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Graph Representation Learning by Hamilton William (19 results)
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Condition: As New. Unread book in perfect condition.
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Graph Representation Learning
Book 14 of 14: Synthesis Lectures on Artificial Intelligence and Machine LearningLanguage: English
Published by Springer International Publishing AG, CH, 2020
ISBN 10: 3031004604 ISBN 13: 9783031004605
Seller: Rarewaves.com USA, London, LONDO, United Kingdom
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Graph Representation Learning (Synthesis Lectures on Artificial Intelligence and Machine Learning)
Book 14 of 14: Synthesis Lectures on Artificial Intelligence and Machine LearningSeller: Ria Christie Collections, Uxbridge, United Kingdom
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Graph Representation Learning
Book 14 of 14: Synthesis Lectures on Artificial Intelligence and Machine LearningUS$ 33.98
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Graph Representation Learning
Book 14 of 14: Synthesis Lectures on Artificial Intelligence and Machine LearningSeller: GreatBookPricesUK, Woodford Green, United Kingdom
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Graph Representation Learning
Book 14 of 14: Synthesis Lectures on Artificial Intelligence and Machine LearningSeller: GreatBookPricesUK, Woodford Green, United Kingdom
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Condition: New. 1st Edition NO-PA16APR2015-KAP.
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Graph Representation Learning
Book 14 of 14: Synthesis Lectures on Artificial Intelligence and Machine LearningSeller: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Ireland
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Graph Representation Learning
Book 14 of 14: Synthesis Lectures on Artificial Intelligence and Machine LearningLanguage: English
Published by Springer International Publishing AG, CH, 2020
ISBN 10: 3031004604 ISBN 13: 9783031004605
US$ 36.70
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Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketPaperback. Condition: New.
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Graph Representation Learning
Book 14 of 14: Synthesis Lectures on Artificial Intelligence and Machine LearningLanguage: English
Published by Springer International Publishing, 2020
ISBN 10: 3031004604 ISBN 13: 9783031004605
Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis.This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs-a nascent but quickly growing subset of graph representation learning.
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Taschenbuch. Condition: Neu. Graph Representation Learning | William L. Hamilton | Taschenbuch | Synthesis Lectures on Artificial Intelligence and Machine Learning | xvii | Englisch | 2020 | Springer | EAN 9783031004605 | 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 Representation Learning (Synthesis Lectures on Artificial Intelligence and Machine Learning, 46)
Book 14 of 14: Synthesis Lectures on Artificial Intelligence and Machine LearningUS$ 139.02
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Add to basketpaperback. Condition: Very Good. Very Good. Dust Jacket may NOT BE INCLUDED.CDs may be missing. SHIPS FROM MULTIPLE LOCATIONS. book.
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Condition: New. This item is printed on demand.
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Graph Representation Learning
Book 14 of 14: Synthesis Lectures on Artificial Intelligence and Machine LearningLanguage: English
Published by Springer International Publishing Sep 2020, 2020
ISBN 10: 3031004604 ISBN 13: 9783031004605
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis.This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs-a nascent but quickly growing subset of graph representation learning. 160 pp. Englisch.
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Graph Representation Learning
Book 14 of 14: Synthesis Lectures on Artificial Intelligence and Machine LearningLanguage: English
Published by Springer, Berlin|Springer International Publishing|Morgan & Claypool|Springer, 2020
ISBN 10: 3031004604 ISBN 13: 9783031004605
Kartoniert / Broschiert. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, re.
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Graph Representation Learning
Book 14 of 14: Synthesis Lectures on Artificial Intelligence and Machine LearningLanguage: English
Published by Springer Nature Switzerland, Springer International Publishing Sep 2020, 2020
ISBN 10: 3031004604 ISBN 13: 9783031004605
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis.This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs¿a nascent but quickly growing subset of graph representation learning.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 160 pp. Englisch.
