Concepts in Action: Representation, Learning, and Application, Bechberger Lucas, Kьhnberger Kai-Uwe, Liu Mingya

Автор: Borg Emma, Picciotto S Sol, Lorange Peter,
Название: Meaning and Representation
ISBN: 0631235779 ISBN-13(EAN): 9780631235774
Издательство: Wiley
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Цена: 19120.00 T
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Описание: * Discusses the relationship between meaning and representation. * Illustrates the differences that exist on the question of how formal representations relate to semantic representations. * Includes contributions by Tim Crane, Jerry Fodor, Paul Horwich, John Hyman, Ernie Lepore, Gregory McCulloch and Mark Sainsbury.

Автор: Bechberger Lucas, Kьhnberger Kai-Uwe, Liu Mingya
Название: Concepts in Action: Representation, Learning, and Application
ISBN: 3030698254 ISBN-13(EAN): 9783030698256
Издательство: Springer
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Цена: 37260.00 T
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Описание: This open access book is a timely contribution in presenting recent issues, approaches, and results that are not only central to the highly interdisciplinary field of concept research but also particularly important to newly emergent paradigms and challenges.

Автор: Gian Piero Zarri
Название: Representation and Management of Narrative Information
ISBN: 1849967237 ISBN-13(EAN): 9781849967235
Издательство: Springer
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Цена: 144410.00 T
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Описание: Written from a multidisciplinary perspective, this book supplies an exhaustive description of NKRL and of the associated knowledge representation principles. It also constitutes an invaluable source of reference for practitioners, researchers and graduates.

Автор: Michel Chein; Marie-Laure Mugnier
Название: Graph-based Knowledge Representation
ISBN: 1849967695 ISBN-13(EAN): 9781849967693
Издательство: Springer
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Цена: 135090.00 T
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Описание: In addressing the question of how far it is possible to go in knowledge representation and reasoning through graphs, the authors cover basic conceptual graphs, computational aspects, and kernel extensions. The basic mathematical notions are summarized.

Автор: Hamilton William L.
Название: Graph Representation Learning
ISBN: 1681739631 ISBN-13(EAN): 9781681739632
Издательство: Mare Nostrum (Eurospan)
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Цена: 57290.00 T
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Описание: 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.