178 citations · 347 across the 12 of their papers we have counts for
4 papers · 1 filter
When to Trust Aggregated Gradients: Addressing Negative Client Sampling in Federated Learning
Wenkai Yang, Yankai Lin, Guangxiang Zhao +3
Federated Learning has become a widely-used framework which allows learning a global model on decentralized local datasets under the condition of protecting local data privacy. How…
Topology-Imbalance Learning for Semi-Supervised Node Classification
Deli Chen, Yankai Lin, Guangxiang Zhao +4
The class imbalance problem, as an important issue in learning node representations, has drawn increasing attention from the community. Although the imbalance considered by existin…
Well-classified Examples are Underestimated in Classification with Deep Neural Networks
Guangxiang Zhao, Wenkai Yang, Xuancheng Ren +3
The conventional wisdom behind learning deep classification models is to focus on bad-classified examples and ignore well-classified examples that are far from the decision boundar…
Understanding and Improving Layer Normalization
Jingjing Xu, Xu Sun, Zhiyuan Zhang +2
Layer normalization (LayerNorm) is a technique to normalize the distributions of intermediate layers. It enables smoother gradients, faster training, and better generalization accu…