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Self-Supervised Learning of Graph Neural Networks: A Unified Review
Deep models trained in supervised mode have achieved remarkable success ...
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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....
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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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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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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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Second-Order Pooling for Graph Neural Networks
Graph neural networks have achieved great success in learning node repre...
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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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Non-Local Graph Neural Networks
Modern graph neural networks (GNNs) learn node embeddings through multil...
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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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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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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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