Image classification has always been a hot research direction in the world, and the
emergence of deep learning has promoted the development of this field. Convolutional …

Over the past decade, deep-learning-based representations have demonstrated remarkable
performance in academia and industry. The learning capability of convolutional neural …

A growing body of work underlines striking similarities between biological neural networks
and recurrent, binary neural networks. A relatively smaller body of work, however, addresses …

Engineering gene and protein sequences with defined functional properties is a major goal
of synthetic biology. Deep neural network models, together with gradient ascent-style …

On-chip edge intelligence has necessitated the exploration of algorithmic techniques to
reduce the compute requirements of current machine learning frameworks. This work aims …

Advancements in artificial intelligence algorithms and models, along with embedded device
support, have resulted in the issue of high energy consumption and poor compatibility when …

Artificial Intelligence (Al) models are being produced and used to solve a variety of current
and future business and technical problems. Therefore, AI model engineering processes …

Deep networks often possess a vast number of parameters, and their significant redundancy
in parameterization has become a widely-recognized property. This presents significant …

Networking is crucial for smart city projects nowadays, as it offers an environment where
people and things are connected. This paper presents a chronology of factors on the …

Recent advances demonstrate that irregularly wired neural networks from Neural
Architecture Search (NAS) and Random Wiring can not only automate the design of deep …