2008


Generative Latent Space Models for Text and Image

Lecture slides:

  • Outline
  • Apoptosis + Medicine
  • Probabilistic Graphical Model Primer
  • What is a graphical model?
  • Recap of Basic Prob. Concepts
  • Dependencies among variables
  • Graphical Models
  • Graphical Models, con'd
  • Two types of GMs
  • Directed Graphical Models
  • Bayesian Network
  • Conditional probability tables (CPTs)
  • Conditional probability density func. (CPDs)
  • Markov Random Fields
  • An (incomplete) genealogy of graphical models
  • GM application: Speech recognition
  • GM application: Evolution
  • GM application: Solid State Physics
  • Probabilistic Inference
  • Learning Graphical Models
  • A GM course
  • The Problem
  • Modeling document collections
  • Probabilistic Modeling of Text Documents
  • Questions of Interest
  • Connecting Probability Models to Data
  • Conditionally Independent Observations
  • “Plate” Notation
  • Example: Gaussian Model
  • Example: Bayesian Gaussian Model
  • Latent Semantic Structure
  • GENERATIVE PROCESS
  • A generative model for documents
  • Probabilistic LSI
  • Latent Dirichlet Allocation
  • LDA
  • Correlated Topic Model
  • Inference Tasks
  • Bayesian inference
  • Approximate Inference
  • Bayesian model selection
  • The desired LL curve
  • Integrating Topics and Syntax
  • Topic Hierarchies
  • Modeling Topic Evolution
  • Latent Space Models for Images
  • Image representation
  • Exchangeability
  • Corr-LDA
  • Automatic annotation
  • Text-based image retrieval
  • Appendix: approximate inference

Author: Eeric Xing, Carnegie Mellon University

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