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 ...

Enterprise information is growing at a phenomenal rate. An abundance of storage capability, a multiplicity of formats in which information can occur, and policies that encourage saving information… ...

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 ...

Eye-Tracking, Machine Learning, Distance Learning, Online Learning, E-Learning, Bibliometric Analysis Share and Cite: Ayan, E ...

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 ...

This course focuses on computational and modeling challenges in real world graphs (networks), with a particular emphasis ... Students should have a strong interest in conducting (or learning how to ...