Context: Advancements in machine learning (ML) lead to a shift from the traditional view of
software development, where algorithms are hard-coded by humans, to ML systems …

Machine learning components are essential for today's software systems, causing a need to
adapt traditional software engineering practices when developing machine-learning-based …

The explorative and iterative nature of developing and operating ML applications leads to a
variety of artifacts, such as datasets, features, models, hyperparameters, metrics, software …

Many machine learning projects for new application areas involve teams of humans who
label data for a particular purpose, from hiring crowdworkers to the paper's authors labeling …

Deep learning has improved the state-of-the-art results in an ever-growing number of
domains. This success heavily relies on the development and training of deep learning …

Supervised machine learning, in which models are automatically derived from labeled
training data, is only as good as the quality of that data. This study builds on prior work that …

Responding to calls to take a more active role in communicating their research findings,
scientists are increasingly using open online platforms, such as Twitter, to engage in science …

Abstract The use of Artificial Intelligence solutions keeps raising in the business domain.
However, this adoption has not brought the expected results to companies so far. There are …

Machine learning tasks over image databases often generate masks that annotate image
content (eg, saliency maps, segmentation maps, depth maps) and enable a variety of …

The duration of the life cycle in deep neural networks (DNN) depends on the data
configuration decisions that lead to success in obtaining models. Analyzing …