This article discusses the use of graph neural networks (GNNs) in intelligent perception, specifically in the field of federated learning and the Internet of…
Browsing: Graph Neural Networks
Topological Deep Learning (TDL) is a cutting-edge approach that goes beyond traditional Graph Neural Networks (GNNs) by modeling complex multi-way relationships. TDL has shown…
This article discusses the use of graph neural networks (GNNs) in representing biological data and their applications in predicting isoform function, RBP binding sites,…
Nvidia continues to dominate machine learning benchmarks with their Hopper architecture, setting records in five out of nine benchmarks, including two new ones for…
A team of researchers has developed a new Machine Learning method, called Message-Passing Monte Carlo (MPMC), for producing low-discrepancy point sets. This method utilizes…
Scientists at La Jolla Institute for Immunology have developed a new computational method for linking molecular marks on our DNA to gene activity, which…
The article discusses the challenges of structural rigidity in deep neural networks and explores methods such as Indirect Encoding and Graph Neural Networks to…
NVIDIA offers top AI courses on various advanced topics like generative AI, graph neural networks, and diffusion models, empowering individuals with the knowledge and…
Georgia Tech receives new state-of-the-art processing chip for AI and high-performance computing research. The GH200 Grace Hopper Superchip from NVIDIA is designed for large-scale…
Cryptocurrencies have enabled cybercriminals to commit ransomware attacks by allowing them to quickly extort large sums of money that can be easily hidden and…