Abstract. This paper describes a novel approach to recovering a parametric deformation that optimally registers one image to another. The method proceeds by constructing a global c...
We combine three threads of research on approximate dynamic programming: sparse random sampling of states, value function and policy approximation using local models, and using lo...
We study approaches that fit a linear combination of basis functions to the continuation value function of an optimal stopping problem and then employ a greedy policy based on the...
We introduce a new sequential importance sampling (SIS) algorithm which propagates in time a Monte Carlo approximation of the posterior fixed-lag smoothing distribution of the symb...
The schedulability analysis problem for many realistic task models is intractable. Therefore known algorithms either have exponential complexity or at best can be solved in pseudo...