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, ...

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 ...

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 ...

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 ...

TensorFlow 1.x was all about building static graphs in a very un-Python manner, but with the TensorFlow 2.x line, ... JAX is a deep learning framework that is built, maintained, ...