28 citations · 33 across the 3 of their papers we have counts for
3 papers
cs.LG2023
Joint Edge-Model Sparse Learning is Provably Efficient for Graph Neural Networks
Shuai Zhang, Meng Wang, Pin-Yu Chen +3
Due to the significant computational challenge of training large-scale graph neural networks (GNNs), various sparse learning techniques have been exploited to reduce memory and sto…
cs.LG2022★ 5 cited
Interpretable Geometric Deep Learning via Learnable Randomness Injection
Siqi Miao, Yunan Luo, Mia Liu +1
Point cloud data is ubiquitous in scientific fields. Recently, geometric deep learning (GDL) has been widely applied to solve prediction tasks with such data. However, GDL models a…
cs.LG2022★ 28 cited
Interpretable and Generalizable Graph Learning via Stochastic Attention Mechanism
Siqi Miao, Miaoyuan Liu, Pan Li
Interpretable graph learning is in need as many scientific applications depend on learning models to collect insights from graph-structured data. Previous works mostly focused on u…