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A Multi-Stage Attentive Transfer Learning Framework for Improving COVID-19 Diagnosis
Computed tomography (CT) imaging is a promising approach to diagnosing t...
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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...
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Node2Seq: Towards Trainable Convolutions in Graph Neural Networks
Investigating graph feature learning becomes essentially important with ...
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Explainability in Graph Neural Networks: A Taxonomic Survey
Deep learning methods are achieving ever-increasing performance on many ...
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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...
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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...
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Noise2Same: Optimizing A Self-Supervised Bound for Image Denoising
Self-supervised frameworks that learn denoising models with merely indiv...
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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...
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Line Graph Neural Networks for Link Prediction
We consider the graph link prediction task, which is a classic graph ana...
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Topology-Aware Graph Pooling Networks
Pooling operations have shown to be effective on computer vision and nat...
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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...
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Global Voxel Transformer Networks for Augmented Microscopy
Advances in deep learning have led to remarkable success in augmented mi...
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Machine Learning Explanations to Prevent Overtrust in Fake News Detection
Combating fake news and misinformation propagation is a challenging task...
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Second-Order Pooling for Graph Neural Networks
Graph neural networks have achieved great success in learning node repre...
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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...
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Kronecker Attention Networks
Attention operators have been applied on both 1-D data like texts and hi...
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XGNN: Towards Model-Level Explanations of Graph Neural Networks
Graphs neural networks (GNNs) learn node features by aggregating and com...
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Non-Local Graph Neural Networks
Modern graph neural networks (GNNs) learn node embeddings through multil...
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XFake: Explainable Fake News Detector with Visualizations
In this demo paper, we present the XFake system, an explainable fake new...
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Graph Representation Learning via Hard and Channel-Wise Attention Networks
Attention operators have been widely applied in various fields, includin...
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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...
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Graph U-Nets
We consider the problem of representation learning for graph data. Convo...
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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...
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Learning Graph Pooling and Hybrid Convolutional Operations for Text Representations
With the development of graph convolutional networks (GCN), deep learnin...
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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...
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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...
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Smoothed Dilated Convolutions for Improved Dense Prediction
Dilated convolutions, also known as atrous convolutions, have been widel...
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Large-Scale Learnable Graph Convolutional Networks
Convolutional neural networks (CNNs) have achieved great success on grid...
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Efficient and Invariant Convolutional Neural Networks for Dense Prediction
Convolutional neural networks have shown great success on feature extrac...
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Dense Transformer Networks
The key idea of current deep learning methods for dense prediction is to...
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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...
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Learning Convolutional Text Representations for Visual Question Answering
Visual question answering is a recently proposed artificial intelligence...
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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...
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Pixel Deconvolutional Networks
Deconvolutional layers have been widely used in a variety of deep models...
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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...
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