Bethe free energy and contrastive divergence approximations for undirected graphical models
As the machine learning community tackles more complex and harder problems, the graphical models needed to solve such problems become larger and more complicated. As a result performing inference and learning exactly for such graphical models become ever more expensive, and approximate inference and learning techniques become ever more prominent. 
There are a variety of techniques for approximate inference and learning in the literature. This thesis contributes some new ideas in the products of experts (PoEs) class of models (Hinton, 2002), and the Bethe free energy approximations (Yedidia et al., 2001). 
For PoEs, our contribution is in developing new PoE models for continuous-valued domains. We developed RBMrate, a model for discretized continuous-valued data. We applied it to face recognition to demonstrate its abilities. We also developed energy-based models (EBMs)—flexible probabilistic models where the building blocks consist of energy terms computed using a feed-forward network. We show that standard square noiseless independent components analysis (ICA) (Bell and Sejnowski, 1995) can be viewed as a restricted form of EBMs. Extending this relationship with ICA, we describe sparse and over-complete representations of data where the inference process is trivial since it is simply an EBM. 
For Bethe free energy approximations, our contribution is a theory relating belief propagation and iterative scaling. We show that both belief propagation and iterative scaling updates can be derived as fixed point equations for constrained minimization of the Bethe free energy. This allows us to develop a new algorithm to directly minimize the Bethe free energy, and to apply the Bethe free energy to learning in addition to inference. We also describe improvements to the efficiency of standard learning algorithms for undirected graphical models (Jirousek and Preucil, 1995).