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9096 results in Communications, Signal Processing and Information Theory

Page 61 of 455

High-Dimensional Data Analysis with Low-Dimensional Models
  • Principles, Computation, and Applications
  • John Wright, Yi Ma
  • Published online:
    11 March 2022
    Print publication:
    13 January 2022
  • Connecting theory with practice, this systematic and rigorous introduction covers the fundamental principles, algorithms and applications of key mathematical models for high-dimensional data analysis. Comprehensive in its approach, it provides unified coverage of many different low-dimensional models and analytical techniques, including sparse and low-rank models, and both convex and non-convex formulations. Readers will learn how to develop efficient and scalable algorithms for solving real-world problems, supported by numerous examples and exercises throughout, and how to use the computational tools learnt in several application contexts. Applications presented include scientific imaging, communication, face recognition, 3D vision, and deep networks for classification. With code available online, this is an ideal textbook for senior and graduate students in computer science, data science, and electrical engineering, as well as for those taking courses on sparsity, low-dimensional structures, and high-dimensional data. Foreword by Emmanuel Candès.

Fundamentals of Classical and Modern Error-Correcting Codes
  • Shu Lin, Juane Li
  • Published online:
    02 February 2022
    Print publication:
    09 December 2021
  • Using easy-to-follow mathematics, this textbook provides comprehensive coverage of block codes and techniques for reliable communications and data storage. It covers major code designs and constructions from geometric, algebraic, and graph-theoretic points of view, decoding algorithms, error control additive white Gaussian noise (AWGN) and erasure, and dataless recovery. It simplifies a highly mathematical subject to a level that can be understood and applied with a minimum background in mathematics, provides step-by-step explanation of all covered topics, both fundamental and advanced, and includes plenty of practical illustrative examples to assist understanding. Numerous homework problems are included to strengthen student comprehension of new and abstract concepts, and a solutions manual is available online for instructors. Modern developments, including polar codes, are also covered. An essential textbook for senior undergraduates and graduates taking introductory coding courses, students taking advanced full-year graduate coding courses, and professionals working on coding for communications and data storage.

  • 16 - Deep Networks for Classification

  • from Part III - Applications to Real-World Problems
  • John Wright, Columbia University, New York, Yi Ma, University of California, Berkeley
  • Book:
    High-Dimensional Data Analysis with Low-Dimensional Models
    Published online:
    11 March 2022
    Print publication:
    13 January 2022, pp 526-572

  • Summary

    In the past decade or so, (deep) neural networks have captured people’s imagination through their empirical success in learning problems involving real-world high-dimensional data such as images, speech, and text [LBH15]. Nevertheless, there is quite a bit of mystery as to how deep networks achieve such striking results. Modern deep networks are typically designed through trial and error.

Page 61 of 455