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...
Physics-informed Graph Neural Networks have achieved remarkable performance in learning through graph-structured data by mitigating common GNN challenges s
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 conventional digital hardware, including the slowdown of Moore’s law due to transistor scaling...
GemNet: Universal Directional Graph Neural Networks for Molecules Johannes Klicpera (Technical University of Munich) · Florian Becker (Department of Informatics, Technical University Munich) · Stephan Günnemann (Technical University of Munich) Learning Graph Models for Retrosynthesis Prediction Vignesh Ram...
(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,...
GNN: graph neural network Contributed by Jie Zhou, Ganqu Cui, Zhengyan Zhang and Yushi Bai. Content 1. Survey 2. Models 2.1 Basic Models 2.2 Graph Types 2.3 Pooling Methods 2.4 Analysis 2.5 Efficiency 2.6 Explainability 3. Applications 3.1 Physics 3.2 Chemistry an...
In such scenarios, leveraging complex machine learning models such as GRAPE proves beneficial. 4. Conclusion This study presents GRAPE, a pioneering framework grounded in Graph Neural Networks (GNNs) tailored to predict chemical-species toxicity relations. GRAPE provides a significant advancement in ...
The experimental results demonstrate that the DTD-GNN model outperforms other graph neural network models in terms of AUC, Precision, and F1-score. The study has important implications for gaining a comprehensive understanding of the relationships between drugs and diseases, as well as for further ...
GNN: graph neural network Contributed by Jie Zhou, Ganqu Cui, Zhengyan Zhang and Yushi Bai. Content 1. Survey 2. Models 2.1 Basic Models 2.2 Graph Types 2.3 Pooling Methods 2.4 Analysis 2.5 Efficiency 3. Applications 3.1 Physics 3.2 Chemistry and Biology 3.3 ...