Conventional approaches to sampling signals or images follow Shannon's theorem: the
sampling rate must be at least twice the maximum frequency present in the signal (Nyquist …

Since their inception in the 1930–1960s, the research disciplines of computational imaging
and machine learning have followed parallel tracks and, during the last two decades …

Data-driven discovery is revolutionizing how we model, predict, and control complex
systems. Now with Python and MATLAB®, this textbook trains mathematical scientists and …

High-dimensional probability offers insight into the behavior of random vectors, random
matrices, random subspaces, and objects used to quantify uncertainty in high dimensions …

Machine learning is one of the fastest growing areas of computer science, with far-reaching
applications. The aim of this textbook is to introduce machine learning, and the algorithmic …

This first chapter formulates the objectives of compressive sensing. It introduces the
standard compressive problem studied throughout the book and reveals its ubiquity in many …

While logistic sigmoid neurons are more biologically plausible than hyperbolic tangent
neurons, the latter work better for training multi-layer neural networks. This paper shows that …

Theoretical results suggest that in order to learn the kind of complicated functions that can
represent high-level abstractions (eg, in vision, language, and other AI-level tasks), one may …

This paper introduces a novel algorithm to approximate the matrix with minimum nuclear
norm among all matrices obeying a set of convex constraints. This problem may be …

This paper demonstrates theoretically and empirically that a greedy algorithm called
Orthogonal Matching Pursuit (OMP) can reliably recover a signal with m nonzero entries in …