Uncertainty quantification in deep learning via Bayesian inference mechanism

Abstract

Deep learning models have demonstrated outstanding performance in complex tasks, but the uncertainty in their prediction results limits their application in engineering and high-reliability scenarios. This paper proposes a deep learning framework that integrates Bayesian inference mechanisms to achieve systematic uncertainty quantification. By introducing Bayesian weight layers into the network and combining variational inference with reparameterization techniques, the model can capture the uncertainty of parameters and data during forward propagation. This paper further designs a modular architecture and parallel training mechanism to optimize computational efficiency and memory consumption, and proposes scalable training strategies to adapt to deep network structures. Experimental evaluations show that this method outperforms traditional deep learning methods in terms of uncertainty quantification accuracy, training stability, and resource utilization, providing a technical foundation for the development of deep learning models in high-reliability systems.