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 …

Neural architecture search (NAS) has attracted increasing attention. In recent years,
individual search methods have been replaced by weight-sharing search methods for higher …

Abstract Machine learning (ML), as the most promising paradigm to discover deep
knowledge from data, has been widely applied to practical applications, such as …

With the advent of Industry 4.0, making accurate predictions of the remaining useful life
(RUL) of industrial components has become a crucial aspect in predictive maintenance …

Recording the trajectory of table tennis balls in real-time enables the analysis of the
opponent's attacking characteristics and weaknesses. The current analysis of the ball paths …

While many companies are currently leveraging on Cloud, data centres and specialized
hardware (eg, GPUs and TPUs) to train very accurate Machine Learning models, the need to …

Full-reference image quality measures are a fundamental tool to approximate the human
visual system in various applications for digital data management: from retrieval to …

The pursuit of self-evolving neural networks has driven the emerging field of Evolutionary
Deep Learning, which combines the strengths of Deep Learning and Evolutionary …

Searching for accurate machine and deep learning models is a computationally expensive
and awfully energivorous process. A strategy which has been recently gaining importance to …

CGP has often been applied for neural architecture search (NAS). However, the
performance of Cartesian genetic programming (CGP) is less than ideal when searching for …