Abstract Machine learning algorithms have been used widely in various applications and
areas. To fit a machine learning model into different problems, its hyper-parameters must be …

Deep learning has made substantial breakthroughs in many fields due to its powerful
automatic representation capabilities. It has been proven that neural architecture design is …

Since deep neural networks were developed, they have made huge contributions to
everyday lives. Machine learning provides more rational advice than humans are capable of …

Objective This work aims to provide a review of the existing literature in the field of
automated machine learning (AutoML) to help healthcare professionals better utilize …

The remarkable progress in deep learning in recent years is largely driven by improvements
in scale, where bigger models are trained on larger datasets for longer schedules. To predict …

The purpose of this study is to introduce new design-criteria for next-generation
hyperparameter optimization software. The criteria we propose include (1) define-by-run API …

Deep learning (DL) techniques have obtained remarkable achievements on various tasks,
such as image recognition, object detection, and language modeling. However, building a …

Simultaneously optimizing many design parameters in time-consuming experiments causes
bottlenecks in a broad range of scientific and engineering disciplines,. One such example is …

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

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