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

Automotive perception involves understanding the external driving environment and the
internal state of the vehicle cabin and occupants using sensor data. It is critical to achieving …

Reliable uncertainty from deterministic single-forward pass models is sought after because
conventional methods of uncertainty quantification are computationally expensive. We take …

The posterior over Bayesian neural network (BNN) parameters is extremely high-
dimensional and non-convex. For computational reasons, researchers approximate this …

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …

Bayesian neural networks (BNN) and deep ensembles are principled approaches to
estimate the predictive uncertainty of a deep learning model. However their practicality in …

A recent trend in artificial intelligence is the use of pretrained models for language and
vision tasks, which have achieved extraordinary performance but also puzzling failures …

The full acceptance of Deep Learning (DL) models in the clinical field is rather low with
respect to the quantity of high-performing solutions reported in the literature. End users are …

Ensembles over neural network weights trained from different random initialization, known
as deep ensembles, achieve state-of-the-art accuracy and calibration. The recently …

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 …