Описание: Deep Learning models are at the core of artificial intelligence research today. It is well known that deep learning techniques are disruptive for Euclidean data, such as images or sequence data, and not immediately applicable to graph-structured data such as text. This gap has driven a wave of research for deep learning on graphs, including graph representation learning, graph generation, and graph classification. The new neural network architectures on graph-structured data (graph neural networks, GNNs in short) have performed remarkably on these tasks, demonstrated by applications in social networks, bioinformatics, and medical informatics. Despite these successes, GNNs still face many challenges ranging from the foundational methodologies to the theoretical understandings of the power of the graph representation learning. This book provides a comprehensive introduction of GNNs. It first discusses the goals of graph representation learning and then reviews the history, current developments, and future directions of GNNs. The second part presents and reviews fundamental methods and theories concerning GNNs while the third part describes various frontiers that are built on the GNNs. The book concludes with an overview of recent developments in a number of applications using GNNs. This book is suitable for a wide audience including undergraduate and graduate students, postdoctoral researchers, professors and lecturers, as well as industrial and government practitioners who are new to this area or who already have some basic background but want to learn more about advanced and promising techniques and applications.
Описание: This monograph builds on Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 1 Low-Rank Tensor Decompositions by discussing tensor network models for super-compressed higher-order representation of data/parameters and cost functions, together with an outline of their applications in machine learning and data analytics. A particular emphasis is on elucidating, through graphical illustrations, that by virtue of the underlying low-rank tensor approximations and sophisticated contractions of core tensors, tensor networks have the ability to perform distributed computations on otherwise prohibitively large volume of data/parameters, thereby alleviating the curse of dimensionality. The usefulness of this concept is illustrated over a number of applied areas, including generalized regression and classification, generalized eigenvalue decomposition and in the optimization of deep neural networks. The monograph focuses on tensor train (TT) and Hierarchical Tucker (HT) decompositions and their extensions, and on demonstrating the ability of tensor networks to provide scalable solutions for a variety of otherwise intractable large-scale optimization problems. Tensor Networks for Dimensionality Reduction and Large-scale Optimization Parts 1 and 2 can be used as stand-alone texts, or together as a comprehensive review of the exciting field of low-rank tensor networks and tensor decompositions. See also: Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 1 Low-Rank Tensor Decompositions. ISBN 978-1-68083-222-8
Автор: Galeone, Paolo Название: Hands-on neural networks with tensorflow 2.0 ISBN: 1789615550 ISBN-13(EAN): 9781789615555 Издательство: Неизвестно Рейтинг: Цена: 7363.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book is a guide to the TensorFlow (TF) framework, from the static graph architecture of TF 1.x to the eager execution and all the new features introduced in TF 2.0. Neural Networks applications are developed throughout the book with the aim of making the reader capable of developing neural networks-based solutions to real problems using TF 2.0
Автор: Marius Leordeanu Название: Unsupervised Learning in Space and Time ISBN: 3030421279 ISBN-13(EAN): 9783030421274 Издательство: Springer Рейтинг: Цена: 20962.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book addresses one of the most important unsolved problems in artificial intelligence: the task of learning, in an unsupervised manner, from massive quantities of spatiotemporal visual data that are available at low cost. The book covers important scientific discoveries and findings, with a focus on the latest advances in the field. Presenting a coherent structure, the book logically connects novel mathematical formulations and efficient computational solutions for a range of unsupervised learning tasks, including visual feature matching, learning and classification, object discovery, and semantic segmentation in video.
The final part of the book proposes a general strategy for visual learning over several generations of student-teacher neural networks, along with a unique view on the future of unsupervised learning in real-world contexts. Offering a fresh approach to this difficult problem, several efficient, state-of-the-art unsupervised learning algorithms are reviewed in detail, complete with an analysis of their performance on various tasks, datasets, and experimental setups. By highlighting the interconnections between these methods, many seemingly diverse problems are elegantly brought together in a unified way.
Serving as an invaluable guide to the computational tools and algorithms required to tackle the exciting challenges in the field, this book is a must-read for graduate students seeking a greater understanding of unsupervised learning, as well as researchers in computer vision, machine learning, robotics, and related disciplines.
This book addresses one of the most important unsolved problems in artificial intelligence: the task of learning, in an unsupervised manner, from massive quantities of spatiotemporal visual data that are available at low cost. The book covers important scientific discoveries and findings, with a focus on the latest advances in the field.
