Deep learning faces challenges in complex language and scene understanding, reasoning about structured data, transferring learning beyond the training conditions, and learning from small amounts of ...

Deep learning, knowledge graphs, and the future of AI. Featuring scientist, best-selling author, and entrepreneur Gary Marcus Preview E Jan 4 · Orchestrate all the Things ...

This repository contains research on graph deep learning models and model explainability, focusing on Graph Convolutional Networks (GCN), Graph Isomorphism Networks (GIN), and their applications to ...

Recent progress in research on deep graph networks (DGNs) has led to a maturation of the domain of learning on graphs. Despite the growth of this research field, there are still important challenges ...

Course: Graph Machine Learning focuses on the application of machine learning algorithms on graph-structured data. Some of the key topics that are covered in the course include graph representation ...

Decoding cosmic evolution depends on accurately predicting the complex chemical reactions in the harsh environment of space.

In short, the end game is deep, wide reinforcement learning, or more simply, building networks that improve with use. And as one might imagine, the computational load of such a task is immense and far ...