Bayesian Last Layer

Bayesian Last Layer (BLL) models focus on efficiently incorporating uncertainty estimation into neural networks by treating only the final layer's weights probabilistically. Current research emphasizes developing more flexible BLL architectures, such as those employing implicit priors and diffusion-based sampling, to handle complex data distributions and improve uncertainty quantification, including disentangling aleatoric and epistemic uncertainty. These advancements aim to enhance model accuracy, calibration, and out-of-distribution detection capabilities, while maintaining computational efficiency, making BLL a promising approach for various applications requiring reliable uncertainty estimates. The extension of BLL to multivariate regression tasks further broadens its applicability and reveals fundamental properties of neural network training dynamics.