103 citations · 113 across the 3 of their papers we have counts for
7 papers
Fast Quantum Property Prediction via Deeper 2D and 3D Graph Networks
Meng Liu, Cong Fu, Xuan Zhang +7
Molecular property prediction is gaining increasing attention due to its diverse applications. One task of particular interests and importance is to predict quantum chemical proper…
On Explainability of Graph Neural Networks via Subgraph Explorations
Hao Yuan, Haiyang Yu, Jie Wang +2
We consider the problem of explaining the predictions of graph neural networks (GNNs), which otherwise are considered as black boxes. Existing methods invariably focus on explainin…
Node2Seq: Towards Trainable Convolutions in Graph Neural Networks
Hao Yuan, Shuiwang Ji
Investigating graph feature learning becomes essentially important with the emergence of graph data in many real-world applications. Several graph neural network approaches are pro…
Towards Improved and Interpretable Deep Metric Learning via Attentive Grouping
Xinyi Xu, Zhengyang Wang, Cheng Deng +2
Grouping has been commonly used in deep metric learning for computing diverse features. However, current methods are prone to overfitting and lack interpretability. In this work, w…
XFake: Explainable Fake News Detector with Visualizations
Fan Yang, Shiva K. Pentyala, Sina Mohseni +6
In this demo paper, we present the XFake system, an explainable fake news detector that assists end-users to identify news credibility. To effectively detect and interpret the fake…
Global Pixel Transformers for Virtual Staining of Microscopy Images
Yi Liu, Hao Yuan, Zhengyang Wang +1
Visualizing the details of different cellular structures is of great importance to elucidate cellular functions. However, it is challenging to obtain high quality images of differe…