116 citations · 360 across the 29 of their papers we have counts for
4 papers · 1 filter
Rethinking Graph Regularization for Graph Neural Networks
Han Yang, Kaili Ma, James Cheng
The graph Laplacian regularization term is usually used in semi-supervised representation learning to provide graph structure information for a model . However, with the rece…
Towards Cross-Granularity Few-Shot Learning: Coarse-to-Fine Pseudo-Labeling with Visual-Semantic Meta-Embedding
Jinhai Yang, Hua Yang, Lin Chen
Few-shot learning aims at rapidly adapting to novel categories with only a handful of samples at test time, which has been predominantly tackled with the idea of meta-learning. How…
Self-Enhanced GNN: Improving Graph Neural Networks Using Model Outputs
Han Yang, Xiao Yan, Xinyan Dai +2
Graph neural networks (GNNs) have received much attention recently because of their excellent performance on graph-based tasks. However, existing research on GNNs focuses on design…
Convolutional Embedding for Edit Distance
Xinyan Dai, Xiao Yan, Kaiwen Zhou +3
Edit-distance-based string similarity search has many applications such as spell correction, data de-duplication, and sequence alignment. However, computing edit distance is known…