Building on recent progress at the intersection of combinatorial optimization and deep
learning, we propose an end-to-end trainable architecture for deep graph matching that …

In this paper, we present a new approach for establishing correspondences between sparse
image features related by an unknown nonrigid mapping and corrupted by clutter and …

We propose a new family of message passing techniques for MAP estimation in graphical
models which we call Sequential Reweighted Message Passing (SRMP). Special cases …

This monograph is about combinatorial optimization. More precisely, about a special class of
combinatorial problems known as energy minimization or maximum a posteriori (MAP) …

Several earth observation satellites acquire image bands with different spatial resolutions,
eg, a panchromatic band with high resolution and spectral bands with lower resolution …

Abstract Dense, discrete Graphical Models with pairwise potentials are a powerful class of
models which are employed in state-of-the-art computer vision and bio-imaging …

We present a new proximal bundle method for Maximum-A-Posteriori (MAP) inference in
structured energy minimization problems. The method optimizes a Lagrangean relaxation of …

Graphical models provide compact representations of complex probability distributions of
many random variables through a collection of potential functions defined on small subsets …

We study a class of convex-concave saddle-point problems of the form $\min_x\max_y⟨ Kx,
y⟩+ f_ {\cal P}(x)-h^*(y) $ where $ K $ is a linear operator, $ f_ {\cal P} $ is the sum of a …

The current financial crisis motivates the study of correlated defaults in financial systems. In
this paper we focus on such a model, which is based on Markov random fields. This is a …