Search results with tag "Graph structure"
Convolutional Neural Networks on Graphs with Fast ...
proceedings.neurips.ccfers the same linear computational complexity and constant learning complexity as classical CNNs, while being universal to any graph structure. Experiments on MNIST and 20NEWS demonstrate the ability of this novel deep learning system to learn local, stationary, and compositional features on graphs. 1Introduction
![arXiv:2106.06090v1 [cs.CL] 10 Jun 2021](/cache/preview/7/6/a/1/c/a/d/d/thumb-76a1caddd29bcbb1b857bf1a9d94f541.jpg)
arXiv:2106.06090v1 [cs.CL] 10 Jun 2021
arxiv.orgDeep learning has become the dominant approach in coping with various tasks in Natural Language Processing (NLP). Although text inputs are typically represented as a sequence of tokens, there is a rich variety of NLP problems that can be best expressed with a graph structure. As a result, there
Graph Transformer Networks - NeurIPS
proceedings.neurips.ccheterogeneous graph and learns node representations via convolution on the learnt graph structures for a given problem. Our contributions are as follows:(i)We propose a novel framework Graph Transformer Networks, to learn a new graph structure which involves identifying useful meta-paths and multi-hop connections