Bayesian Neural Network

Bayesian neural networks (BNNs) aim to combine the power of deep learning with probabilistic reasoning, enabling models to not only make predictions but also quantify their uncertainty. Current research focuses on improving the efficiency and accuracy of BNN inference, exploring various architectures (like U-Nets and Transformers) and algorithms (including variational inference, MCMC, and ensemble methods) to address challenges like high dimensionality and multimodality in the posterior distribution. This work is significant because reliable uncertainty quantification is crucial for trustworthy AI in safety-critical applications such as medical diagnosis, autonomous driving, and scientific modeling, leading to more robust and explainable AI systems.