Neural networks have shown tremendous growth in recent years to solve numerous
problems. Various types of neural networks have been introduced to deal with different types …

In neural networks literature, there is a strong interest in identifying and defining activation
functions which can improve neural network performance. In recent years there has been a …

Face mask detection had seen significant progress in the domains of Image processing and
Computer vision, since the rise of the Covid-19 pandemic. Many face detection models have …

A central problem in learning from sequential data is representing cumulative history in an
incremental fashion as more data is processed. We introduce a general framework (HiPPO) …

Inspired by biological neurons, the activation functions play an essential part in the learning
process of any artificial neural network (ANN) commonly used in many real-world problems …

In-memory computing using resistive memory devices is a promising non-von Neumann
approach for making energy-efficient deep learning inference hardware. However, due to …

Deep neural networks (DNNs) continue to make significant advances, solving tasks from
image classification to translation or reinforcement learning. One aspect of the field receiving …

Sign language recognition is challenged by problems, such as accurate tracking of hand
gestures, occlusion of hands, and high computational cost. Recently, it has benefited from …

We propose a new type of neural networks, Kronecker neural networks (KNNs), that form a
general framework for neural networks with adaptive activation functions. KNNs employ the …

Deep neural networks have gained remarkable achievements in many research areas,
especially in computer vision, and natural language processing. The great successes of …