Deep Learning through Sparse and Low-Rank Modeling 2024 pdf epub mobi 电子书


Deep Learning through Sparse and Low-Rank Modeling

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Deep Learning through Sparse and Low-Rank Modeling 2024 pdf epub mobi 电子书 著者简介

[Zhangyang Wang]

During 2012-2016, he was a Ph.D. student in the Electrical and Computer Engineering (ECE) Department, at the University of Illinois at Urbana-Champaign (UIUC), working with Professor Thomas S. Huang. Prior to that, he obtained the B.E. degree at the University of Science and Technology of China (USTC), in 2012. Dr. Wang's research has been addressing machine learning, computer vision and image processing problems using advanced feature learning techniques. He has co-authored over 30 papers, and published the book “Sparse Coding and Its Applications in Computer Vision”. He has been granted 3 patents.

[Yun Fu]

Dr. Fu is an interdisciplinary faculty member affiliated with College of Engineering and the College of Computer and Information Science at Northeastern University. He received the B.Eng. degree in information engineering and the M.Eng. degree in pattern recognition and intelligence systems from Xi'an Jiaotong University, China, respectively, and the M.S. degree in statistics and the Ph.D. degree in electrical and computer engineering from the University of Illinois at Urbana-Champaign, respectively. Dr. Fu's research interests are Interdisciplinary research in Machine Learning and Computational Intelligence, Social Media Analytics, Human-Computer Interaction, and Cyber-Physical Systems. He has extensive publications in leading journals, books/book chapters and international conferences/workshops.

[Thomas Huang]

Thomas S. Huang received his B.S. Degree in Electrical Engineering from National Taiwan University, Taipei, Taiwan, China; and his M.S. and Sc.D. Degrees in Electrical Engineering from the Massachusetts Institute of Technology, Cambridge, Massachusetts. He was on the Faculty of the Department of Electrical Engineering at MIT from 1963 to 1973; and on the Faculty of the School of Electrical Engineering and Director of its Laboratory for Information and Signal Processing at Purdue University from 1973 to 1980. Dr. Huang's professional interests lie in the broad area of information technology, especially the transmission and processing of multidimensional signals. He has published 21 books, and over 600 papers in Network Theory, Digital Filtering, Image Processing, and Computer Vision. Among his many honors and awards: Honda Lifetime Achievement Award, IEEE Jack Kilby Signal Processing Medal, and the King-Sun Fu Prize of the International Association for Pattern Recognition.


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发表于2024-11-20

Deep Learning through Sparse and Low-Rank Modeling 2024 pdf epub mobi 电子书

Deep Learning through Sparse and Low-Rank Modeling 2024 pdf epub mobi 电子书

Deep Learning through Sparse and Low-Rank Modeling 2024 pdf epub mobi 电子书



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出版者:Academic Press
作者:Zhangyang Wang
出品人:
页数:296
译者:
出版时间:2019-4-12
价格:USD 99.95
装帧:平装
isbn号码:9780128136591
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图书标签: Machine_Learning  Clustering   


Deep Learning through Sparse and Low-Rank Modeling 2024 pdf epub mobi 电子书 图书描述

https://www.elsevier.com/books/deep-learning-through-sparse-and-low-rank-modeling/wang/978-0-12-813659-1

Description:

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.

Key Features:

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

Readership:

Researchers and graduate students in computer vision, machine learning, signal processing, optimization, and statistics

Deep Learning through Sparse and Low-Rank Modeling 2024 pdf epub mobi 电子书

Deep Learning through Sparse and Low-Rank Modeling 2024 pdf epub mobi 电子书
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Deep Learning through Sparse and Low-Rank Modeling 2024 pdf epub mobi 电子书


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