Graph neural networks (GNNs) have emerged as a powerful framework for analyzing and learning from structured data represented as graphs. GNNs operate directly on graphs, as opposed to conventional ...
Franz Inc., an early innovator in AI and leading supplier of graph database technology, is releasing AllegroGraph 7.2, providing organizations with essential data fabric tools, including graph neural ...
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 demand for immersive, realistic graphics in mobile gaming and AR or VR is pushing the limits of mobile hardware. Achieving lifelike simulations of fluids, cloth, and other materials historically ...
Graph neural networks (GNNs) are a relatively recent development in the field of machine learning. Like traditional graphs, a core principle of GNNs is that they model the dependencies and ...
Expect to hear increasing buzz around graph neural network use cases among hyperscalers in the coming year. Behind the scenes, these are already replacing existing recommendation systems and traveling ...
The core of quantum network research lies in efficiently and reliably establishing entanglement between nodes; however, the challenges of maintaining fragile quantum states are far more complex than ...
Guangzhou Haofang Automotive Parts Co., Ltd. and Guangzhou Qianlang Technology Co., Ltd. recently announced the successful acquisition of a patent titled "An Intelligent Detection Method for Defective ...
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