Shuiwang Ji

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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Machine Learning Explanations to Prevent Overtrust in Fake News Detection

Combating fake news and misinformation propagation is a challenging task...
07/24/2020 ∙ by Sina Mohseni, et al. ∙ 16 ∙ 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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Deep Learning of High-Order Interactions for Protein Interface Prediction

Protein interactions are important in a broad range of biological proces...
07/18/2020 ∙ by YI LIU, et al. ∙ 26 ∙ 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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XGNN: Towards Model-Level Explanations of Graph Neural Networks

Graphs neural networks (GNNs) learn node features by aggregating and com...
06/03/2020 ∙ by Hao Yuan, et al. ∙ 39 ∙ 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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XFake: Explainable Fake News Detector with Visualizations

In this demo paper, we present the XFake system, an explainable fake new...
07/08/2019 ∙ by Fan Yang, et al. ∙ 0 ∙ 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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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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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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On Attribution of Recurrent Neural Network Predictions via Additive Decomposition

RNN models have achieved the state-of-the-art performance in a wide rang...
03/27/2019 ∙ by Mengnan Du, 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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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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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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Dense Transformer Networks

The key idea of current deep learning methods for dense prediction is to...
05/24/2017 ∙ by Jun Li, 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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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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Multi-Task Feature Learning Via Efficient l2,1-Norm Minimization

The problem of joint feature selection across a group of related tasks h...
05/09/2012 ∙ by Jun Liu, et al. ∙ 0 ∙ share

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Shuiwang Ji

Featured Co-authors

Jun Li

Fan Yang

Jun Liu

Jieping Ye

Jiliang Tang

Xia Hu

YI LIU

Dinggang Shen

Mengnan Du

Ian Davidson

Xin Luna Dong

CorDEL: A Contrastive Deep Learning Approach for Entity Linkage

Global Voxel Transformer Networks for Augmented Microscopy

Machine Learning Explanations to Prevent Overtrust in Fake News Detection

Second-Order Pooling for Graph Neural Networks

Deep Learning of High-Order Interactions for Protein Interface Prediction

Kronecker Attention Networks

XGNN: Towards Model-Level Explanations of Graph Neural Networks

Non-Local Graph Neural Networks

XFake: Explainable Fake News Detector with Visualizations

Graph Representation Learning via Hard and Channel-Wise Attention Networks

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

Graph U-Nets

On Attribution of Recurrent Neural Network Predictions via Additive Decomposition

Learning Graph Pooling and Hybrid Convolutional Operations for Text Representations

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

Efficient and Invariant Convolutional Neural Networks for Dense Prediction

Dense Transformer Networks

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

Learning Convolutional Text Representations for Visual Question Answering

Spatial Variational Auto-Encoding via Matrix-Variate Normal Distributions

Pixel Deconvolutional Networks

Multi-Task Feature Learning Via Efficient l2,1-Norm Minimization