Abstract In this paper we propose a reduced-reference quality assessment algorithm which computes an approximation of the Structural SIMilarity (SSIM) metrics exploiting coding too...
Marco Tagliasacchi, Giuseppe Valenzise, Matteo Nac...
We present a new algorithm for minimizing a convex loss-function subject to regularization. Our framework applies to numerous problems in machine learning and statistics; notably,...
We devise the first constant-approximate feasibility test for sporadic multiprocessor real-time scheduling. We give an algorithm that, given a task system and > 0, correctly de...
Vincenzo Bonifaci, Alberto Marchetti-Spaccamela, S...
The class of algorithms for approximating reasoning tasks presented in this paper is based on approximating the general bucket elimination framework. The algorithms have adjustabl...