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 …

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 …

In this paper, we compare the three most popular algorithms for hyperparameter
optimization (Grid Search, Random Search, and Genetic Algorithm) and attempt to use them …

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

Domain-specific hardware is becoming a promising topic in the backdrop of improvement
slow down for general-purpose processors due to the foreseeable end of Moore's Law …

Neural architecture search (NAS) has a great impact by automatically designing effective
neural network architectures. However, the prohibitive computational demand of …

Abstract Model quantization is a widely used technique to compress and accelerate deep
neural network (DNN) inference. Emergent DNN hardware accelerators begin to support …

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 …

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 …

Abstract Neural Architecture Search (NAS) has attracted increasingly more attention in
recent years because of its capability to design deep neural network automatically. Among …