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Название: Graph Representation Learning
Автор: William L. Hamilton
Издательство: Morgan & Claypool
Серия: Synthesis Lectures on Artificial Intelligence and Machine Learning
Год: 2020
Страниц: 161
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB
Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into Deep Learning (DL) 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. Machine Learning (ML) is inherently a problem-driven discipline. We seek to build models that can learn from data in order to solve particular tasks, and Machine Learning models are often categorized according to the type of task they seek to solve.
Автор: William L. Hamilton
Издательство: Morgan & Claypool
Серия: Synthesis Lectures on Artificial Intelligence and Machine Learning
Год: 2020
Страниц: 161
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB
Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into Deep Learning (DL) 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. Machine Learning (ML) is inherently a problem-driven discipline. We seek to build models that can learn from data in order to solve particular tasks, and Machine Learning models are often categorized according to the type of task they seek to solve.