Graph Representation Learning
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…
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Produktdetails
- ISBN: 978-3-031-00460-5
- EAN: 9783031004605
- Produktnummer: 39048234
- Verlag: Springer International Publishing
- Sprache: Englisch
- Erscheinungsjahr: 2020
- Seitenangabe: 160 S.
- Masse: H23.5 cm x B19.1 cm x D0.8 cm 312 g
- Abbildungen: Paperback
- Gewicht: 312
Über den Autor
William L. Hamilton is an Assistant Professor of Computer Science at McGill University and a Canada CIFAR Chair in AI. His research focuses on graph representation learning as well as applications in computational social science and biology. In recent years, he has published more than 20 papers on graph representation learning at top-tier venues across machine learning and network science, as well as co-organized several large workshops and tutorials on the topic. Williams work has been recognized by several awards, including the 2018 Arthur L. Samuel Thesis Award for the best doctoral thesis in the Computer Science department at Stanford University and the 2017 Cozzarelli Best Paper Award from the Proceedings of the National Academy of Sciences.
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