Researchers have demonstrated a new training technique that significantly improves the accuracy of graph neural networks (GNNs)—AI systems used in applications from drug discovery to weather ...
The advent of high-density recording technologies, such as Neuropixels and large-scale calcium imaging, has provided an unprecedented look into the ...
Abstract: Graph Neural Networks (GNNs) are now a powerful means of learning about data in a graph format, with special support of recommendation systems, where the interactions between users and items ...
Abstract: This article provides a systematic review of research advances in Temporal Knowledge Graph (TKG) reasoning. TKGs extend static knowledge graphs by incorporating timestamps into quadruples ...
The 2024 Nobel Prize in Chemistry was recently granted to David Baker, Demis Hassabis and John M. Jumper, renowned for their pioneering works in protein design. In addition, Nature has recently ...
Dynamic Graph Neural Networks (Dynamic GNNs) have emerged as powerful tools for modeling real-world networks with evolving topologies and node attributes over time. A survey by Professors Zhewei Wei, ...
Cloud infrastructure anomalies cause significant downtime and financial losses (estimated at $2.5 M/hour for major services). Traditional anomaly detection methods fail to capture complex dependencies ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results