… and uncertainty propagation in reliability and safety analysis. … of a run-to-failure approach.
RUL prediction is an important … models in reliability and safety application, namely support …

… An innovative, but controversial, approach retrieves Bayesian uncertainty estimates via
batch normalization [38]. This work uses MCDropout and bootstrapping to give computationally …

… Through this work, we suggest a risk assessment approach based on machine learning.
In … a clear set of rules of operation for uncertain contexts, designing advanced training for …

… for practical applications and related issues such as safety requirements, … approach is deep
learning and neural networks, and especially interesting from the point of view of uncertainty …

… As machine learning becomes further integrated into … ” when uncertain is a necessary capability
to enable safe clinical … of predictions provided by each approach. Both methods are easy …

… uncertainty-aware machine fault diagnosis method in the probabilistic Bayesian deep learning
… From a probabilistic perspective, the model parameters w are obtained or trained in the …

… perspective. In this article, we provide a bird's-eye view of the most recent work in learning-…
are interested in the problem of safe decision-making under uncertainties using machine …

… to machine intelligence in cases where patient safety is at stake. To address some of these
challenges, medical AI, especially in its modern data-rich deep learning … of this Perspective, in …

… model uncertainty in safety-critical control using a data-driven machine learning approach.
Our goal is to benefit from the recent successes of learning-based control in highly uncertain …

… To illustrate the generality of our approach, we apply the same framework to two different
tasks: protein engineering and imputing gene expression values. First, we show that …