In this paper, we present the Gauss-Newton method as a unified approach to optimizing non-linear noise compensation models, such as vector Taylor series (VTS), data-driven parall...
A distributed adaptive algorithm to estimate a time-varying signal, measured by a wireless sensor network, is designed and analyzed. The presence of measurement noises and of packe...
Carlo Fischione, Alberto Speranzon, Karl Henrik Jo...
We consider the problem of actively learning the mean values of distributions associated with a finite number of options. The decision maker can select which option to generate t...
In IP-based TV distribution, coding degradation is sometimes evident in critical scenes because the bit rate for compression is rather low. Prefiltering is an effective counterme...
Policy gradient (PG) reinforcement learning algorithms have strong (local) convergence guarantees, but their learning performance is typically limited by a large variance in the e...