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Mixed Finite Element Discretization for Maxwell Viscoelastic Model of Wave Propagation
This paper considers semi-discrete and fully discrete mixed finite eleme...
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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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Self-Supervised Contrastive Learning for Efficient User Satisfaction Prediction in Conversational Agents
Turn-level user satisfaction is one of the most important performance me...
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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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XGNN: Towards Model-Level Explanations of Graph Neural Networks
Graphs neural networks (GNNs) learn node features by aggregating and com...
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Large-scale Hybrid Approach for Predicting User Satisfaction with Conversational Agents
Measuring user satisfaction level is a challenging task, and a critical ...
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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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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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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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