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 …

A variety of methods have been applied to the architectural configuration and learning or
training of artificial deep neural networks (DNN). These methods play a crucial role in the …

To use deep learning, one needs to be familiar with various software tools like TensorFlow
or Keras, as well as various model architecture and optimization best practices. Despite …

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 …

Over the past half-decade, many methods have been considered for neural architecture
search (NAS). Bayesian optimization (BO), which has long had success in hyperparameter …

Medical image segmentation is an important tool for current clinical applications. It is the
backbone of numerous clinical diagnosis methods, oncological treatments and computer …

Neural architecture search (NAS) has attracted a lot of attention and has been illustrated to
bring tangible benefits in a large number of applications in the past few years. Architecture …

While early AutoML frameworks focused on optimizing traditional ML pipelines and their
hyperparameters, a recent trend in AutoML is to focus on neural architecture search. In this …

ABSTRACT Neural Architecture Search (NAS) is a logical next step in the automatic learning
of representations, but the development of NAS methods is slowed by high computational …

Early methods in the rapidly developing field of neural architecture search (NAS) required
fully training thousands of neural networks. To reduce this extreme computational cost …