The increase of the worldwide installed photovoltaic (PV) capacity and the intermittent
nature of the solar resource highlights the importance of power forecasting for the grid …

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 …

We present a method to formulate algorithm discovery as program search, and apply it to
discover optimization algorithms for deep neural network training. We leverage efficient …

Deep learning has revolutionized the fields of computer vision, natural language
understanding, speech recognition, information retrieval, and more. However, with the …

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 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 …

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 …

In the past decade, advances in deep learning have resulted in breakthroughs in a variety of
areas, including computer vision, natural language understanding, speech recognition, and …