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9096 results in Communications, Signal Processing and Information Theory

Page 99 of 455

  • 5 - Discrepancy

  • from Part I - Communication
  • Anup Rao, University of Washington, Amir Yehudayoff, Technion - Israel Institute of Technology, Haifa
  • Book:
    Communication Complexity
    Published online:
    30 January 2020
    Print publication:
    20 February 2020, pp 67-92

  • Summary

    Explains the notion of discrepancy. Describesconnections to randomized communicationcomplexity. Proves lower bounds using convexityand concentration of measure.

  • 8 - Lifting

  • from Part I - Communication
  • Anup Rao, University of Washington, Amir Yehudayoff, Technion - Israel Institute of Technology, Haifa
  • Book:
    Communication Complexity
    Published online:
    30 January 2020
    Print publication:
    20 February 2020, pp 144-154

  • Summary

    Lifting is about simulating one computataionalmodel by another. Use lifting to provecommunication complexity lower bounds.

  • 2 - Rank

  • from Part I - Communication
  • Anup Rao, University of Washington, Amir Yehudayoff, Technion - Israel Institute of Technology, Haifa
  • Book:
    Communication Complexity
    Published online:
    30 January 2020
    Print publication:
    20 February 2020, pp 33-45

  • Summary

    Using matrix rank to prove lower bounds oncommunication complexity. Using the structure oflow-rank Boolean matrices to deduce efficientprotocols.

Machine Learning Refined
  • Foundations, Algorithms, and Applications
  • 2nd edition
  • Jeremy Watt, Reza Borhani, Aggelos K. Katsaggelos
  • Published online:
    05 February 2020
    Print publication:
    09 January 2020
  • With its intuitive yet rigorous approach to machine learning, this text provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products. The authors prioritize geometric intuition and algorithmic thinking, and include detail on all the essential mathematical prerequisites, to offer a fresh and accessible way to learn. Practical applications are emphasized, with examples from disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology. Over 300 color illustrations are included and have been meticulously designed to enable an intuitive grasp of technical concepts, and over 100 in-depth coding exercises (in Python) provide a real understanding of crucial machine learning algorithms. A suite of online resources including sample code, data sets, interactive lecture slides, and a solutions manual are provided online, making this an ideal text both for graduate courses on machine learning and for individual reference and self-study.

Communication Complexity
  • and Applications
  • Anup Rao, Amir Yehudayoff
  • Published online:
    30 January 2020
    Print publication:
    20 February 2020
  • Communication complexity is the mathematical study of scenarios where several parties need to communicate to achieve a common goal, a situation that naturally appears during computation. This introduction presents the most recent developments in an accessible form, providing the language to unify several disjoint research subareas. Written as a guide for a graduate course on communication complexity, it will interest a broad audience in computer science, from advanced undergraduates to researchers in areas ranging from theory to algorithm design to distributed computing. The first part presents basic theory in a clear and illustrative way, offering beginners an entry into the field. The second part describes applications including circuit complexity, proof complexity, streaming algorithms, extension complexity of polytopes, and distributed computing. Proofs throughout the text use ideas from a wide range of mathematics, including geometry, algebra, and probability. Each chapter contains numerous examples, figures, and exercises to aid understanding.

Page 99 of 455