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graph representation learning di hamilton william (25 risultati)
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Graph Representation Learning (Synthesis Lectures on Artificial Intelligence and Machine Learning)
Libro 14 di 14: Synthesis Lectures on Artificial Intelligence and Machine LearningDa: BooksRun, Philadelphia, PA, U.S.A.
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Graph Representation Learning
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Graph Representation Learning (Synthesis Lectures on Artificial Intelligence and Machine Learning)
Libro 14 di 14: Synthesis Lectures on Artificial Intelligence and Machine LearningDa: Ria Christie Collections, Uxbridge, Regno Unito
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Graph Representation Learning
Libro 14 di 14: Synthesis Lectures on Artificial Intelligence and Machine LearningDa: Chiron Media, Wallingford, Regno Unito
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Graph Representation Learning
Libro 14 di 14: Synthesis Lectures on Artificial Intelligence and Machine LearningDa: GreatBookPrices, Columbia, MD, U.S.A.
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Graph Representation Learning
Libro 14 di 14: Synthesis Lectures on Artificial Intelligence and Machine LearningLingua: Inglese
Editore: Springer International Publishing AG, CH, 2020
ISBN 10: 3031004604 ISBN 13: 9783031004605
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Graph Representation Learning
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Graph Representation Learning
Libro 14 di 14: Synthesis Lectures on Artificial Intelligence and Machine LearningDa: GreatBookPricesUK, Woodford Green, Regno Unito
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Graph Representation Learning (Synthesis Lectures on Artificial Intelligence and Machine Learning)
Libro 14 di 14: Synthesis Lectures on Artificial Intelligence and Machine LearningDa: California Books, Miami, FL, U.S.A.
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Graph Representation Learning
Libro 14 di 14: Synthesis Lectures on Artificial Intelligence and Machine LearningLingua: Inglese
Editore: Springer International Publishing AG, CH, 2020
ISBN 10: 3031004604 ISBN 13: 9783031004605
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Graph Representation Learning
Libro 14 di 14: Synthesis Lectures on Artificial Intelligence and Machine LearningDa: Books Puddle, New York, NY, U.S.A.
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Graph Representation Learning
Libro 14 di 14: Synthesis Lectures on Artificial Intelligence and Machine LearningDa: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlanda
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Graph Representation Learning
Libro 14 di 14: Synthesis Lectures on Artificial Intelligence and Machine LearningDa: Kennys Bookstore, Olney, MD, U.S.A.
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Graph Representation Learning
Libro 14 di 14: Synthesis Lectures on Artificial Intelligence and Machine LearningLingua: Inglese
Editore: Springer International Publishing AG, CH, 2020
ISBN 10: 3031004604 ISBN 13: 9783031004605
Da: Rarewaves.com UK, London, Regno Unito
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Graph Representation Learning
Libro 14 di 14: Synthesis Lectures on Artificial Intelligence and Machine LearningLingua: Inglese
Editore: Springer International Publishing AG, CH, 2020
ISBN 10: 3031004604 ISBN 13: 9783031004605
Da: Rarewaves USA United, OSWEGO, IL, U.S.A.
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Graph Representation Learning
Libro 14 di 14: Synthesis Lectures on Artificial Intelligence and Machine LearningLingua: Inglese
Editore: Springer International Publishing, 2020
ISBN 10: 3031004604 ISBN 13: 9783031004605
EUR 58,84
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Aggiungi al carrelloTaschenbuch. Condizione: 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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Graph Representation Learning
Libro 14 di 14: Synthesis Lectures on Artificial Intelligence and Machine LearningEUR 54,90
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Aggiungi al carrelloTaschenbuch. Condizione: 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)
Libro 14 di 14: Synthesis Lectures on Artificial Intelligence and Machine LearningDa: Mispah books, Redhill, SURRE, Regno Unito
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Graph Representation Learning (Synthesis Lectures on Artificial Intelligence and William L. Hamilton
Libro 14 di 14: Synthesis Lectures on Artificial Intelligence and Machine LearningDa: BUCHSERVICE / ANTIQUARIAT Lars Lutzer, Wahlstedt, Germania
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Graph Representation Learning
Libro 14 di 14: Synthesis Lectures on Artificial Intelligence and Machine LearningDa: Majestic Books, Hounslow, Regno Unito
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Graph Representation Learning
Libro 14 di 14: Synthesis Lectures on Artificial Intelligence and Machine LearningLingua: Inglese
Editore: Springer International Publishing Sep 2020, 2020
ISBN 10: 3031004604 ISBN 13: 9783031004605
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
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Aggiungi al carrelloTaschenbuch. Condizione: 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
Libro 14 di 14: Synthesis Lectures on Artificial Intelligence and Machine LearningDa: Biblios, Frankfurt am main, HESSE, Germania
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Graph Representation Learning
Libro 14 di 14: Synthesis Lectures on Artificial Intelligence and Machine LearningLingua: Inglese
Editore: Springer, Berlin|Springer International Publishing|Morgan & Claypool|Springer, 2020
ISBN 10: 3031004604 ISBN 13: 9783031004605
EUR 51,51
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Aggiungi al carrelloKartoniert / Broschiert. Condizione: 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
Libro 14 di 14: Synthesis Lectures on Artificial Intelligence and Machine LearningLingua: Inglese
Editore: Springer, Springer Sep 2020, 2020
ISBN 10: 3031004604 ISBN 13: 9783031004605
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
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Aggiungi al carrelloTaschenbuch. Condizione: 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.
