During the past five years the Bayesian deep learning community has developed
increasingly accurate and efficient approximate inference procedures that allow for …

Bayesian neural networks (BNNs) demonstrate promising success in improving the
robustness and uncertainty quantification of modern deep learning. However, they generally …

Abstract Hamiltonian Monte Carlo (HMC) is a Markov chain Monte Carlo (MCMC) approach
that exhibits favourable exploration properties in high-dimensional models such as neural …

The Bayesian treatment of neural networks dictates that a prior distribution is specified over
their weight and bias parameters. This poses a challenge because modern neural networks …

Aleatoric uncertainty captures the inherent randomness of the data, such as measurement
noise. In Bayesian regression, we often use a Gaussian observation model, where we …

Deep learning is gaining increasing popularity for spatiotemporal forecasting. However,
prior works have mostly focused on point estimates without quantifying the uncertainty of the …

Neural networks utilize the softmax as a building block in classification tasks, which contains
an overconfidence problem and lacks an uncertainty representation ability. As a Bayesian …

Neural network (NN) potentials promise highly accurate molecular dynamics (MD)
simulations within the computational complexity of classical MD force fields. However, when …

Deep neural networks (DNNs) are poorly calibrated when trained in conventional ways. To
improve confidence calibration of DNNs, we propose a novel training method, distance …

While low-precision optimization has been widely used to accelerate deep learning, low-
precision sampling remains largely unexplored. As a consequence, sampling is simply …