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. ...
Execution and communication traces are central to performance modeling and analysis. Since the traces can be very long, meaningful compression and extraction of representative beha...
Kernel machines are a popular class of machine learning algorithms that achieve state of the art accuracies on many real-life classification problems. Kernel perceptrons are among...
Abstract. We propose measures for compressed data structures, in which space usage is measured in a data-aware manner. In particular, we consider the fundamental dictionary problem...
—Reliable wireless communications often requires accurate knowledge of the underlying multipath channel. This typically involves probing of the channel with a known training wave...
Waheed Uz Zaman Bajwa, Jarvis Haupt, Gil M. Raz, R...