Deep Learning through Sparse and Low-Rank Modeling
  • Release Date : 15 May 2019
  • Publisher : Academic Press
  • Genre : Computers
  • Pages : 300 pages
  • ISBN 13 : 9780128136591
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Download or read book entitled Deep Learning through Sparse and Low-Rank Modeling by author: Zhangyang Wang which was release on 15 May 2019 and published by Academic Press with total page 300 pages . This book available in PDF, EPUB and Kindle Format. Deep Learning through Sparse Representation and Low-Rank Modeling bridges classical sparse and low rank models-those that emphasize problem-specific Interpretability-with recent deep network models that have enabled a larger learning capacity and better utilization of Big Data. It shows how the toolkit of deep learning is closely tied with the sparse/low rank methods and algorithms, providing a rich variety of theoretical and analytic tools to guide the design and interpretation of deep learning models. The development of the theory and models is supported by a wide variety of applications in computer vision, machine learning, signal processing, and data mining. This book will be highly useful for researchers, graduate students and practitioners working in the fields of computer vision, machine learning, signal processing, optimization and statistics. Combines classical sparse and low-rank models and algorithms with the latest advances in deep learning networks Shows how the structure and algorithms of sparse and low-rank methods improves the performance and interpretability of Deep Learning models Provides tactics on how to build and apply customized deep learning models for various applications

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Author : Shuguang Cui,Alfred O. Hero, III,Zhi-Quan Luo,José M. F. Moura
Publisher : Cambridge University Press
Genre : Technology & Engineering
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