Graph Representation Learning by Hamilton William (25 results)
Language: English
Published by Springer (edition 1), 2020
Series: Synthesis Lectures on Artificial Intelligence and Machine Learning, Book 14 of 14. Book 14 of 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
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Language: English
Published by Springer, 2020
Series: Synthesis Lectures on Artificial Intelligence and Machine Learning, Book 14 of 14. Book 14 of 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
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Language: English
Published by Springer, 2020
Series: Synthesis Lectures on Artificial Intelligence and Machine Learning, Book 14 of 14. Book 14 of 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
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Language: English
Published by Springer 2020-09, 2020
Series: Synthesis Lectures on Artificial Intelligence and Machine Learning, Book 14 of 14. Book 14 of 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
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Language: English
Published by Springer, 2020
Series: Synthesis Lectures on Artificial Intelligence and Machine Learning, Book 14 of 14. Book 14 of 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
- Softcover
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Language: English
Published by Springer International Publishing AG, CH, 2020
Series: Synthesis Lectures on Artificial Intelligence and Machine Learning, Book 14 of 14. Book 14 of 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
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Language: English
Published by Springer, 2020
Series: Synthesis Lectures on Artificial Intelligence and Machine Learning, Book 14 of 14. Book 14 of 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
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Language: English
Published by Springer, 2020
Series: Synthesis Lectures on Artificial Intelligence and Machine Learning, Book 14 of 14. Book 14 of 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
- Softcover
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Language: English
Published by Springer, 2020
Series: Synthesis Lectures on Artificial Intelligence and Machine Learning, Book 14 of 14. Book 14 of 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
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Language: English
Published by Springer International Publishing AG, CH, 2020
Series: Synthesis Lectures on Artificial Intelligence and Machine Learning, Book 14 of 14. Book 14 of 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
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Language: English
Published by Springer, 2020
Series: Synthesis Lectures on Artificial Intelligence and Machine Learning, Book 14 of 14. Book 14 of 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
- Softcover
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Condition: New. 1st Edition NO-PA16APR2015-KAP.
Language: English
Published by Springer, 2020
Series: Synthesis Lectures on Artificial Intelligence and Machine Learning, Book 14 of 14. Book 14 of 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
- Softcover
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Language: English
Published by Springer, 2020
Series: Synthesis Lectures on Artificial Intelligence and Machine Learning, Book 14 of 14. Book 14 of 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
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Language: English
Published by Springer International Publishing AG, CH, 2020
Series: Synthesis Lectures on Artificial Intelligence and Machine Learning, Book 14 of 14. Book 14 of 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
- Softcover
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Language: English
Published by Springer International Publishing AG, CH, 2020
Series: Synthesis Lectures on Artificial Intelligence and Machine Learning, Book 14 of 14. Book 14 of 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
- Softcover
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Language: English
Published by Springer International Publishing, 2020
Series: Synthesis Lectures on Artificial Intelligence and Machine Learning, Book 14 of 14. Book 14 of 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
- Softcover
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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 lea…rn, 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.
Language: English
Published by Springer, 2020
Series: Synthesis Lectures on Artificial Intelligence and Machine Learning, Book 14 of 14. Book 14 of 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
- Softcover
Seller: preigu, Osnabrück, Germanypreigu
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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]ha…rtmann[at]springer[dot]com | Anbieter: preigu.
Language: English
Published by Morgan & Claypool 0, 2020
Series: Synthesis Lectures on Artificial Intelligence and Machine Learning, Book 14 of 14. Book 14 of 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
- Softcover
Seller: Mispah books, Redhill, SURRE, United KingdomMispah books
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paperback. Condition: Acceptable. Acceptable. Dust Jacket NOT present. CD WILL BE MISSING. SHIPS FROM MULTIPLE LOCATIONS. book.
Language: English
Published by Morgan & Claypool, 2020
Series: Synthesis Lectures on Artificial Intelligence and Machine Learning, Book 14 of 14. Book 14 of 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
- Softcover
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Graph Representation Learning (Synthesis Lectures on Artificial Intelligence and William L. Hamilton
Language: English
Published by Morgan & Claypool, 2020
Series: Synthesis Lectures on Artificial Intelligence and Machine Learning, Book 14 of 14. Book 14 of 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
- Hardcover
Seller: BUCHSERVICE / ANTIQUARIAT Lars Lutzer, Wahlstedt, GermanyBUCHSERVICE / ANTIQUARIAT Lars Lutzer
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Condition: gut. Graph Representation Learning (Synthesis Lectures on Artificial Intelligence and In englischer Sprache. pages.
Language: English
Published by Springer, 2020
Series: Synthesis Lectures on Artificial Intelligence and Machine Learning, Book 14 of 14. Book 14 of 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
- Softcover
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Language: English
Published by Springer International Publishing Sep 2020, 2020
Series: Synthesis Lectures on Artificial Intelligence and Machine Learning, Book 14 of 14. Book 14 of 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
- Softcover
- Print on Demand
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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 syst…ems 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.
Language: English
Published by Springer, 2020
Series: Synthesis Lectures on Artificial Intelligence and Machine Learning, Book 14 of 14. Book 14 of 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
- Softcover
- Print on Demand
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Language: English
Published by Springer, Berlin|Springer International Publishing|Morgan & Claypool|Springer, 2020
Series: Synthesis Lectures on Artificial Intelligence and Machine Learning, Book 14 of 14. Book 14 of 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
- Softcover
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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 ar…chitectures is crucial for creating systems that can learn, re.
Language: English
Published by Springer, Springer Sep 2020, 2020
Series: Synthesis Lectures on Artificial Intelligence and Machine Learning, Book 14 of 14. Book 14 of 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
- Softcover
- Print on Demand
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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 KG, Sachsenplatz 4-6, 1201 Wien 160 pp. Englisch.
