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.

Rebooting AI: Deep learning, meet knowledge graphs Gary Marcus, a prominent figure in AI, is on a mission to instill a breath of fresh air to a discipline he sees as in danger of stagnating.

Graph database developer Neo4j Inc. is upping its machine learning game today with a new release of Neo4j for Graph Data Science framework that leverages deep learning and graph convolutional ...

As Toon reminds The Next Platform, deep learning frameworks are capturing a knowledge model from data and the best way to represent those features and represents is via a computational graph. At their ...

A graph approach leaves machine learning users with a structure that can expose a huge amount of parallelism (each of the vertexes might have, for example, 25 million parameters) and that is a lot of ...

TigerGraph, provider of a leading graph analytics platform, is introducing the TigerGraph ML (Machine Learning) Workbench-a powerful toolkit that enables data scientists to significantly improve ML ...

Google Brain, Deep Mind, MIT, University of Edinburgh. The idea is that graph networks are bigger than any one machine-learning approach.

A research team from Kumamoto University has developed a promising deep learning model that significantly enhances the accuracy of subgraph matching—a critical task in fields ranging from drug ...