Deep neural networks (DNNs) have achieved great success in many applications. The
architectures of DNNs play a crucial role in their performance, which is usually manually …

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 …

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

Deep Learning has enabled remarkable progress over the last years on a variety of tasks,
such as image recognition, speech recognition, and machine translation. One crucial aspect …

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 …

Accurate automated medical image recognition, including classification and segmentation,
is one of the most challenging tasks in medical image analysis. Recently, deep learning …

Neural architecture search (NAS) is a promising research direction that has the potential to
replace expert-designed networks with learned, task-specific architectures. In order to help …

Neural architecture search (NAS) has been proposed to automatically tune deep neural
networks, but existing search algorithms, eg, NASNet, PNAS, usually suffer from expensive …

This paper introduces NSGA-Net---an evolutionary approach for neural architecture search
(NAS). NSGA-Net is designed with three goals in mind:(1) a procedure considering multiple …

Significant progress has been made with deep neural networks recently. Sharing trained
models of deep neural networks has been a very important in the rapid progress of research …