Presenting a coherent structure, the book logically connects novel mathematical formulations and efficient computational solutions for a range of unsupervised learning tasks, including visual feature matching, learning and classification, object discovery, and semantic segmentation in video. The final part of the book proposes a general strategy for visual learning over several generations of student-teacher neural networks, along with a unique view on the future of unsupervised learning in real-world contexts.
Offering a fresh approach to this difficult problem, several efficient, state-of-the-art unsupervised learning algorithms are reviewed in detail, complete with an analysis of their performance on various tasks, datasets, and experimental setups. By highlighting the interconnections between these methods, many seemingly diverse problems are elegantly brought together in a unified way.
Serving as an invaluable guide to the computational tools and algorithms required to tackle the exciting challenges in the field, this book is a must-read for graduate students seeking a greater understanding of unsupervised learning, as well as researchers in computer vision, machine learning, robotics, and related disciplines.
Автор: Ma, Yao (michigan State University) Tang, Jiliang (michigan State University) Название: Deep learning on graphs ISBN: 1108831745 ISBN-13(EAN): 9781108831741 Издательство: Cambridge Academ Рейтинг: Цена: 7126.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This comprehensive text on the theory and techniques of graph neural networks takes students, practitioners, and researchers from the basics to the state of the art. It systematically introduces foundational topics such as filtering pooling, robustness, and scalability and then demonstrates applications in NLP, data mining, vision and healthcare.
Автор: Shi Название: Advances in Graph Neural Networks ISBN: 3031161734 ISBN-13(EAN): 9783031161735 Издательство: Springer Рейтинг: Цена: 7685.00 р. Наличие на складе: Нет в наличии.
Описание: This book provides a comprehensive introduction to the foundations and frontiers of graph neural networks. In addition, the book introduces the basic concepts and definitions in graph representation learning and discusses the development of advanced graph representation learning methods with a focus on graph neural networks. The book providers researchers and practitioners with an understanding of the fundamental issues as well as a launch point for discussing the latest trends in the science. The authors emphasize several frontier aspects of graph neural networks and utilize graph data to describe pairwise relations for real-world data from many different domains, including social science, chemistry, and biology. Several frontiers of graph neural networks are introduced, which enable readers to acquire the needed techniques of advances in graph neural networks via theoretical models and real-world applications.
Автор: Liu, Zhiyuan Zhou, Jie Название: Introduction to Graph Neural Networks ISBN: 3031004590 ISBN-13(EAN): 9783031004599 Издательство: Springer Рейтинг: Цена: 8384.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Then several variants of the vanilla model are introduced such as graph convolutional networks, graph recurrent networks, graph attention networks, graph residual networks, and several general frameworks.
Автор: Anthony, Martin Bartlett, Peter Название: Neural network learning ISBN: 052111862X ISBN-13(EAN): 9780521118620 Издательство: Cambridge Academ Рейтинг: Цена: 7920.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book describes theoretical advances in the study of artificial neural networks. It explores probabilistic models of supervised learning problems, and addresses the key statistical and computational questions. It is intended to be accessible to researchers and graduate students in computer science, engineering, and mathematics.
Автор: Chong Название: Smart Grid and Innovative Frontiers in Telecommunications ISBN: 3319949640 ISBN-13(EAN): 9783319949642 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book constitutes the proceedings of the Third International Conference on Smart Grid and Innovative Frontiers in Telecommunications, SmartGIFT, held in Auckland, New Zealand, in April 2018.
Автор: Anthony Название: Neural Network Learning ISBN: 052157353X ISBN-13(EAN): 9780521573535 Издательство: Cambridge Academ Рейтинг: Цена: 18850.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book describes theoretical advances in the study of artificial neural networks. It explores probabilistic models of supervised learning problems, and addresses the key statistical and computational questions. It is intended to be accessible to researchers and graduate students in computer science, engineering, and mathematics.
Автор: Borgwardt Karsten, Ghisu Elisabetta, Llinares-Lуpez Felipe Название: Graph Kernels: State-Of-The-Art and Future Challenges ISBN: 1680837702 ISBN-13(EAN): 9781680837704 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 12197.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Provides a review of existing graph kernels, their applications, software plus data resources, and an empirical comparison of state-of-the-art graph kernels. The book focuses on the theoretical description of common graph kernels, and on a large-scale empirical evaluation of graph kernels.
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