Deep Learning through Sparse and Low Rank Modeling

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

Produk Detail:

  • Author : Zhangyang Wang
  • Publisher : Academic Press
  • Pages : 300 pages
  • ISBN : 0128136596
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKDeep Learning through Sparse and Low Rank Modeling

Deep Learning through Sparse and Low-Rank Modeling

Deep Learning through Sparse and Low-Rank Modeling
  • Author : Zhangyang Wang,Yun Fu,Thomas S. Huang
  • Publisher : Academic Press
  • Release : 15 May 2019
GET THIS BOOKDeep Learning through Sparse and Low-Rank Modeling

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

Handbook of Robust Low-Rank and Sparse Matrix Decomposition

Handbook of Robust Low-Rank and Sparse Matrix Decomposition
  • Author : Thierry Bouwmans,Necdet Serhat Aybat,El-hadi Zahzah
  • Publisher : CRC Press
  • Release : 20 September 2016
GET THIS BOOKHandbook of Robust Low-Rank and Sparse Matrix Decomposition

Handbook of Robust Low-Rank and Sparse Matrix Decomposition: Applications in Image and Video Processing shows you how robust subspace learning and tracking by decomposition into low-rank and sparse matrices provide a suitable framework for computer vision applications. Incorporating both existing and new ideas, the book conveniently gives you one-stop access to a number of different decompositions, algorithms, implementations, and benchmarking techniques. Divided into five parts, the book begins with an overall introduction to robust principal component analysis (PCA) via decomposition

Generalized Low Rank Models

Generalized Low Rank Models
  • Author : Madeleine Udell,Corinne Horn,Reza Zadeh,Stephen Boyd
  • Publisher : Unknown Publisher
  • Release : 03 May 2016
GET THIS BOOKGeneralized Low Rank Models

Principal components analysis (PCA) is a well-known technique for approximating a tabular data set by a low rank matrix. Here, we extend the idea of PCA to handle arbitrary data sets consisting of numerical, Boolean, categorical, ordinal, and other data types. This framework encompasses many well-known techniques in data analysis, such as nonnegative matrix factorization, matrix completion, sparse and robust PCA, k-means, k-SVD, and maximum margin matrix factorization. The method handles heterogeneous data sets, and leads to coherent schemes for

Artificial Intelligence, Evolutionary Computing and Metaheuristics

Artificial Intelligence, Evolutionary Computing and Metaheuristics
  • Author : Xin-She Yang
  • Publisher : Springer
  • Release : 27 July 2012
GET THIS BOOKArtificial Intelligence, Evolutionary Computing and Metaheuristics

Alan Turing pioneered many research areas such as artificial intelligence, computability, heuristics and pattern formation. Nowadays at the information age, it is hard to imagine how the world would be without computers and the Internet. Without Turing's work, especially the core concept of Turing Machine at the heart of every computer, mobile phone and microchip today, so many things on which we are so dependent would be impossible. 2012 is the Alan Turing year -- a centenary celebration of the life

Statistical Learning with Sparsity

Statistical Learning with Sparsity
  • Author : Trevor Hastie,Robert Tibshirani,Martin Wainwright
  • Publisher : CRC Press
  • Release : 07 May 2015
GET THIS BOOKStatistical Learning with Sparsity

Discover New Methods for Dealing with High-Dimensional Data A sparse statistical model has only a small number of nonzero parameters or weights; therefore, it is much easier to estimate and interpret than a dense model. Statistical Learning with Sparsity: The Lasso and Generalizations presents methods that exploit sparsity to help recover the underlying signal in a set of data. Top experts in this rapidly evolving field, the authors describe the lasso for linear regression and a simple coordinate descent algorithm

Sparse Representation, Modeling and Learning in Visual Recognition

Sparse Representation, Modeling and Learning in Visual Recognition
  • Author : Hong Cheng
  • Publisher : Springer
  • Release : 25 May 2015
GET THIS BOOKSparse Representation, Modeling and Learning in Visual Recognition

This unique text/reference presents a comprehensive review of the state of the art in sparse representations, modeling and learning. The book examines both the theoretical foundations and details of algorithm implementation, highlighting the practical application of compressed sensing research in visual recognition and computer vision. Topics and features: describes sparse recovery approaches, robust and efficient sparse representation, and large-scale visual recognition; covers feature representation and learning, sparsity induced similarity, and sparse representation and learning-based classifiers; discusses low-rank matrix approximation,

Generalized Principal Component Analysis

Generalized Principal Component Analysis
  • Author : René Vidal,Yi Ma,Shankar Sastry
  • Publisher : Springer
  • Release : 11 April 2016
GET THIS BOOKGeneralized Principal Component Analysis

This book provides a comprehensive introduction to the latest advances in the mathematical theory and computational tools for modeling high-dimensional data drawn from one or multiple low-dimensional subspaces (or manifolds) and potentially corrupted by noise, gross errors, or outliers. This challenging task requires the development of new algebraic, geometric, statistical, and computational methods for efficient and robust estimation and segmentation of one or multiple subspaces. The book also presents interesting real-world applications of these new methods in image processing, image

Deep Learning

Deep Learning
  • Author : Ian Goodfellow,Yoshua Bengio,Aaron Courville
  • Publisher : MIT Press
  • Release : 10 November 2016
GET THIS BOOKDeep Learning

An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts.

