Hongyang Gao

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Washington State University ∙ Texas A&M University

Kronecker Attention Networks

Attention operators have been applied on both 1-D data like texts and hi...
07/16/2020 ∙ by Hongyang Gao, et al. ∙ 54 ∙ share

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Graph Representation Learning via Hard and Channel-Wise Attention Networks

Attention operators have been widely applied in various fields, includin...
07/05/2019 ∙ by Hongyang Gao, et al. ∙ 0 ∙ share

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Graph U-Nets

We consider the problem of representation learning for graph data. Convo...
05/11/2019 ∙ by Hongyang Gao, et al. ∙ 0 ∙ share

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Learning Graph Pooling and Hybrid Convolutional Operations for Text Representations

With the development of graph convolutional networks (GCN), deep learnin...
01/21/2019 ∙ by Hongyang Gao, et al. ∙ 0 ∙ share

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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...
09/05/2018 ∙ by Hongyang Gao, et al. ∙ 0 ∙ share

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Large-Scale Learnable Graph Convolutional Networks

Convolutional neural networks (CNNs) have achieved great success on grid...
08/12/2018 ∙ by Hongyang Gao, et al. ∙ 0 ∙ share

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Efficient and Invariant Convolutional Neural Networks for Dense Prediction

Convolutional neural networks have shown great success on feature extrac...
11/24/2017 ∙ by Hongyang Gao, et al. ∙ 0 ∙ share

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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...
05/19/2017 ∙ by Lei Cai, et al. ∙ 0 ∙ share

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Pixel Deconvolutional Networks

Deconvolutional layers have been widely used in a variety of deep models...
05/18/2017 ∙ by Hongyang Gao, et al. ∙ 0 ∙ share

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Hongyang Gao

Featured Co-authors

Shuiwang Ji

Zhengyang Wang

Hao Yuan

Lei Cai

Yongjun Chen

Kronecker Attention Networks

Graph Representation Learning via Hard and Channel-Wise Attention Networks

Graph U-Nets

Learning Graph Pooling and Hybrid Convolutional Operations for Text Representations

ChannelNets: Compact and Efficient Convolutional Neural Networks via Channel-Wise Convolutions

Large-Scale Learnable Graph Convolutional Networks

Efficient and Invariant Convolutional Neural Networks for Dense Prediction

Multi-Stage Variational Auto-Encoders for Coarse-to-Fine Image Generation

Pixel Deconvolutional Networks