Deep Learning through Sparse and Low-Rank Modeling

出版者:Academic Press

作者:Zhangyang Wang

页数:296

出版时间:2019-4-12

价格:USD 99.95

装帧:平装

isbn号码:9780128136591

具体描述

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

作者简介

[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.

目录信息

1. Introduction
2. Bi-Level Sparse Coding: A Hyperspectral Image Classification Example
3. Deep Encoders: A Model Unfolding Example
4. Single Image Super-Resolution: From Sparse Coding to Deep Learning
5. From Bi-Level Sparse Clustering to Deep Clustering
6. Signal Processing
7. Dimensionality Reduction
8. Action Recognition
9. Style Recognition and Kinship Understanding
10. Image Dehazing: Improved Techniques
11. Biomedical Image Analytics: Automated Lung Cancer Diagnosis
· · · · · · (收起)

用户评价

作为一名对深度学习理论边界充满好奇的研究者，我希望这本书能够填补我在理解模型内在机理方面的一些空白。我期待它能提供一种不同于传统深度学习教材的视角，更加侧重于从信息论和优化理论的角度来剖析深度学习模型的性能。书中关于“稀疏”的论述，是否会深入到稀疏表示定理、稀疏编码等基础理论，并将其与深度神经网络中的激活函数、正则化技术联系起来？而“低秩”建模，我猜想它可能与矩阵分解、张量分解等技术息息相关，并有望在处理图像、视频、推荐系统等高维数据时展现出强大的优势。我希望书中不仅会介绍这些数学工具，更会展示它们在深度学习模型中的具体应用，例如，如何利用低秩假设来加速大规模矩阵运算，或者如何通过低秩分解来学习数据的潜在因子。我更期待书中能够探讨稀疏性和低秩性如何共同作用，在提升模型效率和泛化能力的同时，也可能为模型的理论分析提供新的工具和框架。

初次翻开这本书，我怀揣着对深度学习领域深入探索的好奇心，尤其被“稀疏”和“低秩”这两个关键词所吸引。我预期这本书会像一把精密的钥匙，打开我理解深度学习模型背后数学原理的新视角。想象中，它会详细阐述为何在海量数据和高维参数空间中，稀疏性和低秩性能够扮演如此关键的角色，例如，它们如何在特征选择、模型压缩、噪声消除以及提升模型泛化能力等方面发挥作用。我期待书中能够有详实的理论推导，清晰的数学公式，以及对这些数学概念在实际深度学习应用中如何体现的生动解读。例如，在卷积神经网络（CNN）中，滤波器的稀疏性是否与局部感受野和权重共享紧密相关？在循环神经网络（RNN）中，低秩表示又如何帮助我们捕捉序列中的长期依赖关系？书中是否会涵盖一些经典稀疏学习算法（如L1正则化）或低秩分解技术（如SVD）在深度学习模型中的具体实现和优化策略？我希望这本书不仅能给我带来理论上的启迪，更能让我对接下来的深度学习实践有更深刻的认识，甚至能够启发我思考新的模型设计思路。

拿到这本书，我首先想到的是它可能提供的一些“高级”的技巧，能够帮助我提升深度学习模型的性能，尤其是在处理大规模、高维数据集时。我期待书中能够详细阐述稀疏建模如何在特征提取和特征选择阶段发挥作用，例如，如何通过Lasso、Elastic Net等方法来识别出对预测任务最重要的特征，从而简化模型，提高效率。对于低秩建模，我猜想它可能在数据降维、噪声去除以及模型压缩方面有独特的贡献。我希望能看到书中介绍如何利用低秩近似来近似复杂的权重矩阵，从而大幅减少模型的参数数量，使得模型更易于部署和推理。此外，我很好奇书中是否会探讨稀疏性和低秩性在不同深度学习架构中的具体体现，例如，如何在Transformer模型中使用稀疏注意力机制，或者如何通过低秩分解来优化循环神经网络的状态表示。总而言之，我希望这本书能够提供一套实用的方法论，帮助我构建出更高效、更鲁棒的深度学习模型。

这本书封面上的“稀疏”与“低秩”字样，让我联想到自己在处理实际数据时常常遇到的挑战：维度灾难和计算效率。我希望这本书能提供一种系统性的方法来应对这些问题。我设想书中会深入探讨如何利用稀疏性来构建更简洁、更具解释性的模型，比如通过非零权重来识别重要的特征，从而帮助我们理解模型决策的过程。同时，我对低秩建模在数据表示和降维方面的应用尤为感兴趣。我期待书中能够解释，为什么很多高维数据在高秩的表象下，实际上隐藏着低维的本质结构，以及如何通过低秩近似来有效地捕捉这些结构。书中是否会介绍一些常用的低秩分解方法，比如主成分分析（PCA）的变种，或者一些更适合深度学习场景的低秩表示学习技术？我更希望能看到这些技术如何与深度学习框架相结合，比如如何用低秩约束来优化神经网络的权重矩阵，从而减少参数量，加速训练，并可能缓解过拟合。这本书能否成为一本指导我如何从庞杂的数据中挖掘出有价值的低维信息，并将其高效地应用于深度学习任务的实践手册？

翻阅这本书，我的大脑中充满了对深度学习模型背后数学原理的探求。我期望书中能像一位经验丰富的向导，带领我深入“稀疏”和“低秩”这两个概念的精髓，理解它们如何成为深度学习的基石。我设想书中会从基础的线性代数和优化理论出发，逐步引出稀疏表示、稀疏恢复以及低秩近似等核心概念，并用清晰的数学语言和直观的图示加以解释。我期待书中能具体说明，在神经网络的训练过程中，如何利用这些数学工具来优化模型的学习过程，例如，如何通过稀疏性约束来鼓励模型学习到更加紧凑和易于理解的表示，或者如何利用低秩假设来简化复杂的模型结构，从而提升计算效率和泛化能力。我希望书中能够提供一些具体的案例研究，展示稀疏和低秩建模在图像识别、自然语言处理、推荐系统等不同领域的应用，并分析这些方法在实际任务中带来的性能提升。总的来说，我期待这本书能成为我理解深度学习模型内在机制的“秘密武器”。