We present a new algorithm for bound-constrained totalvariation (TV) regularization that in comparison with its predecessors is simple, fast, and flexible. We use a splitting app...
This paper proposes a guaranteed robust bounded-error distributed estimation algorithm. It may be employed to perform parameter estimation from data collected in a network of wire...
One potential strength of recurrent neural networks (RNNs) is their – theoretical – ability to find a connection between cause and consequence in time series in an constraint-...
We introduce the terms strong sub- and super-Gaussianity to refer to the previously introduced class of densities log-concave is x2 and log-convex in x2 respectively. We derive rel...
Jason A. Palmer, Kenneth Kreutz-Delgado, Scott Mak...
In this work, the problem of the estimation of parameters in case of mixtures of models composed by the sum of multiple Gaussians is considered. It will be shown how this estimati...