Sparse coding--that is, modelling data vectors as sparse linear combinations of basis elements--is widely used in machine learning, neuroscience, signal processing, and statistics...
Julien Mairal, Francis Bach, Jean Ponce, Guillermo...
Owing to the stochastic nature of discrete processes such as photon counts in imaging, a variety of real-world data are well modeled as Poisson random variables whose means are in...
Abstract Multi-agent cooperation can in several cases be used in order to mitigate problems relating to task sharing within physical processes. In this paper we apply agent based s...
Christian Johansson, Fredrik Wernstedt, Paul David...
We consider state estimation of a Markov stochastic process using an ad hoc wireless sensor network (WSN) based on noisy linear observations. Due to power and bandwidth constraint...
Eric J. Msechu, Alejandro Ribeiro, Stergios I. Rou...
We treat the problem of Blind Deconvolution of Single Input - Single Output (SISO) systems with real or complex binary sources. We explicate the basic mathematical idea by focusing...
Konstantinos I. Diamantaras, Theophilos Papadimitr...