Efficient Methods for Unsupervised Learning of Probabilistic Models

This addresses the problem of handling complex probabilistic models for researchers in machine learning, but it appears incremental based on the vague description.

The thesis developed techniques for training, evaluating, and sampling from intractable and high-dimensional probabilistic models, but no concrete results or numbers are provided in the abstract.