Large language models (LLMs), such as the model underpinning the functioning of OpenAI's platform ChatGPT, are now widely ...
Determining the least expensive path for a new subway line underneath a metropolis like New York City is a colossal planning challenge—involving thousands of potential routes through hundreds of city ...
The reliability of public polling has been under scrutiny since major electoral mispredictions in 2016 and 2020, when ...
In an era driven by complex data, scientists are increasingly encountering information that doesn't lie neatly on flat, Euclidean surfaces. From 3D medical scans to robot orientations and AI ...
Abstract: Heterogeneous Graph Neural Networks (HetGNN) have garnered significant attention and demonstrated success in tackling various tasks. However, most existing HetGNNs face challenges in ...
Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Protein function prediction is essential for elucidating biological processes and ...
This article introduces a model-based design, implementation, deployment, and execution methodology, with tools supporting the systematic composition of algorithms from generic and domain-specific ...
The Graph, the decentralized indexing system that works much like Google for blockchains, has introduced a data standard for Web3. Called GRC-20, the standard would define how information is ...
In the contemporary technological landscape, ensuring confidentiality is a paramount concern addressed through various skillsets. Cryptography stands out as a scientific methodology for safeguarding ...
Abstract: Empowered by their remarkable advantages, graph neural networks (GNN) serve as potent tools for embedding graph-structured data and finding applications across various domains. Particularly, ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results
Feedback