Learning Energy-Based Models
of
High-Dimensional Data
Discovering causal structure as a goal for unsupervised learning
A different kind of hidden structure
A trade-off between how well the model fits the data and the tractability of inference
Energy-Based Models with deterministic hidden units
Maximum likelihood learning is hard
Backpropagation can compute the gradient that Hybrid Monte Carlo needs
The online HMC learning procedure
Frequently Approximately Satisfied constraints
Learning constraints from
natural images
(Yee-Whye Teh)