396 citations · 605 across the 23 of their papers we have counts for
6 papers · 1 filter
Variational Metric Scaling for Metric-Based Meta-Learning
Jiaxin Chen, Li-Ming Zhan, Xiao-Ming Wu +1
Metric-based meta-learning has attracted a lot of attention due to its effectiveness and efficiency in few-shot learning. Recent studies show that metric scaling plays a crucial ro…
Clustering Uncertain Data via Representative Possible Worlds with Consistency Learning
Han Liu, Xianchao Zhang, Xiaotong Zhang +2
Clustering uncertain data is an essential task in data mining for the internet of things. Possible world based algorithms seem promising for clustering uncertain data. However, the…
Dimensionwise Separable 2-D Graph Convolution for Unsupervised and Semi-Supervised Learning on Graphs
Qimai Li, Xiaotong Zhang, Han Liu +2
Graph convolutional neural networks (GCN) have been the model of choice for graph representation learning, which is mainly due to the effective design of graph convolution that com…
Graph Transfer Learning via Adversarial Domain Adaptation with Graph Convolution
Quanyu Dai, Xiao-Ming Wu, Jiaren Xiao +2
This paper studies the problem of cross-network node classification to overcome the insufficiency of labeled data in a single network. It aims to leverage the label information in…
Attributed Graph Clustering via Adaptive Graph Convolution
Xiaotong Zhang, Han Liu, Qimai Li +1
Attributed graph clustering is challenging as it requires joint modelling of graph structures and node attributes. Recent progress on graph convolutional networks has proved that g…
Label Efficient Semi-Supervised Learning via Graph Filtering
Qimai Li, Xiao-Ming Wu, Han Liu +2
Graph-based methods have been demonstrated as one of the most effective approaches for semi-supervised learning, as they can exploit the connectivity patterns between labeled and u…