Zhengyang Wang

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Washington State University ∙ Texas A&M University

Self-Supervised Learning of Graph Neural Networks: A Unified Review

Deep models trained in supervised mode have achieved remarkable success ...
02/22/2021 ∙ by Yaochen Xie, et al. ∙ 5 ∙ share

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Edge Sparse Basis Network: An Deep Learning Framework for EEG Source Localization

EEG source localization is an important technical issue in EEG analysis....
02/18/2021 ∙ by Chen Wei, et al. ∙ 43 ∙ share

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Towards Improved and Interpretable Deep Metric Learning via Attentive Grouping

Grouping has been commonly used in deep metric learning for computing di...
11/17/2020 ∙ by Xinyi Xu, et al. ∙ 10 ∙ share

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Noise2Same: Optimizing A Self-Supervised Bound for Image Denoising

Self-supervised frameworks that learn denoising models with merely indiv...
10/22/2020 ∙ by Yaochen Xie, et al. ∙ 4 ∙ share

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Deep Low-Shot Learning for Biological Image Classification and Visualization from Limited Training Samples

Predictive modeling is useful but very challenging in biological image a...
10/20/2020 ∙ by Lei Cai, et al. ∙ 5 ∙ share

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CorDEL: A Contrastive Deep Learning Approach for Entity Linkage

Entity linkage (EL) is a critical problem in data cleaning and integrati...
09/15/2020 ∙ by Zhengyang Wang, et al. ∙ 0 ∙ share

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Global Voxel Transformer Networks for Augmented Microscopy

Advances in deep learning have led to remarkable success in augmented mi...
08/05/2020 ∙ by Zhengyang Wang, et al. ∙ 107 ∙ share

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Second-Order Pooling for Graph Neural Networks

Graph neural networks have achieved great success in learning node repre...
07/20/2020 ∙ by Zhengyang Wang, et al. ∙ 90 ∙ share

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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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Non-Local Graph Neural Networks

Modern graph neural networks (GNNs) learn node embeddings through multil...
05/29/2020 ∙ by Meng Liu, et al. ∙ 34 ∙ share

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Global Transformer U-Nets for Label-Free Prediction of Fluorescence Images

Visualizing the details of different cellular structures is of great imp...
07/01/2019 ∙ by YI LIU, et al. ∙ 0 ∙ share

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Global Deep Learning Methods for Multimodality Isointense Infant Brain Image Segmentation

An important step in early brain development study is to perform automat...
12/10/2018 ∙ by Zhengyang Wang, 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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Smoothed Dilated Convolutions for Improved Dense Prediction

Dilated convolutions, also known as atrous convolutions, have been widel...
08/27/2018 ∙ by Zhengyang Wang, 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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Learning Convolutional Text Representations for Visual Question Answering

Visual question answering is a recently proposed artificial intelligence...
05/18/2017 ∙ by Zhengyang Wang, et al. ∙ 0 ∙ share

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Spatial Variational Auto-Encoding via Matrix-Variate Normal Distributions

The key idea of variational auto-encoders (VAEs) resembles that of tradi...
05/18/2017 ∙ by Zhengyang Wang, 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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Zhengyang Wang

Featured Co-authors

YI LIU

Dinggang Shen

Shuiwang Ji

Cheng Deng

Xin Luna Dong

Hao Yuan

Hongyang Gao

Chen Wei

Lei Cai

Quanying Liu

Na Zou

Self-Supervised Learning of Graph Neural Networks: A Unified Review

Edge Sparse Basis Network: An Deep Learning Framework for EEG Source Localization

Towards Improved and Interpretable Deep Metric Learning via Attentive Grouping

Noise2Same: Optimizing A Self-Supervised Bound for Image Denoising

Deep Low-Shot Learning for Biological Image Classification and Visualization from Limited Training Samples

CorDEL: A Contrastive Deep Learning Approach for Entity Linkage

Global Voxel Transformer Networks for Augmented Microscopy

Second-Order Pooling for Graph Neural Networks

Kronecker Attention Networks

Non-Local Graph Neural Networks

Global Transformer U-Nets for Label-Free Prediction of Fluorescence Images

Global Deep Learning Methods for Multimodality Isointense Infant Brain Image Segmentation

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

Smoothed Dilated Convolutions for Improved Dense Prediction

Large-Scale Learnable Graph Convolutional Networks

Learning Convolutional Text Representations for Visual Question Answering

Spatial Variational Auto-Encoding via Matrix-Variate Normal Distributions

Pixel Deconvolutional Networks