Deep Learning with PyTorch

Deep Learning with PyTorch
  • Author : Eli Stevens,Luca Antiga,Thomas Viehmann
  • Publisher : Manning Publications
  • Release : 04 August 2020
GET THIS BOOKDeep Learning with PyTorch

Every other day we hear about new ways to put deep learning to good use: improved medical imaging, accurate credit card fraud detection, long range weather forecasting, and more. PyTorch puts these superpowers in your hands, providing a comfortable Python experience that gets you started quickly and then grows with you as you—and your deep learning skills—become more sophisticated. Deep Learning with PyTorch will make that journey engaging and fun. Summary Every other day we hear about new

Example-Based Super Resolution

Example-Based Super Resolution
  • Author : Jordi Salvador
  • Publisher : Academic Press
  • Release : 22 September 2016
GET THIS BOOKExample-Based Super Resolution

Example-Based Super Resolution provides a thorough introduction and overview of example-based super resolution, covering the most successful algorithmic approaches and theories behind them with implementation insights. It also describes current challenges and explores future trends. Readers of this book will be able to understand the latest natural image patch statistical models and the performance limits of example-based super resolution algorithms, select the best state-of-the-art algorithmic alternative and tune it for specific use cases, and quickly put into practice implementations of

Hands-On Machine Learning with R

Hands-On Machine Learning with R
  • Author : Brad Boehmke,Brandon M. Greenwell
  • Publisher : CRC Press
  • Release : 15 November 2019
GET THIS BOOKHands-On Machine Learning with R

Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today’s most popular machine learning methods. This book serves as a practitioner’s guide to the machine learning process and is meant to help the reader learn to apply the machine learning stack within R, which includes using various R packages such as glmnet, h2o, ranger, xgboost, keras, and others to effectively model and gain insight from their data. The book

Low-Rank Models in Visual Analysis

Low-Rank Models in Visual Analysis
  • Author : Zhouchen Lin,Hongyang Zhang
  • Publisher : Academic Press
  • Release : 06 June 2017
GET THIS BOOKLow-Rank Models in Visual Analysis

Low-Rank Models in Visual Analysis: Theories, Algorithms, and Applications presents the state-of-the-art on low-rank models and their application to visual analysis. It provides insight into the ideas behind the models and their algorithms, giving details of their formulation and deduction. The main applications included are video denoising, background modeling, image alignment and rectification, motion segmentation, image segmentation and image saliency detection. Readers will learn which Low-rank models are highly useful in practice (both linear and nonlinear models), how to solve

An Invitation to 3-D Vision

An Invitation to 3-D Vision
  • Author : Yi Ma,Stefano Soatto,Jana Kosecká,S. Shankar Sastry
  • Publisher : Springer Science & Business Media
  • Release : 06 November 2012
GET THIS BOOKAn Invitation to 3-D Vision

This book introduces the geometry of 3-D vision, that is, the reconstruction of 3-D models of objects from a collection of 2-D images. It details the classic theory of two view geometry and shows that a more proper tool for studying the geometry of multiple views is the so-called rank consideration of the multiple view matrix. It also develops practical reconstruction algorithms and discusses possible extensions of the theory.

Sparse Modeling for Image and Vision Processing

Sparse Modeling for Image and Vision Processing
  • Author : Julien Mairal,Francis Bach,Jean Ponce
  • Publisher : Now Publishers
  • Release : 19 December 2014
GET THIS BOOKSparse Modeling for Image and Vision Processing

Sparse Modeling for Image and Vision Processing offers a self-contained view of sparse modeling for visual recognition and image processing. More specifically, it focuses on applications where the dictionary is learned and adapted to data, yielding a compact representation that has been successful in various contexts.

Deep Learning

Deep Learning
  • Author : Li Deng,Dong Yu
  • Publisher : Unknown Publisher
  • Release : 18 May 2021
GET THIS BOOKDeep Learning

Provides an overview of general deep learning methodology and its applications to a variety of signal and information processing tasks