601 citations · 996 across the 16 of their papers we have counts for
11 papers · 1 filter
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…
Graph Structure Learning for Robust Graph Neural Networks
Wei Jin, Yao Ma, Xiaorui Liu +3
Graph Neural Networks (GNNs) are powerful tools in representation learning for graphs. However, recent studies show that GNNs are vulnerable to carefully-crafted perturbations, cal…
Self-supervised Learning on Graphs: Deep Insights and New Direction
Wei Jin, Tyler Derr, Haochen Liu +4
The success of deep learning notoriously requires larger amounts of costly annotated data. This has led to the development of self-supervised learning (SSL) that aims to alleviate…
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…
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…
GRACE: Generating Concise and Informative Contrastive Sample to Explain Neural Network Model's Prediction
Thai Le, Suhang Wang, Dongwon Lee
Despite the recent development in the topic of explainable AI/ML for image and text data, the majority of current solutions are not suitable to explain the prediction of neural net…