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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;

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Low Rank and Sparse Modeling for Visual Analysis

This book provides a view of low-rank and sparse computing, especially approximation, recovery, representation, scaling, coding, embedding;

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Robust Representation for Data Analytics

the theory of low-rank and sparse modeling, the authors develop robust feature representations under various learning paradigms, including;

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Handbook of Robust Low-Rank and Sparse Matrix Decomposition

learning and tracking by decomposition into low-rank and sparse matrices provide a suitable framework for computer vision applications;

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Machine Learning, Low-Rank Approximations and Reduced Order Modeling in Computational Mechanics

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Latent Variable Analysis and Signal Separation

fusion, and related topics; advances in nonlinear blind source separation; sparse and low rank modeling for acoustic signal processing.;

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Turbo Message Passing Algorithms for Structured Signal Recovery

the affine rank minimization (ARM) problem), 3) a mixture of a sparse matrix and a low-rank matrix (which corresponds to the robust principal;

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Feature Learning and Understanding: Algorithms and Applications

analysis, and geometrical-structure-based methods, but also advanced feature learning methods, such as sparse learning, low-rank decomposition;

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Sparse Modeling

up to speed on the latest sparsity-related developments and will motivate you to continue learning about the field. The authors first;

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Sparse Modeling

book gets you up to speed on the latest sparsity-related developments and will motivate you to continue learning about the field. The;

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Feature Learning and Understanding

analysis, and geometrical-structure-based methods, but also advanced feature learning methods, such as sparse learning, low-rank decomposition;

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Low-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;

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Inpainting and Denoising Challenges

and low-rank models, and recent deep learning architectures with autoencoders and variants. The following chapters present results from the;

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Inpainting and Denoising Challenges

and low-rank models, and recent deep learning architectures with autoencoders and variants. The following chapters present results from the;

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Embedded Deep Learning

This book covers algorithmic and hardware implementation techniques to enable embedded deep learning. The authors describe synergetic;

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Hands-On Machine Learning with R

to powerful algorithms such as regularized regression, random forests, gradient boosting machines, deep learning, generalized low rank models;

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Oracle Inequalities in Empirical Risk Minimization and Sparse Recovery Problems: École d'Été de Probabilités de Saint-Flour XXXVIII-2008

recovery based on l_1-type penalization and low rank matrix recovery based on the nuclear norm penalization are other active areas of research;

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Low overhead Communications in IoT Networks

. By utilizing underlying system structures, including sparsity and low rank, these methods can achieve significant performance gains. This;

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Low overhead Communications in IoT Networks

. By utilizing underlying system structures, including sparsity and low rank, these methods can achieve significant performance gains. This;

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Introduction to High-Dimensional Statistics

sensing, estimation with convex constraints, the slope estimator, simultaneously low rank and row sparse linear regression, or aggregation of a;

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Machine Learning

Machine Learning: A Bayesian and Optimization Perspective, 2nd edition, gives a unified perspective on machine learning by;

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Sparse Coding and Its Applications in Computer Vision

introduces sparse coding in the context of representation learning, illustrates the fundamental concepts, and summarizes the most active research;

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Fundamentals of Matrix Computations

decomposition based on adaptive over-complete dictionary, lower bounds for the low-rank matrix approximation and a semi-smoothing augmented lagrange;

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Financial Signal Processing and Machine Learning

market data available. Financial Signal Processing and Machine Learning unifies a number of recent advances made in signal processing and;

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Data Orchestration in Deep Learning Accelerators

the increasing growth in deep learning and other AI applications has led to the emergence of custom Deep Neural Network (DNN) accelerators for;

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Hands-On Deep Learning with Apache Spark

Speed up the design and implementation of deep learning solutions using Apache Spark Key Features Explore the world of distributed deep;

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Learning Approaches in Signal Processing

forests, sparsity, and low-rank matrix estimation and cutting-edge visual/signal processing techniques, including face recognition, Kalman;

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