Lingfei (13 Ergebnisse)
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Sprache: Englisch
Verlag: Peter Lang Inc., International Academic Publishers, 2023
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Sprache: Englisch
Verlag: Peter Lang Inc., International Academic Publishers, 2023
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Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Located in Nujiang Lisu Autonomous Prefecture, Yunnan Province, Yushichang is a typical village of the Pumi ethnic group. With a current population of more than 360, the village has been home to the Pumi people for more than 500 years. Over the first six de…cades of the People's Republic of China, Yushichang village has managed to protect both its ecological environment and the Pumi culture. Drawing on a rich trove of oral history and ethnographic studies, this book tries to understand, at both the micro and macro levels, how this population has navigated the tension between tradition and modernity and what resources it might bring to bear to meet future challenges.
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Taschenbuch. Zustand: Neu. Graph Neural Networks: Foundations, Frontiers, and Applications | Lingfei Wu (u. a.) | Taschenbuch | xxxvi | Englisch | 2023 | Springer | EAN 9789811660566 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter…: preigu.
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Graph Neural Networks: Foundations, Frontiers, and Applications
Wu, Lingfei (Editor) / Cui, Peng (Editor) / Pei, Jian (Editor) / Zhao, Liang (Editor)
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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.
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Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 a…s 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.
Graph Neural Networks: Foundations, Frontiers, and Applications
Wu, Lingfei (Edited by)/ Cui, Peng (Edited by)/ Pei, Jian (Edited by)/ Zhao, Liang (Edited by)
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Hardcover. Zustand: Brand New. 725 pages. 9.25x6.10x1.54 inches. In Stock.
