Search Results for author:
Found 5 papers, 4 papers with code
Deformable Graph Transformer
Transformer-based models have recently shown success in representation learning on graph-structured data beyond natural language processing and computer vision.
Neo-GNNs: Neighborhood Overlap-aware Graph Neural Networks for Link Prediction
Graph Neural Networks (GNNs) have been widely applied to various fields for learning over graph-structured data.
Graph Transformer Networks: Learning Meta-path Graphs to Improve GNNs
Despite the success of GNNs, most existing GNNs are designed to learn node representations on the fixed and homogeneous graphs.
Graph Transformer Networks
In this paper, we propose Graph Transformer Networks (GTNs) that are capable of generating new graph structures, which involve identifying useful connections between unconnected nodes on the original graph, while learning effective node representation on the new graphs in an end-to-end fashion.
Ranking Paragraphs for Improving Answer Recall in Open-Domain Question Answering
Recently, open-domain question answering (QA) has been combined with machine comprehension models to find answers in a large knowledge source.
