Most machine learning algorithms are configured by a set of hyperparameters whose values
must be carefully chosen and which often considerably impact performance. To avoid a time …

The application of Artificial Intelligence (AI) across a wide range of domains comes with both
high expectations of its benefits and dire predictions of misuse. While AI systems have …

The spread of AI-embedded systems involved in human decision making makes studying
human trust in these systems critical. However, empirically investigating trust is challenging …

Today, the prominence of data science within organizations has given rise to teams of data
science workers collaborating on extracting insights from data, as opposed to individual data …

Artificial intelligence (AI) technology has been increasingly used in the implementation of
advanced Clinical Decision Support Systems (CDSS). Research demonstrated the potential …

Artificial Intelligent (AI) and Machine Learning (ML) algorithms are coming out of research
labs into the real-world applications, and recent research has focused a lot on Human-AI …

In the last few years, electron microscopy has experienced a new methodological paradigm
aimed to fix the bottlenecks and overcome the challenges of its analytical workflow. Machine …

After Deep Learning (DL) regained popularity recently, the Artificial Intelligence (AI) or
Machine Learning (ML) field is undergoing rapid growth concerning research and real-world …

Efforts to make machine learning more widely accessible have led to a rapid increase in
Auto-ML tools that aim to automate the process of training and deploying machine learning …

Generative machine learning models have recently been applied to source code, for use
cases including translating code between programming languages, creating documentation …