The Indonesian Throughflow carries both warm water and fresh water from the Pacific into the Indian Ocean. As the only ...
Bayesian neural networks (BNNs) offer a principled framework for uncertainty quantification by treating model parameters as stochastic variables rather than fixed values. However, their practical ...
1 Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, Rochester, NY, United States 2 Department of Electrical and Computer Engineering, The University of Texas Rio Grande ...
Abstract: Graph neural networks (GNNs) have achieved significant success in a variety of graph-related tasks, such as node classification, link prediction, and graph classification. However, GNNs ...
With the rapid development of industrialization and urbanization, the issue of water quality deterioration has become increasingly severe. Accurately assessing water quality is crucial for ...
The mathematics behind artificial intelligence (AI) and machine learning (ML) rely on linear algebra, calculus, probability, and statistics. These provide the foundation for developing the needed ...
Welcome to the Epoch of Reionization repository. This project leverages advanced Bayesian Convolutional Neural Networks (BCNNs) to identify and segment high-probability mass regions within large-scale ...