Abstract Knowledge extraction through machine learning techniques has been successfully
applied in a large number of application domains. However, apart from the required …

Automated machine learning (AutoML) is a young research area aiming at making high-
performance machine learning techniques accessible to a broad set of users. This is …

Today deep learning is widely used for building software. A software engineering problem
with deep learning is that finding an appropriate convolutional neural network (CNN) model …

Machine learning (ML) has been widely adopted in modern software, but the manual
configuration of ML (eg, hyper-parameter configuration) poses a significant challenge to …

Machine learning (ML) is increasingly being used in critical decision-making software, but
incidents have raised questions about the fairness of ML predictions. To address this issue …

Embedded Artificial Intelligence (AI) is becoming increasingly important in the field of
healthcare where such AI enabled devices are utilized to assist physicians, clinicians, and …

Automated machine learning (AutoML) promises to democratize machine learning by
automatically generating machine learning pipelines with little to no user intervention …

Abstract Machine learning (ML) has become essential to a vast range of applications, while
ML experts are in short supply. To alleviate this problem, AutoML aims to make ML easier …

Deep learning models, like traditional software systems, provide a large number of
configuration options. A deep learning model can be configured with different …

Deep Learning models have become an integrated component of modern software systems.
In response to the challenge of model design, researchers proposed Automated Machine …