
Inferring Javascript types using Graph Neural Networks
The recent use of `Big Code' with stateoftheart deep learning methods...
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Graph Neural Processes: Towards Bayesian Graph Neural Networks
We introduce Graph Neural Processes (GNP), inspired by the recent work i...
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A Survey on The Expressive Power of Graph Neural Networks
Graph neural networks (GNNs) are effective machine learning models for v...
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Generalizable Machine Learning in Neuroscience using Graph Neural Networks
Although a number of studies have explored deep learning in neuroscience...
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Graph Neural Networks for IceCube Signal Classification
Tasks involving the analysis of geometric (graph and manifoldstructure...
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Automating Botnet Detection with Graph Neural Networks
Botnets are now a major source for many network attacks, such as DDoS at...
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Computing Graph Neural Networks: A Survey from Algorithms to Accelerators
Graph Neural Networks (GNNs) have exploded onto the machine learning sce...
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A Practical Guide to Graph Neural Networks
Graph neural networks (GNNs) have recently grown in popularity in the field of artificial intelligence due to their unique ability to ingest relatively unstructured data types as input data. Although some elements of the GNN architecture are conceptually similar in operation to traditional neural networks (and neural network variants), other elements represent a departure from traditional deep learning techniques. This tutorial exposes the power and novelty of GNNs to the average deep learning enthusiast by collating and presenting details on the motivations, concepts, mathematics, and applications of the most common types of GNNs. Importantly, we present this tutorial concisely, alongside worked code examples, and at an introductory pace, thus providing a practical and accessible guide to understanding and using GNNs.
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