CSC321 2007
Lecture 23: Sigmoid Belief Nets and the
wake-sleep algorithm
Bayes Nets:
Directed Acyclic Graphical models
Ways to define the conditional probabilities
What is easy and what is hard in a DAG?
The learning rule for sigmoid belief nets
The flaws in the wake-sleep algorithm
Why its hard to learn sigmoid belief nets one layer at a time
Using complementary priors to eliminate explaining away
An example of a complementary prior
Inference in a DAG with replicated weights
A picture of the Boltzmann machine learning algorithm for an RBM
Another explanation of the contrastive divergence learning procedure
The up-down algorithm:
A contrastive divergence version of wake-sleep