Scientific Research Publishing

Spatiotemporal Evolution Patterns and Intelligent Forecasting of Passenger Flow in Megacity High-Speed Rail Hubs: A Case Study of Guangzhou South Railway Station ()

Spatiotemporal Evolution Patterns and Intelligent Forecasting of Passenger Flow in Megacity High-Speed Rail Hubs: A Case ...
IEEE

Informed Prior Initialization for Bayesian Convolutional Neural Networks Through Deterministic Pre-Training

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 ...
Frontiers

Trustworthy navigation with variational policy in deep reinforcement learning

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 ...
IEEE

Bayesian Graph Convolutional Neural Network with Noise Injection

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 ...
Frontiers

Diagnosis and activity prediction of SLE based on serum Raman spectroscopy combined with a two-branch Bayesian network

Objective: This study aims to examine the impact of systemic lupus erythematosus (SLE) on various organs and tissues throughout the body. SLE is a chronic autoimmune disease that, if left untreated, ...
Scientific Research Publishing

Reliable Water Quality Prediction Using Bayesian Multi-Scale Convolutional Attention Network ()

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 ...
GitHub

Galactic Halo Segmentation with Bayesian Convolutional Networks

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 ...