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 …

Mathematical optimization is now widely regarded as an indispensable modeling and
solution tool for the design of wireless communications systems. While optimization has …

Attention layers, as commonly used in transformers, form the backbone of modern deep
learning, yet there is no mathematical description of their benefits and deficiencies as …

Starting from where a first course in convex optimization leaves off, this text presents a
unified analysis of first-order optimization methods–including parallel-distributed algorithms …

Many performance gains achieved by massive multiple-input and multiple-output depend on
the accuracy of the downlink channel state information (CSI) at the transmitter (base station) …

Recently, over-parameterized deep networks, with increasingly more network parameters
than training samples, have dominated the performances of modern machine learning …

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

Dual encoders perform retrieval by encoding documents and queries into dense low-
dimensional vectors, scoring each document by its inner product with the query. We …

By providing explanations for users and system designers to facilitate better understanding
and decision making, explainable recommendation has been an important research …

Movable antenna (MA) is a new technology with great potential to improve communication
performance by enabling local movement of antennas for pursuing better channel …