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20182021
most citedInvestigating and Mitigating Degree-Related Biases in Graph Convolutional Networks

96 citations · 120 across the 6 of their papers we have counts for

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Showing cs.LGShow all

8 papers · 1 filter

cs.LG2021

Labeled Data Generation with Inexact Supervision

Enyan Dai, Kai Shu, Yiwei Sun +1

The recent advanced deep learning techniques have shown the promising results in various domains such as computer vision and natural language processing. The success of deep neural…

cs.LG20205 cited

Explainable Multivariate Time Series Classification: A Deep Neural Network Which Learns To Attend To Important Variables As Well As Informative Time Intervals

Tsung-Yu Hsieh, Suhang Wang, Yiwei Sun +1

Time series data is prevalent in a wide variety of real-world applications and it calls for trustworthy and explainable models for people to understand and fully trust decisions ma…

cs.LG202096 cited

Investigating and Mitigating Degree-Related Biases in Graph Convolutional Networks

Xianfeng Tang, Huaxiu Yao, Yiwei Sun +5

Graph Convolutional Networks (GCNs) show promising results for semi-supervised learning tasks on graphs, thus become favorable comparing with other approaches. Despite the remarkab…

cs.LG201913 cited

Joint Modeling of Local and Global Temporal Dynamics for Multivariate Time Series Forecasting with Missing Values

Xianfeng Tang, Huaxiu Yao, Yiwei Sun +3

Multivariate time series (MTS) forecasting is widely used in various domains, such as meteorology and traffic. Due to limitations on data collection, transmission, and storage, rea…

cs.LG2019

Node Injection Attacks on Graphs via Reinforcement Learning

Yiwei Sun, Suhang Wang, Xianfeng Tang +2

Real-world graph applications, such as advertisements and product recommendations make profits based on accurately classify the label of the nodes. However, in such scenarios, ther…

cs.LG2019

Transferring Robustness for Graph Neural Network Against Poisoning Attacks

Xianfeng Tang, Yandong Li, Yiwei Sun +3

Graph neural networks (GNNs) are widely used in many applications. However, their robustness against adversarial attacks is criticized. Prior studies show that using unnoticeable m…