Markov random field models provide a robust formulation of low-level vision problems. Among the problems, stereo vision remains the most investigated field. The belief propagation...
In this paper, we investigate the use of parallelization in reinforcement learning (RL), with the goal of learning optimal policies for single-agent RL problems more quickly by us...
In this paper, we suggest a parallel algorithm based on a shared memory SIMD architecture for solving an n item subset-sum problem in time O(2n/2 /p) by using p = 2q processors, 0...
Carlos Alberto Alonso Sanches, Nei Yoshihiro Soma,...
Graph algorithms are becoming increasingly important for solving many problems in scientific computing, data mining and other domains. As these problems grow in scale, parallel c...
Andrew Lumsdaine, Douglas Gregor, Bruce Hendrickso...
We present a new class of parallel and distributed audio concealment (PDAC) algorithms which recover lost audio packets at the receiver to fight against channel impairment. The m...