Abstract: Graph neural networks (GNNs) exhibit a robust capability for representation learning on graphs with complex structures, demonstrating superior performance across various applications. Most ...
In forecasting economic time series, statistical models often need to be complemented with a process to impose various constraints in a smooth manner. Systematically imposing constraints and retaining ...
1 Department of Computer Science and Engineering, Kishoreganj University, Kishoreganj, Bangladesh. 2 Department of Electrical and Computer Engineering, North South University, Dhaka, Bangladesh. 3 ...
Graph neural networks (GNNs) have gained traction and have been applied to various graph-based data analysis tasks due to their high performance. However, a major concern is their robustness, ...
Imagine a world where AI-powered bots can buy or sell cryptocurrency, make investments, and execute software-defined contracts at the blink of an eye, depending on minute-to-minute currency prices, ...
A weird phrase is plaguing scientific papers – and we traced it back to a glitch in AI training data
Aaron J. Snoswell receives funding from the Australian Research Council funded Discovery Project "Generative AI and the future of academic writing and publishing" (DP250100074) and has previously ...
models: Deep learning models developed for surrogating the hydraulic one: contains a base class with common inputs and functions and one for the SWE-GNN and mSWE-GNN models. results: Contains results ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results