A key factor for ensuring safety in Autonomous Vehicles (AVs) is to avoid any abnormal
behaviors under undesirable and unpredicted circumstances. As AVs increasingly rely on …

Deep neural networks (DNNs) have achieved tremendous success in making accurate
predictions for computer vision, natural language processing, as well as science and …

Over the last decades, deep neural networks (DNNs) have penetrated all fields of science
and the real world. As a result of the lack of quantifiable data and model uncertainty, deep …

Accurately estimating uncertainties in neural network predictions is of great importance in
building trusted DNNs-based models, and there is an increasing interest in providing …

Deep neural network controllers for autonomous driving have recently benefited from
significant performance improvements, and have begun deployment in the real world. Prior …

A rise in popularity of Deep Neural Networks (DNNs), attributed to more powerful GPUs and
widely available datasets, has seen them being increasingly used within safety-critical …

Reliable uncertainty quantification in deep neural networks is very crucial in safety-critical
applications such as automated driving for trustworthy and informed decision-making …

Deep neural networks (DNNs) have achieved enormous success in a wide range of
domains, such as computer vision, natural language processing and scientific areas …

Uncertainty quantification is a fundamental yet unsolved problem for deep learning. The
Bayesian framework provides a principled way of uncertainty estimation but is often not …

Predictive uncertainty estimation is essential for safe deployment of Deep Neural Networks
in real-world autonomous systems. However, disentangling the different types and sources …