Deep learning algorithms provide plausible benefits for efficient prediction and analysis of
nuclear reactor safety phenomena. However, research works that discuss the critical …

Modern deep neural networks can achieve high accuracy when the training distribution and
test distribution are identically distributed, but this assumption is frequently violated in …

In real-world applications of machine learning, reliable and safe systems must consider
measures of performance beyond standard test set accuracy. These other goals include out …

Background The recent development of metagenomic sequencing makes it possible to
massively sequence microbial genomes including viral genomes without the need for …

Artificial intelligence (AI) has undergone rapid development in recent years and has been
successfully applied to real-world problems such as drug design. In this chapter, we review …

Supervised deep learning models have proven to be highly effective in classification of
dermatological conditions. These models rely on the availability of abundant labeled training …

Data-driven AI systems are increasingly used to augment human decision-making in
complex, social contexts, such as social work or legal practice. Yet, most existing design …

Placing a human in the loop may help abate the risks of deploying AI systems in safety-
critical settings (eg, a clinician working with a medical AI system). However, mitigating risks …

Reliable application of machine learning-based decision systems in the wild is one of the
major challenges currently investigated by the field. A large portion of established …

Out-of-Distribution (OoD) detection is important for building safe artificial intelligence
systems. However, current OoD detection methods still cannot meet the performance …