4 accepted papers
Recent works suggests an interesting interplay between the information flow between inputs features and hidden representations of a learning and the ability of the algorithm to generalize from trained samples to unobserved data. For instance, some of regularization techniques used to control general…
We consider a distributed detection problem with statistically spatial dependent measurements in a sensor network, when there is not a fusion center. Thus, each node takes some measurements, does some processing, exchanges messages with its neighbors and finally makes a decision (typically the same…
We consider the problem of distributed detection over a network of sensors using energy measurements. Assuming that the problem is to detect the presence of stochastic signal with arbitrary temporal correlation, we exactly compute the characteristic function of the energy measurement at each sensing…
This paper investigates, from information theoretic principles, a learning problem based on the principle that any regularity in a given dataset can be exploited to extract compact features from data, in order to build meaningful representations of a relevant content. We begin by introducing the fun…