The formalism of probabilistic graphical models provides a unifying framework for capturing
complex dependencies among random variables, and building large-scale multivariate …

We show how the success of deep learning could depend not only on mathematics but also
on physics: although well-known mathematical theorems guarantee that neural networks …

Situation-aware technologies enabled by multitarget tracking will lead to new services and
applications in fields such as autonomous driving, indoor localization, robotic networks, and …

Dual decomposition, and more generally Lagrangian relaxation, is a classical method for
combinatorial optimization; it has recently been applied to several inference problems in …

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 …

This paper introduces a new rigorous theoretical framework to address discrete MRF-based
optimization in computer vision. Such a framework exploits the powerful technique of Dual …

We present a novel message passing algorithm for approximating the MAP problem in
graphical models. The algorithm is similar in structure to max-product but unlike max-product …

The canonical problem of solving a system of linear equations arises in numerous contexts
in information theory, communication theory, and related fields. In this contribution, we …

Inference problems in graphical models can be represented as a constrained optimization of
a free-energy function. In this paper, we treat both forms of probabilistic inference, estimating …

We propose a new algorithm for approximate MAP inference on factor graphs, by combining
augmented Lagrangian optimization with the dual decomposition method. Each slave …