Abstract The Neural Architecture Search (NAS) problem is typically formulated as a graph
search problem where the goal is to learn the optimal operations over edges in order to …

Abstract Neural Architecture Search (NAS) benchmarks significantly improved the capability
of developing and comparing NAS methods while at the same time drastically reduced the …

Convolutional neural networks (CNN) have transformed the field of computer vision by
enabling the automatic extraction of features, obviating the need for manual feature …

This paper presents a new approach to design the architecture and optimize the
hyperparameters of a deep convolutional neural network (CNN) via of the Fractal …

The Neural Architecture Search (NAS) problem is typically formulated as a graph search
problem where the goal is to learn the optimal operations over edges in order to maximise a …

Downsampling layers, including pooling and strided convolutions, are crucial components of
the convolutional neural network architecture that determine both the granularity/scale of …

Abstract Neural Architecture Search (NAS) methods have been successfully applied to
image tasks with excellent results. However, NAS methods are often complex and tend to …

Downsampling layers, including pooling and strided convolutions, are crucial components of
the convolutional neural network architecture that determine both the granularity/scale of …

Neural architecture search (NAS) aims to automate neural network design process and has
shown promising results for image classification tasks. Owing to combinatorially huge neural …

Abstract Machine Learning (ML) has revolutionized various fields, enabling the development
of intelligent systems capable of solving complex problems. However, the process of …