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Challenges and opportunities in deep reinforcement learning with graph neural networks: A comprehensive review of algorithms and applications
Deep reinforcement learning (DRL) has empowered a variety of artificial intelligence fields,
including pattern recognition, robotics, recommendation systems, and gaming. Similarly …
including pattern recognition, robotics, recommendation systems, and gaming. Similarly …
BLoG: Bootstrapped graph representation learning with local and global regularization for recommendation
With the explosive growth of online information, the significant application value of
recommender systems has received considerable attention. Since user–item interactions …
recommender systems has received considerable attention. Since user–item interactions …
Weisfeiler and lehman go topological: Message passing simplicial networks
The pairwise interaction paradigm of graph machine learning has predominantly governed
the modelling of relational systems. However, graphs alone cannot capture the multi-level …
the modelling of relational systems. However, graphs alone cannot capture the multi-level …
Graph deep learning: State of the art and challenges
The last half-decade has seen a surge in deep learning research on irregular domains and
efforts to extend convolutional neural networks (CNNs) to work on irregularly structured data …
efforts to extend convolutional neural networks (CNNs) to work on irregularly structured data …
A mutually supervised graph attention network for few-shot segmentation: The perspective of fully utilizing limited samples
Fully supervised semantic segmentation has performed well in many computer vision tasks.
However, it is time-consuming because training a model requires a large number of pixel …
However, it is time-consuming because training a model requires a large number of pixel …
Permutation equivariant graph framelets for heterophilous graph learning
The nature of heterophilous graphs is significantly different from that of homophilous graphs,
which causes difficulties in early graph neural network (GNN) models and suggests …
which causes difficulties in early graph neural network (GNN) models and suggests …
Are graph convolutional networks with random weights feasible?
Graph Convolutional Networks (GCNs), as a prominent example of graph neural networks,
are receiving extensive attention for their powerful capability in learning node …
are receiving extensive attention for their powerful capability in learning node …
Dual-graph attention convolution network for 3-D point cloud classification
Three-dimensional point cloud classification is fundamental but still challenging in 3-D
vision. Existing graph-based deep learning methods fail to learn both low-level extrinsic and …
vision. Existing graph-based deep learning methods fail to learn both low-level extrinsic and …
Pushing the limits of fewshot anomaly detection in industry vision: Graphcore
In the area of fewshot anomaly detection (FSAD), efficient visual feature plays an essential
role in memory bank M-based methods. However, these methods do not account for the …
role in memory bank M-based methods. However, these methods do not account for the …
ByteGNN: efficient graph neural network training at large scale
Graph neural networks (GNNs) have shown excellent performance in a wide range of
applications such as recommendation, risk control, and drug discovery. With the increase in …
applications such as recommendation, risk control, and drug discovery. With the increase in …