Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …

While the choice of prior is one of the most critical parts of the Bayesian inference workflow,
recent Bayesian deep learning models have often fallen back on vague priors, such as …

Deep learning (DL) has shown great potential in medical image enhancement problems,
such as super-resolution or image synthesis. However, to date, most existing approaches …

Abstract We propose Radial Bayesian Neural Networks (BNNs): a variational approximate
posterior for BNNs which scales well to large models. Unlike scalable Bayesian deep …

Continuous Normalizing Flows (CNFs) are a class of generative models that transform a
prior distribution to a model distribution by solving an ordinary differential equation (ODE) …

We challenge the longstanding assumption that the mean-field approximation for variational
inference in Bayesian neural networks is severely restrictive, and show this is not the case in …

Regularization/prior-based approach appears to be one of the critical strategies in continual
learning, considering its mechanism for preserving and preventing forgetting the learned …

Deep learning (DL) has shown great potential in medical image enhancement problems,
such as super-resolution or image synthesis. However, to date, little consideration has been …

Lung cancer is one of the most malignant tumors. If it can be detected early and treated
actively, it can effectively improve a patient's survival rate. Therefore, early diagnosis of lung …

Approximate inference in Bayesian deep networks exhibits a dilemma of how to yield high
fidelity posterior approximations while maintaining computational efficiency and scalability …