Molecular machine learning (ML) underpins critical workflows in drug discovery, material science, and catalyst optimization ...

Another contribution of Lin's work is on what is called symbolic compositionality of knowledge graph relations in embedding approaches. Embedding is a technique widely used in machine learning ...

Schad referred to his experience building machine learning ... which other graph databases offer, would be an obvious benefit. ArangoDB's team noted there is community work going on in that ...

Scientist Yi Nian is sharing his machine-learning expertise with the world in his latest co-authored publication, “Globally Interpretable Graph Learning ... to existing works that have primarily ...

a fully managed and intuitive graph machine learning platform. In today’s unpredictable times, businesses must balance cost-efficiency, customer satisfaction, and revenue growth while protecting ...

The result is a machine learning framework that is easier to work with—for example ... and gain introspection into TensorFlow apps. Each graph operation can be evaluated and modified separately ...

In separate work, Williamson used machine learning to refine an old conjecture that connects graphs and polynomials. Computers have aided in mathematical research for years, as proof assistants that ...

Machine learning and recently, graph neural networks become ubiquitous, e.g., in cheminformatics, bioinformatics, fraud detection, question answering, and recommendation over knowledge graphs. In this ...

A new study in Small introduces OptiMate, a machine learning model that predicts optical properties and identifies ...