The theory of compressed sensing shows that samples in the form of random projections are optimal for recovering sparse signals in high-dimensional spaces (i.e., finding needles ...
Rui M. Castro, Jarvis Haupt, Robert Nowak, Gil M. ...
— Machine learning has made great progress during the last decades and is being deployed in a wide range of applications. However, current machine learning techniques are far fro...
— Representation of knowledge within a neural model is an active field of research involved with the development of alternative structures, training algorithms, learning modes an...
Abstract—Multicast-based network tomography enables inference of average loss rates and delay distributions of internal network links from end-to-end measurements of multicast pr...
—Structured Peer-to-Peer (P2P) systems are highly scalable, self-organizing, and support efficient lookups. Furthermore, Distributed Hash Tables (DHTs), due to their features, a...