Powerful statistical models that can be learned efficiently from large amounts of data are
currently revolutionizing computer vision. These models possess a rich internal structure …

Graphical models, a marriage between probability theory and graph theory, provide a
natural tool for dealing with two problems that occur throughout applied mathematics and …

Likelihood-based learning of graphical models faces challenges of computational
complexity and robustness to model misspecification. This paper studies methods that fit …

Research into methods for reasoning under uncertainty is currently one of the most exciting
areas of artificial intelligence, largely because it has recently become possible to record …

Several decades of research in the field of machine learning have resulted in a multitude of
different algorithms for solving a broad range of problems. To tackle a new application, a …

Graphical models are often used “inappropriately,” with approximations in the topology,
inference, and prediction. Yet it is still common to train their parameters to approximately …

This paper is a multidisciplinary review of empirical, statistical learning from a graphical
model perspective. Well-known examples of graphical models include Bayesian networks …

The following quotation, from the Preface of [Jor99], provides a very concise introduction to
graphical models. Graphical models are a marriage between probability theory and graph …

Graphical models for structured domains are powerful tools, but the computational
complexities of combinatorial prediction spaces can force restrictions on models, or require …

We propose a general modeling and inference framework that combines the complementary
strengths of probabilistic graphical models and deep learning methods. Our model family …