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Published by Morgan & Claypool, 2020
Series: Book 14 of 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
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Published by Morgan & Claypool Publishers, 2014
Series: Book 3 of 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
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Published by Springer, 2020
Series: Book 14 of 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
- Softcover
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Published by Springer, 2017
Series: Book 8 of 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
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Series: Book 3 of 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
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Series: Book 5 of 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
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Published by Springer, 2012
Series: Book 2 of 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
- Softcover
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Published by Springer 3/18/2014, 2014
Series: Book 4 of 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
- Softcover
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Paperback or Softback. Condition: New. Judgment Aggregation: A Primer. Book.
Language: English
Published by Springer 3/18/2014, 2014
Series: Book 5 of 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
- Softcover
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Published by Springer, 2012
Series: Book 2 of 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
- Softcover
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Series: Book 3 of 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
- Softcover
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Published by Springer, 2017
Series: Book 8 of 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
- Softcover
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Published by Springer (edition 1), 2012
Series: Book 2 of 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
- Softcover
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Published by Springer (edition 1), 2014
Series: Book 4 of 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
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Published by Springer, 2014
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Series: Book 5 of 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
- Softcover
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Statistical Relational Artificial Intelligence: Logic, Probability, and Computation (Synthesis Lectures on Artificial Intelligence and Machine Learning)
De Raedt, Luc,Kersting, Kristian,Poole, David,Natarajan, Sriraam
Language: English
Published by Springer, 2016
Series: Book 7 of 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
- Hardcover
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Published by Springer, 2014
Series: Book 4 of 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
- Softcover
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Published by Springer, 2014
Series: Book 5 of 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
- Softcover
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Published by Springer 2020-09, 2020
Series: Book 14 of 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
- Softcover
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Published by Springer, 2020
Series: Book 14 of 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
- Softcover
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Language: English
Published by Springer, 2020
Series: 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: Book 14 of 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
- Softcover
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Paperback. Condition: New. 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.…
Language: English
Published by Springer, 2020
Series: Book 14 of 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
- Softcover
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Published by Springer, 2012
Series: Book 2 of 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
- Softcover
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Published by Springer, 2018
Series: Book 10 of 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
- Hardcover
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Published by Springer 2017-04, 2017
Series: Book 8 of 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
- Softcover
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Published by Springer 2014-03, 2014
Series: Book 3 of 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
- Softcover
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Published by Springer, 2020
Series: Book 14 of 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
- Softcover
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