2019
NIPS
NeurIPS 2019
Graph Normalizing Flows
Abstract
We introduce graph normalizing flows: a new, reversible graph neural network model for prediction and generation. On supervised tasks, graph normalizing flows perform similarly to message passing neural networks, but at a significantly reduced memory footprint, allowing them to scale to larger graphs. In the unsupervised case, we combine graph normalizing flows with a novel graph auto-encoder to create a generative model of graph structures. Our model is permutation-invariant, generating entire graphs with a single feed-forward pass, and achieves competitive results with the state-of-the art auto-regressive models, while being better suited to parallel computing architectures.
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Interdisciplinary Bridge
— Deep Learning and Machine Learning and Mathematics & Optimization
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Keyword Pioneer
— reversible model
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Machine Learning > Core Methods > Representation Learning
Machine Learning > Learning Types > Unsupervised Learning
Deep Learning > Architectures > Graph Neural Networks
Mathematics & Optimization > Mathematics > Graph Theory
Machine Learning > Core Methods > Graph Neural Networks
Deep Learning > Learning Types > Generative Models