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Topology-Aware Graph Pooling Networks
Pooling operations have shown to be effective on computer vision and nat...
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Kronecker Attention Networks
Attention operators have been applied on both 1-D data like texts and hi...
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Graph Representation Learning via Hard and Channel-Wise Attention Networks
Attention operators have been widely applied in various fields, includin...
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Graph U-Nets
We consider the problem of representation learning for graph data. Convo...
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Learning Graph Pooling and Hybrid Convolutional Operations for Text Representations
With the development of graph convolutional networks (GCN), deep learnin...
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ChannelNets: Compact and Efficient Convolutional Neural Networks via Channel-Wise Convolutions
Convolutional neural networks (CNNs) have shown great capability of solv...
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Large-Scale Learnable Graph Convolutional Networks
Convolutional neural networks (CNNs) have achieved great success on grid...
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Efficient and Invariant Convolutional Neural Networks for Dense Prediction
Convolutional neural networks have shown great success on feature extrac...
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Multi-Stage Variational Auto-Encoders for Coarse-to-Fine Image Generation
Variational auto-encoder (VAE) is a powerful unsupervised learning frame...
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Pixel Deconvolutional Networks
Deconvolutional layers have been widely used in a variety of deep models...
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