21 citations · 52 across the 11 of their papers we have counts for
6 papers · 1 filter
DRAG: Divergence-based Adaptive Aggregation in Federated learning on Non-IID Data
Feng Zhu, Jingjing Zhang, Shengyun Liu +1
Local stochastic gradient descent (SGD) is a fundamental approach in achieving communication efficiency in Federated Learning (FL) by allowing individual workers to perform local u…
Unsupervised Multiplex Graph Learning with Complementary and Consistent Information
Liang Peng, Xin Wang, Xiaofeng Zhu
Unsupervised multiplex graph learning (UMGL) has been shown to achieve significant effectiveness for different downstream tasks by exploring both complementary information and cons…
Graph Meets LLMs: Towards Large Graph Models
Ziwei Zhang, Haoyang Li, Zeyang Zhang +3
Large models have emerged as the most recent groundbreaking achievements in artificial intelligence, and particularly machine learning. However, when it comes to graphs, large mode…
Adversarially Robust Neural Architecture Search for Graph Neural Networks
Beini Xie, Heng Chang, Ziwei Zhang +5
Graph Neural Networks (GNNs) obtain tremendous success in modeling relational data. Still, they are prone to adversarial attacks, which are massive threats to applying GNNs to risk…
Block Format Error Bounds and Optimal Block Size Selection
Ilya Soloveychik, Ilya Lyubomirsky, Xin Wang +1
The amounts of data that need to be transmitted, processed, and stored by the modern deep neural networks have reached truly enormous volumes in the last few years calling for the…
Interpretable CNNs for Object Classification
Quanshi Zhang, Xin Wang, Ying Nian Wu +2
This paper proposes a generic method to learn interpretable convolutional filters in a deep convolutional neural network (CNN) for object classification, where each interpretable f…