Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering Daniil Sorokin, Iryna Gurevych COLING 2018 Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks Diego Marcheggiani, Joost Bastings, Ivan Titov NAACL 2018 Linguistically-Informed Self-Attent...
Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering Daniil Sorokin, Iryna Gurevych COLING 2018 Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks Diego Marcheggiani, Joost Bastings, Ivan Titov NAACL 2018 Linguistically-Informed Self-Attent...
Recently, graph neural networks (GNN) have shown strength in learning low-dimensional representations of individual cells by propagating neighbor cell features and constructing cell-cell relations in a global cell graph9,10. For example, our in-house tool scGNN, a GNN model, has demonstrated superi...
Ewenwan/Graph-neural-networks master 1Branch0Tags Code This branch is1 commit ahead of,1 commit behindSeongokRyu/Graph-neural-networks:master. README Graph-neural-networks 图(graph)是一种数据格式,它可以用于表示社交网络、通信网络、蛋白分子网络等, 图中的节点表示网络中的个体,连边表示个体之间的...
(loss value) of the final layout which we find to strongly correlate with the quality of the layout. But we also explore two additional measures, such as cluster separation and link length distribution. We begin by comparing the performance of FDL with the three proposed neural network models,...
Labeling Trick: A Theory of Using Graph Neural Networks for Multi-Node Representation LearningMuhan Zhang (Peking University) · Pan Li (Stanford University) · Yinglong Xia (University of Southern California) · Kai Wang (Facebook) · Long Jin (Facebook) ...
Liang YixuanWuhan University of TechnologyWan YuanApplied Intelligence: The International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving Technologies
DGL provides a powerful graph object that can reside on either CPU or GPU. It bundles structural data as well as features for better control. We provide a variety of functions for computing with graph objects including efficient and customizable message passing primitives for Graph Neural Networks....
基于历史上的神经图灵机[8]、可微神经计算机[9]等神经执行器(neural executors)的成功,受益于现在广为使用的各种图机器学习工具包,2020 年发表的一些研究工作从理论上探究了神经执行器的缺陷[5,10,11],提出了一些基于 GNN 的强大的新推理架构[12-15],并且在神经推理任务上具有完美的泛化性能[16]。在 2021 年...
Recent years have witnessed a surge of interest in learning representations of graph-structured data, with applications from social networks to drug discovery. However, graph neural networks, the machine learning models for handling graph-structured data, face significant challenges when running on conven...