Deep Learning and Machine Learning has made breakthroughs in recent years. There is tens of billions of dollars going into development of the new AI. Google and Deep Mind are recognizing that Deep ...

Deep Learning for Graphs Has a Long-Standing History. The deep learning for graphs field is rooted in neural networks for graphs research and early 1990s works on Recursive Neural Networks (RecNN) for ...

This repository contains code for deep learning on graphs using various graph neural network models. You can use this code to train and evaluate models such as GCN (Graph Convolutional Network), GIN ...

Deep learning, meet knowledge graphs . When asked if he thinks knowledge graphs can have a role in the hybrid approach he advocates for, Marcus was positive. One way to think about it, ...

This repository contains a curated list of papers on deep graph learning for drug discovery (DGL4DD), which are categorized based on their published years and corresponding tasks. Matching receptor to ...

• GCNAT is a deep learning algorithm that combines graph convolutional networks (GCN) and graph attention networks (GAT). After building a heterogeneous network, this approach combines the embeddings ...