Hongyang Gao

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Washington State University

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Iowa State University of Science and Technology

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Texas A&M University

Featured Co-authors

YI LIU
101 publications
Jia Liu
81 publications
Shuiwang Ji
46 publications
Xuan Zhang
42 publications
Zhengyang Wang
18 publications
Hao Yuan
18 publications
Hailiang Liu
16 publications
Hridesh Rajan
15 publications
Tianxiang Gao
9 publications
Lei Cai
8 publications
Yongjun Chen
7 publications

∙ 05/16/2022

Gradient Descent Optimizes Infinite-Depth ReLU Implicit Networks with Linear Widths

Implicit deep learning has recently become popular in the machine learni...

Tianxiang Gao, et al. ∙

∙ 02/01/2022

Molecular Graph Representation Learning via Heterogeneous Motif Graph Construction

We consider feature representation learning problem of molecular graphs....

Zhaoning Yu, et al. ∙

∙ 02/01/2022

MotifExplainer: a Motif-based Graph Neural Network Explainer

We consider the explanation problem of Graph Neural Networks (GNNs). Mos...

Zhaoning Yu, et al. ∙

∙ 10/11/2021

A global convergence theory for deep ReLU implicit networks via over-parameterization

Implicit deep learning has received increasing attention recently due to...

Tianxiang Gao, et al. ∙

∙ 03/15/2021

Sent2Matrix: Folding Character Sequences in Serpentine Manifolds for Two-Dimensional Sentence

We study text representation methods using deep models. Current methods,...

Hongyang Gao, et al. ∙

∙ 10/19/2020

Topology-Aware Graph Pooling Networks

Pooling operations have shown to be effective on computer vision and nat...

Hongyang Gao, et al. ∙

∙ 07/16/2020

Kronecker Attention Networks

Attention operators have been applied on both 1-D data like texts and hi...

Hongyang Gao, et al. ∙

∙ 07/05/2019

Graph Representation Learning via Hard and Channel-Wise Attention Networks

Attention operators have been widely applied in various fields, includin...

Hongyang Gao, et al. ∙

∙ 05/11/2019

Graph U-Nets

We consider the problem of representation learning for graph data. Convo...

Hongyang Gao, et al. ∙

∙ 01/21/2019

Learning Graph Pooling and Hybrid Convolutional Operations for Text Representations

With the development of graph convolutional networks (GCN), deep learnin...

Hongyang Gao, et al. ∙

∙ 09/05/2018

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

Convolutional neural networks (CNNs) have shown great capability of solv...

Hongyang Gao, et al. ∙

∙ 08/12/2018

Large-Scale Learnable Graph Convolutional Networks

Convolutional neural networks (CNNs) have achieved great success on grid...

Hongyang Gao, et al. ∙

∙ 11/24/2017

Efficient and Invariant Convolutional Neural Networks for Dense Prediction

Convolutional neural networks have shown great success on feature extrac...

Hongyang Gao, et al. ∙

∙ 05/19/2017

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

Variational auto-encoder (VAE) is a powerful unsupervised learning frame...

Lei Cai, et al. ∙

∙ 05/18/2017

Pixel Deconvolutional Networks

Deconvolutional layers have been widely used in a variety of deep models...

Hongyang Gao, et al. ∙