Agarwal, Chirag, Himabindu Lakkaraju, and Marinka Zitnik. "Towards a Unified Framework for Fair and Stable Graph Representation Learning." In Proceedings of the 37th Conference on Uncertainty in ...

This article explores what knowledge graphs are, why they are becoming a favourable data storage format, and discusses their potential to improve artificial intelligence and machine learning ...

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 ...

Graphs are among the most widely-used data structures in machine learning. Their power comes from the flexibility of capturing relations (edges) of collections of entities (nodes) which arise in a ...

Graph analytics platform TigerGraph has just released its new TigerGraph ML Workbench, a Jupyter-based Python development framework. TigerGraph says this machine learning toolkit “enables data ...

TensorFlow 2.0, released in October 2019, revamped the framework significantly based on user feedback. The result is a machine learning framework that is easier to work with—for example, by ...

Linux supports a wide array of AI and ML frameworks that cater to different aspects of machine learning, from deep learning to statistical modeling. Below are some of the most popular frameworks ...