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. Google and Deep Mind are recognizing that Deep ...

Deep learning, meet knowledge graphs . When asked if he thinks knowledge graphs can have a role in the hybrid approach he advocates for, Marcus was positive. One way to think about it, ...

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 neural ...

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 ...

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 ...

The master plan for neural networks is to create ever-larger networks that loop into one another, learning and improving. These must be recurrent—not just in the RNN way, but recurrent over many ...

The paper, "Relational inductive biases, deep learning, and graph networks," posted on the arXiv pre-print service, is authored by Peter W. Battaglia of Google's DeepMind unit, along with ...

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 ...