AWS enters the context layer race with a graph that learns from agents, not manual curation

AWS Context is a self-learning knowledge graph for enterprise data — it propagates agent-discovered relationships ...
Nature

Transfer learning with graph neural networks for improved molecular property prediction in the multi-fidelity setting

We investigate the potential of graph neural networks (GNNs) for transfer learning and improved molecular property prediction in the context of funnels or screening cascades characteristic of drug ...
Nature

Molecular contrastive learning of representations via graph neural networks

Molecular machine learning bears promise for efficient molecular property prediction and drug discovery. However, labelled molecule data can be expensive and time consuming to acquire. Due to the ...
lse

Learning from a single network: How new mathematics reveals the hidden geometry of graphs

Giulia Livieri sets out remarkable new research with results that clarify how learning works on complex graphs and how quickly any method (including Graph Convolutional Networks) can learn from them, ...
Frontiers

Numerical foundations for scalable statistical and machine learning methods

Across modern data-intensive disciplines, the union of numerical computation, statistics, and machine learning has become ...