We address the problem of finding the most likely assignment or MAP estimation in a Markov random field. We analyze the linear programming formulation of MAP through the lens of...
We study the functions from Fm 2 into Fm 2 for odd m which oppose an optimal resistance to linear cryptanalysis. These functions are called almost bent. It is known that almost ben...
—This paper aims to develop a novel framework to systematically trade-off computational complexity with output distortion in linear multimedia transforms, in an optimal manner. T...
We present a two-step method for identifying SISO Hammerstein systems. First, using a persistent input with retrospective cost optimization, we estimate a parametric model of the l...
Anthony M. D'Amato, Kenny S. Mitchell, Bruno Ot&aa...
A key problem in reinforcement learning is finding a good balance between the need to explore the environment and the need to gain rewards by exploiting existing knowledge. Much ...