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

Modern deep learning methods constitute incredibly powerful tools to tackle a myriad of
challenging problems. However, since deep learning methods operate as black boxes, the …

General conditions for organic reactions are important but rare, and efforts to identify them
usually consider only narrow regions of chemical space. Discovering more general reaction …

Bayesian formulations of deep learning have been shown to have compelling theoretical
properties and offer practical functional benefits, such as improved predictive uncertainty …

Cellular senescence is an important factor in aging and many age-related diseases, but
understanding its role in health is challenging due to the lack of exclusive or universal …

Fault diagnosis is efficient to improve the safety, reliability, and cost-effectiveness of
industrial machinery. Deep learning has been extensively investigated in fault diagnosis …

The study on improving the robustness of deep neural networks against adversarial
examples grows rapidly in recent years. Among them, adversarial training is the most …

Modern machine learning methods including deep learning have achieved great success in
predictive accuracy for supervised learning tasks, but may still fall short in giving useful …

Deep ensembles have been empirically shown to be a promising approach for improving
accuracy, uncertainty and out-of-distribution robustness of deep learning models. While …

Accurate automated medical image recognition, including classification and segmentation,
is one of the most challenging tasks in medical image analysis. Recently, deep learning …