Hao Yuan

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Washington State University ∙ Texas A&M University

Mixed Finite Element Discretization for Maxwell Viscoelastic Model of Wave Propagation

This paper considers semi-discrete and fully discrete mixed finite eleme...
01/22/2021 ∙ by Hao Yuan, et al. ∙ 0 ∙ 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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Self-Supervised Contrastive Learning for Efficient User Satisfaction Prediction in Conversational Agents

Turn-level user satisfaction is one of the most important performance me...
10/21/2020 ∙ by Mohammad Kachuee, et al. ∙ 0 ∙ 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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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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Large-scale Hybrid Approach for Predicting User Satisfaction with Conversational Agents

Measuring user satisfaction level is a challenging task, and a critical ...
05/29/2020 ∙ by Dookun Park, et al. ∙ 0 ∙ 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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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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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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Hao Yuan

Featured Co-authors

Fan Yang

YI LIU

Jiliang Tang

Xia Hu

Shuiwang Ji

Cheng Deng

Sungjin Lee

Mengnan Du

Zhengyang Wang

Young-Bum Kim

Xiaoping Xie

Mixed Finite Element Discretization for Maxwell Viscoelastic Model of Wave Propagation

Node2Seq: Towards Trainable Convolutions in Graph Neural Networks

Explainability in Graph Neural Networks: A Taxonomic Survey

Towards Improved and Interpretable Deep Metric Learning via Attentive Grouping

Self-Supervised Contrastive Learning for Efficient User Satisfaction Prediction in Conversational Agents

Deep Learning of High-Order Interactions for Protein Interface Prediction

XGNN: Towards Model-Level Explanations of Graph Neural Networks

Large-scale Hybrid Approach for Predicting User Satisfaction with Conversational Agents

XFake: Explainable Fake News Detector with Visualizations

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

Spatial Variational Auto-Encoding via Matrix-Variate Normal Distributions

Pixel Deconvolutional Networks