Graph neural networks (GNNs) have rapidly emerged as a central methodology for analysing complex datasets presented as graphs, where entities are interconnected through diverse relationships. By ...
BingoCGN employs cross-partition message quantization to summarize inter-partition message flow, which eliminates the need for irregular off-chip memory access and utilizes a fine-grained structured ...
Researchers have proposed a Fourier graph neural network for estimating the state of health of lithium-ion batteries while simultaneously considering spatial and temporal feature relationships. The ...
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 ...
Fine-grained spatial data are critical for informed decision-making in domains ranging from economic planning to ...
Hosted on MSN
Cracking the code of online communities
From Facebook friend circles to hidden influencer groups, community detection in social networks is evolving fast. Researchers are combining deep learning, graph neural networks, and advanced ...
Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of neural network quantile regression. The goal of a quantile regression problem is to predict a single numeric ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results