Lingfei wu (40 resultados)
Graph Neural Networks : Foundations, Frontiers, and Applications
Wu, Lingfei (EDT); Cui, Peng (EDT); Pei, Jian (EDT); Zhao, Liang (EDT)
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Librería: Ria Christie Collections, Uxbridge, Reino UnidoRia Christie Collections
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EUR 79,74
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Librería: Chiron Media, Wallingford, Reino UnidoChiron Media
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EUR 76,97
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Graph Neural Networks : Foundations, Frontiers, and Applications
Wu, Lingfei (EDT); Cui, Peng (EDT); Pei, Jian (EDT); Zhao, Liang (EDT)
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Librería: GreatBookPrices, Columbia, MD, Estados Unidos de AmericaGreatBookPrices
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EUR 94,05
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Graph Neural Networks : Foundations, Frontiers, and Applications
Wu, Lingfei (EDT); Cui, Peng (EDT); Pei, Jian (EDT); Zhao, Liang (EDT)
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Librería: GreatBookPricesUK, Woodford Green, Reino UnidoGreatBookPricesUK
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EUR 79,72
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Librería: Basi6 International, Irving, TX, Estados Unidos de AmericaBasi6 International
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EUR 98,56
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Graph Neural Networks : Foundations, Frontiers, and Applications
Wu, Lingfei (EDT); Cui, Peng (EDT); Pei, Jian (EDT); Zhao, Liang (EDT)
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Librería: GreatBookPricesUK, Woodford Green, Reino UnidoGreatBookPricesUK
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EUR 87,45
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Librería: Rarewaves.com USA, London, LONDO, Reino UnidoRarewaves.com USA
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EUR 107,89
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Paperback. Condición: New. 2022 ed. 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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Librería: California Books, Miami, FL, Estados Unidos de AmericaCalifornia Books
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EUR 117,75
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Graph Neural Networks : Foundations, Frontiers, and Applications
Wu, Lingfei (EDT); Cui, Peng (EDT); Pei, Jian (EDT); Zhao, Liang (EDT)
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Librería: GreatBookPrices, Columbia, MD, Estados Unidos de AmericaGreatBookPrices
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EUR 118,95
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Librería: Basi6 International, Irving, TX, Estados Unidos de AmericaBasi6 International
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EUR 121,32
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Condición: Brand New. New. Delivery takes 25-30 days. Excellent Customer Service.
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Librería: Ria Christie Collections, Uxbridge, Reino UnidoRia Christie Collections
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EUR 116,77
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Condición: New. In.
Graph Neural Networks : Foundations, Frontiers, and Applications
Wu, Lingfei (EDT); Cui, Peng (EDT); Pei, Jian (EDT); Zhao, Liang (EDT)
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Librería: GreatBookPrices, Columbia, MD, Estados Unidos de AmericaGreatBookPrices
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EUR 131,33
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Condición: As New. Unread book in perfect condition.
Graph Neural Networks : Foundations, Frontiers, and Applications
Wu, Lingfei (EDT); Cui, Peng (EDT); Pei, Jian (EDT); Zhao, Liang (EDT)
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Librería: GreatBookPricesUK, Woodford Green, Reino UnidoGreatBookPricesUK
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EUR 116,76
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Librería: Grand Eagle Retail, Bensenville, IL, Estados Unidos de AmericaGrand Eagle Retail
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EUR 136,22
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Hardcover. Condición: new. Hardcover. 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. This gap has driven a wave of research for deep learning on graphs, including graph representation learning, graph generation, and graph classification. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Librería: Kennys Bookshop and Art Galleries Ltd., Galway, GY, IrlandaKennys Bookshop and Art Galleries Ltd.
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EUR 133,55
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Condición: New.
Graph Neural Networks : Foundations, Frontiers, and Applications
Wu, Lingfei (EDT); Cui, Peng (EDT); Pei, Jian (EDT); Zhao, Liang (EDT)
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Librería: GreatBookPricesUK, Woodford Green, Reino UnidoGreatBookPricesUK
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EUR 130,05
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Condición: As New. Unread book in perfect condition.
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Librería: California Books, Miami, FL, Estados Unidos de AmericaCalifornia Books
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EUR 148,97
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Condición: New.
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Librería: Books Puddle, New York, NY, Estados Unidos de AmericaBooks Puddle
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EUR 152,47
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Condición: New.
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Librería: preigu, Osnabrück, Alemaniapreigu
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EUR 94,05
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Taschenbuch. Condición: 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 | Anbiet…er: preigu.
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Librería: Kennys Bookstore, Olney, MD, Estados Unidos de AmericaKennys Bookstore
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EUR 167,04
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Condición: New.
Graph Neural Networks: Foundations, Frontiers, and Applications
Wu, Lingfei (Editor) / Cui, Peng (Editor) / Pei, Jian (Editor) / Zhao, Liang (Editor)
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Librería: Revaluation Books, Exeter, Reino UnidoRevaluation Books
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EUR 160,27
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Paperback. Condición: Brand New. 725 pages. 9.25x6.10x1.73 inches. In Stock.
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Librería: Rarewaves.com UK, London, Reino UnidoRarewaves.com UK
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EUR 101,51
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Paperback. Condición: New. 2022 ed.
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Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
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EUR 113,44
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Taschenbuch. Condición: 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 da…ta 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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Librería: Books Puddle, New York, NY, Estados Unidos de AmericaBooks Puddle
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EUR 193,04
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Condición: New. pp. 689.
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Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
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EUR 145,22
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Buch. Condición: 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.
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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Librería: Revaluation Books, Exeter, Reino UnidoRevaluation Books
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EUR 202,97
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Hardcover. Condición: Brand New. 725 pages. 9.25x6.10x1.54 inches. In Stock.
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Librería: AussieBookSeller, Truganina, VIC, AustraliaAussieBookSeller
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EUR 204,35
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Hardcover. Condición: new. Hardcover. 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. This gap has driven a wave of research for deep learning on graphs, including graph representation learning, graph generation, and graph classification. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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Librería: Brook Bookstore On Demand, Napoli, NA, ItaliaBrook Bookstore On Demand
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EUR 86,24
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Condición: new. Questo è un articolo print on demand.
Idioma: Inglés
Editorial: Springer Nature Singapore, Springer Nature Singapore Jan 2023, 2023
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AlemaniaBuchWeltWeit Ludwig Meier e.K.
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EUR 90,94
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 gra…ph-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. 728 pp. Englisch.
