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 …

Models that combine quantum mechanics (QM) with machine learning (ML) promise to
deliver the accuracy of QM at the speed of ML. This hands‐on tutorial introduces the reader …

Recent interest in complex and computationally expensive machine learning models with
many hyperparameters, such as automated machine learning (AutoML) frameworks and …

The success of machine learning in a broad range of applications has led to an ever-
growing demand for machine learning systems that can be used off the shelf by non-experts …

Modern supervised machine learning algorithms involve hyperparameters that have to be
set before running them. Options for setting hyperparameters are default values from the …

Abstract Model selection strategies for machine learning algorithms typically involve the
numerical optimisation of an appropriate model selection criterion, often based on an …

Automated Machine Learning (AutoML) supports practitioners and researchers with the
tedious task of designing machine learning pipelines and has recently achieved substantial …

Cross-validation using randomized subsets of data—known as k-fold cross-validation—is a
powerful means of testing the success rate of models used for classification. However, few if …

We address the sparse signal recovery problem in the context of multiple measurement
vectors (MMV) when elements in each nonzero row of the solution matrix are temporally …

The K-fold Cross Validation (KCV) technique is one of the most used approaches by
practitioners for model selection and error estimation of classifiers. The KCV consists in …