McGill University Publications
Author : McGill University
Publisher :
Page : 38 pages
File Size : 27,12 MB
Release : 1921
Category : Economics
ISBN :
Author : McGill University
Publisher :
Page : 38 pages
File Size : 27,12 MB
Release : 1921
Category : Economics
ISBN :
Author : McGill University (MONTREAL)
Publisher :
Page : pages
File Size : 11,25 MB
Release : 1921
Category :
ISBN :
Author : McGill University
Publisher :
Page : 874 pages
File Size : 46,64 MB
Release : 1921
Category :
ISBN :
Author : Dempsey
Publisher :
Page : 0 pages
File Size : 42,11 MB
Release : 2005
Category :
ISBN :
Author : McGill University. Libraries
Publisher :
Page : pages
File Size : 49,32 MB
Release : 1930
Category :
ISBN :
Author : William L. William L. Hamilton
Publisher : Springer Nature
Page : 141 pages
File Size : 17,83 MB
Release : 2022-06-01
Category : Computers
ISBN : 3031015886
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.
Author :
Publisher :
Page : 576 pages
File Size : 39,4 MB
Release : 1921
Category :
ISBN :
Some nos. are reprints from: Annual report of the governors, principal and fellows.
Author : McGill University
Publisher :
Page : pages
File Size : 18,43 MB
Release : 1927
Category : Canada
ISBN :
Author : Louis Nicolas
Publisher : McGill-Queen's Press - MQUP
Page : 573 pages
File Size : 39,86 MB
Release : 2011
Category : Art
ISBN : 0773538763
A natural history and illustrations of the New World in the seventeenth century.
Author : Raymond Klibansky
Publisher :
Page : 257 pages
File Size : 13,64 MB
Release : 2014-11
Category : Libraries
ISBN : 9781770962200