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A comprehensive survey on graph neural networks
Deep learning has revolutionized many machine learning tasks in recent years, ranging
from image classification and video processing to speech recognition and natural language …
from image classification and video processing to speech recognition and natural language …
[HTML][HTML] Graph neural networks: A review of methods and applications
Lots of learning tasks require dealing with graph data which contains rich relation
information among elements. Modeling physics systems, learning molecular fingerprints …
information among elements. Modeling physics systems, learning molecular fingerprints …
How powerful are graph neural networks?
Graph Neural Networks (GNNs) are an effective framework for representation learning of
graphs. GNNs follow a neighborhood aggregation scheme, where the representation vector …
graphs. GNNs follow a neighborhood aggregation scheme, where the representation vector …
Weisfeiler and leman go neural: Higher-order graph neural networks
In recent years, graph neural networks (GNNs) have emerged as a powerful neural
architecture to learn vector representations of nodes and graphs in a supervised, end-to-end …
architecture to learn vector representations of nodes and graphs in a supervised, end-to-end …
Reinforcement learning for solving the vehicle routing problem
We present an end-to-end framework for solving the Vehicle Routing Problem (VRP) using
reinforcement learning. In this approach, we train a single policy model that finds near …
reinforcement learning. In this approach, we train a single policy model that finds near …
Computing graph neural networks: A survey from algorithms to accelerators
Graph Neural Networks (GNNs) have exploded onto the machine learning scene in recent
years owing to their capability to model and learn from graph-structured data. Such an ability …
years owing to their capability to model and learn from graph-structured data. Such an ability …
Generalization and representational limits of graph neural networks
We address two fundamental questions about graph neural networks (GNNs). First, we
prove that several important graph properties, eg, shortest/longest cycle, diameter, or certain …
prove that several important graph properties, eg, shortest/longest cycle, diameter, or certain …
Graph networks as learnable physics engines for inference and control
Understanding and interacting with everyday physical scenes requires rich knowledge
about the structure of the world, represented either implicitly in a value or policy function, or …
about the structure of the world, represented either implicitly in a value or policy function, or …
Dual encoder-based dynamic-channel graph convolutional network with edge enhancement for retinal vessel segmentation
Retinal vessel segmentation with deep learning technology is a crucial auxiliary method for
clinicians to diagnose fundus diseases. However, the deep learning approaches inevitably …
clinicians to diagnose fundus diseases. However, the deep learning approaches inevitably …
What can neural networks reason about?
Neural networks have succeeded in many reasoning tasks. Empirically, these tasks require
specialized network structures, eg, Graph Neural Networks (GNNs) perform well on many …
specialized network structures, eg, Graph Neural Networks (GNNs) perform well on many …