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2190 results in Pattern Recognition and Machine Learning

Page 29 of 110

Control Systems and Reinforcement Learning
  • Sean Meyn
  • Published online:
    17 May 2022
    Print publication:
    09 June 2022
  • A high school student can create deep Q-learning code to control her robot, without any understanding of the meaning of 'deep' or 'Q', or why the code sometimes fails. This book is designed to explain the science behind reinforcement learning and optimal control in a way that is accessible to students with a background in calculus and matrix algebra. A unique focus is algorithm design to obtain the fastest possible speed of convergence for learning algorithms, along with insight into why reinforcement learning sometimes fails. Advanced stochastic process theory is avoided at the start by substituting random exploration with more intuitive deterministic probing for learning. Once these ideas are understood, it is not difficult to master techniques rooted in stochastic control. These topics are covered in the second part of the book, starting with Markov chain theory and ending with a fresh look at actor-critic methods for reinforcement learning.

Introduction to Graph Signal Processing
  • Antonio Ortega
  • Published online:
    09 May 2022
    Print publication:
    09 June 2022
  • An intuitive and accessible text explaining the fundamentals and applications of graph signal processing. Requiring only an elementary understanding of linear algebra, it covers both basic and advanced topics, including node domain processing, graph signal frequency, sampling, and graph signal representations, as well as how to choose a graph. Understand the basic insights behind key concepts and learn how graphs can be associated to a range of specific applications across physical, biological and social networks, distributed sensor networks, image and video processing, and machine learning. With numerous exercises and Matlab examples to help put knowledge into practice, and a solutions manual available online for instructors, this unique text is essential reading for graduate and senior undergraduate students taking courses on graph signal processing, signal processing, information processing, and data analysis, as well as researchers and industry professionals.

The Principles of Deep Learning Theory
  • An Effective Theory Approach to Understanding Neural Networks
  • Daniel A. Roberts, Sho Yaida
  • With contributions by Boris Hanin
  • Published online:
    05 May 2022
    Print publication:
    26 May 2022
  • This textbook establishes a theoretical framework for understanding deep learning models of practical relevance. With an approach that borrows from theoretical physics, Roberts and Yaida provide clear and pedagogical explanations of how realistic deep neural networks actually work. To make results from the theoretical forefront accessible, the authors eschew the subject's traditional emphasis on intimidating formality without sacrificing accuracy. Straightforward and approachable, this volume balances detailed first-principle derivations of novel results with insight and intuition for theorists and practitioners alike. This self-contained textbook is ideal for students and researchers interested in artificial intelligence with minimal prerequisites of linear algebra, calculus, and informal probability theory, and it can easily fill a semester-long course on deep learning theory. For the first time, the exciting practical advances in modern artificial intelligence capabilities can be matched with a set of effective principles, providing a timeless blueprint for theoretical research in deep learning.

  • 18 - Group Testing

  • from Part Three - Compressive Sensing
  • Simon Foucart, Texas A & M University
  • Book:
    Mathematical Pictures at a Data Science Exhibition
    Published online:
    21 April 2022
    Print publication:
    28 April 2022, pp 149-156

  • Summary

    A Boolean analog of the standard compressive sensing problem, known as nonadaptive group testing, is analyzed in this chapter. Its success is characterized via the notion of separability (intimately related to disjunctiveness and strong selectivity) of the testing procedure. The minimal number of tests making separability possible is determined, and a deterministic procedure using roughly this number of tests is presented. Finally, it is shown that solving a linear feasibility program allows one to exactly recover sparse binary vectors from the outcomes of a separable testing procedure.

Page 29 of 110