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

Over the last decade, neural networks have reached almost every field of science and
become a crucial part of various real world applications. Due to the increasing spread …

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

Approximate Bayesian inference for neural networks is considered a robust alternative to
standard training, often providing good performance on out-of-distribution data. However …

Neural networks (NNs) have demonstrated their potential in a variety of domains ranging
from computer vision (CV) to natural language processing. Among various NNs, two …

Abstract Machine learning approaches to medical image synthesis have shown outstanding
performance, but often do not convey uncertainty information. In this chapter, we survey …

Abstract Bayesian Neural Networks and deep ensembles represent two modern paradigms
of uncertainty quantification in deep learning. Yet these approaches struggle to scale mainly …

Artificial Neural Network (ANN)-based inference on battery-powered devices can be made
more energy-efficient by restricting the synaptic weights to be binary, hence eliminating the …

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

Encoding domain knowledge into the prior over the high-dimensional weight space of a
neural network is challenging but essential in applications with limited data and weak …