Scalability of Node-Based BNNs Versus Dropout Uncertainty Estimation on Multimodal Benchmarks Under Covariate Shift

Given the use of machine learning-based tools for monitoring the Water Quality Indicators (WQIs) over lakes and coastal waters, understanding the properties of such models, including the uncertainties inherent in their predictions is essential. This has led to the development of two probabilistic NN-algorithms: Mixture Density Network (MDN) and Bayesian Neural Network via Monte Carlo Dropout (BNN-MCD). These NNs are complex, featuring thousands of trainable parameters and modifiable hyper-parameters, and have been independently trained and tested. The model uncertainty metric captures the unce

Research goal: How does the scalability of node-based BNNs compare to dropout-based uncertainty estimation methods in terms of log-likelihood scores on multimodal benchmarks under covariate shift?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 8.2/10.