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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

Chinese AI start-up Megvii will make its deep learning framework MegEngine open-source, in a move that could reduce the country’s reliance on US-originated frameworks such as Tensor Flow and ...