b'Shuiwang Ji'

A Multi-Stage Attentive Transfer Learning Framework for Improving COVID-19 Diagnosis

Computed tomography (CT) imaging is a promising approach to diagnosing t...
01/14/2021 ∙ by YI LIU, et al. ∙ 10 ∙ share

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CleftNet: Augmented Deep Learning for Synaptic Cleft Detection from Brain Electron Microscopy

Detecting synaptic clefts is a crucial step to investigate the biologica...
01/12/2021 ∙ by YI LIU, et al. ∙ 10 ∙ share

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Node2Seq: Towards Trainable Convolutions in Graph Neural Networks

Investigating graph feature learning becomes essentially important with ...
01/06/2021 ∙ by Hao Yuan, et al. ∙ 22 ∙ share

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Explainability in Graph Neural Networks: A Taxonomic Survey

Deep learning methods are achieving ever-increasing performance on many ...
12/31/2020 ∙ by Hao Yuan, et al. ∙ 227 ∙ 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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Augmented Equivariant Attention Networks for Electron Microscopy Image Super-Resolution

Taking electron microscopy (EM) images in high-resolution is time-consum...
11/06/2020 ∙ by Yaochen Xie, et al. ∙ 1 ∙ 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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Line Graph Neural Networks for Link Prediction

We consider the graph link prediction task, which is a classic graph ana...
10/20/2020 ∙ by Lei Cai, et al. ∙ 18 ∙ share

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Topology-Aware Graph Pooling Networks

Pooling operations have shown to be effective on computer vision and nat...
10/19/2020 ∙ by Hongyang Gao, et al. ∙ 6 ∙ 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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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

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