10 citations · 22 across the 6 of their papers we have counts for
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cs.LG2022★ 10 cited
GNNInterpreter: A Probabilistic Generative Model-Level Explanation for Graph Neural Networks
Xiaoqi Wang, Han-Wei Shen
Recently, Graph Neural Networks (GNNs) have significantly advanced the performance of machine learning tasks on graphs. However, this technological breakthrough makes people wonder…
cs.LG2022★ 10 cited
On the Importance and Applicability of Pre-Training for Federated Learning
Hong-You Chen, Cheng-Hao Tu, Ziwei Li +2
Pre-training is prevalent in nowadays deep learning to improve the learned model's performance. However, in the literature on federated learning (FL), neural networks are mostly in…
cs.LG2022
SmartGD: A GAN-Based Graph Drawing Framework for Diverse Aesthetic Goals
Xiaoqi Wang, Kevin Yen, Yifan Hu +1
While a multitude of studies have been conducted on graph drawing, many existing methods only focus on optimizing a single aesthetic aspect of graph layouts, which can lead to sub-…