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2190 results in Pattern Recognition and Machine Learning

Page 7 of 110

Deep Learning for Biomedical Image Reconstruction
  • Edited by Jong Chul Ye, Yonina C. Eldar, Michael Unser
  • Published online:
    15 September 2023
    Print publication:
    12 October 2023
  • Discover the power of deep neural networks for image reconstruction with this state-of-the-art review of modern theories and applications. The background theory of deep learning is introduced step-by-step, and by incorporating modeling fundamentals this book explains how to implement deep learning in a variety of modalities, including X-ray, CT, MRI and others. Real-world examples demonstrate an interdisciplinary approach to medical image reconstruction processes, featuring numerous imaging applications. Recent clinical studies and innovative research activity in generative models and mathematical theory will inspire the reader towards new frontiers. This book is ideal for graduate students in Electrical or Biomedical Engineering or Medical Physics.

Adversarial Learning and Secure AI
  • David J. Miller, Zhen Xiang, George Kesidis
  • Published online:
    07 September 2023
    Print publication:
    31 August 2023
  • Providing a logical framework for student learning, this is the first textbook on adversarial learning. It introduces vulnerabilities of deep learning, then demonstrates methods for defending against attacks and making AI generally more robust. To help students connect theory with practice, it explains and evaluates attack-and-defense scenarios alongside real-world examples. Feasible, hands-on student projects, which increase in difficulty throughout the book, give students practical experience and help to improve their Python and PyTorch skills. Book chapters conclude with questions that can be used for classroom discussions. In addition to deep neural networks, students will also learn about logistic regression, naïve Bayes classifiers, and support vector machines. Written for senior undergraduate and first-year graduate courses, the book offers a window into research methods and current challenges. Online resources include lecture slides and image files for instructors, and software for early course projects for students.

  • 1 - Introduction to Robust Statistics

  • Ilias Diakonikolas, University of Wisconsin-Madison, Daniel M. Kane, University of California, San Diego
  • Book:
    Algorithmic High-Dimensional Robust Statistics
    Published online:
    24 August 2023
    Print publication:
    07 September 2023, pp 1-28
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Page 7 of 110