Deep Learning Through Sparse And Low Rank Modeling
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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-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
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.
Solving with Bees
This book is a comprehensive volume, which delves into the versatile world of Artificial Bee Colony (ABC) algorithms, their variants, and myriad applications in a wide range of fields. This book is designed to be an essential resource for researchers, practitioners, students, and anyone intrigued by the fascinating realm of swarm intelligence and optimization. This book serves as a bridge between the theoretical foundations of ABC algorithms and their practical implementations across diverse domains. The book offers a deep understanding of these algorithms and how they can be harnessed to tackle complex real-world challenges.